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All Journal Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Journal of Software Engineering and Information System (SEIS)
Sherly Fillia
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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.
Pemodelan Topik Pada Ulasan Kegiatan Dakwah Menggunakan Algoritma Latent Dirichlet Allocation Elfizar; Sherly Fillia; Rahmad Kurniawan; Sukamto; Tisha Melia; Fitra Lestari
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 plays an important role in dakwah (Islamic preaching) development, yet its evaluation methods remain limited. Understanding congregant feedback is crucial, but manually analyzing thousands of comments is ineffective. This research aims to apply topic modeling to automatically identify the main themes within congregant opinions. The algorithm used is Latent Dirichlet Allocation (LDA), analyzing 2,581 comments collected from the MUI Riau Smart Evaluation System. The research phase involved text preprocessing, such as cleaning, case folding, tokenizing, stopword removal, and stemming to produce clean data. This data was then converted into a Bag-of-Words (BoW) representation as input for the LDA model. The optimal number of topics was determined through evaluation using Coherence Score and Perplexity. Experimental results show that a configuration with 16 topics provides the best balance between semantic coherence and model generalizability, with a Coherence Score of 0.5008 and a Perplexity of -7.7787. The identified topics reflect diverse aspects, including prayers, appreciation for preachers, respect, discussions on Islamic values, and spiritual reflections. The LDA method proved effective in extracting thematic patterns from congregant opinions, providing a foundation for developing a real-time evaluation system.
Co-Authors Aidha Tita Irani Elfizar Ernia Juliastuti Finanta Okmayura Fitra Lestari Lukas, Ivander Muhammad Amirulhaq rahmad Kurniawan RIZKY ARDIANSYAH Sukamto Tisha Melia