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Implementation Smart Automatic Transfer Switch Multi-Input Multi-Output (MIMO) On DC House Wildan Surya Wijaya; Sagita Rochman; Adi Winarno; Mohamad Ilham; Aditya Ilham
Jurnal JE-UNISLA : Electronic Control, Telecomunication, Computer Information and Power System Vol 10 No 1 (2025): MARET
Publisher : Universitas Islam Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30736/je-unisla.v10i1.1384

Abstract

In the development of technology, the specialization in New and Renewable Energy (NRE) is increasing, this is supported by the infrastructure of using NRE as a need for transportation of electric cars, electric bicycles, electric vehicle charging stations, home electricity needs, and the fulfillment of industrial needs. The electrical basis of the system is DC electricity where it is more efficient because there is no need to convert to AC electricity. In this study, it supports the transition and full use of DC electrical energy with an ATS system with various sources and equipped with a variable output of DC voltage used. The test results showed that the solar panels performed optimally at a voltage of 13.4 V and a current of 0.8 A, while the wind turbine had a fluctuating performance with a voltage between 7.2 V to 14.2 V and a current of 0.21 A to 0.5 A. Picohydro had a stable voltage in the range of 11.4 V to 13.2 V, but the current was low due to limited water flow. PSUs, as a backup power source, show stable performance with voltages between 14.3 V to 14.5 V and currents of 1.05 A to 1.3 A. MIMO ATS (Automatic Transfer Switch) systems are proven to be able to prioritize resource priorities to maintain power stability.
Design and Construction of Door Security System using Radio Frequency Identification based on The Internet of Things at The Main Power House Warehouse of Juanda Airport Surabaya Solikin, Akhmad; Wildan Surya Wijaya
BEST Vol 6 No 1 (2024): BEST
Publisher : Universitas PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/best.vol6.no1.8829

Abstract

In an increasingly advanced world, people have valuable documents and personal items such as cell phones, laptops, power banks, especially for people who work in office areas because they really need these valuable items for work purposes. However, there are several things that become problems when bringing these valuable items to the office where the office still uses manual door locks.
IMPLEMENTASI SISTEM PENGATURAN LAMPU PADA INKUBATOR TELUR AYAM MENGGUNAKAN SENSOR DHT 11 DAN ARDUINO UNO R3: Sistem Irigasi Otomatis untuk Budidaya Kecambah Kaya Nutrisi Menggunakan Arduino Uno wildan surya wijaya; Mochamad Choirul, Fu'ad; Atmiasri, Atmiasri
BEST Vol 6 No 2 (2024): BEST (Buana Electrical Science Technology): Journal of Applied Electrical, Scienc
Publisher : Universitas PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/best.vol6.no2.9553

Abstract

This research develops a prototype of a lamp setting system in a chicken incubator using DHT11 and Arduino Uno R3 sensors. The purpose of this system is to control the temperature and humidity inside the incubator automatically to create optimal conditions for hatching chicken eggs. The DHT11 sensor serves to measure temperature and humidity, while the Arduino Uno R3 is used as a microcontroller that processes data and regulates the heating lamp. This system works by comparing the sensor measurement results with a set setpoint value. If the temperature or humidity is outside the desired limits, the Arduino will turn the heating light on or off to return the ambient conditions to the optimal range. Tests show that this system is effective in maintaining the appropriate temperature and humidity, thus increasing the success rate of hatching chicken eggs. The results of this study show that the prototype developed can be a practical and efficient solution for the process of hatching chicken eggs.
Design, Build, and Implement Monitoring Applications Student Grades and Violations Puteri Nurul Ma'rifah; Achmad Zain Nur; Luthfi Awwalia; Wildan Surya Wijaya; Puteri Nurul Magfirah
BEST Vol 7 No 1 (2025): BEST
Publisher : Universitas PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/best.vol7.no1.10198

Abstract

Information and communication technology development has entered the era of Industry 4.0, requiring the educational sector to adapt more rapidly and efficiently. Thus, information technology has been integrated into various learning activities. However, the monitoring of information on students' learning progress is mostly carried out by the schools, while parents typically receive report cards at the end of the term. This does not provide effectiveness, as it limits parental involvement in early supervision. This research is designed to help both teachers and parents in supervising students more directly. This research utilizes NFC (Near Field Communication) technology for recording student attendance at school. Based on the results of application testing, users gave high ratings to the system in terms of interface design, NFC card reading accuracy, ease of managing student permissions, recording student behavior and academic grades, as well as the tool’s completeness and functionality. The average success rate of horizontal NFC card reading reached 100%, while vertical reading achieved 90%. The maximum reading distance was 4 cm for Samsung A50s (version 10.0) and Samsung A30s (version 10.0), and 3 cm for the Redmi Note 8 Pro (version 9.0).
Design and Development of a Geographic Information System for Historical Tourism Sites in Sumenep Regency Using the Location-Based Service Method Zain Nur, Achmad; Puteri Nurul Ma'rifah; Wildan Surya Wijaya
BEST Vol 7 No 2 (2025): BEST
Publisher : Universitas PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/v7n71q66

Abstract

This study presents the development of a Web-based Geographic Information System (Web GIS) aimed at mapping historical tourism sites in Sumenep Regency. The system integrates Location-Based Services (LBS) to enhance the delivery of spatial information and support tourism promotion. A prototype-based methodology was used in designing and building the application, which includes planning, requirements analysis, system design, data collection, and testing. The system architecture was modeled using UML diagrams, including use case, activity, and entity-relationship diagrams to ensure functionality and data integrity. Spatial and attribute data were collected through literature reviews and transformed into an interactive web platform that allows users to search for and visualize historical tourism locations. The system supports administrative features for data input and updates, alongside public-facing features such as maps and visitor statistics. Testing was conducted using black-box methods to verify the system’s performance and reliability. The final output is a fully functional Web GIS platform that enhances public access to tourism information and assists local authorities in managing tourism data effectively. This research contributes to the advancement of digital tourism infrastructure and promotes cultural heritage visibility in the region.
Integration of Concatenated Deep Learning Models with ResNet Backbone for Automated Corn Leaf Disease Identification imam sudianto, Achmad; Sigit Susanto Putro; Eka Mala Sari; Ika Oktavia Suzanti; Aeri Rachmad; Wildan Surya Wijaya
BEST Vol 7 No 2 (2025): BEST
Publisher : Universitas PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/3kct9e57

Abstract

Corn is one of Indonesia's food commodities, which is an alternative food that supports food diversification in Indonesia. However, leaf infections in corn plants often cause significant yield losses and threaten food security. Early detection of this disease is very important, especially for small farmers, because conventional diagnostic methods that rely on agronomists are expensive and time-consuming. Recent advances in Agricultural Artificial Intelligence (AI) and image processing have facilitated automatic plant disease recognition through Convolutional Neural Networks (CNN), with ResNet as the main backbone combined through concatenation with MobileNetV3, DenseNet161, and GoogleNet. The dataset consists of 4,000 images divided into 2,560 training data, 640 validation data, and 800 test data, with image sizes adjusted to 224×224 pixels. The dataset consists of 4,000 images distributed across four categories: gray leaf spot, common rust, northern leaf blight, and healthy leaf. The testing was conducted using three different optimizers, namely Adam, RMSprop, and SGD, with a learning rate of 0.01. The experimental results showed that the SGD optimizer provided the best performance with a loss value of 0.2275, accuracy of 0.9513, precision of 0.9536, recall of 0.9513, and F1-score of 0.9512. These findings confirm that the combination of ResNet, MobileNetV3, DenseNet161, and GoogleNet architectures with the SGD optimizer can significantly improve the accuracy of corn leaf disease detection, making it a potential application for automatic detection systems in support of smart farming practices.