Prediksi Tingkat Produksi Bawang Goreng menggunakan Metode K-Means dan Fuzzy Inference System


Authors

  • Priska Wisudawaty Sekolah Tinggi Teknologi Bandung, Bandung, Indonesia

DOI:

https://doi.org/10.47065/bulletincsr.v4i1.297

Keywords:

Fried Onions; Clustering; Fuzzy Sugeno; K-Means; Forecasting; Prediction

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

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Submitted: 2023-11-26
Published: 2023-12-31

Abstract View: 87 times
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How to Cite

Wisudawaty, P. (2023). Prediksi Tingkat Produksi Bawang Goreng menggunakan Metode K-Means dan Fuzzy Inference System. Bulletin of Computer Science Research, 4(1), 1-9. https://doi.org/10.47065/bulletincsr.v4i1.297

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