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All Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Jurnal Teknik Komputer AMIK BSI Jusikom : Jurnal Sistem Komputer Musirawas JUTIM (Jurnal Teknik Informatika Musirawas) Jurnal Teknologi Informasi MURA KOMPUTA : Jurnal Ilmiah Komputer dan Informatika JUSIM (Jurnal Sistem Informasi Musirawas) Journal of Computer System and Informatics (JoSYC) Jurnal Digital Teknologi Informasi Jurnal Pengabdian kepada Masyarakat Nusantara Prosiding Seminar Riset Mahasiswa Jurnal Teknologi Informasi Mura Jurnal Edukasi EJESH : Journal Islamic Economics and Social Edutik : Jurnal Pendidikan Teknologi Informasi dan Komunikasi Jurnal UNIV.BI MENGABDI
sobri, ahmad
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Journal : Journal of Computer System and Informatics (JoSYC)

 1 
Model Hybrid dalam Penentuan Stok Barang Bangunan Melalui Pendekatan Machine Learning Adinda, Intan Bintang; Irawan, Davit; Karman, Joni; Sobri, Ahmad
Journal of Computer System and Informatics (JoSYC) Vol 7 No 1 (2025): November 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v7i1.8065

Abstract

This study aims to develop a machine learning-based construction material stock prediction model using a hybrid approach that combines K-Means Clustering as a sales pattern grouping method and Support Vector Machine (SVM) as a classification method to predict material sales levels. This research was motivated by the problem of stock management at Toko Usaha Jaya in Lubuklinggau City, which is still done manually, thus potentially causing excess stock that increases storage costs and stock shortages that can lead to lost sales opportunities and decreased customer satisfaction. The data used includes material names, initial stock quantities, quantities sold, remaining stock, and selling prices collected during the period from January to December 2023. The results show that the hybrid model is capable of grouping materials into three categories, namely very popular, fairly popular, and less popular, with a Silhouette Score of 0.42, indicating fairly good clustering quality. Furthermore, the SVM model produced a classification accuracy rate of 99%, reflecting an increase in stock prediction accuracy compared to manual management methods. These findings indicate that the application of the K-Means and SVM hybrid model can improve inventory management efficiency and support more accurate and effective data-driven decision making.
Co-Authors Adinda, Intan Bintang Aktavera, Beni Andri Anto Tri Susilo, Andri Anto Aprillian, Alif Armanto, Armanto Athallah, Muhammad Rafif aviani, tri hasanah bimastari Bayani, Ahmad Bimastari, Tri Hasanah Daulay, Nelly Khairaini Dwi Budi Santoso Elmayati, Elmayati Fadila, Nurul Fitri, Maysade Hidayat, Asep Toyib Intan, Bunga Irawan, Davit Karman, Joni Lestari, Novi Lingga Wijaya, Harma Oktafia Lukman Hakim Maya Sari, Wisdalia Muhammad Husein Nurdiana, Hestin Nurdiansyah, Deni Padilah, Salwa Pratama, Dafa Ananda Rahmawati, Diah Ayu Regina Regina Rizki, Fido Rizqa, Rachma Fatihatul Robby, Robby Rudi Kurniawan Rusdiyanto Rusdiyanto, Rusdiyanto Sari, Wisdalia Maya Savitri, Ratna Sunardi, Lukman Surya Putra, Raihan Rama Syahvini, Salsabila Nasywa Wulandari, Cindi Zulius, Antoni