Implementasi Data Mining Tingkat Kepemimpinan Siswa dengan K-Nearest Neighbor, Decision Tree, dan Naïve Bayes
DOI:
https://doi.org/10.30865/mib.v7i1.5351Keywords:
Data Mining, Leadership, Student, K-NN, Decision Tree, Naïve BayesAbstract
The process of monitoring and evaluating high school student leadership is deemed necessary because the level of student leadership is one of the prerequisites for high school students to face real challenges in the future. Data mining can be used to classify the level of leadership among high school students. The purpose of the research conducted in this case is to apply data mining using the K-NN, Decision Trees, and Naive Bayes models. This research is located in two different public high schools, namely SMA A as training data and SMA B as test data. This data was obtained in the same year, namely 2022. The data obtained were analyzed with the help of the Rapidminer application using K-NN, Decision Tree, and Naive Bayes. Student data that is processed is Basic Education Data (DAPODIK) in excel format. Before being analyzed, the text is processed first, namely tokenization, case folding, stop words, and details. The main goal of the steps above is also the main goal of this study to get the most accurate algorithm for classifying student leadership levels and knowing the results for comparison. The conclusion of this study is when measuring the performance of the three algorithms, the test results use confusion matrix validation. The K-NN algorithm was found to have the highest accuracy score compared to the Decision Tree and Naive Bayes. The accuracy value of the K-NN method using a dataset of high school students is 95.86%, the accuracy value of the Decision Tree algorithm is 94.65%, and the accuracy value of the Naïve Bayes algorithm is 79.55%.References
Trimanto,_F._Faqih,_I._M._Irfani,_and_S._Timur, “Penerapan_Data_Mining_Untuk Evaluasi Status_Kelulusan_Mahasiswa_Fakultas_Teknologi_Pertanian_Tahun_2015_Menggunakan_AlgoritmaNaïve_Bayes_Classifier,â€_in_Seminar_Nasional_Informatika,_Amikom_University,_2015
Fakultas Teknik Universitas Islam Madura, “Rencana Operasional Fakultas Teknik Universitas Islam Madura,†2022
E. P. Rohmawan, “Prediksi Kelulusan Mahasiswa Tepat Waktu Menggunakan Metode Desicion Tree Dan Artificial Neural Network,†J. Ilm. MATRIK, vol. 20, no. 1, pp. 21–30, 2018
I. A. A. Amra and A. Y. A. Maghari, “Students performance prediction using KNN and Naive Bayes ian,†in ICIT 2017 - 8th International Conference on Information Technology, Proceedings, 2017, no. December 2018, pp. 909–913
S. P. Nabila, N. Ulinnuha, and A. Yusuf, “Model Prediksi Kelulusan Tepat Waktu Dengan Metode Fuzzy C-Means Dan K-Nearest Neighbors Menggunakan Data Registrasi Mahasiswa,†Netw. Eng. Res. Oper., vol. 6, no. 1, p. 39, 2021
D. Iskandar and Y. K. Suprapto, “Perbandingan Akurasi Klasifikasi Tingkat,†Netw. Eng. Res. Oper., vol. 2, no. 1, pp. 37–43, 2015
K. D. Maisari, D. Andreswari, and R. Efendi, “Implementasi Metode TOPSIS dengan Pembobotan Entropy untuk Penentuan Calon Penerima Bantuan Siswa Miskin (BSM) APBD Kota Bengkulu( Studi Kasus : SMAN 8 Kota Bengkulu ),†J. Rekursif, vol. 5, no. 2, pp. 179–194, 2017
A. Aditya, I. Jovian, and B. N. Sari, “Implementasi K-Means Clustering Ujian Nasional Sekolah Menengah Pertama di Indonesia Tahun 2018/2019,†J. Media Inform. Budidarma, vol. 4, no. 1, p. 51, 2020, doi: 10.30865/mib.v4i1.1784
N. Rofiqo, A. P. Windarto, and D. Hartama, “Penerapan Clustering Pada Penduduk Yang Mempunyai Keluhan Kesehatan Dengan Datamining K-Means,†KOMIK (Konferensi Nas. Teknol. Inf. dan Komputer), vol. 2, no. 1, pp. 216–223, 2018, doi: 10.30865/komik.v2i1.929.
E. Sugiyarti and A. Maseleno, “Sistem Pendukung Keputusan (Dss) Penyeleksian Pemilihan Penerima Beasiswa Sma N 1 Ulubelu Tanggamus Mengunakan Data Mining,†Konf. Mhs. Sist. Inf., vol. 6, no. 1, pp. 62–69, 2018
R. S. Harahap, “Komparasi Algoritma Klasifikasi Decision Tree, Naive Bayes Dan Neural Network Untuk Prediksi Penyakit Ginjal Kronis,†Konf. Nas. Ilmu Pengetah. dan Teknol., vol. 2, no. 1, pp. 239-INF.244, 2016, [Online]. Available: http://konferensi.nusamandiri.ac.id/prosiding/index.php/knit/article/view/129.
S. Defiyanti, “Integrasi Metode Clustering dan Klasifikasi untuk Data Numerik,†Citee, no. July, pp. 256–261, 2017.
E. Buulolo, Buku Data Mining Untuk Perguruan Tinggi, I. 2020.
F. L. Gaol, T. Matsuo, and S. City, “The Digital Transformation of Enterprise Architecture on Culinary SMEs : A Case Study – Culinary SMEs in DKI Jakarta Province,†vol. 14, no. 2, pp. 275–289, 2020.
N. Rofiqo, A. P. Windarto, and D. Hartama, “KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) PENERAPAN CLUSTERING PADA PENDUDUK YANG MEMPUNYAI KELUHAN KESEHATAN DENGAN DATAMINING K-MEANS,†[Online]. Available: http://ejurnal.stmik-budidarma.ac.id/index.php/komik.
J. Han, M. Kamber, and J. Pei, “Data Mining: Concepts and Techniques,†Data Min. Concepts Tech., 2012, doi: 10.1016/C2009-0-61819-5
Ellis and Hartler. Nursing in Today’s World. Lippincot Williams & Wilkins. Philadelphia. 2012
Davies and P. Beynon, Database Systems Third Edition. New York: Palgrave Macmillan, 2004.
I Pramudiono. (2007) Ilmu Komputer. [Online]. http://www.ilmukomputer.org/wp-content/uploads/2006/08/iko-datamining.zip
B. Santosa, Data Mining Teknik Pemanfaatan Data untuk Keperluan Bisnis. Yogyakarta: Graha Ilmu, 2007.
M. Berry and G. Linoff, Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, Ney York: John Wiley & Sons, 2004.
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