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All Journal Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi Seminar Nasional Teknologi Informasi Komunikasi dan Industri Jurnal Sistem Cerdas
Siti Monalisa
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Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Other

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Comparison Of K-Means, K-Medoids, and Fuzzy C-Means Algorithms for Clustering Drug User’s Addiction Levels Annisa Nadaa Shabrina; M. Afdal; Siti Monalisa
Jurnal Sistem Cerdas Vol. 6 No. 2 (2023)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v6i2.313

Abstract

Narcotics, psychotropics, and addictive substances are drugs that can activate brain systems, affect dopamine levels, and cause addiction. In Indonesia, there is a law requiring drug addicts to receive treatment and care. To properly treat a drug addict, it is first necessary to determine the level of addiction. Data mining methods such as clustering can be used to assess a user's level of drug addiction. This study uses the clustering algorithms Fuzzy C-means, K-Medoids, and K-means. The performance of the three clustering algorithms will then be evaluated based on the average similarity of clusters. Data such as how many types of drugs that used, the length of time they were used, the psychiatric status, and the physical condition status, are used. Clustering was accomplished using the data mining software RStudio. The clustering algorithms were then evaluated with the Davies Bouldin Index (DBI). The K-Medoids algorithm was found to have the best average similarity value of cluster for determining drug users' addiction levels based on the results of the analysis.
Customer Segmentation Using the RFMD Model and Fuzzy C-Means Algorithm Muhammad Hafis Zikri; Siti Monalisa; Fitriani Muttakin
Jurnal Sistem Cerdas Vol. 7 No. 3 (2024)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v7i3.481

Abstract

Many businesses face challenges in optimizing customer data processing, which often limits the ability to understand customer behavior and improve marketing strategies. This research addresses these challenges by applying the RFMD (Recency, Frequency, Monetary, Diversity) model combined with the Fuzzy C-Means (FCM) clustering algorithm to segment customers based on transaction data. The results identified five distinct customer segments based on Customer portfolio Analysis (CPA), which were validated using the Davies-Bouldin Index (DBI), with each segment showing diverse levels of engagement and behavioral patterns. The results show that the best clusters of Superstar and Golden customers are clusters 4 and 2, while Typical and Occasional customers are clusters 1 and 3. The lowest cluster of Everyday customers is found in cluster 5. The findings provide applicable insights to improve customer retention and optimize data-driven marketing strategies.
Customer Segmentation Analysis Through RFM-D Model and K-Means Algorithm Refri Martiansah; Siti Monalisa; Fitriani Muttakin; Mona Fronita
Jurnal Sistem Cerdas Vol. 8 No. 1 (2025)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i1.504

Abstract

This research analyzes customer segmentation through the RFM-D (Recency, Frequency, Monetary, and Diversity) model and the K-Means algorithm. The data comes from sales transactions at Café Z from January 2023 to February 2024, with 10,212 entries. The applied methodology includes several stages: data pre-processing, cleaning, transformation, normalization, and clustering. Clustering validation was carried out using the Davies-Bouldin Index (DBI) to ensure the quality of the clusters formed. The analysis results identified three customer clusters based on purchasing behavior, indicating that the K-Means algorithm effectively groups customers. These findings provide insight for companies to design marketing strategies that are more focused and appropriate to the characteristics of each customer segment. Companies can improve operational efficiency, increase customer satisfaction, and maximize profitability by utilizing this segmentation. This research contributes to optimizing resource allocation and personalizing marketing approaches, ultimately strengthening customer relationships.
Co-Authors Annisa Nadaa Shabrina Aprisa Aprisa Dewi Anjainah Dwi Putri Setia fhadilatul juniati Fitra Kurnia Fitriani Muttakin Herni Ramadhani Ikhsan Khoiru Julhandri Julhandri Lia Waroka M. Afdal Misra Hartati Mona Fronita Muhammad Azlan Shah Putra Muhammad Hafis Zikri Nur Arifin Presdi Ponti Anggara Refri Martiansah Revilia Zafa Tengku Nurainun Wresni Anggraini Yundari, Yundari