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All Journal Jurnal Teknologi dan Manajemen Informatika JAST : Jurnal Aplikasi Sains dan Teknologi
Hendrawan, Nofrian Deny
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Other

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Implementasi IoT pada Mesin Gibrik Pengering Sampah Anorganik berbasis Graphical User Interface Hendrawan, Nofrian Deny; Arief, Rizza Muhammad; Prihatiningsih, Bekti
JAST : Jurnal Aplikasi Sains dan Teknologi Vol 8, No 2 (2024): EDISI DESEMBER 2024
Publisher : Universitas Tribhuwana Tunggadewi Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33366/jast.v8i2.6317

Abstract

Problems in inorganic waste management in Junrejo Village, RW 07, Batu City, are often related to the lack of efficiency of the drying process, which can affect the quality of recycling. This activity aims to develop an inorganic waste drying gibrik machine equipped with an Internet of Things (IoT) system and Graphical User Interface (GUI) to improve efficiency and ease of operation. The IoT system is implemented to enable remote monitoring and control of the machine via a mobile application, while the GUI is designed to provide an intuitive user interface. The methods used include hardware and software design, sensor integration to monitor temperature and humidity, and the development of Android-based applications as a communication medium between users and machines. This tool was implemented in Junrejo Village, RW 07, Batu City, by involving the local community as partners. The test results show that implementing this system can increase drying efficiency by up to 25% compared to conventional methods, so the Junrejo Village community can optimize its waste management process.ABSTRAKPermasalahan dalam pengelolaan sampah anorganik di Desa Junrejo, RW 07, Kota Batu, seringkali berkaitan dengan kurangnya efisiensi proses pengeringan, yang dapat mempengaruhi kualitas daur ulang. Kegiatan ini bertujuan untuk mengembangkan sebuah mesin gibrik pengering sampah anorganik yang dilengkapi dengan sistem Internet of Things (IoT) dan Graphical User Interface (GUI) guna meningkatkan efisiensi dan kemudahan pengoperasian. Sistem IoT diterapkan untuk memungkinkan pemantauan dan pengendalian mesin secara jarak jauh melalui aplikasi mobile, sedangkan GUI dirancang untuk memberikan antarmuka pengguna yang intuitif. Metode yang digunakan meliputi perancangan perangkat keras dan perangkat lunak, integrasi sensor untuk memantau suhu dan kelembaban, serta pengembangan aplikasi berbasis Android sebagai media komunikasi antara pengguna dan mesin. Implementasi alat ini dilakukan di Desa Junrejo, RW 07, Kota Batu, dengan melibatkan masyarakat setempat sebagai mitra. Hasil pengujian menunjukkan bahwa implementasi sistem ini dapat meningkatkan efisiensi pengeringan hingga 25% dibandingkan dengan metode konvensional, sehingga masyarakat  Desa  Junrejo  dalam  mengoptimalkan  proses pengelolaan sampah mereka.
A Comparative Study of YOLOv8 and YOLO - NAS Performance in Human Detection Image Hendrawan, Nofrian Deny; Kolandaisamy, Raenu
Jurnal Teknologi dan Manajemen Informatika Vol. 9 No. 2 (2023): Desember 2023
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v9i2.12192

Abstract

In the realm of computer vision, object detection holds immense importance across applications such as surveillance and autonomous vehicles. This study addresses the critical challenge of human detection under low-light conditions, essential for nocturnal surveillance and autonomous driving systems. Focusing on the evolution of YOLO models, particularly YOLO - NAS and YOLOv8, a research gap is identified concerning their performance in low-light scenarios. The research conducts a detailed analysis of YOLO - NAS and YOLOv8 effectiveness in human detection under reduced ambient illumination. Object detection, vital in computer vision, faces challenges in low-light scenarios. This study concentrates on human detection due to its significance in night-time surveillance and autonomous driving. Despite YOLO models' evolution, a research gap exists in comparing their performance in low-light conditions. The study aims to fill this gap, providing insights for enhancing human detection methodologies in challenging lighting environments.
Co-Authors Bekti Prihatiningsih Kolandaisamy, Raenu Rizza Muhammad Arief