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All Journal Knowledge Engineering and Data Science Jurnal Teknologi Terpadu Akuntansi dan Humaniora: Jurnal Pengabdian Masyarakat
Purnama, Ida Bagus Irawan
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Humanities Computer Science & IT Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Social Sciences

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Rancang Bangun Sistem Pengukuran Kinerja Baterai pada Baggage Towing Tractor berbasis NodeMCU ESP8266 Aplikasi Android Suardana, I Gede Made Putra; Nugraha, Ida Bagus Made Harisanjaya Adi; Pemayun, Dewa Gede Agung Padmanaba; Purnama, Ida Bagus Irawan; Budarsa, I Gede Ketut Sri; Sugirianta, Ida Bagus Ketut; Sapteka, Anak Agung Ngurah Gde
Jurnal Teknologi Terpadu Vol 8 No 2 (2022): Desember, 2022
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v8i2.491

Abstract

An electric vehicle uses increasing in Indonesia. Airport services are also advancing the development of electric cars on Baggage Towing Tractors (BTT). BTT is used to attract aircraft baggage to the terminal baggage queue. BTT uses an electric engine as a driving force and an 80V battery as a voltage source. If the BTT battery is not managed correctly, then it is an effect on the battery life. This battery is prone to over-voltage, under-voltage, and over-heat. The project is necessary to develop a system that makes it easier for users to monitor battery performance and technicians during maintenance. The concept of this system is based on an android application. This application will provide real-time information about battery voltage, temperature, and humidity through Firebase Database. The data received by sensors is sent to the database via a microcontroller, which is connected to a Wi-Fi network. The android application will access the database and display data in real-time by ignoring previous data. This system is designed for users to determine the condition of the BTT battery that will be used. The system was tested to evaluate measurement accuracy and speed of updating data. From the test result, the level of accuracy system is around 97%, and it’s rated as working optimally.
Design and Implementation of Solar-Powered Submersible Water Pump for Irrigation System in Subak Munduk Babakan Sangeh, Bali PURNAMA, Ida Bagus Irawan; ELFAROSA, Ketut Vini; SUGIRIANTA, Ida Bagus Ketut; INDRAYANTI, Anak Agung Putri; WIDIANTARA, Made
Akuntansi dan Humaniora: Jurnal Pengabdian Masyarakat Vol. 3 No. 2 (2024): Akuntansi dan Humaniora: Jurnal Pengabdian Masyarakat (Juni – September 2024)-I
Publisher : Indonesia Strategic Sustainability

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38142/ahjpm.v3i2.1140

Abstract

This paper presents a comprehensive study on the design, implementation, and performance evaluation of a Solar-Powered Submersible Water Pump (SPSWP) system tailored for agricultural irrigation in Subak Munduk Babakan, Sangeh, Bali. With a focus on sustainable agriculture and water resource management, the system addresses the challenges of water scarcity during the dry season in the absence of natural irrigation sources. The SPSWP system, consisting of solar panels with a pump controller, a submersible pump, and a water tank, harnesses solar energy to power the pump, eliminating the need for extensive infrastructure. The research encompasses site survey and mapping, analysis and design, installation, benefits beyond irrigation, and technical measurements. The implementation shows promising results in overcoming water scarcity issues. The system's advantages include minimal maintenance, cost savings, and enhanced reliability. Additionally, the SPSWP system serves as an educational site for renewable energy study and awareness. Performance metrics are measured and discussed, including solar irradiance, voltage, current, solar panel temperature, and water discharge. The results indicate fluctuating solar energy availability, with voltage and current aligning with solar panel specifications. The system demonstrates a water discharge rate of 0.56 liters/second, showcasing promising outcomes in addressing water scarcity challenges for agricultural irrigation. Further research and monitoring are required to assess the long-term performance and sustainability of the system.
A Novel Approach to Defect Detection in Arabica Coffee Beans Using Deep Learning: Investigating Data Augmentation and Model Optimization Ardian, Yusriel; Irawan, Novta Danyel; Sutoko, Sutoko; Astawa, I Nyoman Gede Arya; Purnama, Ida Bagus Irawan; Dwiyanto, Felix Andika
Knowledge Engineering and Data Science Vol 7, No 1 (2024)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v7i12024p117-127

Abstract

Arabica coffee beans have valuable market worth because of their taste and quality, and there are defects like wholly and partially black beans that can lower the standards of a product, especially in the premium coffee sector. However, the manual processes used to detect the defects take an inordinate amount of time and are inefficient. This study aims to bridge the knowledge gap on the automated detection and recognition of the defects present in the Arabica coffee beans by creating and optimizing a CNN model based on a modified VGG16 architecture. The model applies data augmentation, rotation, cropping, and Bayesian hyperparameter optimization to improve defect detectability and expedite the training period. During testing, the defined model demonstrated excellent efficiency in defect detection, with a 97.29% confidence level, which was higher than that of the modified VGG16 and Slim-CNN models. The goal of the second optimization was an improvement of the practical application of the model. In terms of the time it takes for a model to be trained, approximately 30% of the time was saved. These findings present a consistent and effective way for the mass production processes of coffee to have quality control procedures automated. The model's ability to detect defects in other agricultural items makes it attractive, thus serving as a practical example of how AI can impact effective management in the inspection processes. The research further enriches the study of deep learning applications in agriculture by demonstrating how to efficiently address specific defect detection problems through an optimized convolutional neural network model.
Co-Authors Anak Agung Ngurah Gde Sapteka Anak Agung Putri Indrayanti Budarsa, I Gede Ketut Sri Dwiyanto, Felix Andika Elfarosa, Ketut Vini I Nyoman Gede Arya Astawa Irawan, Novta Danyel Nugraha, Ida Bagus Made Harisanjaya Adi Pemayun, Dewa Gede Agung Padmanaba Suardana, I Gede Made Putra Sugirianta, Ida Bagus Ketut Sutoko, Sutoko WIDIANTARA, Made Yusriel Ardian