JURIKOM (Jurnal Riset Komputer) discusses knowledge in the field of Informatics, Information Systems, Informatics Management, DSS, AI, ES, and Networks, as a forum for expressing research results both conceptually and technically related to Information Technology and Computers.
Optimasi Jadwal Tanam Padi di Kabupaten Tuban melalui Prediksi Curah Hujan Menggunakan Random Forest
Weather uncertainty due to climate change increasingly threatens rice harvest success, especially when farmers still rely on traditional forecasts that are not always accurate. This study developed a decision support system to determine the timing of rice planting based on daily rainfall predictions using Random Forest Regression. Daily climate data from the BMKG in Tuban, East Java, for the period 2022–2025 was used as the basis for training, with the addition of time features such as month, day of the year, and season to capture seasonal patterns. In East Java, the rainy season usually lasts from October to April and the dry season from May to September, but climate change has caused shifts in the timing, duration, and intensity of rainfall, making traditional seasonal classifications less reliable for determining the optimal planting time. The model was tested on 2025 data and showed improved performance compared to the baseline model. The tuned model produced an MAE of 5.78 mm, an RMSE of 9.75 mm, and a coefficient of determination (R²) of 0.177, an improvement over the baseline, which had an MAE of 6.02 mm, an RMSE of 10.16 mm, and an R² of 0.107. Although the R² value is still relatively low, the decrease in MAE and RMSE indicates that the tuned model is more accurate in predicting daily rainfall, especially in the light to moderate range, which is most relevant for planting decisions
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Implemetasi HHKBRM Pada Model Rekomendasi Hibrida untuk Perencanaan Kompetensi SDM Rumah Sakit
The development of human resources (HR) competencies in hospitals has become increasingly important in the era of digital healthcare transformation, which requires healthcare professionals to possess adaptive and data-driven capabilities. However, competency planning still faces several challenges, including limited training data (data sparsity), high variability in competency needs, and the limitations of conventional recommendation systems that are not yet capable of providing personalized and contextual recommendations. This study aims to develop a hybrid recommendation model, namely the Hierarchical Hybrid Knowledge-Based Recommendation Model (HHKBRM), to support more effective competency planning for hospital HR. This research adopts a quantitative experimental approach using secondary data, including HR profiles, training data, training histories, organizational data, and competency standards. The proposed model integrates knowledge-based, content-based filtering, and collaborative filtering approaches, supported by a competency level categorization technique. The analytical process includes data preprocessing, semantic mapping using term weighting and similarity measurement, and hybrid score computation to generate relevant training recommendations. The evaluation results indicate that the model achieves a Precision@10 of 0,054, Recall@10 of 0,300, NDCG@10 of 0,299, Hit Rate@10 of 0,300, and Diversity@10 of 0,849. These results demonstrate that the model is capable of providing relevant recommendations with good ranking quality and high diversity. Overall, the proposed model is effective in supporting data-driven competency planning for hospital human resources by balancing recommendation relevance and diversity.
Aceh Cultural Asset Data Management System Using Website-Based Business Process Reengineering Method
This study aims to design and implement a cultural asset data management system in Aceh using a website-based Business Process Reengineering (BPR) approach. Aceh possesses abundant cultural assets with significant historical, traditional, and artistic value; however, their management has been constrained by manual, fragmented, and inefficient processes, resulting in difficulties in data monitoring, maintenance scheduling, and information dissemination. To address these challenges, this research applies the BPR method to analyze and redesign existing workflows, followed by the development of an integrated web-based management system. The proposed system features a user-friendly interface that enables administrators and stakeholders to manage asset records, update asset conditions, perform data searches, monitor preservation status, and generate reports efficiently. The novelty of this study lies in the integration of BPR methodology with a web-based cultural asset management platform specifically tailored to Aceh’s cultural preservation needs, combining workflow optimization, centralized data storage, and real-time monitoring within a single system. Unlike previous studies that mainly focus on digitization or database development, this research emphasizes comprehensive business process transformation to improve operational efficiency and governance quality. System evaluation was conducted through functionality testing, usability assessment, and efficiency analysis. The results show that the implementation of the proposed system reduced average data processing time by 68%, improved data retrieval speed by 74%, and increased data accuracy and consistency from 71% to 94% compared to the previous manual system. In addition, usability testing involving 30 respondents achieved a System Usability Scale (SUS) score of 86.5, indicating excellent user acceptance and system effectiveness. These findings demonstrate that the application of BPR significantly enhances cultural asset management performance while supporting more sustainable preservation and decision-making processes. This study is expected to serve as a reference for other regions seeking to implement information technology solutions for cultural heritage preservation and management.
Analisis Pengaruh Preprocessing Regex dan Cosine Similarity terhadap Performa IndoBERT dalam Klasifikasi Berita Hoaks Berbahasa Indonesia
The rapid growth of online media has significantly improved access to information, but it has also accelerated the spread of misinformation and hoax news. In hoax detection research, datasets are commonly derived from fact-checking platforms, which typically contain structured components such as claims, narratives, and clarification statements explicitly indicating that certain information is false. The presence of such clarification sentences has the potential to cause bias, a condition in which the model learns text patterns that explicitly indicate the label, thereby reducing the model's ability to fully understand the content of the news. This study aims to analyze the impact of preprocessing techniques based on regular expression (regex) and cosine similarity on the performance of the IndoBERT model for Indonesian hoax news classification. Both approaches are employed to identify and handle clarification sentences, enabling the model to focus more on contextual and semantic understanding of the news content. Experimental results show that the cosine similarity-based preprocessing outperforms the regex-based approach, achieving accuracy, precision, recall, and F1-score of 92.8%. In comparison, the regex-based method obtains an accuracy of 90.7%, precision of 91.3%, recall of 90.7%, and F1-score of 90.6%. These findings indicate that the semantic-based approach is more effective in handling linguistic variability and reducing potential bias caused by explicit clarification patterns. Overall, this study highlights the importance of appropriate preprocessing strategies in improving classification performance and provides insights into the impact of clarification statements in fact-checking datasets on transformer-based hoax detection models
Pemanfaatan Yolo11 dan Bytetrack untuk Penghitungan Telur Berbasis Visi Komputer pada Konveyor Secara Real-time
Egg counting is an important stage in the laying hen farming industry because it directly affects productivity and the efficiency of production management. Manual counting methods still have several limitations, including relatively long processing time, the need for a large workforce, and a fairly high error rate due to human factors. These conditions indicate the need for the implementation of automated technology capable of improving speed, accuracy, and consistency in the egg counting process. This study aims to develop an automatic egg counting system based on computer vision by integrating YOLO11 as the object detection method and ByteTrack as the object tracking method. The egg dataset was collected through image acquisition under various conditions, followed by annotation and data augmentation processes before being used in the model training stage using Google Colab.The test results show that the developed system is capable of detecting and counting eggs with a high level of accuracy, where precision and recall values exceed 0.90, and the average counting accuracy reaches 94.4% under various testing conditions. The main factors affecting system errors are high conveyor speed, which causes motion blur, and lighting variations that can lead to false positives and false negatives. The main contribution of this research is the development of a camera-based counting system design, thereby reducing errors in counting the number of eggs. The results of this study indicate that the integration of YOLO11 and ByteTrack has the potential to improve the efficiency of automated egg counting processes and contribute to the advancement of computer vision technology in the modern poultry industry.
Information Security Risk Analysis and Identification in the Tulang Bawang Data Portal Application Using the OCTAVE Allegro Method
Information technology has become an essential component in supporting organizational and governmental operations. In line with the implementation of E-Government and the Satu Data Indonesia initiative, the Tulang Bawang Regency Government, through its Communications and Information Office (Diskominfo), developed the Tulang Bawang Data Portal to facilitate centralized data management. Despite its strategic role, the system is exposed to various information security risks that may threaten the confidentiality, integrity, and availability of data if not properly addressed. This study aims to systematically identify, assess, and prioritize information security risks within the Tulang Bawang Data Portal using the OCTAVE Allegro method. A qualitative risk assessment approach was employed, incorporating asset identification, threat analysis, impact evaluation, and risk prioritization based on defined risk measurement criteria. The findings indicate the presence of four significant risks, with Risk Relative Scores (RRS) ranging from 14 to 23. Notably, 75% of the identified risks are classified as high priority. The most critical risk is associated with database misuse, which poses a substantial threat to sensitive government data. To address these risks, several mitigation strategies are recommended, including the enhancement of access control mechanisms, periodic security audits, user awareness programs, and the establishment of comprehensive data governance policies. This study contributes to the field by providing a structured and practical risk assessment framework tailored to government-based data portal systems, thereby supporting more effective and informed decision-making in information security management.
Perbandingan Model Deep Learning DenseNet121, EfficientNetB0 dan Resnet-50 pada Klasifikasi Anemia Citra Telapak Tangan
Anemia is a global health problem affecting approximately 1.2 billion people worldwide, with the highest prevalence among pregnant women and adolescent girls. Conventional diagnosis through laboratory blood tests is invasive, requires trained medical personnel, and is unaffordable for communities in remote areas. This study aims to evaluate and compare the performance of three deep learning architectures, namely DenseNet121, EfficientNetB0, and ResNet-50, in classifying anemia and non-anemia conditions non-invasively based on palm images. The dataset used is a public dataset called anemiatangan from the Kaggle platform, consisting of 10,200 images with two classes, Anemia and Non-Anemia, divided into 80% training data (8,200 images), 10% validation (1,000 images), and 10% testing (1,000 images). All three models were trained using a transfer learning approach with pre-trained weights from ImageNet, accompanied by preprocessing and data augmentation. Evaluation was performed based on accuracy, precision, recall, F1-Score, and AUC-ROC (Area Under the Receiver Operating Characteristic Curve) metrics. The test results indicate that DenseNet121 and EfficientNetB0 achieved the highest accuracy of 99% with precision, recall, and F1-Score values approaching perfection at 0.99, while ResNet-50 recorded an accuracy of 97%. Therefore, DenseNet121 and EfficientNetB0 are proven to be the most optimal architectures for implementing a non-invasive anemia screening system based on palm images, with the potential to be integrated into mobile applications and telemedicine systems to support early detection of anemia in remote areas.
Penerapan Algoritma Genetika Pada Aplikasi Optimasi Penentuan Kelompok KKM Reguler UIN Maliki Berbasis Web
A genetic algorithm was implemented in a web-based application to optimize the formation of Regular KKM groups at UIN Maulana Malik Ibrahim Malang. The main contribution of this study was reflected in the formulation of constraint rules that were aligned with the requirements of KKM group assignment, so that a fitness function different from those used in previous studies was established [15]. Group formation was carried out by considering four constraints, namely the presence of at least one HTQ member in each group, a low ratio of duplicated majors within a group, a gender proportion aligned with the data distribution, and an even number of members across groups. In addition, the algorithm was integrated into a web-based application so that the group formation process was not only optimized, but also supported by a more interactive system with a high level of usability. The system interface was developed using Laravel on the front-end side. The computational process was executed using Python on the back-end side. The relatively long computation time of the genetic algorithm was handled by applying a flagging-process mechanism in the database so that request timeouts could be avoided. Parameter testing was conducted on Popsize, Generation, Crossover Rate, and Mutation Rate to obtain the best configuration. The test results showed that the best solution was produced at the configuration of Popsize 70, Generation 400, Crossover Rate 0.5, and Mutation Rate 0.5, with a average fitness value of 0.983684211. The evaluation results showed that the number of groups fulfilling all criteria was increased from 82 groups to 177 groups after optimization. Thus, a more optimal, structured, and institutionally appropriate KKM group formation was achieved through the implementation of an interactive web-based system using a genetic algorithm.
Analisis Sentimen Ulasan Mobile Banking Bank Kalbar pada Google Play Store Menggunakan IndoBert
User reviews on the Google Play Store can serve as an important data source for understanding public perceptions of mobile banking services. At the time this paper was written, the application’s average user review score was 3.2, indicating a poor rating. Therefore, the bank needs to further examine the factors that could improve this rating in order to minimize reputational risk, since most users who intend to install an application tend to check its rating first before deciding whether to install it. In general, a rating considered very good is at least 4.5. This study aims to analyze user review sentiment toward the Bank Kalbar Mobile Banking application using a natural language processing approach with IndoBERT, along with two comparison models: TF-IDF with Logistic Regression and RNN BiLSTM. The dataset consists of 2,465 reviews classified into three sentiment classes: positive, negative, and neutral. The data distribution shows class imbalance, with 1,445 positive reviews, 894 negative reviews, and 126 neutral reviews. The data were split using a stratified method into 70% training data and 30% testing data. The research stages included text cleaning, data splitting, model training, and evaluation using accuracy, macro-F1, precision, recall, and a confusion matrix. The experimental results show that the TF-IDF + Logistic Regression model achieved the best performance, with an accuracy of 0.8581 and a macro-F1 score of 0.6777. The RNN BiLSTM model obtained an accuracy of 0.8311 and a macro-F1 score of 0.6777, while IndoBERT achieved an accuracy of 0.8041 and a macro-F1 score of 0.6774. Although IndoBERT did not achieve the highest accuracy, it demonstrated better capability in identifying the neutral class, as indicated by a recall score of 0.6316. These findings indicate that, for a relatively small and imbalanced dataset, a classical TF-IDF-based model can still deliver competitive performance compared with deep learning and transformer-based models.
Optimasi Strategi Inventory dan Mitigasi Knowledge Loss pada Industri Otomotif Melalui Integrasi Algoritma K-Means Clustering dan Framework SECI
Digital transformation within used automotive industry today demands paradigm shift from intuitive decision-making towards data-driven approach to face increasingly intense market competition dynamics. The primary problem identified in this research is high level of subjectivity in stock management and dependence on individual experience triggering organizational knowledge loss risks or knowledge loss. This study aims to optimize stock management strategy and mitigate these risks through integration of K-Means clustering algorithm and Socialization, Externalization, Combination, Internalization framework. The research method involves in-depth analysis of five hundred fifty-eight thousand eight hundred thirty-seven vehicle transaction data using data mining techniques to discover hidden patterns from automotive market behavior. Research results show that the algorithm successfully classified stock into three optimal clusters, where symbol k represents cluster number of three, with high performance proven by Calinski-Harabasz Index score of 283,364.95. These clusters differentiate assets into medium, high-risk, and premium categories based on physical condition and mileage, which allows companies to determine liquidation or retention strategies accurately. Integration with the framework ensures that data mining findings are transformed into permanently documented organizational knowledge management. The implementation of this model provides a significant impact for companies in improving operational efficiency and reducing dependence on individual memory. This research study provides a real contribution in creating an objective foundation for more measurable, systematic, and sustainable managerial decision-making for national industry sectors and other related complex business environment systems.
Implementasi dan Analisis Kinerja Komputasi Paralel Menggunakan Ray pada Lingkungan Multi-Core untuk Pemrosesan Audio
Parallel computing has become an effective approach to improving data processing efficiency, particularly for CPU-bound applications. This study aims to implement parallel computing using the Ray framework for audio feature extraction in a multi-core single-node environment. The dataset consists of 1000 .wav audio files from the RAVDESS dataset, which are processed through feature extraction in the time, frequency, and time–frequency domains using the Librosa library. The extracted features include zero-crossing rate, spectral features, and Mel-Frequency Cepstral Coefficients (MFCC), which provide a comprehensive representation of audio signal characteristics. Each audio file is treated as an independent task and distributed across multiple workers for parallel processing. This approach allows data processing to be performed independently without inter-task dependencies. Experiments are conducted by varying the number of workers from 1 to 4 to observe their impact on execution time. The primary parameter observed is the total execution time required to complete the feature extraction process. The results show that the implementation of parallel computing reduces execution time compared to serial processing. However, the performance improvement is not strictly linear due to system overhead and hardware resource limitations. These findings indicate that a task parallelism approach using Ray can serve as a practical solution to improve audio data processing efficiency in resource-constrained environments without requiring complex distributed computing infrastructure.
Analisis Komparatif Information Gain Dan Gain Ratio Pada Algoritma C4.5 Untuk Klasifikasi Produk ATK Terlaris Segmen Business-to-School
Uncertainty in stock management, such as the risk of overstock and stock-out, is a major challenge for Office Stationery (ATK) distributors in facing fluctuating seasonal demand patterns in the Business-to-School (B2S) segment. This study aims to analyze the comparative attribute selection criteria in the C4.5 algorithm, namely Information Gain and Gain Ratio, in the classification of best-selling ATK products. The dataset used consists of 647 sales transaction data from January to December 2024. The novelty of this study lies in the comparative analysis of the two criteria in a sales dataset with specific seasonal characteristics, which has not been widely discussed in previous studies that generally only focus on the application of a single algorithm. The research methodology follows the Knowledge Discovery in Database (KDD) stages systematically. The results show that Information Gain produces a slightly higher accuracy value, namely 78.98%, while Gain Ratio (77.89%) produces a model with a simpler, more stable, and easier to interpret decision tree structure. The Procurement Type attribute is identified as the most dominant factor in determining the level of product sales. As a main conclusion, this study establishes that Gain Ratio is a more optimal method for strategic business decision making because through Split Information normalization, this method successfully reduces bias towards highly variable attributes and produces a more concise decision tree structure and avoids overfitting compared to Information Gain.
Forecasting Volume Penumpang Harian KRL Yogyakarta–Solo Menggunakan SARIMA, LSTM, dan SARIMA-LSTM
Passenger volume forecasting in public transportation is an important aspect of supporting data-driven operational decision-making. The Yogyakarta–Solo Commuter Rail is a strategic public transportation mode that supports interregional mobility. Fluctuations in passenger volume influenced by daily patterns, weekends, national holidays, and collective leave periods require forecasting models capable of capturing seasonal patterns and possible nonlinear changes in time series data. This study aims to compare the performance of Seasonal Autoregressive Integrated Moving Average (SARIMA), Long Short-Term Memory (LSTM), and hybrid SARIMA-LSTM models in forecasting the daily passenger volume of the Yogyakarta–Solo Commuter Rail. The dataset consists of daily passenger volume data from January to December 2025. The research stages include data preprocessing, calendar-based feature engineering, chronological training and testing data splitting, SARIMA modeling, LSTM modeling, residual modeling using LSTM, and performance evaluation using MAE, RMSE, and MAPE. The results show that the SARIMA model obtained an MAE of 2644.81, an RMSE of 3299.78, and a MAPE of 10.56%. The LSTM model obtained an MAE of 1977.50, an RMSE of 2528.75, and a MAPE of 7.28%. Meanwhile, the hybrid SARIMA-LSTM model achieved an MAE of 2634.78, an RMSE of 3294.24, and a MAPE of 10.52%. Based on these results, the LSTM model achieved the best forecasting performance compared to SARIMA and hybrid SARIMA-LSTM. The hybrid SARIMA-LSTM model provided only a slight improvement over SARIMA but did not outperform the LSTM model. These findings indicate that forecasting model selection should consider dataset characteristics, residual patterns, and the adequacy of historical data. Future research is recommended to use longer historical data and incorporate relevant external variables to improve forecasting accuracy.
Prediksi Persetujuan Pinjaman Bank Ritel Menggunakan CatBoost dengan Optimasi Hyperparameter Berbasis Optuna dan Analisis Interpretabilitas SHAP
The banking sector faces significant challenges in accurately classifying loan applications, particularly for datasets dominated by categorical features with imbalanced class distribution (71%:29%). This study proposes the application of CatBoost (Categorical Boosting) with Optuna-based hyperparameter optimization a Bayesian optimization framework using Tree-structured Parzen Estimator (TPE) for bank loan approval prediction. Two class imbalance handling scenarios are comparatively evaluated: SMOTE and CatBoost built-in class_weight. Experiments are conducted on a dataset of 381 samples with 15 active features (12 original features and 3 engineered features) using 5-fold stratified cross-validation. Results show LightGBM achieves the best overall performance with Accuracy 93.42%, Precision 94.83%, Recall 96.49%, F1-Score 95.65%, ROC-AUC 0.9215, and MCC 0.8220. CatBoost (SMOTE) achieves competitive performance with AUC-CV 0.9030 and F1 94.02%. SHAP (SHapley Additive exPlanations) analysis identifies Credit_History as the dominant feature (mean|SHAP|=3.2225), followed by ApplicantIncome (0.6896) and Property_Area_Semiurban (0.5222). This study contributes as the first investigation integrating CatBoost+Optuna+XAI-SHAP in retail bank loan approval prediction with dominant categorical features, while providing systematic comparison against LightGBM, XGBoost, and Random Forest.
Perbandingan Moving Average dan Exponential Smoothing untuk Prediksi Harga Saham BBRI pada Dataset 2019–2026
This study compares four time series forecasting methods Simple Moving Average (SMA), Double Moving Average (DMA), Single Exponential Smoothing (SES), and Double Exponential Smoothing (DES/Holt) for predicting the closing stock price of BBRI.JK. The dataset comprises 1,768 daily observations spanning January 2019 to December 2026, split into training (80%) and testing (20%) sets. Each method's parameters were optimized via grid search minimizing MAPE, then evaluated across three metrics: MAPE, MAE, and RMSE. SES (α = 0.9) emerged as the best-performing model, achieving a MAPE of 0.3763%, MAE of IDR 14.93, and RMSE of IDR 24.31 substantially outperforming SMA (3.1591%), DMA (2.7561%), and DES (3.6973%). These findings offer methodological guidance for researchers and practical insight for investors operating in emerging market equities with near weak-form efficiency.
Evaluating Unsupervised Clustering for Credit Card Fraud Detection Under Extreme Class Imbalance
The exponential rise in digital payments has elevated the difficulty of identifying fraudulent credit card activities, especially considering the extreme class imbalance inherent in financial records, where illicit actions typically represent a minuscule fraction of overall traffic. This research aims to assess the efficacy of unsupervised machine learning techniques for anomaly recognition within a public, anonymized dataset. The proposed methodology establishes K-Means clustering as a foundational baseline to understand broader structural patterns. Subsequently, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is deployed as the principal mechanism to isolate dense anomalous regions. To enhance processing speed and determine optimal hyperparameters, specifically epsilon and minimum points, initial tuning occurs on a representative data sample, followed by a comprehensive evaluation across the entire dataset. System performance is systematically evaluated through confusion matrix metrics, prioritizing the accurate classification of minority fraud cases. Experimental outcomes reveal that the DBSCAN algorithm attains an 88.5% detection rate for illegitimate transactions, substantially exceeding the 42.3% threshold achieved by the baseline model. Nevertheless, this heightened sensitivity introduces a trade-off, generating a 10.2% false-positive rate regarding legitimate operations. Ultimately, the density-based approach proves robust for isolating rare fraudulent behaviors in massive data environments, demonstrating substantial viability for practical deployment despite the slight increase in false alarms
Implementation of YOLO11 for Disease Detection in Strawberry Plants Based on Android Application
A Diseases in strawberry plants, particularly leaf spot and powdery mildew, represent major challenges that can diminish fruit quality and production quantity. Manual diagnosis by farmers is often subjective, time-consuming, and prone to error. This research aims to develop an automated strawberry disease detection system by implementing and comparing three variants of the latest deep learning architecture, YOLO11n (Nano), YOLO11s (Small), and YOLO11m (Medium), into an Android-based application. The results indicate that the YOLO11n (Nano) variant, as the baseline, provides the most optimal performance for mobile use, achieving a mean Average Precision (mAP@50) of 91.7%, a precision of 0.888, and a recall of 0.841. After integration into Android devices using the TensorFlow Lite format, the model recorded a real-time inference time ranging from 104-125 ms at a speed of 8 FPS. This study contributes an empirical framework for deploying cutting-edge deep learning models on resource-constrained edge devices, establishing that YOLO11n effectively bridges the gap between state-of-the-art detection accuracy and mobile operational efficiency. Furthermore, it provides a practical roadmap for the digital transformation of early-stage crop monitoring, enabling farmers to perform reliable, real-time diagnostics directly in the field.
Implementation Of Attention Mechanism And Explainable Ai For Skin Lesion Classification Using CNN
Skin lesions are critical dermatological indicators that require early detection to prevent severe outcomes such as melanoma. Traditional Convolutional Neural Network (CNN) architectures employed for categorizing these lesions frequently encounter significant hurdles, notably disproportionate class distributions and a lack of transparency, functioning essentially as opaque "black boxes" during inferential processes. To mitigate these limitations, the current research deploys a ResNet-50 framework augmented by a Convolutional Block Attention Module (CBAM) to refine spatial and channel feature prioritization, alongside the integration of Gradient-Weighted Class Activation Mapping (Grad-CAM) to yield interpretable visualizations. The empirical analysis utilized the HAM10000 repository, incorporating a preprocessing pipeline that encompassed spatial resizing, pixel normalization, and data augmentation, subsequently trained via a bipartite transfer learning methodology. Quantitative metrics reveal that the CBAM-integrated architecture elevates the baseline global accuracy from 82.00% to 86.83%, while simultaneously augmenting the Macro F1-Score from 68.00% to 77.00%. Qualitative evaluation using Grad-CAM shows sharper and more localized heatmaps, indicating that the attention mechanism successfully guides the model to focus on clinically relevant lesion areas. These findings suggest that combining attention mechanisms with explainable AI not only enhances classification performance but also provides visual transparency, supporting clinical interpretation. This approach is expected to improve trust and reliability in automated skin lesion classification systems
Evaluasi Komparatif Algoritma Machine Learning dalam Analisis Sentimen Program Makan Bergizi Gratis di Media Sosial X
The analysis of public opinion on social media platforms through sentiment analysis plays a crucial role in understanding how the public responds to government policies, including the Free Nutritious Meal Program (MBG). However, the imbalanced nature of social media data and the use of informal language such as sarcasm pose challenges in the sentiment classification process. Therefore, this study aims to examine public perceptions of the MBG program on platform X while also evaluating the effectiveness of several machine learning algorithms in categorizing sentiment. The dataset used in this study consists of 2,000 comments collected between February and April 2026. The data were labeled using a lexicon-based approach and processed through preprocessing and feature extraction using TF-IDF. The classification process was carried out using six algorithms: Naïve Bayes, K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), Decision Tree, Random Forest, and Logistic Regression. The results show that Random Forest achieved the highest accuracy, reaching 92%, supported by a cross-validation score of 89%, indicating strong model stability. Based on the classification results, public sentiment is predominantly neutral at 66.3%, followed by negative sentiment at 22.6% and positive sentiment at 11.1%. These findings suggest that public opinion toward the MBG program tends to be neutral, with a stronger inclination toward criticism than support. Furthermore, the results highlight the importance of selecting appropriate algorithms to improve the accuracy of sentiment analysis on complex and imbalanced textual data
Klasifikasi Kematangan Buah Kelapa Sawit Dengan SimCLR Berbasis HSV Controlled Color Augmentasi
Self-supervised learning with the SimCLR method requires appropriate data augmentation as the foundation for contrastive representation learning. In the task of classifying oil palm fruit maturity, color is the main discriminative feature, so the standard RGB-based ColorJitter augmentation has the potential to distort critical color information. This study proposes an augmentation framework based on the HSV color space with eight parameter configurations (C1–C8) determined in a data-driven manner through Eta-squared (η²) analysis, designed to preserve the semantic integrity of maturity color while generating varied positive pairs. Experiments were conducted on 1,016 oil palm fruit images across three classes (Unripe, Ripe, Overripe) using ResNet-18 as the SimCLR encoder with a linear evaluation protocol. The results show that all HSV configurations outperform the Baseline (82.22%), with HSV_C1 being the best configuration, achieving a test accuracy of 94.44% and Macro F1-Score of 94.46% (+12.22 pp). The η² analysis confirms that the Hue component is the most discriminative feature between classes, so constraining it during augmentation proves essential for the quality of the resulting representations. The proposed framework consistently improves SimCLR performance in color-based classification domains with minimal labeled data requirements.
Perbandingan Random Forest dan Gradient Boosting pada Prediksi Hasil Belajar Siswa
Student learning outcomes are important indicators for evaluating the success of the learning process and can be used to support data-driven academic decision-making. This study aims to compare the performance of the Random Forest and Gradient Boosting algorithms in predicting student learning outcomes. The study employed a quantitative approach with a comparative experimental design using the student dataset containing 500 student records and 11 attributes. The target variable was passed, while several attributes such as gender, age, study_hours_per_week, attendance_rate, parent_education, internet_access, extracurricular, and previous_score were used as predictors. The data preprocessing stage included data cleaning, missing value handling, categorical data transformation, feature selection, and train-test splitting with an 80:20 ratio. Model evaluation was conducted using confusion matrix, accuracy, precision, recall, F1-score, and ROC-AUC, supported by 10-fold cross validation. The results showed that Random Forest slightly outperformed Gradient Boosting on most evaluation metrics. On the test data, Random Forest achieved an accuracy of 87.00%, precision of 90.63%, recall of 89.23%, F1-score of 89.92%, and ROC-AUC of 93.27%, while Gradient Boosting obtained an accuracy of 86.00%, precision of 90.48%, recall of 87.69%, F1-score of 89.06%, and ROC-AUC of 93.10%. These findings indicate that both algorithms performed well in predicting student learning outcomes, with Random Forest showing more stable performance on the dataset used.
Analisis Sinyal Fisiologis Frekuensi Tinggi untuk Ekstraksi Fitur Kebugaran Berbasis HRV dan Signal Processing
Heart Rate Variability (HRV) is a non-invasive biomarker that reflects the activity of the autonomic nervous system and is closely related to physical fitness and recovery. This study aims to analyze physiological signals based on frequency domain components to extract fitness-related features using signal processing techniques. However, previous studies have shown limitations in utilizing comprehensive HRV frequency analysis for fitness evaluation, thus motivating this study to focus on frequency-based physiological interpretation. Electrocardiogram (ECG) signals were obtained from the PhysioNet database and processed through filtering, R peak detection, and RR interval extraction. Frequency domain analysis was performed using Power Spectral Density (PSD) to obtain spectral features, including Low Frequency (LF) (0.04–0.15 Hz), High Frequency (HF) (0.15–0.40 Hz), and LF/HF ratio. The results showed that the LF component exhibited a dominant peak around 0.1 Hz with values ranging from 0.008–0.009 s²/Hz, while the HF component ranged from 0.002–0.003 s²/Hz and had a broader distribution. An LF/HF ratio greater than 2 indicated a predominance of sympathetic activity. These findings suggest that HRV energy distribution is concentrated in the low-frequency band, reflecting a stable physiological state that has not yet reached optimal recovery. This study demonstrates that frequency-based HRV analysis using signal processing provides meaningful physiological insights for fitness evaluation without relying on machine learning models
Implementasi Algoritma C4.5 Untuk Klasifikasi Menu Favorit Pelanggan Pada UMKM Warung Mie Aceh Seafood
The rapidly growing culinary sector requires Micro, Small, and Medium Enterprises (MSMEs) to implement targeted operational strategies. The main problem often faced by the management of the Warung Mie Aceh Seafood MSME is the inability to accurately predict the level of menu popularity based on external factors. This directly impacts the inefficiency of managing wet raw material (seafood) inventory due to its perishable nature. Therefore, this study aims to implement data mining technology using the C4.5 algorithm to predictively classify customers' favorite menus. As a solution to these problems, this study processed 586 historical sales transaction data records from July to September 2025. Unlike previous studies, the novelty of this research lies in integrating external environmental variables, namely weather conditions and day types, along with menu type attributes into a single multidimensional decision tree model evaluated using the 10-fold cross-validation method. The test results show that the C4.5 algorithm classification model has proven to be excellent, achieving an accuracy rate of 96.08% and a class precision value of 100% for the "Very Favorite" and "Not Favorite" classes. Practically, extracting rules from this model provides a specific contribution as a decision support system; for instance, guiding management to reduce cold beverage stocks during heavy rain, or maximize the 'Snacks' menu inventory on weekends, enabling the MSME to avoid material losses while efficiently minimizing the risk of lost potential revenue.
Internet of Things-Based Water Quality Monitoring and Automatic Feeding for Catfish Ponds Using Mamdani Fuzzy Logic
Manual water-quality monitoring and schedule-based feeding may delay responses to unsuitable pond conditions and lead to inappropriate feed dispensing in catfish cultivation. This study developed an Internet of Things (IoT)-based monitoring and automatic feeding system that evaluates water temperature and pH using Mamdani fuzzy inference. The prototype integrates an ESP32 microcontroller, temperature and pH sensors, an ultrasonic sensor for feed-level monitoring, and a servo motor for feed dispensing. Temperature and pH measurements are classified into five linguistic categories and evaluated through a 25-rule base to produce a binary Feed ON or Feed OFF decision. Sensor validation yielded accuracies of 95.52% for temperature measurement, 95.87% for pH measurement, and 95.97% for ultrasonic distance measurement. Validation using five representative input combinations showed that all program outputs matched the expected rule-base decisions, resulting in 100% decision conformity for the tested cases. During seven days of field monitoring, all 21 evaluations conducted at 09:00, 15:00, and 21:00 produced Feed ON because the paired temperature and pH measurements satisfied the minimum rule-base requirements. Web and mobile dashboards displayed sensor measurements, feed availability, device status, operating mode, and feeding activity in real time. The main contribution is a compact and interpretable mechanism that performs condition-based feeding decisions locally on the ESP32 while supporting remote supervision through IoT interfaces. The results indicate adequate sensor performance and consistent fuzzy decision implementation under the tested conditions.
Analisis Komparasi Algoritma XGBoost dan Logistic Regression Berbasis Explainable AI (SHAP) untuk Deteksi Hipertensi
Hypertension represents a significant global health challenge due to its frequently asymptomatic nature until serious complications arise. This study aims to conduct an in-depth comparison between XGBoost and Logistic Regression algorithms for the early detection of hypertension, with a specific focus on explainability utilizing the SHAP Permutation Explainer method. The primary issue addressed is the tendency of previous research to manipulate datasets through oversampling techniques, which risks altering the natural distribution of clinical features, alongside the presence of "explainer bias" resulting from the use of non-uniform explanation methods. This research utilizes original data from the 2015 Behavioral Risk Factor Surveillance System (BRFSS) to ensure clinically reliable results. The results demonstrate that the XGBoost model consistently outperforms Logistic Regression across all evaluation metrics, with XGBoost achieving an AUC of 0.805 and an accuracy of 73.4%, compared to Logistic Regression's AUC of 0.802 and accuracy of 73.1%. SHAP-based interpretation reveals that BMI and Age are the most dominant predictors in both models. The primary contribution of this study is the provision of a predictive framework leveraging the original data distribution, yielding more objective predictions compared to synthetic manipulation, thereby serving as a credible reference for medical practitioners to understand hypertension risk factors in a transparent and accountable manner.
Smart Virtual Guide: Chatbot Cerdas Sistem Informasi Goa Terawang dengan Analisis User Adoption
Most local tourist destinations in Indonesia still rely on static information systems that are unable to provide real-time responses to visitors, including the Terawang Ecopark Cave in Blora Regency, Central Java. This research developed an intelligent chatbot called Smart Virtual Guide based on the Large Language Model (LLM) which is integrated into the information system of Goa Terawang Ecopark, as well as analyzing the level of user acceptance using the Technology Acceptance Model (TAM). Different from previous tourism chatbot research which is generally based on conventional NLP and only supports one language, the novelty of this research lies in the integration of LLM with the support of Indonesian and Javanese at the same time in local tourist destinations. The development of the system uses the Waterfall method with functional testing through Black Box Testing and data collection from 30 respondents using a Likert scale questionnaire. The results of the Black Box Testing test showed that all features were valid, while the TAM results obtained an average percentage of 80.95% (Good category) on the PEOU, PU, BI, and ATU variables. These findings prove that LLM-based approaches with regional language support are effective in improving the quality of digital tourism information services that are more interactive, inclusive, and adaptive.
Comparison of Logistic Regression and Random Forest Performance in Student Dropout Prediction based on Multi Source Data
The high rate of student dropouts is one of the important challenges in higher education because it can affect academic quality, learning effectiveness, and the performance of educational institutions. This condition encourages the need for a prediction system that is able to identify students at risk of dropouts early so that preventive measures can be taken appropriately. This study aims to compare the performance of Logistic Regression and Random Forest algorithms in predicting student dropout based on multi-source. The dataset consists of 4,424 student data with 34 attributes covering academic, demographic, socioeconomic, and academic administration aspects. The research stages include data preprocessing, target transformation into binary classification, feature scaling, data sharing using an 80:20 scheme, and handling class imbalances using the Synthetic Minority Oversampling Technique (SMOTE). Furthermore, a modeling process was carried out using Logistic Regression and Random Forest algorithms to predict the risk of student dropout. Model evaluation was carried out using accuracy, precision, recall, F1-score, and Area Under Curve Receiver Operating Characteristic (AUC-ROC). The results showed that Random Forest performed better than Logistic Regression with an accuracy of 0.884, precision of 0.842, recall of 0.785, F1-score of 0.812, and AUC-ROC of 0.930. Meanwhile, Logistic Regression obtained an accuracy of 0.871, precision of 0.780, recall of 0.835, F1-score of 0.806, and AUC-ROC of 0.928. These results show that Random Forest is more effective in handling complex relationships in multi-source data for student dropout predictions
Analisis Pengaruh Kadar Amonia terhadap Biota pada Sistem Akuaponik Berbasis IoT Menggunakan Sensor Fusion dan K-Means
Aquaponic systems integrate fish and plant cultivation in a single production cycle, but their success depends heavily on water quality particularly ammonia levels, which can be harmful to living organisms if left unmonitored. This study develops an IoT-based monitoring system using an ESP32 microcontroller equipped with eight sensors (pH, water temperature, air temperature, humidity, DO, EC, TDS, and ammonia) integrated through a sensor fusion approach. Sensor data were processed using mean imputation and Z-score normalization, then analyzed with Pearson Correlation for feature selection and K-Means clustering for anomaly detection in an aquaponic system cultivating Channa striata and Amaranthus sp. Results show that ammonia correlates most strongly with pH (r = 0.50), while correlations with other parameters were relatively low. K-Means successfully distinguished normal from anomalous conditions automatically, and biological testing confirmed that optimal growth occurred at ammonia levels below 1 mg/L. Compared to single-parameter monitoring systems, this multivariate approach provides a more comprehensive picture of environmental conditions and offers a foundation for developing smart, efficient, and sustainable aquaponic systems.
Informasi Evaluasi Tata Kelola Teknologi Informasi Pada Klinik Menggunakan Framework Cobit 5
The rapid advancement of information technology has driven healthcare facilities to optimize the use of information systems to support operations and data management. The UINSU Medan Primary Outpatient Clinic has been using the eClinic system; however, a standardized evaluation of information technology governance has not been systematically established. This study aims to design and implement a web-based information system for evaluating IT governance using the COBIT 5 framework. The method employed is quantitative descriptive with an evaluative approach through the Process Assessment Model (PAM) in the Evaluate, Direct, and Monitor (EDM) domain, covering processes EDM01, EDM02, and EDM05. Data were collected via a web-based questionnaire from five respondents consisting of administrators, doctors, and nurses. Black-box testing confirmed that all system features function as intended. Evaluation results indicate that EDM02 and EDM05 achieved the target Capability Level 3, while EDM01 only reached Level 2, with a gap of +1 level due to the unmet Process Definition attribute (PA 3.1). Improvement recommendations focus on standardizing and documenting IT governance operational procedures at the clinic.
Implementasi Business Intelligence untuk Visualisasi Data Akademik Menggunakan Power BI Dalam Meningkatkan Kualitas Layanan Akademik
Improving the quality of higher education requires the effective, integrated, and measurable management and analysis of students’ academic data. However, many higher education institutions still face challenges in collecting, integrating, and analyzing academic data efficiently. Consequently, the monitoring of academic performance has not yet been conducted in a standardized manner and has not fully supported the achievement of additional performance indicators. In this context, the implementation of Business Intelligence (BI) is highly relevant, as it provides a systematic platform for managing students’ academic performance data and presenting it through informative visualizations. This study aims to implement a Business Intelligence system using Power BI architecture to integrate and manage students’ academic performance data obtained from various academic sources. The data used in this study consisted of 80 student records, including grades, semester grade point average, cumulative grade point average, academic status, study program, and cohort year for the 2023 - 2024 period. The data integration process was carried out through the stages of extraction, transformation, and loading into the Power BI model. The implementation results show that the system was able to process 98% of the available academic data and generate three interactive dashboards, including visualizations of GPA trends, student attendance distribution, and student grade distribution. Based on the testing results, the use of the Power BI dashboard improved the efficiency of the academic reporting process from three days to one hour, representing an increase of 93.1%. In addition, the level of conformity between the information displayed on the dashboard and the source data achieved an accuracy of 97.5%, indicating that the system can support information validity in the academic decision-making process. The implementation of Business Intelligence contributes to improving academic data management, accelerating the monitoring of student achievement, assisting stakeholders in interpreting data more effectively, and supporting the enhancement of academic service quality and higher education performance evaluation.
Implementasi Website Pendataan Dan Monitoring Kelompok Tani Menggunakan Algoritma Decision Tree
This research focuses on developing a web-based information system for data management and monitoring of farmer groups in Minahasa Regency. The system aims to enhance the efficiency, accuracy, and transparency of agricultural data collection and supervision processes. Utilizing technologies such as Laravel framework and Decision Tree algorithms, the system provides real-time data visualization, prediction of assistance needs, and facilitates farmer participation in reporting activities, applying for aid, and accessing training online. The Decision Tree algorithm is employed to generate predictive insights for future aid requirements based on historical data, supporting more informed decision-making by the relevant authorities. The implementation of this system is expected to streamline administrative tasks, improve data accuracy, and promote good governance within the agricultural sector. Overall, this system contributes to advancing digital transformation in agricultural management, enabling better planning, resource allocation, and service delivery for farmers in Minahasa.
Perbandingan Kinerja Model Forecasting Nilai Perdagangan Komoditas HS pada Evaluasi Time-Based
Global economic uncertainty, trade-regime shifts, and supply-chain disruptions have made export-import trade-value forecasting increasingly complex. This study compares the performance of Random Forest, Extra Trees Regression, and SARIMA in predicting monthly trade values of HS commodities in the apparel and footwear sector, covering HS 61, HS 62, HS 63, and HS 64. The dataset was obtained from Indonesia?s Central Bureau of Statistics (BPS) and organized as a monthly time series using a leakage-safe workflow through a time-based train-validation-test split. The modeling stage employed 19 predictive features consisting of historical, local statistical, calendar-seasonal, and exogenous variables, and each model was tuned on the validation set before being evaluated on the holdout test. Performance was assessed using MAE, RMSE, MAPE, sMAPE, and wMAPE, with MAPE as the primary ranking metric. The main contribution of this study lies in providing a fair and replicable comparison of three forecasting models under a time-based evaluation protocol for HS commodity trade data, making the model selection results more representative of real implementation settings. The results show that Random Forest achieved the best MAPE at 22.7746%, slightly outperforming Extra Trees Regression at 22.9469%, while SARIMA recorded 28.9794%. These findings indicate that tree-based ensemble models are more adaptive to volatile trade data, whereas SARIMA remains relevant as a statistical baseline for structured seasonal patterns.
Systematic Literature Review: Application of Deep Learning in Tuberculosis Diagnosis Using Chest X-Ray Images – A Focus on Models, Challenges, and Research Opportunities
Tuberculosis (TB) is an infectious disease with a high mortality rate worldwide. Deep learning offers promising opportunities for automated TB diagnosis from chest X-ray (CXR) images. This systematic literature review (SLR), conducted following PRISMA 2020 guidelines, analyzes 66 articles from Scopus (2021–2026) to examine deep learning models, datasets, evaluation methods, challenges, and research opportunities. Findings reveal that CNN models remain dominant (42.42%), followed by hybrid CNN-Transformer models (37.88%), while public datasets are most frequently used (63.64%). Key challenges include dataset limitations, poor generalization, computational complexity, and lack of interpretability. This review contributes a comprehensive taxonomy of deep learning architectures for TB detection, identifies emerging trends toward hybrid and ensemble approaches, and provides actionable recommendations for future research, including federated learning, explainable AI, and clinical integration. These findings offer valuable guidance for researchers and practitioners developing reliable AI-based TB diagnostic systems.
Sistem Monitoring Kualitas Udara Ruangan Berbasis IoT Dengan Algoritma Fuzzy Logic
Indoor air quality is often not monitored directly, although exposure to pollutant gases, fine particles, unsuitable temperature, and humidity may increase the potential for respiratory health problems. The main problem is that air monitoring devices are commonly expensive, difficult to access, and present technical data that is not easy for users to understand. This study aims to design an Internet of Things (IoT)-based indoor air quality monitoring system that processes sensor data using Fuzzy Logic to produce air quality risk categories rather than disease diagnosis. The system uses an ESP32 DevKit v1, DHT22, MQ-135, and GP2Y1010AU0F sensors, then sends data in real-time through the MQTT protocol to a Mosquitto broker and Python Bridge Server for visualization on a web dashboard. The testing results show that the DHT22 sensor has a maximum error of 0.90% for temperature and 2.17% for humidity, the MQ-135 can distinguish normal to dangerous air conditions, the GP2Y1010AU0F detects PM2.5 up to 89.3 ug/m3, and MQTT communication remains stable with latency below 200 ms. Fuzzy Logic testing produces scores of 15-91 that map clean, moderate, unhealthy, and dangerous air scenarios. The contribution of this study is a low-cost air monitoring prototype that presents real-time air quality information in an understandable form and can support early prevention actions against poor air exposure.