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All Journal SAMUDERA JURIKOM (Jurnal Riset Komputer) Jurnal Tika KLIK: Kajian Ilmiah Informatika dan Komputer Aceh Journal of Community Engagement (AJCE)
Dedy Armiady
Fakultas Ilmu Komputer Universitas Almuslim Bireuen - Aceh
Religion Agriculture, Biological Sciences & Forestry Humanities Chemistry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Mathematics

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Journal : KLIK: Kajian Ilmiah Informatika dan Komputer

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Klasifikasi Kualitas Buah Pisang Berdasarkan Citra Buah Menggunakan Stochastic Gradient Descent Dedy Armiady; Imam Muslem R
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 2 (2023): Oktober 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i2.1243

Abstract

Banana fruit quality is an important factor in meeting consumer demand and maintaining product quality in the supply chain. The development of automatic methods for classifying the quality of bananas is becoming increasingly important as the worldwide consumption of bananas grows. In this study, we propose a classification method for banana fruit quality using the Stochastic Gradient Descent (SGD) algorithm. This study aims to evaluate the performance of SGD in classifying the quality of bananas and to analyze the effect of selecting hyperparameters on the classification results. The dataset collected is a dataset containing pictures of bananas with various levels of ripeness and conditions. This dataset is used to train and test a classification model using SGD. During the experiment, hyperparameter tuning processes such as learning rate, momentum, and batch size were carried out to understand how these parameters affect the performance of SGD in classification. We report the results of evaluating the classification based on accuracy and analyze changes in performance with variations in hyperparameters. The results of this study indicate that SGD has the potential to classify the quality of bananas, where the optimal SGD model obtained a classification accuracy of 99.9%, compared to the standard SGD model which only obtained a classification accuracy of 94.7%.
Co-Authors ,, Mukhlis Munar Muslem R, Imam Riyadhul Fajri Rizky Mauliza T. Rafli A