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All Journal Jurnal Penelitian Pendidikan IPA (JPPIPA) Buletin Ilmiah Sarjana Teknik Elektro Indonesia Berdaya Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Kewirausahaan dan Bisnis
Aji Pamungkas
STMIK AMIKOM Yogyakarta
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Journal : Buletin Ilmiah Sarjana Teknik Elektro

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Optimizing Banana Type Identification: An Support Vector Machine Classification-Based Approach for Cavendish, Mas, and Tanduk Varieties Pamungkas, Aji; Fadlil, Abdul
Buletin Ilmiah Sarjana Teknik Elektro Vol. 5 No. 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v5i4.9145

Abstract

This research focuses on addressing the need for improved efficiency in the agricultural sector, particularly in banana processing in Indonesia, where the demand for bananas is consistently high. To improve the efficiency of banana processing, the research proposes the development of a machine learning based solution for automatic banana type selection. This solution uses image data of three banana types (Cavendish, Mas, and Tanduked) captured by a microscopic camera. The images are subjected to feature extraction, and a Support Vector Machine (SVM) algorithm is used to train the model. The results are implemented in a graphical user interface (GUI). The experimental results show promising results, with an accuracy of 86.67%, a precision of 87.78%, and an error rate of 13.33%, achieved with SVM parameters of C = 1000 and a linear kernel. This automated approach provides a practical and sustainable solution to the labor-intensive manual banana variety selection process, thus increasing the efficiency of the banana processing industry.
Co-Authors Abdul Fadlil Dian Citaningtyas Ari Kadi, Dian Citaningtyas Fauzi, Rizal Ula Ananta Kristyaningrum, Dwi Hesty Mentari, Delta Purna PUTRI WULANDARI Rahman, Abi Fathur Riziq, Kamal Ali Rossanto, Selly Andan Ria Saputra , Anggi Susanto, Erliyan Redi Winarto Winarto