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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Sains dan Teknologi Jurnal Ilmiah KOMPUTASI Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Information System for Educators and Professionals : Journal of Information System Sebatik MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Ilmiah Media Sisfo EXPLORE JOISIE (Journal Of Information Systems And Informatics Engineering) Jurnal Teknologi Dan Sistem Informasi Bisnis Indonesian Journal of Electrical Engineering and Computer Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Majalah Ilmiah UPI YPTK Journal of Computer Scine and Information Technology Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) EXPLORE Jurnal Komtekinfo INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System Jurnal Pengabdian Masyarakat Dharma Andalas
Yanto, Musli
Universitas Putra Indonesia YPTK Padang
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Social Sciences Other

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Optimalisasi Strategi Pembelajaran Siswa Melalui Identifikasi Gaya Belajar Menggunakan Klasterisasi K-Means dan Klasifikasi K Nearest Neighbor Ilsa Hidayat; Musli Yanto; Rini Sovia
Journal of Information System Research (JOSH) Vol 7 No 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i3.9322

Abstract

Accuracy in adjusting teaching strategies to student learning characteristics is important because it can determine the effectiveness of the learning process. One of the key factors in improving the quality of learning is the suitability between teachers' teaching strategies and students' learning styles. The mismatch between the two aspects can reduce the effectiveness of the learning process and have an impact on low learning outcomes. Based on this, this study aims to optimize students' learning strategies through the application of the K-Means clustering model and the K-Nearest Neighbor classification. The performance of the K-Means Algorithm is able to classify learning styles and determine the labeling of learning styles, K-Nearest Neighbor is used to classify data that has been labeled by the K-Means algorithm. This research dataset amounted to 200 student data sourced from SMP Negeri 1 Panyabungan from the results of 20 questions answered by students. The results showed that the combination of the K-Means and K-Nearest Neighbor algorithms produced good performance with an accuracy value of 0.92, precision of 0.92, recall of 0.92, and F1-score of 0.91. The contribution of this research is expected to enrich the literature related to the application of the K-Means and K-Nearest Neighbor models in optimizing learning strategies, as well as assisting teachers at SMP Negeri 1 Panyabungan in designing and implementing learning strategies that are more effective and in accordance with the needs of students.
Identifikasi Status Gizi Balita Menggunakan Metode K-Means Clustering Dan Naïve Bayes Sela Ramadani; Musli Yanto; Gunadi Widi Nurcahyo
INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System Vol 11 No 1 (2026): INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS (Juni 2026)
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Bina Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51211/isbi.v11i1.3820

Abstract

Penelitian ini dilatarbelakangi oleh pentingnya kesehatan balita sebagai indikator keberhasilan pembangunan yang berkaitan erat dengan kondisi gizi serta berdampak langsung pada pertumbuhan dan perkembangan anak. Di wilayah kerja Puskesmas Cubadak, pemantauan kondisi gizi balita dilakukan berdasarkan data antropometri, sehingga diperlukan metode analisis yang mampu mengolah dan mengelompokkan data secara tepat dan objektif. Penelitian ini bertujuan menerapkan algoritma K-Means Clustering dan Naïve Bayes dalam mengelompokkan serta mengklasifikasikan kondisi gizi balita sebagai pendukung pengambilan keputusan. Metode penelitian menggunakan pendekatan machine learning dengan data antropometri balita yang meliputi usia, jenis kelamin, berat badan, dan tinggi badan. Algoritma K-Means Clustering digunakan untuk mengelompokkan data berdasarkan tingkat kemiripan karakteristik, sedangkan algoritma Naïve Bayes digunakan untuk melakukan klasifikasi kondisi gizi balita. Hasil penelitian menunjukkan bahwa kombinasi kedua metode menghasilkan akurasi sebesar 97%, sehingga model mampu mengelompokkan dan mengklasifikasikan kondisi gizi balita secara akurat dan konsisten sebagai sistem pendukung pengambilan keputusan di Puskesmas Cubadak.
Identifikasi Status Gizi Balita Menggunakan Metode K-Means Clustering Dan Naïve Bayes Sela Ramadani; Musli Yanto; Gunadi Widi Nurcahyo
INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System Vol 11 No 1 (2026): INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS (Juni 2026)
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Bina Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51211/isbi.v11i1.3820

Abstract

Penelitian ini dilatarbelakangi oleh pentingnya kesehatan balita sebagai indikator keberhasilan pembangunan yang berkaitan erat dengan kondisi gizi serta berdampak langsung pada pertumbuhan dan perkembangan anak. Di wilayah kerja Puskesmas Cubadak, pemantauan kondisi gizi balita dilakukan berdasarkan data antropometri, sehingga diperlukan metode analisis yang mampu mengolah dan mengelompokkan data secara tepat dan objektif. Penelitian ini bertujuan menerapkan algoritma K-Means Clustering dan Naïve Bayes dalam mengelompokkan serta mengklasifikasikan kondisi gizi balita sebagai pendukung pengambilan keputusan. Metode penelitian menggunakan pendekatan machine learning dengan data antropometri balita yang meliputi usia, jenis kelamin, berat badan, dan tinggi badan. Algoritma K-Means Clustering digunakan untuk mengelompokkan data berdasarkan tingkat kemiripan karakteristik, sedangkan algoritma Naïve Bayes digunakan untuk melakukan klasifikasi kondisi gizi balita. Hasil penelitian menunjukkan bahwa kombinasi kedua metode menghasilkan akurasi sebesar 97%, sehingga model mampu mengelompokkan dan mengklasifikasikan kondisi gizi balita secara akurat dan konsisten sebagai sistem pendukung pengambilan keputusan di Puskesmas Cubadak.
Analysis of Student Selection Models Using K-Means Clustering and K-Nearest Neighbor Classification Algorithms Imam Fakhri Muhammad; Syafri Arlis; Musli Yanto
Jurnal KomtekInfo Vol. 13 No. 2 (2026): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v13i2.683

Abstract

The high level of student interest in the selection process poses challenges, including student admission management. The selection process generally consists of several stages, ranging from administrative tests, academic tests, psychological tests, and physical fitness tests. Based on this, the purpose of this study is to develop an approach that can help evaluate student readiness objectively and based on data. This study aims to analyze student selection by applying the concept of data mining using the K-Means and K-Nearest Neighbor (KNN) algorithms. The K-Means algorithm is used to group student data into several clusters based on the similarity of characteristics. Meanwhile, the K-Nearest Neighbor algorithm works by classifying new data based on similarity or the closest distance. The research dataset consists of 124 student data points obtained from the Arka Padang tutoring center headquarters. Based on this study, the results show that the application of the K-Means and K-Nearest Neighbor (KNN) algorithms demonstrates that both methods are capable of processing student data to identify patterns and levels of readiness for selection, achieving an accuracy of 92.10%. Thus, this method is considered reliable in supporting the process of evaluating student readiness. This research contributes to the understanding of the application of data mining concepts to evaluate and analyze student readiness levels and demonstrates how the K-Means and KNN algorithms can be used in the process of classifying students objectively and based on data.
Co-Authors - Khairiazaz Ariandi, Vicky Aulia Fitrul Hadi Billy Hendrik Darma Syahrullah Ekajaya Dewi Eka Putri Dewi Eka Putri Dhio Saputra Dina Selvia Dito Jurinaldo Dodi Guswandi Eka Naufaldi Novri Eka Praja Wiyata Mandala Febri Hadi Firdaus daus Gunadi Widi Nurcahyo Hadi Syahputra Ilsa Hidayat Imam Fakhri Muhammad Irzal Arif Wisky Jamhur, Annisak Izzaty Julsapargi Nursam Khairiazaz Liga Mayola M. Hafizh M. Hafizh Muhammad Hafizh Muhammad Reza Putra Muhammad Reza Putra Nursam, Julsapargi Putri Melati Putri Melati Rahma Yanti Randy Permana Riandana Afira Safitri, Wifra Sela Ramadani Selvi Zola Fenia SIGIT SANJAYA, SIGIT Sitti Rizki Mulyani Sovia, Rini Sumijan Syafri Arlis Widya Nursanty Yogi Wiyandra Yuhandri Yuhandri, Yuhandri Yulasmi Yuliana Pertiwi