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All Journal Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Journal of Software Engineering and Information System (SEIS)
Aidha Tita Irani
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Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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PEMODELAN RFM & K-MEANS CLUSTERING UNTUK SEGMENTASI PELANGGAN DALAM PENJUALAN ONLINE Lukas, Ivander; Finanta Okmayura; Aidha Tita Irani; Ernia Juliastuti; Muhammad Amirulhaq; Rizky Ardiansyah; Sherly Fillia
Jurnal Rekayasa Perangkat Lunak dan Sistem Informasi Vol. 5 No. 2 (2025)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v5i2.9556

Abstract

The exponential growth of e-commerce platforms necessitates sophisticated customer analytics to maintain competitive advantage and optimize revenue streams. This study addresses the critical challenge of understanding heterogeneous customer purchasing behaviors in online retail environments through advanced data mining techniques. The research implements RFM (Recency, Frequency, Monetary) modeling integrated with K-Means clustering algorithm to achieve comprehensive customer segmentation for strategic marketing optimization. A quantitative-exploratory methodology was employed, utilizing a comprehensive online sales dataset comprising over 40,000 transactional records. The analytical framework involved systematic data preprocessing using Python libraries (Pandas, NumPy), followed by RFM parameter calculation and standardization through StandardScaler normalization. K-Means clustering was subsequently applied with optimal cluster determination via Elbow Method validation, yielding three distinct customer segments. Visualization and interpretation were conducted using Tableau, Matplotlib, and Seaborn for comprehensive segment characterization. Results demonstrate successful identification of strategically significant customer clusters: high-value loyal customers, moderate-engagement prospects, and potential churn-risk segments, each exhibiting distinctive RFM behavioral patterns. The segmentation framework enables targeted marketing strategy formulation, personalized customer retention programs, and optimized resource allocation. This research contributes valuable insights for e-commerce practitioners seeking data-driven approaches to enhance customer relationship management and sustain long-term business profitability in competitive online marketplaces.
Penerapan Non-negative Matrix Factorization untuk Pemodelan Topik pada Opini Kegiatan Dakwah Rahmad Kurniawan; Aidha Tita Irani; Sukamto; Ilyas Husti; Fatayat; Elfizar
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

The Indonesian Ulema Council (MUI) of Riau Province faces challenges in objectively evaluating dakwah (Islamic preaching) as its existing Peta Dakwah Cerdas (PDC) system lacks a feature to analyze congregant feedback. This study aims to design and implement a topic modeling model to identify the main hidden themes within congregant opinions. The study utilized 2,581 comments collected from the MUI Riau Smart Evaluation System. The methodology involved text preprocessing, Term Frequency-Inverse Document Frequency (TF-IDF) word weighting, and topic modeling using the Non-Negative Matrix Factorization (NMF) algorithm. Toward determine the optimal number of topics (k), the model was evaluated using Coherence Score to measure semantic readability and Silhouette Score to measure the resulting topic separation. The experiment identified two topics (k=2) as the best configuration achieving a high Coherence Score of 0.7023 and a Silhouette Score of 0.0163. The two main topics formed represent (1) Prayers and Greetings for the Preacher, and (2) Congregant Participation and Appreciation for the Dakwah. The application of NMF proved effective in identifying thematic patterns in congregant opinions and can serve as a foundation for MUI Riau to develop a real-time Islamic preaching evaluation system.
Co-Authors Elfizar Ernia Juliastuti Fatayat Finanta Okmayura Ilyas Husti Lukas, Ivander Muhammad Amirulhaq rahmad Kurniawan RIZKY ARDIANSYAH Sherly Fillia Sukamto