Melenia, Diah Ayu (2022) Perancangan Demand Forecast Suku Cadang Seal Oring Dengan Mempertimbangkan Harga Jual Unit Dan Jumlah Periodical Service Unit Alat Berat Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS). Other thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
PT. XYZ menggunakan Moving Average (MA) untuk memprediksi demand suku cadang. Namun akurasinya menurun untuk pola data fluktuatif, seperti pada data demand seal Oring 065 dan 125. Oleh karena itu, dilakukan perancangan demand forecast menggunakan metode lain dengan melibatkan variabel yang sekiranya memberikan pengaruh terhadap demand suku cadang. Metode Adaptive Neuro Fuzzy Inference System (ANFIS) dengan mempertimbangkan pengaruh harga penjualan unit dan jumlah unit alat berat yang melakukan periodical service (PS) yang menggunakan seal Oring dan Autoregresive Integrated Moving Average (ARIMA) diusulkan sebagai metode forecast untuk memperoleh error yang lebih rendah. Sampel alat berat yang ditinjau adalah hydraulic excavator PC200 dan bulldozer D85. Uji korelasi menunjukkan adanya korelasi negatif antara harga jual unit alat berat terhadap demand seal Oring. Korelasi terendah oleh korelasi demand seal Oring 065 terhadap harga jual unit D85 dan tertinggi oleh demand seal Oring 125 terhadap harga jual unit D85. Demand seal Oring terhadap unit alat berat yang melakukan PS berkorelasi positif. Demand seal Oring 065 tidak berkorelasi terhadap PS PC200 dan korelasi tertinggi oleh demand seal Oring 125 terhadap PS D85. Error terrendah untuk demand forecast seal Oring 065 menggunakan ANFIS grid partitioning 17 fungsi keanggotaan, sedangkan untuk seal Oring 125 menggunakan ARIMA(1,1,0).
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PT. XYZ uses the Moving Average (MA) to forecast demand of spare parts. However, the accuracy is reduced for fluctuating data patterns, as in the demand seal Oring 065 and 125. Therefore, a demand forecast using other methods involving variables that might affect the demand for spare parts designed. Adaptive Neuro Fuzzy Inference System (ANFIS) by considering the effect of unit sales prices and the number of periodical service (PS) of heavy equipment units which using that seal Oring and Autoregresive Integrated Moving Average (ARIMA) is proposed as forecast method to obtain a lower error value. Heavy equipment reviewed are hydraulic excavator PC200 and bulldozer D85. Result shows that are there is a negative correlation between the PC200 and D85 sales price to demand of seal Oring. The lowest correlation is by demand seal Oring 065 to the D85 sales price and the highest by demand seal Oring 125 to the D85 sales price. Demand of seal Oring to the number of heavy equipment which do PS is positively correlated. Demand seal Oring 065 is not correlated with PS hydraulic excavator PC200 and the highest correlation is demand seal Oring 125 to PS D85. Demand forecast seal Oring 065 produced the lowest error using ANFIS grid partitioning 17 membership functions, while demand forecast seal Oring 125 gives the lowest error using ARIMA(1,1,0).
| Item Type: | Thesis (Other) |
|---|---|
| Additional Information: | RSF 629.836 Mel p-1 2022 |
| Uncontrolled Keywords: | Adaptive Neuro Fuzzy Inference System, Autoregresive Integrated Moving Average, Moving Average, Peramalan, Suku Cadang. Adaptive Neuro Fuzzy Inference System, Autoregresive Integrated Moving Average, Forecasting, Moving Average, Spare Parts. |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) > TA167.5 Neurotechnology. Neuroadaptive systems |
| Divisions: | Faculty of Industrial Technology and Systems Engineering (INDSYS) > Physics Engineering > 30201-(S1) Undergraduate Thesis |
| Depositing User: | Mr. Marsudiyana - |
| Date Deposited: | 11 May 2026 02:26 |
| Last Modified: | 11 May 2026 02:26 |
| URI: | http://repository.its.ac.id/id/eprint/133101 |
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