The IJICS (International Journal of Informatics and Computer Science)
International Journal of Informatics and Computer Science (IJICS) is a reputable scientific journal that publishes the latest research results in the fields of informatics and computer science. This journal focuses on the development of theories, methods, and applications of intelligent technologies, covering topics such as artificial intelligence, machine learning, big data, natural language processing, computational intelligence, robotics, computer vision, knowledge systems, and other innovative applications in various sectors. IJICS is committed to being an open access publication platform that provides high-quality research accessible globally to academics, practitioners, and technology developers. With a broad scope ranging from biologically inspired systems to high-performance computing, this journal serves as an important forum for the exchange of ideas and interdisciplinary collaboration in the fields of computer science and informatics.
The Impact of Dominant Color in the Shopee Application's User Interface Design on User Focus Levels
The design of mobile application interfaces plays a crucial role in shaping user experience, where color influences not only aesthetics but also user cognition and focus. This study aims to analyze the effect of the dominant orange color in the Shopee application interface on user focus levels. A quantitative correlational approach was employed, involving 32 respondents who completed a Likert-scale questionnaire, with data analyzed using SPSS. The results show a strong and statistically significant correlation between color perception and user focus (r = 0.681; p < 0.05), indicating that better evaluation of the dominant color is associated with higher focus levels. Furthermore, the coefficient of determination (R² = 0.464) reveals that nearly half of the variation in user focus is explained by color alone. These findings confirm that the dominant orange color enhances attention, supports sustained concentration, and improves navigation efficiency. This study highlights color as a key functional factor in optimizing user focus in mobile commerce applications.
Copyright (c) 2026 The IJICS (International Journal of Informatics and Computer Science)
Development of a Web-Based Digital Dictionary for the Most Common Vocabulary in the Qur'an
Access to learning the most common Quranic vocabulary remains a challenge, particularly for the Deaf and Hard of Hearing community who face a significant lack of inclusive digital resources. Existing digital dictionaries often overlook this demographic, failing to provide crucial accessibility features such as sign language video translations. This research aims to bridge this accessibility gap by designing and developing a specialized, website-based digital dictionary focused on the most common Quranic terms. The primary contribution is an interactive and inclusive platform that integrates Arabic script, transliteration, and meaning with corresponding sign language video interpretations. The Agile Development methodology was employed, allowing for a flexible and iterative process to effectively address user feedback. This lifecycle involved key stages such as requirements planning, system modeling using UML and ERD, backend development with the Laravel framework, and functional validation via black-box testing. The testing phase demonstrated highly positive outcomes; functional validation via Black-Box testing confirmed that all system modules operated correctly according to specifications without any errors. Furthermore, the usability evaluation using the System Usability Scale (SUS) yielded an average score of 71, placing the platform in the "Good" category with an "Acceptable" level of usability.
A Generative AI-Driven Approach to Integrating the SECI Model for Knowledge-Based Systems
This study examines the potential role of Generative Artificial Intelligence (Gen-AI) in extending the Socialization, Externalization, Combination, and Internalization (SECI) model within knowledge management. Using a Systematic Literature Review (SLR) of studies published between 2024 and 2026, it maps the functions of technologies such as Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Logic Augmented Generation (LAG) into Nonaka and Takeuchi’s knowledge spiral. The findings indicate that Gen-AI can support knowledge processes such as retrieval, interpretation, structuring, and integration, which may contribute to more continuous and digitally mediated knowledge flows. Some reviewed studies report improvements in handling tacit and explicit knowledge; for example, a simulation-based study reports a tacit knowledge recall rate of up to 94.9%, although this result is derived from a specific experimental context and should not be generalized. The review also identifies challenges, including AI-generated inaccuracies, overreliance on automated systems, and data security concerns, highlighting the continued importance of human oversight. This study contributes a conceptual mapping of a Generative AI-based Knowledge Framework (GRAI) as an extension of the SECI model; however, this contribution remains theoretical and requires further empirical validation across organizational contexts.
Comparative Study of Naive Bayes and SVM for E-Commerce Sentiment Classification on Shopee
This study compares the performance of Naive Bayes and Support Vector Machine (SVM) for sentiment classification of men’s shirt product reviews on Shopee. A dataset of 500 reviews was collected via web scraping and processed through case folding, tokenizing, stopword removal, and stemming, followed by TF-IDF feature extraction. The data was split at an 80:20 ratio and evaluated using accuracy, precision, recall, and F1-score. The main contribution of this study is demonstrating that despite both algorithms achieving equal overall accuracy of 93%, SVM outperforms Naive Bayes in detecting negative sentiment on a class-imbalanced dataset, with SVM attaining a negative class recall of 0.87 and F1-score of 0.88 compared to 0.80 and 0.87 for Naive Bayes. These findings provide practical guidance for selecting an appropriate classifier in imbalanced e-commerce review classification tasks.
Enhancing Product Recommendations Using ALS Matrix Factorization on Retailrocket with Apache Spark
The rapid growth of e-commerce has generated massive volumes of user interaction data, requiring recommendation systems capable of providing accurate and relevant product suggestions. However, conventional recommendation systems still face sparsity issues and low prediction accuracy in large-scale data environments. This study aims to improve the accuracy of big data-based product recommendation systems by implementing Matrix Factorization using the Alternating Least Squares (ALS) algorithm. The research utilized the Retailrocket E-commerce Dataset consisting of 104,287 interaction records, 18,642 users, and 7,831 products with a sparsity level of 96.8%. Implicit interactions, including product views, add-to-cart activities, and purchases, were transformed into weighted preference values to represent user behavior. The model was implemented using Apache Spark MLlib within a distributed computing environment. Model evaluation was conducted using RMSE, MAE, Precision@10, and Recall@10 metrics with a 5-fold cross-validation approach. The experimental results indicate that the optimal configuration was achieved using 50 latent factors, 0.05 regularization, and 20 iterations, producing an RMSE value of 0.836, MAE of 0.689, Precision@10 of 0.861, and Recall@10 of 0.824. These findings demonstrate that ALS-based Matrix Factorization effectively improves recommendation quality while supporting scalability for large-scale data processing in modern e-commerce environments. Keywords: Matrix Factorization, Alternating Least Squares, Recommendation System, Big Data, Collaborative Filtering.
Classification of Coming‑of‑Age Song Lyrics Using Convolutional Neural Network (CNN) Architecture
The coming-of-age theme in song lyrics is rich in emotional expressions, identity transitions, and nostalgia. This study implements a 1-Dimensional (1D) Convolutional Neural Network (CNN) architecture to automatically classify coming-of-age-themed song lyrics. Raw lyrics data were collected through a custom scraping function get_song_lyrics, which then went through a structured text preprocessing stage to remove stopwords and non-semantic components. The cleaned words were represented into a vector space using a pre-trained 50-dimensional GloVe embedding (GloVe 50d) of size (max_length, 50) to capture semantic relationships between words. A 1D CNN model was applied to extract local features in the form of phrase combinations (n-grams) through filter shifting, followed by a Global Max Pooling layer to filter out the most dominant emotional information before the final classification process. As a comparison method and additional analysis, a rule-based approach using TextBlob was applied to extract polarity and subjectivity scores, while the Word Cloud technique was used to visualize the dominance of transitional lexical terms such as grow, leave, and remember. The gap between training and validation performance suggests that the model learned dataset-specific patterns rather than generalized semantic representations. Similar overfitting behavior has been reported in CNN-based lyric and sentiment classification studies when training data are limited or insufficiently diverse. The results showed that the integration of GloVe 50d semantic representation and local feature extraction by 1D CNN was able to produce high and stable accuracy in recognizing the unique characteristics of song lyrics with a maturity theme.
K-Means Clustering for Adaptive Learning Recommendations Based on Student Academic Performance in Vocational Education
Differences in students' academic abilities create challenges in the learning process because many schools still apply uniform learning strategies without considering individual academic characteristics. This study aims to cluster students based on academic performance using the K-Means Clustering algorithm and to generate adaptive learning recommendations for each cluster. The dataset consisted of academic records from 50 students of SMK Swasta 2 Delima Sari in the 2024/2025 academic year, including assignment scores, Midterm Examination (UTS) scores, Final Examination (UAS) scores, and attendance rates. Data preprocessing was conducted through data cleaning and Min-Max normalization to ensure that each variable contributed proportionally to the clustering process. The K-Means algorithm was implemented with k = 3 to form high, medium, and low academic performance groups. The results show that 12 students (24%) were classified into the High Cluster, 28 students (56%) into the Medium Cluster, and 10 students (20%) into the Low Cluster. The clustering quality was supported by a WCSS value of 2.3714 and a Silhouette Score of 0.6843, indicating reasonably well-separated clusters. Based on the cluster profiles, students in the High Cluster are recommended to receive enrichment activities, students in the Medium Cluster receive regular instruction with periodic feedback, and students in the Low Cluster receive remedial learning and structured mentoring. These findings indicate that K-Means Clustering can support data-driven educational decision-making and provide a practical basis for adaptive learning recommendations in vocational education.
Deep Learning-Based Sentiment Analysis on Social Media Text Using Long Short-Term Memory (LSTM)
The large volume and informal nature of Indonesian social media text make manual sentiment analysis slow, inconsistent, and difficult to scale. This study evaluates a Long Short-Term Memory (LSTM) model for classifying public comments from X/Twitter into positive, neutral, and negative sentiment. A balanced dataset of 3,000 public Indonesian-language posts collected from January to March 2026 was manually labeled into three equal classes. Duplicate, irrelevant, advertising, and empty posts were removed; the remaining text underwent case folding, noise removal, tokenization, and padding. The data were stratified into 2,400 training and 600 testing instances. The model used a 10,000-word vocabulary, 100-token sequences, a 128-dimensional embedding, 128 LSTM units, dropout of 0.5, and a softmax output layer. On the held-out test set, the model obtained 87.00% accuracy, 86.80% precision, 86.50% recall, and 86.60% F1-score. Positive sentiment produced the strongest class-level performance, whereas neutral comments were more difficult because factual, ambiguous, and mixed expressions provide weaker affective cues. The findings show that LSTM provides a useful baseline for three-class Indonesian social media sentiment classification. However, generalization remains limited by the single-platform, topic-dependent dataset and the absence of repeated or cross-domain evaluation.
Performance Analysis of RSA–RC4 Hybrid Encryption Using the EM2B Key Generator
This study evaluates the computational performance of a hybrid encryption scheme that combines RSA for key protection, RC4 for file encryption, and the EM2B mechanism for deterministic key expansion. The prototype was implemented as a PHP-based application and tested on 35 SQL text files. RC4 was used as the baseline, while the hybrid scheme was assessed using ciphertext size, character count, encryption time, decryption time, and restoration success. The reported averages show that RC4 required 1.33 s for encryption and 1.41 s for decryption, whereas RSA–EM2B–RC4 required 5.30 s and 100.09 s, respectively. The hybrid scheme also produced approximately 4.29 times greater ciphertext storage than RC4 alone. All files were restored to the same reported size after decryption; however, byte-level or hash-based integrity verification was not performed. Therefore, the results demonstrate functional implementation and quantify computational overhead, but they do not establish superior cryptographic security. RC4 is retained only as an experimental legacy baseline because current standards deprecate its operational use. Future evaluation should include authenticated encryption, entropy and randomness testing, key-sensitivity analysis, repeated timing trials, and cryptographic hash verification of restored files.
Development of a Web-Based Nutritious Food Consumption Monitoring System
School meal programs require reliable records to determine whether meals are distributed and consumed as planned. At the observed school, consumption was recorded manually, creating risks of incomplete entries, delayed recapitulation, and difficulty retrieving historical data. This study developed a web-based nutritious food consumption monitoring system using the Waterfall model. Requirements were identified through observation, stakeholder interviews, and document review, then translated into Unified Modeling Language and database designs. The implemented modules cover role-based login, student records, food menus, daily consumption entries, and period-based reports. Functional verification used predefined black-box scenarios for authentication, data management, consumption recording, reporting, and logout. All seven primary scenarios produced the expected outputs, while two focused authentication cases correctly accepted valid credentials and rejected invalid credentials. These results show that the prototype satisfies the tested functional requirements and centralizes previously fragmented records. However, the 100% functional success rate applies only to the specified test cases and does not establish usability, performance, security, nutritional adequacy, or effectiveness in improving student health. Field deployment with representative users, measured task performance, security assessment, and validation of nutritional indicators is therefore required before operational adoption.
LSTM-Based Deep Learning Approach for Hoax Detection on Indonesian Social Media
The widespread adoption of social media, particularly Facebook, has drastically accelerated information dissemination while simultaneously amplifying the spread of misinformation. Hoax content poses a serious threat to public trust and social cohesion. This study proposes a hoax classification system based on the Long Short-Term Memory (LSTM) architecture, selected for its inherent ability to model sequential dependencies in textual data. The experimental workflow covered text preprocessing (case folding, tokenization, stopword removal, and stemming), word vectorization using the Keras Tokenizer, and end-to-end LSTM model training on the “Indonesian Fact and Hoax Political News” dataset from Kaggle, totaling 4,502 samples with a balanced 80:20 train-test split. Experimental results demonstrate the proposed model achieves 92.44% accuracy, 92.63% precision, 92.22% recall, and a 92.42% F1-score on the test set. These findings confirm that LSTM effectively captures contextual and sequential linguistic patterns in Indonesian-language content, offering a viable and scalable solution for automated hoax detection on social media platforms.
Digital Evidence Integrity System Using SHA-256 Hashing for Digital Forensic Data Tracking
This study presents the design and implementation of a digital forensic system that integrates an audit trail mechanism with the SHA-256 hashing algorithm to track data changes and detect unauthorized manipulation, without depending on a blockchain architecture. Using a Design Science Research approach, the system called the Digital Evidence Integrity System was built as a web application with PHP and MySQL, covering requirement identification, model design, implementation, an integrity validation mechanism, and testing and evaluation. Evaluation began with a pilot of five records under authorized and unauthorized update scenarios that simulated direct database access, then was expanded to sixteen scenarios spanning seven manipulation categories: insert, update, deletion, replay, concurrent access, stored-hash alteration, and audit-trail-log manipulation. Across the fourteen scenarios usable for a confusion matrix, the system correctly flagged 4 of 9 genuine manipulation attempts (a 44.4% detection rate) with zero false positives on legitimate changes, while consistently failing to detect four categories of manipulation: outright deletion of a record, replay of a superseded but internally valid data-hash pair, an attacker recomputing and overwriting the stored hash together with the data, and tampering with the audit-trail log itself. A repeated one-character perturbation test (60 trials) confirmed the avalanche effect of SHA-256, with a mean Hamming distance of 126.42 of 256 bits (49.38%, SD 8.52) between the hash of the original and the perturbed input. Taken together, these findings indicate that an audit trail combined with SHA-256 hashing is computationally lightweight and reliably detects manipulation that alters data without also updating its stored hash, but is not, in its current form, a sufficient safeguard against an attacker capable of also controlling the hash or the log; protecting the audit-trail log itself, adding a genuine user-authentication mechanism, and closing the deletion, replay, and stored-hash-alteration gaps identified here are the priorities for further development before the system could be relied upon in an operational forensic setting.
Implementation of K-Means Clustering for Student Achievement Classification Using Academic Performance Indicators
Student achievement assessment in educational institutions is often conducted manually by relying on average scores or class rankings, making the results less objective and unable to represent students’ overall academic conditions. This study aims to implement the K-Means Clustering algorithm to classify student achievement using academic performance indicators. The dataset consists of 10 student records with four variables: average score, examination score, attendance percentage, and assignment score. The research stages include data collection, data validation, Min-Max normalization, initial centroid selection, Euclidean Distance calculation, cluster formation, centroid updating, and interpretation of clustering results. The number of clusters was set to K=3, representing high, medium, and low achievement categories. The results show that Cluster C1 consists of 4 students with high achievement, Cluster C2 consists of 3 students with medium achievement, and Cluster C3 consists of 3 students with low achievement. The final centroid values indicate that Cluster C1 has the strongest academic performance, while Cluster C3 requires more intensive academic support. These findings demonstrate that K-Means Clustering can classify student achievement objectively and support data-driven educational decision-making, academic guidance, and targeted learning strategy development.
Laundry Performance Analysis Using KPI and Linear Regression Dashboard
This study develops an integrated approach for measuring and forecasting the revenue performance of a small laundry business using Key Performance Indicators (KPIs), simple linear regression, and a dashboard prototype. The dataset consists of operational records from one Indonesian laundry enterprise covering January–June 2024. Daily records were validated, anonymized, and aggregated into six monthly observations containing transaction volume, customer count, laundry weight, revenue targets, and actual revenue. The KPIs comprised total revenue, month-to-month revenue growth, average revenue per transaction, and target achievement. A chronological holdout was used to evaluate the regression model: January–April formed the training set, whereas May–June formed the test set. The holdout evaluation produced MAE of Rp1,287,500, RMSE of Rp1,319,209, and MAPE of 6.31%. After validation, the model was refitted to all six observations, resulting in Ŷ = 13,216,666.67 + 1,235,714.29X with R² = 0.9198. Total observed revenue was Rp105,250,000; the highest monthly revenue was Rp21,000,000 in June; average revenue per transaction remained Rp50,000; and overall target achievement was 105.25%. The model forecast revenues of Rp21.87 million, Rp23.10 million, and Rp24.34 million for July, August, and September, respectively. The dashboard consolidates the KPI and forecast results into a concise decision-support view. Because the analysis uses one business and only six monthly observations, the forecasts should be interpreted as an exploratory trend estimate rather than a generalizable long-term model.
Random Forest Model for Food Security Index Prediction Using Climate and Socioeconomic Panel Data in North Sumatra
This study develops and evaluates a Random Forest Regression model for predicting the Food Security Index (IKP) using climate and socioeconomic panel data from 33 regencies/cities in North Sumatra, with Deli Serdang Regency as the principal case discussed. The predictors comprise poverty rate, gross regional domestic product (GRDP) per capita, annual rainfall, and annual rainy days. The common modeling period was 2020–2021. Of 66 potential regency-year observations, eight lacked IKP values in 2021, leaving 58 complete observations. To account for the panel structure, model performance was evaluated using nested group-aware cross-validation, with regency/city as the grouping variable. Across five outer folds, Random Forest achieved RMSE 8.450 ± 2.703, MAE 6.289 ± 2.018, and R² −0.501 ± 1.500. Linear regression obtained RMSE 8.282 ± 2.431, MAE 6.401 ± 2.262, and R² −0.363 ± 1.192, while a single decision tree obtained RMSE 9.821 ± 3.482, MAE 8.030 ± 2.849, and R² −0.972 ± 1.871. Thus, no model demonstrated strong or stable out-of-group generalization. Repeated group-aware permutation analysis identified poverty rate as the most consistent predictor. The results are predictive and associational, not causal, and the present model should be regarded as an exploratory proof of concept requiring larger, spatially resolved data for robust regional forecasting.
Decision Support System for Early Detection of Depression Risk Based on Lifestyle Patterns Using the ROC-WASPAS Method
This study develops a decision-support model for prioritizing lifestyle-based depression risk indicators using Rank Order Centroid (ROC) weighting and Weighted Aggregated Sum Product Assessment (WASPAS). The model evaluates five respondents against five criteria: stress level, sleep duration, social interaction, dietary pattern, and physical activity. Criterion priorities were converted into ROC weights, after which the decision matrix was normalized according to cost and benefit orientation. WASPAS then combined the weighted sum and weighted product components using an equal aggregation coefficient. Stress received the largest weight (0.456), followed by sleep duration (0.256), social interaction (0.156), dietary pattern (0.090), and physical activity (0.040). The final preference scores placed A1 first (0.569), followed by A3 (0.557), A2 (0.519), A5 (0.502), and A4 (0.434). These scores represent relative lifestyle profiles within the small study sample; they do not constitute a clinical diagnosis of depression. The model demonstrates a transparent calculation workflow that may support preliminary risk prioritization when its criteria, scales, and weights have been validated by mental-health professionals. Future work should use a larger sample, a validated depression instrument, sensitivity analysis, and external validation before practical screening is considered.