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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JITK (Jurnal Ilmu Pengetahuan dan Komputer) Indonesian Journal of Applied Informatics Jurnal Informatika dan Rekayasa Perangkat Lunak E-Link: Jurnal Teknik Elektro dan Informatika Jurnal Ilmu Komputer dan Sistem Komputer Terapan (JIKSTRA) Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Sistem Informasi dan Teknologi (SINTEK) TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi AMMA : Jurnal Pengabdian Masyarakat Jurnal Informatika
Rudi Kurniawan
STMIK IKMI Cirebon
Humanities Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Nursing

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Optimizing Grocery Sales Data Grouping Using the Fuzzy C-Means Algorithm: Case Study of Nafhan Mart Store Nafhan Khairuddin Fathin; Rudi Kurniawan; Saeful Anwar
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.842

Abstract

The sale of staple food products at Nafhanmart Store, Cirebon Regency, includes essential household items such as rice, cooking oil, sugar, and flour, which maintain stable demand as basic necessities. This study focuses on improving sales clustering models at Nafhanmart using the Fuzzy C-Means (FCM) algorithm, a prominent method in data mining. Key factors influencing sales include price, sales volume, demand, and remaining stock. Accurate clustering analysis is vital for strategic inventory management and profit maximization. The research applies the Knowledge Discovery in Database (KDD) methodology, encompassing data selection, preprocessing, transformation, FCM implementation, and evaluation using the Davies-Bouldin Index (DBI). Attributes analyzed include price, sales volume, demand, and remaining stock. The FCM algorithm clusters data based on patterns, with DBI evaluating clustering quality and determining optimal clusters. Data analysis and visualization were conducted using RapidMiner. Results show that the FCM algorithm achieves optimal clustering quality with a DBI score of 0.452 for two clusters, outperforming three clusters (DBI 0.474) and four clusters (DBI 0.536). Price and demand are identified as critical factors influencing clustering outcomes. These findings enhance the clustering model, offering actionable insights for inventory management and sales strategy, while showcasing the FCM algorithm's adaptability for other SMEs to support data-driven decision-making.
Co-Authors Achmad Fikri Adbdul Muhyi Ade Awaludin Aditia Ahmad Faqih Ahmad Haekal Susanto Ahmad Muhaimin Ahmad Rai Fatkaozi Akmal Mustafa Arif Rinaldi Arif Rinaldi Dikananda Ayura Yufita Bani Nurhakim Bisma Mahendra Cep Lukman Rohmat Dadang Sudrajat Denni Pratama Dian Ade Kurnia Dodi Solihudin Edi Wahyudin Fathurrahman Fatihanursari Dikananda Fikri Firmansyah Fitriani Agustina Gilang Ramadhan Hilda Novia Ramadani Ibnu Fajri Khaerul Anam Martanto . Maulana Sujarwadi Mohamad Alif Subhi Muhamad Taufiq Hidayat Muhammad Hilmy Naufan Muhammad Khoerul Ikhwan Nafhan Khairuddin Fathin Narasati, Riri Narasati Nurul Fathonah Odi Nurdiawan Othman bin Mohd Pratama Putra Sanjaya Puji Pramudya Marta Raditya Danar Dana Ramdan Irawan Rifki Hermawan Ryan Hamonangan Saeful Amri Saeful Anwar Salman Alfarizi Santi Nurjulaiha Shinta Virgiana Suteja Syaiful Imanudin Tati Suprapti Taufik Hidayat Umi Hayati Wartumi Wartumi Windi Herlita Vidila Windy Astuti Yudhistira Arie Wijaya Yudis Firmansyah Zulfa Hana Aqliyah