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Journal of Data Mining and Information Systems
Published by Yayasan Pendidikan Penelitian Pengabdian Algero
ISSN : 29865271     EISSN : 29863473     DOI : https://doi.org/10.54259/jdmis
Core Subject : Science,
Computer Science & IT
Journal of Data Mining and Information Systems (JDMIS) is intended as a medium for scientific studies of research results, thoughts, and critical-analytic studies regarding research in the field of computer science and technology, including Information Technology, Informatics Management, Data Mining, and Information Systems. It is part of the spirit of disseminating knowledge resulting from research and thoughts for the service of the wider community. In addition, it serves as a reference source for academics in Computer Science and Information Technology. JDMIS publishes papers regularly two times a year, namely in February and August. All publications in JDMIS are open, allowing articles to be freely available online without a subscription.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 53 Documents
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Deteksi Anomali Integritas Akademik Siswa Menggunakan Isolation Forest dan Data Mining Yesi Wulan Puspitasari; Achmad Agus Athok Miftachuddin
JDMIS: Journal of Data Mining and Information Systems Vol. 4 No. 2 (2026): August 2026
Publisher : Yayasan Pendidikan Penelitian Pengabdian Algero

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/jdmis.v4i2.8825

Abstract

This study aimed to detect academic integrity anomalies among twelfth-grade students at an Islamic senior high school (Madrasah Aliyah Negeri) using a Python-based data mining approach. Academic monitoring had previously been conducted manually, which risked delaying the identification of students who required guidance. This research applied the Isolation Forest algorithm with an unsupervised learning approach to analyze midterm and final exam scores, project scores, report averages, attendance percentages, disciplinary violation records, and school counseling records from 650 students. The analysis followed the CRISP-DM methodology, covering six stages from business understanding to deployment. The anomaly detection results were further validated using a rule-based approach, producing a more objective and contextual hybrid validation process. The model identified 52 students (8%) as anomalous data. Hybrid validation classified 544 students (83.69%) as Safe, 97 students (14.92%) as Needing Monitoring, and 9 students (1.39%) as Needing Follow-up. The results were visualized through a Streamlit-based dashboard that can support school decision-making in objective, data-driven academic monitoring.
Analisis Data Transaksi Penjualan Menggunakan Metode Apriori Untuk Meningkatkan Keputusan Bisnis Di Lalan Mart Intan Daniar; Kariyamin Kariyamin; Amin Irmawan
JDMIS: Journal of Data Mining and Information Systems Vol. 4 No. 2 (2026): August 2026
Publisher : Yayasan Pendidikan Penelitian Pengabdian Algero

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/jdmis.v4i2.8907

Abstract

The utilization of sales transaction data can help businesses identify consumer purchasing patterns and support business decision-making. Lalan Mart has sales transaction data that has not been optimally utilized to identify relationships among products that are frequently purchased together. This study aims to analyze consumer purchasing patterns using the Apriori Algorithm and provide recommendations to support inventory management, product placement, and promotional strategies. The data consisted of 4,740 transactions involving 157 types of products during the period from January to December 2025. The analysis was conducted using the Apriori Algorithm with a minimum support of 6% and a minimum confidence of 60%. The results produced 11 association rules with lift values greater than 1, indicating positive relationships among products. The highest confidence value was obtained from the rule Rice and Sugar → Cooking Oil, with a confidence value of 99.32%, while the highest lift value was found in the UHT Milk → Oreo rule, with a lift value of 9.576. These results demonstrate that the Apriori Algorithm can help identify consumer purchasing patterns and support business decision-making through inventory management, product placement, and bundling and cross-selling strategies.
Analisis Pola Asosiasi Produk Berdasarkan Data Transaksi Penjualan di Toko Nofi Shop Menggunakan Fp-Growth Wa Ode Dalvin Lestari; Sry Faslia Hamka; Karyamin Yamin
JDMIS: Journal of Data Mining and Information Systems Vol. 4 No. 2 (2026): August 2026
Publisher : Yayasan Pendidikan Penelitian Pengabdian Algero

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/jdmis.v4i2.8908

Abstract

This study aimed to identify product purchasing patterns at Nofi Shop using the Frequent Pattern Growth (FP-Growth) algorithm and to implement the results as recommendations for product bundling, product arrangement, and promotional strategies. The research data consisted of 1,200 sales transactions recorded from September 2024 to May 2026. After preprocessing, 650 transactions that met the analysis criteria were obtained. The data were then transformed using One-Hot Encoding and processed using the FP-Growth algorithm with a minimum support of 4% and a minimum confidence of 60%. The analysis produced 35 association rules that met the criteria, with strong purchasing patterns such as Trash Bin and Broom, Mattress and Bed Sheet, and Chair and Table. The resulting association rules were subsequently implemented in a web-based system as recommendations for product bundling, product arrangement, and promotional strategies. Verification showed consistency among the manual calculations, Python implementation, and the developed system, while Black Box testing demonstrated that the main system functions operated as required. This study produced a system that could assist in identifying purchasing patterns and support data-driven decision-making at Nofi Shop.

Page 6 of 6 | Total Record : 53

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