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All Journal Journal of Electrical Engineering and Computer (JEECOM) Jurnal Teknik Mesin, Industri, Elektro dan Informatika
Bilal Shandyarta Syamsudin
POLITEKNIK NEGERI JEMBER
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Industrial & Manufacturing Engineering

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Perancangan Sistem Monitoring Pergerakan Lobster Air Tawar Berbasis Internet of Things (IoT) Denny Trias Utomo; Abhinaya Fahar Laila; Dinda Amalia Julyandri; Miftakhur Rahman; Bilal Shandyarta Syamsudin
Journal of Electrical Engineering and Computer (JEECOM) Vol 4, No 1 (2022)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v4i1.3625

Abstract

Lobster air tawar mempunya nilai ekonomis yang sangat penting dalam perdagangan dan juga menjadi komoditas unggulan di tingkat lokal maupun internasional. Peluang pengembangan budidaya lobster dipengaruhi oleh tingginya kebutuhan atau permintaan domestik maupun luar negeri. Di Indonesia usaha budidaya lobster air tawar hanya berkembang dalam skala kecil dan belum berkembang sebesar permintaan. Hal ini dapat dibuktikan dari rendahnya pengembangan dan produksi pada budidaya lobster air tawar di Indonesia. Jenis dan ukuran lobster juga dapat memengaruhi harga lobster air tawar menjadi sangat tinggi dan bervariasi. Selain itu, kualitas pada lobster air tawar juga dapat memengaruhi tinggi rendahnya nilai jual lobster. Oleh karena itu, diciptakanlah sebuah teknologi yang mampu memonitoring pergerakan lobster air tawar berbasis Internet of Things (IoT). Alat ini menggunakan 5 komponen yang terdapat pada alat ini. Komponen yang digunakan pada alat ini adalah raspberry pi 4, relay 4 channel, servo, lampu LED, dan power supply 12 volt . Dengan adanya alat ini para pembudidaya lobster air tawar dapat lebih mudah dalam  memantau pergerakan lobster air tawar dari jarak jauh.
Optimizing Coffee Ripeness Classification Using Yolov5 for Automated Detection and Sorting Arfan Astaraja; Bilal Shandyarta Syamsudin; Muhammad Diaz Maulana Dhafin; Fatkhul Hidayah; Niecola Jody Setiawan
Jurnal Teknik Mesin, Industri, Elektro dan Informatika Vol. 4 No. 1 (2025): JURNAL TEKNIK MESIN, INDUSTRI, ELEKTRO DAN INFORMATIKA
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jtmei.v4i1.4821

Abstract

The quality of agricultural products, particularly coffee beans, is crucial in today's global market, which demands precise ripeness classification due to its high commercial value. Traditional manual methods in coffee plantations, heavily reliant on human labor to determine quality, often result in inefficiencies and inaccuracies. To address this issue, this study developed an automated coffee ripeness detection system using the YOLOv5 machine learning algorithm, combined with Raspberry Pi, webcam, and servo motor. By integrating YOLOv5, the system enables real-time classification of coffee beans into three categories: ripe, unripe, and rotten, with an average accuracy of 90% during real-time testing. This system not only reduces dependence on manual labor but also improves process efficiency across various environmental conditions. The findings suggest that the application of this technology can significantly enhance productivity in the coffee industry, while providing a foundation for further advancements in automation and classification methodologies in the agricultural sector.
Co-Authors Abhinaya Fahar Laila Arfan Astaraja Denny Trias Utomo, Denny Trias Dinda Amalia Julyandri Fatkhul Hidayah Miftakhur Rahman Muhammad Diaz Maulana Dhafin Niecola Jody Setiawan