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All Journal ANDHARUPA JITEK (Jurnal Ilmiah Teknosains) Jurnal Teknologi Informasi MURA Martabe : Jurnal Pengabdian Kepada Masyarakat EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Jurnal Mnemonic JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Abdiformatika: Jurnal Pengabdian Masyarakat Informatika Ahsana: Jurnal Penelitian dan Pengabdian kepada Masyarakat Jurnal Teknologi Informasi Mura
Gusmaliza, Debi
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Journal : Jurnal Teknologi Informasi Mura

 1 
RANCANG BANGUN MESIN SORTIR BUAH TOMAT BERDASARKAN TINGKAT KEMATANGAN DENGAN SENSOR TCS3200 Puspita, Desi; Gusmaliza, Debi; Aminah, Siti
Jurnal Teknologi Informasi Mura Vol 17 No 1 (2025): Jurnal Teknologi Informasi Mura JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v17i1.2629

Abstract

The tomato sorting process is currently still done manually by farmers where the results are less accurate and effective. Currently, many still use the manual tomato separation process, so it takes a long time and is not accurate in the process of separating tomatoes. The ripeness of tomatoes can be determined during the harvest process. The level of ripeness of tomatoes is seen from their color, ripe fruit is marked with red. The purpose of the study was to create a prototype for automatic tomato sorting based on the level of ripeness with the TCS3200 sensor. The TCS3200 sensor plays a role in detecting the level of ripeness of tomatoes based on the color of the tomato skin. The system development method used is the Rapid Application Development (RAD) system such as Requirements Planning, RAD Workshop Design, and Implementation. This prototype will work by selecting the color of red, greenish, and yellow tomatoes. This tool is equipped with a motor to separate tomatoes according to the level of ripeness, a TCS3200 color sensor that produces three identification outputs, namely 100% ripe tomatoes, 80% unripe, and 100% half-ripe with an overall identification level of 93.3%.
Co-Authors Aisah, Elpita Alfis Arif Arizki, Angga Azhari, Habi Dedi Setiadi Fitri Ramadhani Inda Anggraini Jangcik, Idi Masdalipa, Risnaini Muntari, Siti Nanda S. Prawira Puspita, Desi Rahmadhani, Fitri Sasmita Sasmita Siti Aminah Yadi, Yadi