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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 JOURNAL OF SCIENCE AND SOCIAL RESEARCH 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 Jurnal Pengabdian Masyarakat Dharma Andalas
Yanto, Musli
Universitas Putra Indonesia YPTK Padang
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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.
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.
ANALISIS ALGORITMA K MEANS DAN C4.5 DALAM PENILAIAN KERJA KARYAWAN SEBAGAI OPTIMALISASI MANAJEMEN SDM Muhammad Idris; Gunadi Widi Nurcahyo; Musli Yanto
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 1 (2026): February 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i1.5662

Abstract

Abstract: Human resources are strategic organizational assets; therefore, an objective, systematic, and data-driven performance evaluation system is required. At Universitas Alifah Padang, employee performance assessment is still dominated by conventional approaches that are subjective and have not been able to comprehensively describe performance patterns. This study aims to apply Data Mining using K-Means Clustering and the C4.5 algorithm to analyze employee performance evaluation. The research method employs the Knowledge Discovery in Database (KDD) framework, which includes data collection, data analysis, variable analysis, K-Means analysis, C4.5 analysis, and evaluation. The K-Means Clustering algorithm is used to group employees based on similarities in performance characteristics, while the C4.5 algorithm is applied to build a decision tree–based classification model to identify patterns and dominant factors influencing performance evaluation. The research dataset consists of 96 employee records assessed using six performance indicators: responsibility, initiative, teamwork, work quality, work quantity, and discipline. The results indicate the formation of three performance clusters, namely very good (42 records), good (48 records), and fairly good (6 records), with consistent results between manual calculations and RapidMiner testing. This study contributes a Data Mining–based employee performance evaluation model that can serve as a foundation for strategic decision-making in higher education institutions. Keywords: Data Mining;K Means Clustering;C4.5;Performa Evaluation Abstrak: Sumber daya manusia merupakan aset strategis organisasi sehingga diperlukan sistem penilaian kinerja yang objektif, sistematis, dan berbasis data. Di Universitas Alifah Padang, penilaian kinerja karyawan masih didominasi pendekatan konvensional yang bersifat subjektif dan belum mampu menggambarkan pola kinerja secara komprehensif. Penelitian ini bertujuan menerapkan konsep Data Mining dengan algoritma K-Means Clustering dan C4.5 untuk menganalisis penilaian kinerja karyawan. Metode penelitian menggunakan kerangka Knowledge Discovery in Database (KDD) yang meliputi kumpul data, analisa data, analisa variabel, analisis k means, C4.5 dan evaluasi. Algoritma K-Means Clustering digunakan untuk mengelompokkan karyawan berdasarkan kemiripan karakteristik kinerja, sedangkan algoritma C4.5 digunakan untuk membangun model klasifikasi berbasis pohon keputusan guna mengidentifikasi pola dan faktor dominan penilaian kinerja. Dataset penelitian terdiri dari 96 data karyawan dengan enam indikator kinerja, yaitu tanggung jawab, inisiatif, kerja sama tim, kualitas kerja, kuantitas kerja, dan disiplin. Hasil pengujian menunjukkan terbentuknya tiga klaster kinerja, yaitu sangat baik (42 data), baik (48 data), dan cukup baik (6 data), dengan hasil yang konsisten antara perhitungan manual dan RapidMiner. Penelitian ini berkontribusi menyediakan model analisis penilaian kinerja berbasis Data Mining sebagai dasar pengambilan keputusan strategis di perguruan tinggi. Abstrak ditulis menggunakan times new roman 11 maksimal 200 kata dalam satu paragraf. Abstrak ditulis dengan ringkas, jelas, dalam bahasa Indonesia dan bahasa inggris. Abstrak minimal memuat tujuan, metodologi, hasil penelitian, dan simpulan. Abstrak didampingi oleh kata kunci. Kata kunci sedapat mungkin menjelaskan isi tulisan, ditulis dengan huruf kecil kecuali singkatan, maksimum 5 (Lima) kata, masing-masing dipisahkan dengan titik koma; Times New Roman 11. Kata kunci: Data Mining; K Means Clustering; C4.5; Penilaian Kinerja
ANALISIS KUALITAS PRODUKSI AYAM BROILER MENGGUNAKAN METODE K-MEANS CLUSTERING DAN ALGORITMA C4.5 Oriza Rama Saputra; Rini Sovia; Musli Yanto
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 1 (2026): February 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i1.5747

Abstract

Abstract: Broiler chicken production is influenced by various factors, such as feed, environment, and maintenance management, which generate large amounts of complex production data. This condition causes the assessment and decision-making processes related to production quality to often be suboptimal and not based on in-depth data analysis. This study aims to implement the K-Means method and C4.5 algorithm to produce an analysis process that can be used as a solution in determining broiler chicken production quality in the South Coast region. The K-Means method is used to classify broiler chicken production data based on similar characteristics to facilitate pattern identification. The C4.5 algorithm was used to build a decision tree model to determine and predict broiler chicken production quality based on the most influential attributes. The research dataset was sourced from farm data in the South Coast region, with a total of 157 data points obtained. The clustering results presented three main segments, namely 94 with good results, 58 data with moderate results, and 5 data with poor results. Meanwhile, the C4.5 algorithm was built based on the clustering results from K-Means. The accuracy was calculated using the F1 score, with an accuracy of 93,75%. Keywords: Broiler Chicken;K-Means; C4.5 Algorithm. Abstrak: Produksi ayam broiler dipengaruhi oleh berbagai faktor, seperti pakan, lingkungan, dan manajemen pemeliharaan, yang menghasilkan data produksi dalam jumlah besar dan bersifat kompleks. Kondisi ini menyebabkan proses penilaian dan pengambilan keputusan terkait kualitas produksi sering kali belum optimal dan kurang didasarkan pada analisis data yang mendalam. Penelitian dilakukan bertujuan untuk Mengimplementasikan metode K-Means dan algoritma C4.5 untuk menghasilkan proses analisis yang dijadikan solusi dalam penentuan kualitas produksi ayam broiler di wiliyah Pesisir Selatan. Metode K-Means digunakan untuk mengklasifikasikan data produksi ayam broiler berdasarkan kesamaan karakteristik untuk memudahkan identifikasi pola. Algoritma C4.5 Digunakan untuk membangun model pohon keputusan dalam menentukan dan memprediksi kualitas produksi ayam broiler berdasarkan atribut yang paling berpengaruh. Dataset penelitian bersumber dari data peternakan di wilayah Pesisir Selatan, Dengan total data ada 157 data yang didapatkan. Hasil klustering menyajikan tiga segmen utama yaitu 94 dengan hasil baik, 58 data dengan hasil sedang dan 5 data dengan hasil buruk, Sedangkan Algoritma C4.5 dibangun berdasarkan hasil klustering dari K-Means. Perhitungan Hasil akurasi dengan f1 score Dengan hasil akurasi 93,75%. Kata Kunci: Ayam Broiler, K-Means, Algoritma C4.5
IDENTIFIKASI SENTIMEN PENGGUNA APLIKASI JKN BERBASIS TERM FREQUENCY-DOCUMENT FREQUENCY DAN SUPPORT VECTOR MACHINE Alicia Mulya Maharani; Musli Yanto; Billy Hendrik
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 4 (2026): August 2026 (1)
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i4.6895

Abstract

Abstract: User reviews of the Jaminan Kesehatan Nasional (JKN) mobile application on app distribution platforms continue to increase in line with the growing use of digital health services in Indonesia. These reviews contain sentiments related to the application, yet they have not been systematically analyzed to reveal user perception and satisfaction toward the JKN application. This study aims to analyze sentiment in JKN application reviews by employing the Term Frequency–Inverse Document Frequency (TF-IDF) method combined with the Support Vector Machine (SVM) algorithm. The review data processing stage began with preprocessing, comprising cleaning, case folding, tokenization, stopword removal, and stemming, followed by weighting using TF-IDF to transform textual data into numerical data in accordance with word occurrence frequency. The weighting results then served as input for the SVM algorithm to construct a separating hyperplane between classes, thereby classifying reviews into positive or negative categories. The research dataset consisted of 10,000 JKN application user reviews obtained through scraping from the Google Play Store platform. The testing results showed an accuracy of 99.83%, precision of 99.92%, recall of 99.83%, and an F1-score of 99.87%. These results demonstrate that the combination of TF-IDF and SVM can produce highly accurate sentiment classification of JKN application user reviews. User perception and satisfaction obtained from this sentiment identification serve as an evaluative basis for JKN application administrators to encourage improvements in the quality of digital health services in Indonesia. Keyword: Sentiment Analysis, TF-IDF, JKN, Support Vector Machine, User Reviews.   Abstrak: Ulasan pengguna aplikasi Jaminan Kesehatan Nasional (JKN) pada platform distribusi aplikasi terus bertambah seiring meningkatnya penggunaan layanan kesehatan digital di Indonesia. Ulasan tersebut memuat sentimen terkait kualitas layanan, kemudahan penggunaan, dan kinerja fitur, namun belum dianalisis secara sistematis untuk mengetahui persepsi dan kepuasan pengguna terhadap aplikasi JKN. Sentimen dalam ulasan pengguna aplikasi JKN dianalisis menggunakan metode Term Frequency Inverse Document Frequency (TF-IDF) dan Support Vector Machine (SVM) sebagai tujuan dari penelitian ini. Tahapan untuk mengolah data ulasan pada penelitian ini merupakan preprocessing (cleaning, case folding, tokenization, stopword removal, dan stemming), disusul pembobotan memakai TF-IDF agar data teks bertransformasi menjadi data numerik selaras dengan frekuensi kemunculan kata. Hasil pembobotan menjadi input bagi algoritma SVM untuk membentuk hyperplane pemisah antar kelas, sehingga ulasan diklasifikasikan ke dalam kategori positif atau negatif. Dataset penelitian berjumlah 10.000 ulasan pengguna aplikasi JKN yang diperoleh melalui scraping dari platform Google Play Store. Hasil pengujian dari penelitian yaitu akurasi sebesar 99,83%, presisi 99,92%, recall 99,83%, dan F1-score 99,87%. Hasil dari pengujian tersebut membuktikan kombinasi TF-IDF dan SVM dapat menghasilkan klasifikasi sentimen ulasan pengguna aplikasi JKN dengan ketepatan tinggi. Gambaran mengenai persepsi dan kepuasan pengguna diperoleh dari hasil identifikasi sentimen ini untuk bahan evaluasi bagi pengelola aplikasi JKN guna mendorong peningkatan kualitas layanan kesehatan digital di Indonesia. Kata kunci: Analisis Sentimen, TF-IDF, JKN, Support Vector Machine, Ulasan Pengguna.
Co-Authors - Khairiazaz Alicia Mulya Maharani 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 Idris Muhammad Reza Putra Muhammad Reza Putra Nursam, Julsapargi Oriza Rama Saputra 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