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All Journal Indonesian Journal of Artificial Intelligence and Data Mining Indonesian Journal of Industrial Engineering & Management (JINSENG) Journal of Industrial Innovation and Safety Engineering
Zamzani, Muhammad Imron
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Industrial & Manufacturing Engineering Transportation

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A Hybrid Traditional and Machine Learning-Based Stacking-Based Ensemble Forecasting Approach for Coal Price Prediction Yaqin, Alvin Muhammad 'Ainul; Hamdi, Rafisal; Zamzani, Muhammad Imron; Hertadi, Christopher Davito Prabandewa; Nabiha, Hilwa Dwi Putri
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 2 (2024): September 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i2.30547

Abstract

Accurate coal price forecasts are crucial, as volatility in coal prices significantly impacts company performance and profitability. Traditional time series forecasting methods, such as exponential smoothing, are known for their simplicity and low data requirements. In contrast, machine learning techniques, such as random forest and neural network, offer higher accuracy in predictions. However, very few attempts have been made to combine the simplicity of traditional methods with the accuracy of machine learning techniques. This paper presents a novel stacking-based model that integrates both traditional statistical methods and machine learning techniques to enhance coal price predictions. Using Indonesian coal price data from January 2009 to October 2021, we trained the models on various combinations of predictors to generate new predictions. Our findings demonstrate that our stacking-based model outperforms other models, with RMSE and MAPE values of 6.44 and 5.97%, respectively. These results indicate that the model closely forecasts actual coal prices, capturing 94.03% of the price movements. The main contribution of this study is the application of stacking-based models to coal price forecasting in Indonesia, which has not been previously explored, thus enriching the literature on this topic.
Analisis Beban Kerja Mental Menggunakan Metode NASA-TLX Pada Mekanik PT. ABC Kota Balikpapan Ramdani, Muhamad; Zamzani, Muhammad Imron
Journal of Industrial Innovation and Safety Engineering (JINSENG) Vol 2 No 1 (2024): Vol 2 No 1 (2024): JINSENG Volume 2 Nomor `1 January 2024
Publisher : Jurusan Teknologi Industri dan Proses

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35718/jinseng.v2i1.8481368

Abstract

Dalam melakukan kegiatan kerja tentu banyak aktivitas kerja fisik dan kerja mental yang terjadi pada mekanik PT. ABC Kota Balikpapan. Beban kerja yang berlebihan mampu mengakibatkan terjadinya stress dan menimbulkan reaksi emosional. Oleh sebab itu diperlukan pengukuran beban kerja mental untuk mengetahui kapasitas kerja mekanik telah sesuai standar atau belum. Pengukuran beban kerja mental pada penelitian ini dilakukan dengan metode NASA-TLX (National Aeronautics and Space Administration Task Load Index) karena mampu memberikan kuantifikasi beban kerja yang berdasarkan rating dari 6 indikator terkait Mental Demand (MD), Physical Demand (PD), Temporal Demand (TD), Own Performance (OP), Effort (EF), Frustration Level (FR). Dalam penelitian ini melakukan pengumpulan data primer yang bersumber dari hasil kuesioner dan data hasil wawancara terhadap seluruh mekanik yang berjumlah 9 orang. Pengolahan data dilakukan dengan berdasarkan perhitungan NASA-TLX dan didapatkan hasil masing-masing skor beban kerja mekanik yang didapatkan yaitu 63.00, 69.27, 66.73, 64.33, 67.00, 67.73, 64.80, 68.60, 67.60. Melihat hasil keseluruhan skor akhir dari NASA-TLX maka dapat disimpulkan beban kerja mental para mekanik pada PT. ABC Kota Balikpapan masih tergolong tinggi karena diantara skor 57-79 berdasarkan rekomendasi NASA-TLX.
Material Planning with ABC Classification, Min-Max Method, and Continuous Review System Method at PT XYZ Wardana, Farhan Jezando; Zamzani, Muhammad Imron; Purba, Arini Anestesia
IJIEM - Indonesian Journal of Industrial Engineering and Management Vol 5, No 1: February 2024
Publisher : Program Pascasarjana Magister Teknik Industri Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/ijiem.v5i1.22661

Abstract

PT XYZ is a petrochemical manufacturing company that produces industrial raw materials. This company has a stock of inventory materials in its warehouse. The background of this research is based on the company's need to optimize raw material inventory management to ensure the smooth running of the methanol production process and prevent overstock and stock of material. This study analyzed company SAP data, which showed a high average overstock level in CR1-B2-C01 storage during 2018-2022. Forty materials were overstocked, and 22 materials were out of stock. This shows the urgency to classify priority inventory materials, determine the research results in minimum and maximum quantities of materials, and determine reorder points. The research methods used include the ABC classification to classify inventory priorities, the Min-Max method to calculate the minimum and maximum amount of material, and the Continuous Review System method to determine reorder points and the amount of inventory to be provided. Data on purchases and usage of Parker O-Lube and O-Ring Parker materials in CR1-B2-C01 storage during 2018-2022 are used as a basis for planning material requirements. The research results found that by applying the ABC classification, the Min-Max method, and the Continuous Review System method, the company can optimize the management of CR1-B2-C01 raw materials. In investing in raw materials, companies can reduce the risk of unnecessary costs, minimize damage to goods, and prioritize the optimal use of storage space.
Co-Authors Alvin Muhammad ‘Ainul Yaqin Hamdi, Rafisal Hertadi, Christopher Davito Prabandewa Nabiha, Hilwa Dwi Putri Purba, Arini Anestesia Ramdani, Muhamad Wardana, Farhan Jezando