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All Journal EDUMATIC: Jurnal Pendidikan Informatika
Feranandah Firdausi
Program Studi Informatika, Universitas Muhammadiyah Malang
Computer Science & IT Education

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Perbandingan Prediksi Kualitas Kopi Arabika dengan Menggunakan Algoritma SGD, Naive Bayes, dan Random Forest Veronica Retno Sari; Feranandah Firdausi; Yufis Azhar
Jurnal Pendidikan Informatika (EDUMATIC) Vol 4, No 2 (2020): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v4i2.2202

Abstract

Classification is one of the techniques that exist in data mining and is useful for grouping a data based on the attachment of the data with the sample data. The dataset that is used in this study is the coffee dataset taken from Dataset Coffee Quality Institute on the GitHub platform. The attributes that contained in the dataset are Aroma, Aftertaste, Flavor, Acidity, Balance, Body, Uniformity, Sweetness, Clean Cup, and Copper points. There are 3 classification methods that are used in this study, Stochastic Gradient Descent, Random Forest and Naive Bayes. The aim of this study is to find out which algorithm is the most effective to predict the coffee quality in the dataset. After that, the prediction results will be tested using K-Fold Cross Validation and Area Under the Curve (AUC) method. The results show that Stochastic Gradient Descent obtained the best accuracy results compared to the other two methods with an accuracy of 98% and increased to 99% after tested using K-fold Cross Validation and AUC method.
Co-Authors Veronica Retno Sari Yufis Azhar