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All Journal Brilliance: Research of Artificial Intelligence Jurnal Algoritma
Zakiah, Azizah
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Computer Science & IT Decision Sciences, Operations Research & Management Mathematics Other

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Implementasi Metode Wavelet Transform dengan ARIMA untuk memprediksi Kebutuhan Bahan Baku Obat di PT. Seikyo Indochem: Studi Kasus Pendekatan Hybrid Time Series pada Industri Farmasi Lolowang, Juan Marten Daniel; Zakiah, Azizah
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2399

Abstract

The optimal availability of pharmaceutical raw materials is a vital aspect in ensuring the continuity of production within the pharmaceutical industry. PT. Seikyo Indochem faces challenges in accurately forecasting raw material requirements due to the fluctuating and complex nature of the data. This study implements the Wavelet Transform method combined with ARIMA (Auto-Regressive Integrated Moving Average) to enhance the accuracy of demand forecasting. Wavelet Transform is utilized to decompose historical data into low- and high-frequency components, enabling a more in-depth analysis of seasonal patterns and trends. The low-frequency component is analyzed using ARIMA to predict long-term patterns, while the high-frequency component is used to capture short-term fluctuations. The results show that this hybrid approach reduces the prediction error (Mean Absolute Percentage Error) by 15 percent compared to using ARIMA alone. This model provides a more reliable predictive solution to support efficient inventory management of pharmaceutical raw materials.
Implementation of C5.0 Algorithm in Cement Stock and Purchase Management at PT. Maktal Maulida, Rezky Salman; Zakiah, Azizah
Brilliance: Research of Artificial Intelligence Vol. 6 No. 1 (2026): Brilliance: Research of Artificial Intelligence, Article Research May 2026
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v6i1.7744

Abstract

Stock management is a crucial activity in the supply chain of any company, including PT. Maktal, which operates in cement distribution. The stock management system, which still relies on experience and manual methods, has the potential to cause a mismatch between demand and supply, ultimately leading to excessive inventory costs (overstock) or product shortages (stockout). The implementation of Machine Learning offers a solution to enhance the accuracy of stock needs planning. This study aims to develop and compare the performance of machine learning models, specifically the Decision Tree (C5.0) and Random Forest algorithms, in predicting the category of cement stock needs (Low, Medium, High) based on historical transaction data. The data used are historical cement sales and ordering transactions of PT. Maktal from 2020 to 2024. The stock quantity data was converted into categorical variables (Low, Medium, High) through a discretization process. Both algorithms were tested and evaluated for their performance using accuracy, precision, recall, and F1-score metrics through a cross-validation test. The comparative results indicate that the Random Forest algorithm provides the best prediction performance with an accuracy level reaching 79.91%. This performance is significantly higher than that of the Decision Tree algorithm. Feature importance analysis identified that the Purpose (customer type) and Month variables are the most influential predictors of the stock needs category. The Random Forest model proved to be effective and reliable as a data-driven decision support system to optimize stock planning and cement purchasing at PT. Maktal, reducing the risk of losses due to demand uncertainty.
Co-Authors Lolowang, Juan Marten Daniel Maulida, Rezky Salman