Entyo, Nabbil Gibran Winitaris (2023) Prediksi Permintaan Suku Cadang pad Container Crane dengan Metode Adaptive Neuro Fuzzy Inference System (ANFIS) berdasarkan Service Maintenance Information. Other thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Proses perencanaan persediaan suku cadang proximity sensor pada container crane menjadi salah satu tantangan yang dialami PT. Pelindo Terminal Petikemas dalam maintenance, akibat angka demand suku cadang yang sangat fluktuatif dan pola demand suku cadang yang tidak menentu. Peneliti melakukan perancangan model demand forecast suku proximity sensor untuk meningkatkan evaluasi prediksi inventori yang optimal bagi PT. Pelindo Terminal Petikemas. Peneliti menggunakan metode Adaptive Neuro Fuzzy Inference System (ANFIS) dengan variabel input yaitu jumlah unit, jumlah periodic maintenance, jumlah breakdown, dan jumlah produksi kontainer. Performansi metode demand forecast yang diusulkan yaitu ANFIS, dibandingkan dengan metode demand forecast yang digunakan oleh PT. Pelindo Terminal Petikemas yaitu Moving Average. Hasil uji akurasi menunjukkan nilai error ANFIS untuk proximity sensor secara berurutan sebesar MAD 0,98, MSE 1,62. Metode moving average menghasilkan nilai error sebesar MAD 1,69 dan MSE 4,41. Metode kualitatif yang dilakukan oleh perusahaan menghasilkan nilai error sebesar MAD 2,35 dan MSE 11,31. Kesimpulan yang didapatkan yaitu metode ANFIS adalah metode demand forecast dengan performansi atau tingkat akurasi yang lebih baik daripada metode eksisting yang digunakan oleh perusahaan baik metode movinga average dan kualitatif.
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The process of planning the supply of proximity sensor spare parts on container cranes is one of the challenges experienced by PT. Pelindo Container Terminal is under maintenance, due to fluctuating demand for spare parts and uncertain patterns of demand for spare parts. Researchers designed a demand forecast model for the proximity sensor tribe to improve the evaluation of optimal inventory predictions for PT. Pelindo Container Terminal. Researchers used the Adaptive Neuro Fuzzy Inference System (ANFIS) method with input variables, namely the number of units, the number of periodic maintenances, the number of breakdowns, and the number of container production. The performance of the proposed demand forecast method is ANFIS, compared to the demand forecast method used by PT. Pelindo Container Terminal namely Moving Average. The results of the accuracy test show that the ANFIS error value for the proximity sensor is MAD 0.98, MSE 1.62 respectively. The moving average method produces an error value of MAD 1.69 and MSE 4.41. The qualitative method used by the company produces an error value of MAD 2.35 and MSE 11.31. The conclusion obtained is that the ANFIS method for demand forecasting has better performance or accuracy than the existing method used by companies, both moving average and qualitative methods
| Item Type: | Thesis (Other) |
|---|---|
| Uncontrolled Keywords: | Adaptive Neuro Fuzzy Inference System, Moving Average, Prediksi, Suku Cadang; Adaptive Neuro Fuzzy Inference System, Forecasting, Moving Average, Spare Parts |
| Subjects: | H Social Sciences > HB Economic Theory > Economic forecasting--Mathematical models. Q Science > QA Mathematics > QA76.87 Neural networks (Computer Science) Q Science > QA Mathematics > QA9.64 Fuzzy logic T Technology > T Technology (General) > T174 Technological forecasting |
| Divisions: | Faculty of Industrial Technology and Systems Engineering (INDSYS) > Physics Engineering > 30201-(S1) Undergraduate Thesis |
| Depositing User: | Nabbil Gibran Winitaris Entyo |
| Date Deposited: | 30 Aug 2023 05:48 |
| Last Modified: | 30 Aug 2023 05:48 |
| URI: | http://repository.its.ac.id/id/eprint/102151 |
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