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All Journal Jurnal Teknologi Industri Pertanian Bulletin of Computer Science Research
Wisudawaty, Priska
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Agriculture, Biological Sciences & Forestry Computer Science & IT Engineering Industrial & Manufacturing Engineering

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Prediksi Tingkat Produksi Bawang Goreng menggunakan Metode K-Means dan Fuzzy Inference System Wisudawaty, Priska
Bulletin of Computer Science Research Vol. 4 No. 1 (2023): December 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v4i1.297

Abstract

Shallots are a strategic commodity because they are needed for household consumption as well as the food industry. Shallots are usually used as a cooking spice, or as a topping for food dishes called fried onions. Shallots are easily damaged, one way to prevent damage is to process shallots into fried onions. Sales of fried onions fluctuate every month due to consumer demand, therefore in this research a grouping of production levels and predictions of fried onion production was carried out. The methods used in this research are K-Means and Fuzzy Sugeno. From the results of research using the K-Means method, there are 3 clusters of fried onion production levels, namely high, medium and small production levels. High production levels were found in months 4, 5, 9, and 10; moderate production levels in months 1, 2, 3, 6, 7, 8 and 11; while a small production level was found in the 12th month. Based on system testing using the fuzzy Sugeno method, data was generated that could be processed and produce 9 rules to serve as a reference in predicting fried onion production for the following years. Apart from that, based on the results of the Mean Absolute Percent Error calculation, the capability of the model created is good and accurate because it has a value of 14.2%. Fried onion production levels in the 4th and 12th months have more accurate predictions compared to other months
SALES PREDICTION OF KACAMPRING CHIPS USING ADAPTIVE NEURO FUZZY INFERENCE SYSTEM METHOD Wisudawaty, Priska; Djatna, Taufik
Jurnal Teknologi Industri Pertanian Vol. 34 No. 1 (2024): Jurnal Teknologi Industri Pertanian
Publisher : Department of Agroindustrial Technology, Bogor Agricultural University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24961/j.tek.ind.pert.2024.34.1.1

Abstract

Industri makanan dan minuman selalu menjadi sektor yang dinamis dan kompetitif. Salah satu produk yang memiliki popularitas yang tinggi adalah keripik kacampring. Kebutuhan akan keripik kacampring yang terus meningkat telah mendorong produsen untuk mencari cara untuk meningkatkan produksi dan memenuhi permintaan pasar yang terus tumbuh. Salah satu pendekatan yang efektif dalam mengelola produksi adalah dengan memanfaatkan teknologi prediksi penjualan yang dapat memberikan wawasan berharga tentang permintaan pelanggan di masa depan. Salah satu metode pemecahan masalah yang digunakan dalam penelitian adalah Adaptive Neuro Fuzzy Inference System (ANFIS). Berdasarkan pengujian sistem prediksi penjualan keripik kacampring didapat beberapa kesimpulan yaitu, dari data penjualan keripik kacampring di UMKM dapat dihasilkan sebuah data yang dapat diproses dan menghasilkan 27 rule untuk menjadi acuan dalam melakukan prediksi penjualan keripik kacampring yang akan mendatang. Pada pengujian menggunakan software Matlab R2016 bahwa proses pelatihan menggunakan MF gbellmf dengan menghasilkan tingkat akurasi 99,902%. Selain itu berdasarkan hasil perhitungan MAPE, terlihat bahwa nilai MAPE 3,96 yang berarti bahwa kemampuan model yang dibuat sangat baik dan akurat karena hasil pengujian lebih rendah dari 10%. Kata kunci: ANFIS, Fuzzy, MAPE, penjualan, prediksi
Co-Authors Erliza Hambali