Ahmad Nafi’uzzahidi, Gentur Wahyu Nyipto Wibowo, Sarwido Sarwido

Peningkatan Akurasi Prediksi Stok Bahan Baku Furnitur Menggunakan Algoritma Random Forest Regressor Berbasis Web

Introduction

Peningkatan akurasi prediksi stok bahan baku furnitur menggunakan algoritma random forest regressor berbasis web. Prediksi akurat stok bahan baku furnitur dengan Random Forest Regressor berbasis web. Optimalkan manajemen persediaan & efisiensi operasional di industri mebel.

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.

Review

The paper "Peningkatan Akurasi Prediksi Stok Bahan Baku Furnitur Menggunakan Algoritma Random Forest Regressor Berbasis Web" addresses a critical challenge within the furniture manufacturing industry: the accurate prediction of raw material inventory amidst fluctuating demand. The authors rightly highlight that inefficiencies stemming from demand variability, leading to either overstocking or material shortages, significantly disrupt production processes and impact operational efficiency. By proposing a data-driven solution, the study positions itself as a practical attempt to mitigate these long-standing issues, aiming to provide more precise stock requirement estimations than traditional methods. This foundational problem statement establishes a clear need for the research undertaken and its potential impact. Methodologically, the study employs a quantitative experimental design, developing a web-based predictive system using the Flask framework and a MySQL database. The choice of the Random Forest Regressor algorithm is well-justified given its robust performance in regression tasks and ability to handle various data types. A commendable aspect of the methodology is the thorough data preprocessing stage, encompassing cleaning, handling missing values, and normalization, which is crucial for minimizing noise and enhancing model performance. The use of historical sales and Bill of Materials (BOM) data for furniture products, coupled with feature engineering based on temporal trends, demonstrates a comprehensive approach to preparing relevant input for the machine learning model, ensuring the reliability and applicability of the developed system. The results presented are compelling, with the Random Forest model achieving a coefficient of determination ($R^2$) of 0.84 and a Mean Absolute Error (MAE) of 5.4. These metrics strongly indicate the model's high accuracy and predictive power in estimating future raw material requirements. The study successfully demonstrates that a machine learning-driven approach offers superior precision compared to conventional inventory management techniques. The practical implications are significant, promising accelerated managerial decision-making and optimized operational efficiency for medium-scale manufacturers. Furthermore, the research contributes theoretically to the development of artificial intelligence-based decision support systems within supply chain management, underscoring its relevance beyond immediate industrial application.

Full Text

Comments

You need to be logged in to post a comment.