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All Journal ComEngApp : Computer Engineering and Applications Journal JSI: Jurnal Sistem Informasi (E-Journal) Annual Research Seminar CESS (Journal of Computer Engineering, System and Science) Sistemasi: Jurnal Sistem Informasi JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jiko (Jurnal Informatika dan komputer) Informatik : Jurnal Ilmu Komputer Data Science: Journal of Computing and Applied Informatics Building of Informatics, Technology and Science Jurnal Generic Malcom: Indonesian Journal of Machine Learning and Computer Science ABDIKOM
Ahmad Rifai
Universitas Sriwijaya
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Library & Information Science Other

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Penerapan Model Recency, Frequency, Monetary untuk Segmentasi Pola Perilaku Pelanggan Indibiz: Application of the Recency, Frequency, Monetary Model for Segmentation of Indibiz Customer Behavior Patterns Aulia Pinkasari; Meiyin Monica Amilia Putri; Gibral Abdurahman; Ahmad Fadhil Rizqi; Ken Ditha Tania; Allsela Meiriza; Ahmad Rifai
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 2 (2026): MALCOM April 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i2.2611

Abstract

PT Telkom Indonesia Witel Sumbagsel menghadapi kondisi Data Rich, Information Poor (DRIP), yaitu melimpahnya data transaksi yang belum dimanfaatkan secara optimal sebagai dasar pengambilan keputusan pada unit Payment Collection. Penelitian ini menerapkan metodologi Knowledge Discovery in Databases (KDD) menggunakan model RFM (Recency, Frequency, Monetary) dan algoritma K-Means Clustering untuk mengidentifikasi pola perilaku pembayaran pelanggan. Dataset terdiri dari 46.355 transaksi periode Januari–Desember 2025. Jumlah cluster optimal ditentukan menggunakan metode Elbow dan menghasilkan empat segmen pelanggan (k=4). Evaluasi menggunakan Silhouette Coefficient memperoleh nilai 0,3463 yang menunjukkan kualitas klaster yang dapat diterima. Hasil segmentasi mengelompokkan pelanggan ke dalam kategori loyal, potential, standard, dan churn-risk, sehingga mendukung penyusunan strategi penagihan yang lebih tepat sasaran dan berbasis data. Penelitian ini menunjukkan bahwa integrasi RFM dan K-Means efektif dalam mengubah data transaksi menjadi wawasan praktis bagi manajemen penagihan telekomunikasi.
Perbandingan Model Klasifikasi Supervised Machine Learning dalam Knowledge Discovery Layanan TI Pertamina Prabumulih: Comparison of Supervised Machine Learning Classification Models in Knowledge Discovery of Pertamina Prabumulih IT Services Shafa Aurelliza Arian; Putri Rahel Alifia; Bagus Prihantoro; Muhammad Iqbal Disriansyah; Ken Ditha Tania; Alsella Meiriza; Ahmad Rifai
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 2 (2026): MALCOM April 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i2.2617

Abstract

Pengelolaan data layanan Teknologi Informasi (TI) di Pertamina Prabumulih memerlukan pendekatan analitik untuk meningkatkan efektivitas penanganan dan mendukung pengambilan keputusan berbasis data. Penelitian ini bertujuan membandingkan performa beberapa model klasifikasi supervised machine learning pada layanan TI periode 2020–2025 menggunakan pendekatan knowledge discovery melalui teknik data mining terhadap 8.627 data awal. Tahapan penelitian meliputi preprocessing, pelabelan kelas, penanganan ketidakseimbangan data dengan Synthetic Minority Over-sampling Technique (SMOTE), serta pembagian data dengan skenario 70:30, 80:20, dan 90:10. Proses klasifikasi dilakukan menggunakan algoritma Naïve Bayes, Support Vector Machine (SVM), dan Random Forest. Evaluasi model menggunakan metrik accuracy, precision, recall, dan F1-score. Hasil menunjukkan bahwa skenario 90:10 memberikan performa terbaik, dengan Support Vector Machine (SVM) mencapai accuracy 0,8806, precision 0,8757, recall 0,8941, dan F1-score 0,8788, melampaui algoritma lainnya. Kategori desktop hardware teridentifikasi sebagai kasus terbanyak selama periode penelitian. Temuan ini dapat dimanfaatkan sebagai dasar strategis untuk prioritas penanganan layanan, alokasi sumber daya, serta penguatan Knowledge Management guna peningkatan layanan TI secara terarah, efektif, dan berkelanjutan.
Comparative Customer Segmentation Pipelines for E-Commerce Using K-Means-KNN and UMAP-K-Means-XGBoost Dzidan Aditya Gumilang; Endang Lestari Ruskan; Ardina Ariani; Ken Dhita Tania; Ahmad Rifai
Building of Informatics, Technology and Science (BITS) Vol 8 No 2 (2026): September 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i2.10794

Abstract

The rapid expansion of e-commerce has generated massive volumes of customer data that remain underutilized for supporting Customer Relationship Management (CRM) strategies. Conventional customer segmentation approaches commonly employ a pipeline consisting of K-Means clustering followed by K-Nearest Neighbors (KNN) classification. However, this approach exhibits limitations in handling high-dimensional data and maintaining classification performance on large-scale datasets. This study presents a comparative analysis of two customer segmentation pipelines: the conventional K-Means-KNN pipeline and the proposed Uniform Manifold Approximation and Projection (UMAP)-K-Means-XGBoost pipeline. The experiments were conducted using the E-Commerce Shopper Behavior & Lifestyle dataset, comprising approximately one million customer records and eight selected features representing transactional, psychographic, and financial behavioral characteristics. Clustering performance was evaluated using the Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index, while classification performance was assessed using accuracy, precision, recall, and F1-score. Experimental results demonstrate that incorporating UMAP improves cluster separability by preserving the intrinsic structure of high dimensional data, whereas XGBoost consistently outperforms KNN in downstream classification, achieving an accuracy exceeding 99%. These findings indicate that the UMAP-K-Means-XGBoost pipeline provides a more robust, scalable, and interpretable framework for customer segmentation, thereby offering more reliable decision support for data-driven CRM strategies in e-commerce environments.
Co-Authors A. Salwa Aurelya Putri Abd. Rasyid Syamsuri Adelia Rizki Putri Ahmad Fadhil Rizqi Al Amin Mulya Al Farissi Ali Ibrahim Alifa Putri Shahabiyah Aliya Faiza Allsela Meiriza, Allsela Allsella Meiriza Alsella Meiriza Alsella Meiriza Ardina Ariani Athiyyah Nuha Rotifa Aulia Pinkasari Bagus Prihantoro Bambang Tutuko Danny Matthew Saputra Dedy Kurniawan Dhio Pratama Wiransyah Dinda Lestarini Dinna Yunika Hardiyanti Donny Giovanna Karo Karo Dzidan Aditya Gumilang Edo Wicaksono Eka Prasetyo Ariefin Endang Lestari Ruskan Endang Lestari Ruskan Fathoni - Fidela Tertia Alfino Fransiska Prihatini Sihotang Gabriel Sebastian Santoso Gibral Abdurahman Haniifah Putriani Hardini Novianti Hardini Novianti Hardini Novianti Hardini Novianti Huda Ubaya Jaidan Jauhari Jeremiah Alwin Siahaan Kemahyanto Exaudi Ken Dhita Tania Ken Dhita Tania Ken Ditha Tania Kesuma, Lucky Indra Lailla Syal Syabilla Lina Oktarina M Raykah Alam Ramadan M. Rudi Sanjaya M. Thoriqul Fadli Mei Intan Natasyah Meiyin Monica Amilia Putri Melisa Tri Cahya Ningsih Mira Afrina Muhammad Bayu Samudra Muhammad Dzaky Hasyim Muhammad Fachri Nuriza Muhammad Iqbal Disriansyah Muhammad Mayda Ary Pratama Muhammad Naufal Rachmamtullah Muhammad Rafly Muhammad Rendi Muhammad Wahyu Hikmalsyah Octa Dama Yanti Osvari Arsalan Pacu Putra Pascal Adhi Kurnia Tarigan Pibriana, Desi Purwita Sari Puti Chalisa Wardhana Putri Eka Sevtiyuni Putri Rahel Alifia Rahmad Fadli Isnanto Rahmat Izwan Heroza Rahmat Izwan Heroza Rayya Ramadhan Simangunsong Richa Pratiwi Rizka Dhini Kurnia Rossi Passarella Samsuryadi - Sarifah Putri Raflesia Sarmayanta Sembiring Satria Ramadhani Shafa Aurelliza Arian Sutarno - Sutarno Sutarno Syifa Alfariani Syifa Naura Milla Celesta Winda Kurnia