Features Selection based on Enhanced KNN to Predict Raw Material Needs on PT. SANM
Raw material inventory must be able to meet production needs. So it is necessary to plan / predict raw material needs in the following month to determine the raw material inventory. Currently PT. SANM uses a manual counting method, the expenditure of raw materials for six months, then deducts the cu...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Program Studi Teknik Informatika Universitas Trilogi
2021-12-01
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| Series: | JISA (Jurnal Informatika dan Sains) |
| Subjects: | |
| Online Access: | https://trilogi.ac.id/journal/ks/index.php/JISA/article/view/912 |
| _version_ | 1818030046208589824 |
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| author | Siti Aisyah Naili Mutia Tjong Wan Sen |
| author_facet | Siti Aisyah Naili Mutia Tjong Wan Sen |
| author_sort | Siti Aisyah Naili Mutia |
| collection | DOAJ |
| description | Raw material inventory must be able to meet production needs. So it is necessary to plan / predict raw material needs in the following month to determine the raw material inventory. Currently PT. SANM uses a manual counting method, the expenditure of raw materials for six months, then deducts the current raw material inventory. As a result, there are raw materials that are over order or lacking, which causes production to be constrained. The manual calculation method is not effective enough to meet the raw material inventory. In this research, the researcher proposes an algorithm which is contained in Data Mining, that is Enhanced KNN using GWO to predict raw material needs. Because GWO and Enhanced KNN algorithms give the results are easy to understand, have good accuracy compared to other machine learning methods, can cover the trapped problem from KNN traditional and capable of improving the accuracy using feature selection method. The method used in this study is to compare Enhanced KNN with and without GWO that gives a significant increase in the accuracy value by 16.5%, from 44.6% to 61.1%. |
| first_indexed | 2024-12-10T05:29:21Z |
| format | Article |
| id | doaj.art-e8ff9c3afb1f4b9285a7f9e76bd0ffc9 |
| institution | Directory Open Access Journal |
| issn | 2776-3234 2614-8404 |
| language | English |
| last_indexed | 2024-12-10T05:29:21Z |
| publishDate | 2021-12-01 |
| publisher | Program Studi Teknik Informatika Universitas Trilogi |
| record_format | Article |
| series | JISA (Jurnal Informatika dan Sains) |
| spelling | doaj.art-e8ff9c3afb1f4b9285a7f9e76bd0ffc92022-12-22T02:00:36ZengProgram Studi Teknik Informatika Universitas TrilogiJISA (Jurnal Informatika dan Sains)2776-32342614-84042021-12-014210010610.31326/jisa.v4i2.912563Features Selection based on Enhanced KNN to Predict Raw Material Needs on PT. SANMSiti Aisyah Naili Mutia0Tjong Wan SenPresident UniversityRaw material inventory must be able to meet production needs. So it is necessary to plan / predict raw material needs in the following month to determine the raw material inventory. Currently PT. SANM uses a manual counting method, the expenditure of raw materials for six months, then deducts the current raw material inventory. As a result, there are raw materials that are over order or lacking, which causes production to be constrained. The manual calculation method is not effective enough to meet the raw material inventory. In this research, the researcher proposes an algorithm which is contained in Data Mining, that is Enhanced KNN using GWO to predict raw material needs. Because GWO and Enhanced KNN algorithms give the results are easy to understand, have good accuracy compared to other machine learning methods, can cover the trapped problem from KNN traditional and capable of improving the accuracy using feature selection method. The method used in this study is to compare Enhanced KNN with and without GWO that gives a significant increase in the accuracy value by 16.5%, from 44.6% to 61.1%.https://trilogi.ac.id/journal/ks/index.php/JISA/article/view/912data mining, classification, enhanced k-nearest neighbor, feature selection |
| spellingShingle | Siti Aisyah Naili Mutia Tjong Wan Sen Features Selection based on Enhanced KNN to Predict Raw Material Needs on PT. SANM JISA (Jurnal Informatika dan Sains) data mining, classification, enhanced k-nearest neighbor, feature selection |
| title | Features Selection based on Enhanced KNN to Predict Raw Material Needs on PT. SANM |
| title_full | Features Selection based on Enhanced KNN to Predict Raw Material Needs on PT. SANM |
| title_fullStr | Features Selection based on Enhanced KNN to Predict Raw Material Needs on PT. SANM |
| title_full_unstemmed | Features Selection based on Enhanced KNN to Predict Raw Material Needs on PT. SANM |
| title_short | Features Selection based on Enhanced KNN to Predict Raw Material Needs on PT. SANM |
| title_sort | features selection based on enhanced knn to predict raw material needs on pt sanm |
| topic | data mining, classification, enhanced k-nearest neighbor, feature selection |
| url | https://trilogi.ac.id/journal/ks/index.php/JISA/article/view/912 |
| work_keys_str_mv | AT sitiaisyahnailimutia featuresselectionbasedonenhancedknntopredictrawmaterialneedsonptsanm AT tjongwansen featuresselectionbasedonenhancedknntopredictrawmaterialneedsonptsanm |