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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JMM (Jurnal Masyarakat Mandiri) JURNAL PENDIDIKAN TAMBUSAI EDUMATIC: Jurnal Pendidikan Informatika JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Bulletin of Computer Science Research Brilliance: Research of Artificial Intelligence Pelita Teknologi : Jurnal Ilmiah Informatika, Arsitektur dan Lingkungan Jurnal Ilmiah SIGMA: Informatics Engineering Journal of UPB Bulletin of Data Science Nautical: Jurnal Ilmiah Multidisiplin Indonesia Jurnal Pengabdian Mandiri Proceeding International Pelita Bangsa GLOBAL: Jurnal Lentera BITEP Jurnal Pelita Pengabdian Journal of Renewable Engineering
Amali, Amali
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Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Automotive Engineering Civil Engineering, Building, Construction & Architecture 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 Mathematics Social Sciences Other

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Journal : Bulletin of Data Science

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Optimalisasi Strategi Penjualan Sparepart Menggunakan Association Rule Berbasis Algoritma Apriori Amali, Amali; Widodo, Edy
Bulletin of Data Science Vol 5 No 2 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletinds.v5i2.9903

Abstract

The development of information technology encourages companies to utilize sales transaction data as a strategic source of information in business decision-making. However, the increasing amount of transaction data is often not optimally utilized to identify consumer purchasing patterns. This study aims to analyze consumer purchasing patterns in spare parts sales transactions using association rules based on the Apriori algorithm to support the optimization of sales strategies and inventory management. The research method used is a quantitative approach consisting of data collection, data preprocessing, transaction data transformation, frequent itemset generation, and association rule formation. The data used in this study consisted of 350 spare parts sales transactions processed using the Apriori algorithm with a minimum support value of 20% and a minimum confidence value of 70%. The results showed that the products Front Bumper and Brake Pads had the strongest association relationship with a confidence value of 76% and support value of 23%. In addition, the relationship between Radiator and Side Mirror products showed a confidence value of 71%. The study proves that the Apriori algorithm is effective in identifying relationships between products and can assist companies in determining promotional strategies, inventory management, and data-driven business decision-making to improve spare parts sales
Co-Authors Al Ayubi, Muhammad Din Andri Firmansyah Andriani Andriani Andriano, Choky Angela, Devani Nadia Vita Kusuma Anggraini, Ariska Nur Anshory, Ahmad Shofwan Antika Zahrotul Kamalia Anwar, Muhammad Syaibani ARIF SUSILO Aswan Supriyadi Sunge Badruzzaman, Aceng Bintang Satria, Yusuf Putra Budiarto, Eko Budiman, Banyu Sugara Butsianto, Sufajar DHANI ARIATMANTO Edy Widodo Edy Widodo Fahmi, Alif Gunawan, Achmad Rezky Hamidah, Jilan Herfiansyah, Raihan Hidayah, Aldy Imawan, Alifia Putri Ismamudi Ismamudi Ismamudi, Ismamudi Kurniasih, Nabila Kurniawan, Jidan Restu Listyanto, Rudy Effendy Maulana, Donny Muhammad Rifa'i, Anggi Muhtajuddin Danny Nawangsih, Ismasari Noor, Viyan Qomarudin Nugroho, Agung Nurhidayanti, Nisa Permana, Yudi Pikriyansyah, Ridho Pratama, Alvian Saputra Pratama, Fauzhan Qhof Priyo, Basuki Edi Putra, Maha Ramadan, Fadli Ramadhan, Arya Syah Ramadhan, Zidan Lutfi Rifa'i, Anggi Muhammad Saepul, Muhammad Saniyah, Luzzatus Saputra, Muhamad Ariel Satpatmantyo, Kurbandi Septianto, Riski Suryadi Suryadi Tasya, Amalia Taufiq Rachman, Taufiq Tedi, Nanang Turmudi Zy, Ahmad Tyas Ismi Trialfhianty Wangsadanureja, Miftah Wibowo, Mohamad Hegar Sukmana Wiyanto -