Jesika, Izza Rachma (2022) Perancangan Demand Forecast Untuk Spare Part Air Cleaner 600-185-6100 Dan 600-185- 6110 Alat Berat Menggunakan Metode Adaptive Neuro Fuzzy Inference System (Anfis). Other thesis, Institut Teknologi Sepuluh Nopember Surabaya.
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Abstract
Indonesia sebagai negara industri otomotif alat berat berdampak pada perekonomian yang membutuhkan ketersediaan spare part. Berdasarkan konsep ekonomi, terdapat keselarasan antara ekonomi dengan proses pembangunan. PT. XYZ, penyedia spare part melakukan demand forecast dengan metode Moving Average (MA) untuk memaksimumkan laba dan masih menghasilkan nilai MAPE > 50%. Spare part yang sering mengalami persediaan tidak menentu adalah air cleaner (filter). Tugas akhir ini mengajukan perancangan demand forecast spare parts air cleaner 600-185-6100 dan 600-185-6110 dengan metode Adaptive Neuro Fuzzy Inference System (ANFIS) dengan tiga variabel input harga promosi, brand trust, dan lama penyinaran matahari. ANFIS bekerja dengan melatih dan menguji data yang digabung dengan pembentukan membership function (MF) dengan model fuzzy sugeno. Hasil terbaik pada ANFIS dengan MF gbell, number of MF sebesar 5 dengan pemilihan grid partition untuk pembuatan Fuzzy Inference System, dan digunakan optim method hybrid. Performansi perancangan demand forecast terbaik dengan nilai MAE sebesar 3.37 dan MSE sebesar 22.57 pada forecast air cleaner 6100 serta nilai MAE sebesar 1.01 dan MSE senilai 10.41 pada forecast air cleaner 6110. Kata Kunci: Adaptive Neuro Fuzzy Inference System (ANFIS), Air cleaner, Forecast, Moving average
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Indonesia as country of heavy equipment automotive industry has impact for economy. Based of economic concept, there is correlation between economy and construction with availability of spare parts. PT. XYZ, spare part provider performs demand forecast using Moving Average to maximize profit, but MAPE value >50%. Spare part that often stockout is air cleaner. Final project proposes demand forecast design for air cleaner 600-185-6100 and 600-185-6110 with ANFIS with three inputs: promotion,, brand trust, duration of sunshine. ANFIS works by training and testing data that combined with the formation of Sugeno fuzzy. Best results in ANFIS with MF gbell, number of MF is 5, grid partition for making Fuzzy Inference System, and use hybrid for optim method. Best demand forecast design performance is with MAE value of 3.37 and MSE value of 22.57 for air cleaner 6100 and MAE value of 1.01, MSE value of 10.41 for air cleaner 6110
| Item Type: | Thesis (Other) |
|---|---|
| Additional Information: | RSF 629.836 Jes p-1 |
| Uncontrolled Keywords: | Adaptive Neuro Fuzzy Inference System (ANFIS), Air cleaner, Forecast, Moving average. |
| Subjects: | T Technology > TJ Mechanical engineering and machinery > TJ217 Adaptive control systems |
| Divisions: | Faculty of Industrial Technology and Systems Engineering (INDSYS) > Physics Engineering > 30201-(S1) Undergraduate Thesis |
| Depositing User: | EKO BUDI RAHARJO |
| Date Deposited: | 07 Dec 2022 06:20 |
| Last Modified: | 07 Dec 2022 06:20 |
| URI: | http://repository.its.ac.id/id/eprint/95176 |
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