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All Journal Jurnal Manajemen Bisnis PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnas Nasional Teknologi dan Sistem Informasi Jurnal ULTIMA InfoSys JURNAL PENDIDIKAN TAMBUSAI EDUKATIF : JURNAL ILMU PENDIDIKAN JISICOM (Journal of Information System, Infomatics and Computing) Jurnal Kependidikan: Jurnal Hasil Penelitian dan Kajian Kepustakaan di Bidang Pendidikan, Pengajaran dan Pembelajaran Jurnal Manajemen Informatika Jayakarta Journal of Students‘ Research in Computer Science (JSRCS) International Journal of Regional Innovation Brilliance: Research of Artificial Intelligence Jurnal Pendidikan Vokasi Raflesia Jurnal Desain Komunikasi Visual Nirmana Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Digital Transformation Technology (Digitech) International Journal of Information Technology and Computer Science Applications (IJITCSA) Jurnal Guru Komputer Journal of Computer Science Contributions (Jucosco) Jurnal PETISI (Pendidikan Teknologi Informasi) Orbit : Jurnal Ilmu Multidisiplin Nusantara Jurnal Komtika (Komputasi dan Informatika)
Rasim, Rasim
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Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Social Sciences Other

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Journal : Journal of Students‘ Research in Computer Science (JSRCS)

 1 
Algoritma Apriori Untuk Pola Penjualan Pada Kedai Kopi Studi Kasus: Kedai Kopioko Juliano, Aryo; Rasim, Rasim; Sugiyatno, Sugiyatno
Journal of Students‘ Research in Computer Science Vol. 3 No. 1 (2022): Mei 2022
Publisher : Program Studi Informatika Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/02t5ze63

Abstract

Effective promotion can increase sales figures. One way is to identify market conditions, namely about consumer purchasing tastes, which can be observed through consumer purchase transaction data. In recent years, transaction data has been widely used as research material, which aims to build some new information related to sales patterns to help manage future business development. In this study, the a priori algorithm method was used to determine sales patterns. The results obtained from the experiments carried out that the application of data mining using yahoo a priori with the association rule method can run well and produce two association rules, by changing the minimum support and confidence parameters. After the experiment using the apriori algorithm, it was found that the combination of menu items that can be made for sales patterns or the development process uses the racist Kopioko package menu, potatoes with a value of 60.34%, and racist Kopioko, Agung with a value of 54.88
Implementasi Algoritma Apriori pada Sistem Informasi Penjualan Web Pujiono, Krisna Dimas; Mugiarso; Handayani, Dwipa; Rasim, Rasim
Journal of Students‘ Research in Computer Science Vol. 6 No. 2 (2025): November 2025
Publisher : Program Studi Informatika Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/kjhjrc12

Abstract

This study focuses to implementing a web-based sales information system leveraging the Apriori algorithm to analyze consumer purchasing patterns at PT. Mura Mitra Sejati. Adopting the Waterfall development methodology, the project progressed systematically through the analysis, design, implementation, testing, and maintenance stages.The system was developed using a native PHP architecture based on the MVC pattern, supported by a MySQL database and Bootstrap for the front-end. Key functionalities of the system include sales transaction recording, real-time inventory management, and powerful association rule mining. Comprehensive black-box testing across all modules—Login, Stock Management, Transactions, Apriori Analysis, and Reporting—confirmed the intended performance of every system function, achieving a 100% success rate.The Apriori algorithm effectively identified strong association rules from 10 transaction datasets, notably revealing the frequent co-purchase of White Paint and 1-inch Brushes, with 40% support and 80% confidence, respectively. Ultimately, the resulting system delivers an efficient, well-structured, and data-driven solution that significantly improves sales management and supports strategic decision-making.
Co-Authors ACHMAD FAUZI Agus Hidayat Al-Kadzim, Muhammad Ghiyats Alexander, Allan D Anisyah, Ani Baszliah Nursabrina Bayu Wicaksono Dwipa Handayani Eka Fitrajaya Rahman Enjang Ali Nurdin Erlangga Erlangga Fajar Ramadhan Fauzi, Indah Resti Fazira, Ira Fried Sinlae Ghaitsa Zahira Shaffa Hambali, Yudi Ahmad Herbert Siregar Herlawati Herlawati Isya, Nadhief Athallah Joniwarta Judie, Rizky Rachman Juliano, Aryo Julianti, Sukma Junaeti, Enjun Khaairi, Mochamad Lavianti, Desi Lubis, Hendarman Mahbub, Asep Ramdhani Mahphoth, Mohd Halim bin Mugiarso Mugiarso, Mugiarso Muhamad Firdaus Muhammad Khaerudin Muhammad Yasir Munir Munir, Munir Nailah Nurul Qalbina Natasya Regita Gandawidjaya Nursalman, Muhammad Nusratullo, Samialloi Piantari, Erna Prima Dina Atika Pujiono, Krisna Dimas Puspo Dewi Dirgantari Putro, Budi Laksono Ranggana, Alfaza Rani Megasari Rawinto, Irsyad Robertus Suraji Rosa, Elisa Rozi, Muhammad Fahru Sari, Fitra Bona Septem Riza, Lala Shafarin, Anti Nuraulia Silaen, Oriza Sativa Dinauni Siregar, Herbert SITI SETIAWATI Sofyan, Muhammad Hilmy Rasyad Sugiyatno Sugiyatno Sumitra, Desfasa Ilham Syafri Syamsudin, Muhammad Ully, Rona Wawan Setiawan Wihardi, Yaya Wijati, Damar Zulkifli, Akmal