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All Journal Jurnal Pendidikan Informatika dan Sains SISFOTENIKA Jurnal Teknologi Terapan Jurnal Ekonomi Manajemen Sistem Informasi Jurnal Ilmiah MADIYA: Masyarakat Mandiri Berkarya Jurnal Sistem Informasi Bisnis (JUNSIBI) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Nusantara Journal of Multidisciplinary Science Jurnal Kecerdasan Buatan dan Teknologi Informasi IKRAM: Jurnal Ilmu Komputer Al Muslim
R Wisnu Prio Pamungkas
Universitas Bhayangkara Jakarta Raya
Religion Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Mechanical Engineering Physics Social Sciences

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Application of K-Means Clustering Algorithm in Edam Burger Sales Information System for Inventory Control Optimization Muhammad Rofiq Ubaidillah; R Wisnu Prio Pamungkas; Prio Kustanto
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 5 No. 3 (2026): September 2026 In progress.
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v5i3.565

Abstract

Manual sales and inventory management in small culinary enterprises often leads to data inaccuracies, stock mismanagement, and underutilized transactional data. This study aims to design a web-based sales information system integrated with the K-Means clustering algorithm to optimize inventory control at Edam Burger & Frozen Foods. Utilizing the Waterfall methodology, the system was developed using the Laravel framework and MySQL. The analytical engine processed five months of transactional data across fifteen products, applying Min-Max Normalization to equalize the scales of sales volume, revenue, and transaction frequency. The K-Means algorithm successfully segmented the product catalog into three distinct categories based on performance: one high-selling core product (6.7%), three medium-selling secondary items (20%), and eleven low-selling complementary products (73.3%). Black Box Testing confirmed a 100% functional success rate across all system modules. The primary novelty of this research lies in seamlessly embedding the K-Means engine directly into the operational dashboard, overcoming the common barrier of offline, standalone data mining. This integration enables real-time, data-driven procurement strategies, providing actionable recommendations: prioritizing continuous stock availability for high-demand items, scheduling regular restocking for medium items, and minimizing capital tied up in low-moving inventory to reduce food waste. Ultimately, this integrated approach empowers small business owners to transition from intuition-based management to systematic, algorithm-driven inventory optimization. This study successfully bridges the gap between routine transactions and strategic analytics.
Web-Based Stock Overstock Warning System for Spare Parts Inventory Using Linear Regression Rizky Fadillah Putra Pratama; R Wisnu Prio Pamungkas; Fried Sinlae
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 5 No. 3 (2026): September 2026 In progress.
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v5i3.568

Abstract

Inventory management in the automotive spare parts industry faces a criticalchallenge in managing overstock conditions, which lead to increased storagecosts and capital freezing. PT. Dipo Internasional Pahala Otomotif currentlymanages spare parts inventory manually, without any predictive alert systemcapable of detecting potential overstock based on sales data. This study developsa web-based overstock warning system using the Simple Linear Regressionalgorithm implemented in the Laravel framework to predict spare parts stockrequirements and automatically trigger overstock alerts. The system was builtfollowing the Waterfall development methodology through seven sequentialphases: planning, analysis, design, coding, testing, implementation, and maintenance.The linear regression model uses time period as the independentvariable (X) and stock quantity as the dependent variable (Y ), forming theprediction equation Y = a + bX. Based on a simulation with n = 4 periods,the resulting equation Y = 7 + 2.7X predicted a stock of 20.5 units in period5, which exceeded the defined overstock threshold. System evaluation usingBlack Box Testing confirmed that all functional modules operated correctly.The system successfully provides automated overstock detection and real-timealert notifications, enabling more accurate and data-driven inventory decisionsat PT. Dipo Internasional Pahala Otomotif.
Co-Authors adam al hafidh Ade Irawan Ahmad Fathurrozi Akmal Najib Arin Novita Dewi Aulia Nada Azizah Bangun Erih Pamungkas Bayu Dwimas Nuansyah Beatric Stevany Zebua Dian Firstian Alfarabi Fairuz Hadi Fried Sinlae Hafiyan Rizqi Sanjaya Hapzi Ali Henryansyah Tawaqal Herland Sunaryo Ilham maulana Istianingsih Sastrodiharjo, Istianingsih Khoiridha Ashkiyyah Kustanto , Prio Linda Fitriyani M. Hadi Prayitno Muammar Yasir Muhammad Rayhan Muhammad Rofiq Ubaidillah Nabiilah Khoirunnisaa Nanda Eka Rindiati Nani Sudiarti Rakhmi Khalida Rakhmi Khalida Rizky Antonio Figo Rizky Fadillah Putra Pratama Rony, Zahara Tussoleha Sultan Bacharuddin Yusuf Hidayat Tri Widyastuti Zia Tabaruk