PREDICTION SYSTEM FOR THE AMOUNT OF SUGAR PRODUCTION USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM
Sugar is one of the staple foods most Indonesians use, so sugar production needs to be done optimally to meet people's needs. This research will design a prediction system for the amount of sugar production in PTPN XI PG Prajekan using the Adaptive Neuro-Fuzzy Inference System (ANFIS) method. A...
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| Format: | Article |
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Universitas Pattimura
2024-10-01
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| Series: | Barekeng |
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| Online Access: | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/13232 |
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| author | Ahmad Kamsyakawuni Walidatush Sholihah Abduh Riski |
| author_facet | Ahmad Kamsyakawuni Walidatush Sholihah Abduh Riski |
| author_sort | Ahmad Kamsyakawuni |
| collection | DOAJ |
| description | Sugar is one of the staple foods most Indonesians use, so sugar production needs to be done optimally to meet people's needs. This research will design a prediction system for the amount of sugar production in PTPN XI PG Prajekan using the Adaptive Neuro-Fuzzy Inference System (ANFIS) method. ANFIS is a combined method of two systems, namely a fuzzy logic system and an artificial neural network system. This research consists of data collection, ANFIS system design, ANFIS training, ANFIS testing, accuracy calculation, and result analysis. The prediction system for the amount of sugar production is designed to predict the variable which is the amount of sugar production in the year using the input variables (sugarcane harvested area in year ), (amount of sugarcane in year ), (average of yield in year ), and (number of milling days in year ). The experiments in this research used variations of the type of membership function and the number of membership functions. The best model obtained in this research is a model with a difference between two sigmoidal membership functions and a product of two sigmoidal membership functions with a total of 2 membership functions for each input variable. Both models have the same Mean Absolute Percentage Error (MAPE) value, which is 1.79% in the training process and 4.82% in the testing process. |
| format | Article |
| id | doaj-art-808261df3f0d4cda9975cea187833e56 |
| institution | Kabale University |
| issn | 1978-7227 2615-3017 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Universitas Pattimura |
| record_format | Article |
| series | Barekeng |
| spelling | doaj-art-808261df3f0d4cda9975cea187833e562025-08-20T03:37:34ZengUniversitas PattimuraBarekeng1978-72272615-30172024-10-011842597261010.30598/barekengvol18iss4pp2597-261013232PREDICTION SYSTEM FOR THE AMOUNT OF SUGAR PRODUCTION USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEMAhmad Kamsyakawuni0Walidatush Sholihah1Abduh Riski2Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Jember, IndonesiaDepartment of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Jember, IndonesiaDepartment of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Jember, IndonesiaSugar is one of the staple foods most Indonesians use, so sugar production needs to be done optimally to meet people's needs. This research will design a prediction system for the amount of sugar production in PTPN XI PG Prajekan using the Adaptive Neuro-Fuzzy Inference System (ANFIS) method. ANFIS is a combined method of two systems, namely a fuzzy logic system and an artificial neural network system. This research consists of data collection, ANFIS system design, ANFIS training, ANFIS testing, accuracy calculation, and result analysis. The prediction system for the amount of sugar production is designed to predict the variable which is the amount of sugar production in the year using the input variables (sugarcane harvested area in year ), (amount of sugarcane in year ), (average of yield in year ), and (number of milling days in year ). The experiments in this research used variations of the type of membership function and the number of membership functions. The best model obtained in this research is a model with a difference between two sigmoidal membership functions and a product of two sigmoidal membership functions with a total of 2 membership functions for each input variable. Both models have the same Mean Absolute Percentage Error (MAPE) value, which is 1.79% in the training process and 4.82% in the testing process.https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/13232prediction systemsugar productionadaptive neuro-fuzzy inference systemmembership function |
| spellingShingle | Ahmad Kamsyakawuni Walidatush Sholihah Abduh Riski PREDICTION SYSTEM FOR THE AMOUNT OF SUGAR PRODUCTION USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM Barekeng prediction system sugar production adaptive neuro-fuzzy inference system membership function |
| title | PREDICTION SYSTEM FOR THE AMOUNT OF SUGAR PRODUCTION USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM |
| title_full | PREDICTION SYSTEM FOR THE AMOUNT OF SUGAR PRODUCTION USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM |
| title_fullStr | PREDICTION SYSTEM FOR THE AMOUNT OF SUGAR PRODUCTION USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM |
| title_full_unstemmed | PREDICTION SYSTEM FOR THE AMOUNT OF SUGAR PRODUCTION USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM |
| title_short | PREDICTION SYSTEM FOR THE AMOUNT OF SUGAR PRODUCTION USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM |
| title_sort | prediction system for the amount of sugar production using adaptive neuro fuzzy inference system |
| topic | prediction system sugar production adaptive neuro-fuzzy inference system membership function |
| url | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/13232 |
| work_keys_str_mv | AT ahmadkamsyakawuni predictionsystemfortheamountofsugarproductionusingadaptiveneurofuzzyinferencesystem AT walidatushsholihah predictionsystemfortheamountofsugarproductionusingadaptiveneurofuzzyinferencesystem AT abduhriski predictionsystemfortheamountofsugarproductionusingadaptiveneurofuzzyinferencesystem |