Machine Learning Methods for Forecasting Intermittent Tin Ore Production
Effective production forecasting is important for resource planning and management in the mining industry. Tin ore production from Cutter Section Dredges (CSD) may fluctuate due to a variety of factors, in which there are periods when the production is zero. This study compares various combinations...
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| Format: | Article |
| Language: | English |
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Ikatan Ahli Informatika Indonesia
2024-10-01
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| Series: | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
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| Online Access: | https://jurnal.iaii.or.id/index.php/RESTI/article/view/5990 |
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| author | Nabila Dhia Alifa Rahmah Budhi Handoko Anindya Apriliyanti Pravitasari |
| author_facet | Nabila Dhia Alifa Rahmah Budhi Handoko Anindya Apriliyanti Pravitasari |
| author_sort | Nabila Dhia Alifa Rahmah |
| collection | DOAJ |
| description | Effective production forecasting is important for resource planning and management in the mining industry. Tin ore production from Cutter Section Dredges (CSD) may fluctuate due to a variety of factors, in which there are periods when the production is zero. This study compares various combinations of machine learning-based classification and forecasting to predict future tin ore production values, which have not been found in previous studies. The presence of zero values in the forecast in the next day's tin ore production forecast is addressed by combining classification and forecasting techniques. Random Forest and CatBoost classification techniques are used to determine the next day's CSD production operating status. Then, for each time point when the CSD is operational, a forecasting model is created using CatBoost and Bi-LSTM. This study's findings show that a serial combination of the Random Forest classification method and CatBoost forecasting can produce accurate tin ore production forecasts for the selected CSD (RMSE = 0.271, MAE = 0.179, MAE = 0.730, F1-score = 0,80). This study demonstrates how a serial combination of classification and forecasting models can improve the accuracy and efficiency of production forecasting for intermittent time series data. |
| format | Article |
| id | doaj-art-1f0efd1490254e43a07e52f8fe1eee0f |
| institution | Kabale University |
| issn | 2580-0760 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Ikatan Ahli Informatika Indonesia |
| record_format | Article |
| series | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| spelling | doaj-art-1f0efd1490254e43a07e52f8fe1eee0f2025-01-13T03:31:56ZengIkatan Ahli Informatika IndonesiaJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)2580-07602024-10-018564465010.29207/resti.v8i5.59905990Machine Learning Methods for Forecasting Intermittent Tin Ore ProductionNabila Dhia Alifa Rahmah0Budhi Handoko1Anindya Apriliyanti Pravitasari2Universitas PadjadjaranUniversitas PadjadjaranUniversitas PadjadjaranEffective production forecasting is important for resource planning and management in the mining industry. Tin ore production from Cutter Section Dredges (CSD) may fluctuate due to a variety of factors, in which there are periods when the production is zero. This study compares various combinations of machine learning-based classification and forecasting to predict future tin ore production values, which have not been found in previous studies. The presence of zero values in the forecast in the next day's tin ore production forecast is addressed by combining classification and forecasting techniques. Random Forest and CatBoost classification techniques are used to determine the next day's CSD production operating status. Then, for each time point when the CSD is operational, a forecasting model is created using CatBoost and Bi-LSTM. This study's findings show that a serial combination of the Random Forest classification method and CatBoost forecasting can produce accurate tin ore production forecasts for the selected CSD (RMSE = 0.271, MAE = 0.179, MAE = 0.730, F1-score = 0,80). This study demonstrates how a serial combination of classification and forecasting models can improve the accuracy and efficiency of production forecasting for intermittent time series data.https://jurnal.iaii.or.id/index.php/RESTI/article/view/5990forecastingclassificationmachine learningminingcatboost |
| spellingShingle | Nabila Dhia Alifa Rahmah Budhi Handoko Anindya Apriliyanti Pravitasari Machine Learning Methods for Forecasting Intermittent Tin Ore Production Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) forecasting classification machine learning mining catboost |
| title | Machine Learning Methods for Forecasting Intermittent Tin Ore Production |
| title_full | Machine Learning Methods for Forecasting Intermittent Tin Ore Production |
| title_fullStr | Machine Learning Methods for Forecasting Intermittent Tin Ore Production |
| title_full_unstemmed | Machine Learning Methods for Forecasting Intermittent Tin Ore Production |
| title_short | Machine Learning Methods for Forecasting Intermittent Tin Ore Production |
| title_sort | machine learning methods for forecasting intermittent tin ore production |
| topic | forecasting classification machine learning mining catboost |
| url | https://jurnal.iaii.or.id/index.php/RESTI/article/view/5990 |
| work_keys_str_mv | AT nabiladhiaalifarahmah machinelearningmethodsforforecastingintermittenttinoreproduction AT budhihandoko machinelearningmethodsforforecastingintermittenttinoreproduction AT anindyaapriliyantipravitasari machinelearningmethodsforforecastingintermittenttinoreproduction |