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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Teknovasi : Jurnal Teknik dan Inovasi Mesin Otomotif, Komputer, Industri dan Elektronika INTECOMS: Journal of Information Technology and Computer Science JURNAL TEKNOLOGI DAN OPEN SOURCE JSAI (Journal Scientific and Applied Informatics) Jurnal Teknologi Informasi dan Pendidikan Jurnal Ilmu Komputer dan Bisnis Jurnal Nasional Ilmu Komputer Zuriah : Jurnal Pendidikan Anak Usia Dini International Journal of Business and Information Technology Jurnal Pustaka Mitra : Pusat Akses Kajian Mengabdi Terhadap Masyarakat Jurnal Pepadun Journal Of Human And Education (JAHE) Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Laporan Upaya Nyata Inovasi Ilmu Komputer (LUNIK) Jurnal Pustaka Paket Jurnal Komputasi
Yulya Muharmi
Department of Computer Science, Universitas Lampung
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Public Health Social Sciences Other

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Analysis of Product Purchase Patterns Using the Apriori Algorithm on FMCG Distributor Transaction Data in the Riau Region Yulya Muharmi; Nurul Azwanti; Dhella Amelia
Jurnal Pepadun Vol. 7 No. 1 (2026): April
Publisher : Department of Computer Science, Faculty of Mathematics and Natural Sciences, University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/pepadun.v7i3.324

Abstract

This study investigates purchasing patterns of fast-moving consumer goods (FMCG) in Riau Province, Indonesia, using the Apriori algorithm within the Market Basket Analysis framework. Transaction data from a distributor comprising 4,422 transactions and 243 unique products across Pekanbaru, Kampar, and Rokan Hulu were analyzed to generate frequent itemsets and association rules, evaluated using support, confidence, and lift metrics. The application of a consistent minimum support and confidence threshold ensures statistically robust rule extraction across regions with different transaction scales.The results reveal strong intra-brand associations within the snack category, with several rules exhibiting lift values above ten, indicating systematic bundling behavior rather than random co-occurrence. These findings suggest that retailers tend to stock complementary product variants simultaneously, reflecting structured purchasing patterns at the outlet level. Regional comparison highlights differences in rule density across districts, shaped by transaction volume and the proportional effect of the support threshold, demonstrating how dataset scale influences association complexity. Overall, the study demonstrates that the Apriori algorithm effectively uncovers meaningful purchasing structures in distributor-level transaction data. The findings provide actionable insights for inventory management, regional distribution planning, and targeted promotions, while contributing to the literature by examining FMCG purchasing behavior in a multi-region distribution context using empirical distributor data.
Information System for Guidance on Student Practical Work Reports in the Computer Science Department of the Web-Based Information Management Study Program Yulya Muharmi; Bambang Hermanto; Viona Almadea
Jurnal Pepadun Vol. 6 No. 3 (2025): December
Publisher : Department of Computer Science, Faculty of Mathematics and Natural Sciences, University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/pepadun.v6i3.301

Abstract

The supervision of practical work (KP) reports in the Computer Science Department has traditionally been carried out manually, which often leads to several issues such as difficulty aligning schedules between students and supervisors and challenges in tracking the history of report revisions. To address these limitations, a web-based internship report supervision information system was developed to support the guidance process in an online, structured, and well-documented manner.This research adopted the Waterfall model as the system development framework, while a descriptive qualitative approach was applied during the requirements analysis and implementation phases. The system includes key features such as consultation scheduling, report submission, revision tracking, and management of user profiles and announcements. System evaluation was conducted through black-box testing and a User Acceptance Test (UAT), yielding a score of 81%, which indicates that users were generally satisfied with the system’s performance.Overall, the presence of this system improves efficiency and transparency in the supervision process and strengthens communication and progress monitoring for both students and supervisors.
Analysis Of Employee Discipline Based On Digital Attendance With The K-Means Algorithm Method Yulya Muharmi; Sri Nadriati
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 5 No. 2 (2022): Jurnal Teknologi dan Open Source, December 2022
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v5i2.2628

Abstract

Employee discipline is one of the most important factors for the progress of the company. PT. Sumatra Core Cellular (PT. SIS) Pekanbaru has implemented a digital attendance application, but the company has not evaluated the application to determine the level of employee discipline. Data mining is the process of extracting useful information from a large database population. One of the data mining methods is the K-Means algorithm. The data mining process uses the method of K-Means algorithm with 2 clusters namely discipline and less disciplined categories. The data used is attendance data of 159 employees, namely data on tardiness, non-attendance (TAP), attendance hours and 4 selected questionnaire questions. Tools for grouping with the Rapidminer application. Using the K-Means algorithm method, it is known that cluster 0 consists of 133 employees or 83.64% with a disciplined category and cluster 1 produces 26 employees or 16.35% with a less disciplined category. Judging from the accuracy of attendance hours, employees in cluster 0 are more likely to be present at 07.45 - 08.15 and in cluster 1 they are more likely to be present at 08.15 - 08.30. In terms of lateness and TAP, there is a lack of discipline in cluster 1. From the level of satisfaction with the application based on 4 selected questions, it can be concluded that the digital attendance application increases the discipline of the employees. The results of this analysis can be used as a reference for evaluating employee discipline, determining promotions and improving employee discipline in the future.
Pengenalan Website Sebagai Media Informasi Bagi Anak Yatim Di Panti Asuhan Muhammadiyah Cabang Pauh IX Mike Febri Mayang Sari; Dhella Amelia; Dian Permata Sari; Yulya Muharmi
Laporan Upaya Nyata Inovasi Ilmu Komputer JPKM Lunik - Vol 3 No 02, November 2025
Publisher : FMIPA Unila

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/lunik.v3i02.50

Abstract

Pengabdian Masyarakat ini bertujuan untuk mengenalkan website sebagai media informasi yang dapat digunakan oleh anak yatim di panti Asuhan Muhammadiyah Cabang Pauh IX. Dalam era digital saat ini , akses informasi sangat penting untuk perkembangan pendidikan dan kesejahteraan anak-anak, termasuk anak yatim . Pelatihan website ini diharapkan dapat menjadi acuan yang efektif untuk menyampaikan informasi yang relevan, seperti kegiatan panti, program pendidikan , serta peluang beasiswa dan pengembangan diri bagi anak yatim. Penggunaan website sebagai media informasi dapat meningkatkan keterlibatan anak yatim dalam berbagai program yang ada di panti, serta memberikan mereka peluang untuk mengakses informasi yang mendukung perkembangan secara lebih mandiri. Oleh karena itu, Pelatihan ini diharapkan dapat menjadi salah satu solusi dalam meningkatkan ilmu pengetahuan dalam menggunakan website.  
Clustering Toko Ritel Berdasarkan Pola Penjualan Produk Menggunakan Algoritma K-Means Amalia Praptiwi, Riska; Muharmi, Yulya; Amelia, Dhella
Jurnal Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence) Vol 6 No 1 (2026): Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence)
Publisher : Pustaka Galeri Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55382/jurnalpustakaai.v6i1.1748

Abstract

Penelitian ini menganalisis pola penjualan toko ritel di Pekanbaru dan melakukan segmentasi berbasis data menggunakan algoritma K-Means. Dataset terdiri dari 965 toko dengan variabel numerik hasil preprocessing, meliputi total penjualan, frekuensi transaksi, rata-rata nilai pembelian, dan variasi produk. Hasil clustering menunjukkan tiga segmen toko: performa tinggi, menengah, dan rendah, dengan evaluasi kualitas menggunakan Silhouette Score sebesar 0.603 dan Davies-Bouldin Index sebesar 0.763. Visualisasi PCA memperkuat pemisahan antar cluster secara intuitif. Kebaruan penelitian ini terletak pada penerapan K-Means pada data ritel lokal Pekanbaru dengan evaluasi cluster komprehensif, sehingga menghasilkan segmentasi yang lebih representatif terhadap perilaku konsumen daerah. Temuan ini dapat dimanfaatkan untuk strategi distribusi produk, target promosi, dan evaluasi kinerja toko berbasis data. Penelitian selanjutnya dapat menambahkan variabel lokasi geografis dan demografi pelanggan untuk meningkatkan presisi segmentasi.
Co-Authors Aji Pulungan, Wahyu Amalia Praptiwi, Riska Amelia, Dhella Arif , Muhammad Azwanti, Nurul Bambang Hermanto Citra, Erin Eka Delpima Suhita Dian Permata Sari Hutabri, Elbert Istiana Ruswita Kiki Farida Ferine Kusuma, Chandra Liga Mayola Melinedri Melinedri Mike Febri Mayang Sari Misnawati Nadriati, Sri Nella Novita Nurul Azwanti Nurul Azwanti Pratiwi, Mariska Putri Rahmat Fauzi Rizky Fitriawan Saipul, Saipul Sawitri, Riza Sri Nadriati SRI NADRIATI Sri Tria Siska Tika Meldina Viona Almadea Wirta Agustin