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All Journal Jurnal DISPROTEK Jurnal Abdimas Madani dan Lestari (JAMALI) Jurnal Teknologi Dan Sistem Informasi Bisnis Amalee: Indonesian Journal of Community Research & Engagement JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Minfo Polgan (JMP) Jurnal Informatika Teknologi dan Sains (Jinteks)
Sarwido Sarwido, Sarwido
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Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Public Health Social Sciences Other

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IMPLEMENTASI DATA MINING MENGGUNAKAN METODE LEAST SQUARE UNTUK MEMPREDIKSI JUMLAH PENJUALAN MEBEL DI UD. MEBEL JATI Sarwido, Sarwido; Shofi'in, Faiz Ali; Saputro, Heru
Jurnal Disprotek Vol 14, No 1 (2023)
Publisher : Universitas Islam Nahdlatul Ulama Jepara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34001/jdpt.v14i1.3850

Abstract

UD Mebel Teak is one of the many shops that provide furniture or household items such as chairs, sideboards, cupboards and so on. The amount of sales transaction data at UD Mebel Jati Stores is currently only used to make sales reports and stock items. In fact, from this sales data mining, it can be searched for an estimate of the amount of sales of an item for a certain month using a calculation method. From here, it can be seen the forecasting of the number of sales for a certain month so that the UD Teak Furniture Store can estimate the supply of furniture. The author will design a data mining implementation system to predict furniture sales using the least squares method to make better use of existing sales transaction data. The design will be implemented using the PHP programming language and MySql database. PHP is a programming language that integrates with HTML to create attractive web pages. This research is expected to produce a datamining implementation system to predict furniture sales using the website-based least squares method. This system is expected to be able to provide information about which furniture is in great demand by consumers in order to provide stock for that furniture. the results of the validation carried out by material experts on sales forecasting decision support systems contained 7 instruments, an ideal score of 63 with an expert score of 63 and a presentation of 100% was declared feasible. And from the verification of media experts from 9 instruments, it was declared feasible with an ideal score of 77 from an expert score of 79 and a presentation of 96.2%.
Peningkatan Akurasi Prediksi Stok Bahan Baku Furnitur Menggunakan Algoritma Random Forest Regressor Berbasis Web Nafi’uzzahidi, Ahmad; Wibowo, Gentur Wahyu Nyipto; Sarwido, Sarwido
Journal of Information System Research (JOSH) Vol 7 No 2 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i2.9095

Abstract

This study aims to address the uncertainty of raw material inventory in the furniture industry through the implementation of the Random Forest Regressor machine learning algorithm. The primary problem addressed is demand fluctuation, which frequently leads to stock management inefficiencies, including overstocking or material shortages that disrupt production processes. The research method employs a quantitative approach with an experimental design, developing a web-based system using the Flask framework and MySQL database. The data sample includes historical sales transaction records and Bill of Materials (BOM) data for furniture products, such as dining tables and minimalist chairs. Prior to modeling, the data underwent a preprocessing stage comprising data cleaning, handling missing values, and normalization to minimize the impact of noise on transaction data. Data collection was conducted through the extraction of internal databases, which were then processed through feature engineering stages based on temporal trends. The results demonstrate that the Random Forest model can predict future raw material requirements with high accuracy, evidenced by a coefficient of determination ($R^2$) of 0.84 and a Mean Absolute Error (MAE) of 5.4.5 These findings prove that a data-driven approach provides more precise stock requirement estimations than conventional methods. In conclusion, the integration of this predictive technology offers practical contributions to accelerating managerial decision-making and optimizing operational efficiency in the medium-scale manufacturing sector. The implications of this study support the theoretical development of artificial intelligence-based decision support systems in supply chain management.
Co-Authors Adi Sucipto, Adi Agustina, Hera Khilya Akhis, Muhammad Ilma Akhis Akhmad Pandhu Wijaya, Akhmad Pandhu Almajid, Ahmad Afan Andriansyach, Dimas Jordy Arif Mustofa Asrori, Yuhda Rachmat Bayu, Muhammad Bayu Nugroho Buang Budi Wahono, Buang Budi Dias Prihatmoko Faturrahman, Kharis Febriansyah, Ahmad Diva Haq, Muhammad Izzul Hartoyo, Nindiya Ika Nugraha Ilma, Safira Putri Zidni Maknun, Muhammad Jauharul Mulyo, Harminto Musyarraf, Fajrul Fahry Nafi’uzzahidi, Ahmad Nur Aeni Widiastuti Pangestu, Binar Ageng Raganta Prasetyo, Doni Agung Putra, Fadhil Athalah R Hadapiningradja Kusumodestoni Raffi, Muhamad Faizal Reviyandi, Riky saputro, heru Septiarani, Diva Herninda Shofi'in, Faiz Ali Syifauddin, Muhammad Rizal Wibowo, Gentur Wahyu Nyipto Zachary, Vivien Varian Zahira, Tiara