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All Journal Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi Sistemasi: Jurnal Sistem Informasi Seminar Nasional Teknologi Informasi Komunikasi dan Industri Jurnal Sistem Cerdas Jurnal Mahasiswa Sistem Informasi (JMSI)
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.
PENERAPAN MODEL UTAUT UNTUK MENGUKUR DAMPAK KECERDASAN BUATAN TERHADAP AKADEMIK MAHASISWA M. Ridho Kurniawan; Siti Monalisa; Arya Bharata Sena; Fenny Purwani
Jurnal Mahasiswa Sistem Informasi (JMSI) Vol. 7 No. 1 (2025): Jurnal Mahasiswa Sistem Informasi (JMSI)
Publisher : Program Studi DIII Sistem Informasi - Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/jmsi.v7i1.10747

Abstract

Perkembangan kecerdasan buatan (Artificial Intelligence/AI) semakin memengaruhi proses akademik mahasiswa, mulai dari pencarian informasi, penyusunan tugas, hingga pembelajaran digital. Penelitian ini bertujuan untuk menganalisis penerimaan dan penggunaan AI oleh mahasiswa menggunakan model UTAUT (Unified Theory of Acceptance and Use of Technology) serta melihat dampaknya terhadap performa akademik. Penelitian menggunakan metode survei dengan kuesioner skala Likert 1–5 kepada 30 mahasiswa program studi Sistem Informasi, dan data dianalisis menggunakan regresi linier berganda. Hasil penelitian menunjukkan bahwa Performance Expectancy, Effort Expectancy, dan Facilitating Conditions berpengaruh signifikan terhadap Behavioral Intention. Behavioral Intention berpengaruh signifikan terhadap Use Behavior, yang selanjutnya berdampak positif terhadap kualitas akademik mahasiswa. Temuan ini menunjukkan bahwa penerimaan teknologi AI memiliki hubungan kuat dengan peningkatan performa akademik.
The Impact of Electronic Customer Relationship Management (E-CRM) Strategy on Customer Loyalty in Mytelkomsel using a Mediation Model M Irvan; Siti Monalisa
SISTEMASI Vol 15, No 6 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i6.5653

Abstract

This study investigates the impact of Electronic Customer Relationship Management (E-CRM) strategies, encompassing Functional Dimensions and Personal Dimensions, on Customer Loyalty in the use of the MyTelkomsel application, using Perceived Customer Relationship Quality (comprising cognitive and emotional dimensions) as the mediating construct. Data were collected from 100 MyTelkomsel users through a survey and analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach. The results indicate that Functional Dimensions have a positive and significant effect on Perceived Customer Relationship Quality (path coefficient = 0.471) and also exert a positive direct effect on Customer Loyalty (path coefficient = 0.256). Meanwhile, Personal Dimensions positively influence Perceived Customer Relationship Quality (path coefficient = 0.335) but exhibit a negative direct effect on Customer Loyalty (path coefficient = −0.109). Furthermore, Perceived Customer Relationship Quality has a positive and significant effect on Customer Loyalty (path coefficient = 0.539) and serves as a mediator in the relationships between both Functional Dimensions and Personal Dimensions and Customer Loyalty. These findings provide deeper insights into the relationship between E-CRM strategies and customer loyalty in the context of the MyTelkomsel application. They also confirm the mediating role of Perceived Customer Relationship Quality and highlight the strategic value of implementing E-CRM to strengthen customer loyalty in the digital era.
Co-Authors Annisa Nadaa Shabrina Aprisa Aprisa Arya Bharata Sena Dewi Anjainah Dwi Putri Setia Fenny Purwani fhadilatul juniati Fitra Kurnia Fitriani Muttakin Herni Ramadhani Ikhsan Khoiru Julhandri Julhandri Lia Waroka M Irvan M. Afdal M. Ridho Kurniawan 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