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All Journal Jurnal Informatika dan Teknik Elektro Terapan Applied Technology and Computing Science Journal Joutica : Journal of Informatic Unisla
Mustain Mustain
Universitas Islam Lamongan
Computer Science & IT Control & Systems Engineering Engineering Social Sciences

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Journal : Applied Technology and Computing Science Journal

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Classification Of Determination Of Sweet Siwalan Fruit Based On Color Feature Using Svm Support Vector Machine Method Mustain mustain; Munif Munif; Kurnia yahya
TEKNOLOGI DITERAPKAN DAN JURNAL SAINS KOMPUTER Vol 5 No 1 (2022): June
Publisher : Universitas Nahdlatul Ulama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33086/atcsj.v5i1.3678

Abstract

Siwalan fruit is usually called a fruity fruit, not everyone knows about this fruit because this fruit only exists in certain regions in Indonesia. This fruit is very nutritious because it contains a lot of nutrients and has a sweet taste. But ordinary people determine the level of sweetness by means of manually tasting it directly. And with this research can help the community to distinguish between sweet and non-sweet siwalan, a system that can help classify siwalan fruit based on color is designed. With the SVM (Support Vector Machine) method and the method used is a learning machine method that can find the best hyperplane that separates 2 classes in the input space. It can be seen that the overall test data of 20 data from 3 taste siwalan (sweet, medium, lacking), the testing data obtained the highest level of accuracy of 90%, and can be said that SVM has a better level of accuracy.
Analysis of the K-Nearest Neighbor Algorithm to Determine the Prediction of Tofu Production Munif Munif; Mustain Mustain; Kurnia Yahya
TEKNOLOGI DITERAPKAN DAN JURNAL SAINS KOMPUTER Vol 5 No 1 (2022): June
Publisher : Universitas Nahdlatul Ulama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33086/atcsj.v5i1.3677

Abstract

The tofu factory in Kedungpring does not yet have a prediction system to estimate the number of tofu that will be predicted for the next month. As a result, companies cannot meet market demand in a timely and appropriate amount. Therefore, it is necessary to make a prediction system to determine the amount of tofu production in Kedungpring District. In this research, the application of K-Nearest Neighbor Algorithm Analysis Application to determine the prediction of the number of Web-Based production to make it easy to predict the number of tofu production. The system functional test results show that all features in the application are able to run properly and functionally. Testing the accuracy of the prediction system K-Nearest Neighbor algorithm to determine the prediction of the number of web-based tofu production that can produces a MAPE of 0.68%
Co-Authors Ahmad Fathoni Danang Bagus Reknadi Kurnia Yahya Kurnia yahya Mochammad Fajrin MUNIF ROCHMAWANTO Retno Wardhani