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Sagita Rochman
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Best : Journal of Applied Electrical, Science and Technology
Published by Universitas PGRI Adi Buana Surabaya
ISSN : 27152871     EISSN : 27145247     DOI : https://doi.org/10.36456/best.vol3.no1
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Physics
A Journal that contain Applied Electrical, Science & Technology. Published twice a year, in March and September. P-ISSN: 2715-2871(print), and E-ISSN: 2714-5247 (online).
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 166 Documents
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Design of IoT-Based Monitoring System on Miniature Automated Storage and Retrieval System (ASRS) with Barcode-Based Item Identification Using ESP32 Khairun Nadia, Amalia; Andrasto, Tatyantoro; Norman Syah, Mario
BEST Vol 8 No 2 (2026): BEST
Publisher : Universitas PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/fd514w79

Abstract

This study aims to design and develop a monitoring system for a miniature Automated Storage and Retrieval System (ASRS) based on Internet of Things (IoT) with barcode-based item identification using the ESP32 microcontroller. The system integrates the GM65-S barcode scanner, two ESP32 units as input and robot arm controllers, a MySQL database with 11 relational tables, and a web monitoring dashboard (SmartASRS) accessible at https://smartasrs.my.id. The research adopted the Research and Development (R&D) method with the Waterfall model. Testing was performed using Black Box Testing on all implemented features. Results showed a 100% success rate across 42 web monitoring test scenarios spanning 6 modules, and 100% across 81 database test scenarios spanning 11 tables. The system supports three item retrieval modes: FIFO (First In First Out), LIFO (Last In First Out), and FEFO (First Expired First Out). All features including real-time storage grid monitoring, barcode identification, robot progress tracking, and activity history functioned as designed. The SmartASRS system demonstrates that an integrated IoT-based ASRS monitoring solution can be effectively realized on a miniature scale as both a functional warehouse management system and an industrial automation learning medium.
Exploration of RTMDet and SAHI for Long-Tailed Macaque Damage in Corn Fields Yunnerja; Finki Dona Marleny; Ayu Ahadi Ningrum
BEST Vol 8 No 2 (2026): BEST
Publisher : Universitas PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/ndc0dc33

Abstract

Attacks by long-tailed macaques (Macaca fascicularis) on corn farmland cause a drastic decline in yield from 7–9 metric tons to only 4–5 metric tons per hectare. Traditional control methods have proven ineffective, necessitating solutions based on smart technology. This study explores the RTMDet (Real-Time Detection Transformer) algorithm combined with the SAHI (Slicing Aided Hyper Inference) framework to evaluate wildlife detection performance. Four RTMDet variants RTMDet-tiny, RTMDet-s, RTMDet-m, and RTMDet-l were tested using a dataset of 2,271 COCO annotated images. Evaluation on the test set showed that the standalone RTMDet-s achieved the best performance with a mAP@0.5 of 0.9070, a Recall of 0.9585, and an F1-Score of 0.9321. Applying SAHI reduced accuracy and caused computational delays (down to 3.02 FPS) because the monkey objects occupy a relatively large area within the image frame. Based on these results, the standard RTMDet-s is highly recommended for integration as a real-time automated trigger system within IoT-based wildlife deterrent devices to instantly mitigate attacks and protect farmers’ crop yields.
An ArcGIS-Based Spatial Decision Support System Using Euclidean Distance and Multicriteria Analysis for Identifying the Optimal Candidate Locations for Low-Voltage Electrical Network Connections Apriyanto, Nanda; Brunner, I Made Indradjaja Marcus
BEST Vol 8 No 2 (2026): BEST
Publisher : Universitas PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/yhpt5d18

Abstract

Determining customer connection locations on a Low-Voltage Distribution Network is a critical stage in electricity distribution planning because it requires simultaneous consideration of spatial conditions and network technical capability. Manual evaluation processes are often time-consuming, depend heavily on the experience of field personnel, and may produce inconsistent recommendations. This study aims to develop a Spatial Decision Support System (SDSS) based on a Python Toolbox in ArcGIS Pro by integrating the Euclidean Distance method and multicriteria analysis to identify the most suitable candidate locations for customer connections on a Low-Voltage Distribution Network. This study employed a Research and Development (R&D) approach consisting of requirements analysis, system design, toolbox development, implementation, and testing. The Euclidean Distance method was applied to identify the five nearest distribution pole candidates from multiple distribution transformers as an initial screening stage. Each candidate was subsequently evaluated using multicriteria analysis based on transformer capacity, network reliability indices comprising the System Average Interruption Duration Index (SAIDI) and the System Average Interruption Frequency Index (SAIFI), transformer voltage conditions, and connection distance. The evaluated parameters were normalized, weighted according to their relative importance, and aggregated into a feasibility score to rank the candidate connection locations. The results demonstrate that the developed toolbox successfully automates candidate identification, technical evaluation, score calculation, and recommendation generation in a rapid, consistent, and objective manner. The integration of spatial analysis and multicriteria analysis within ArcGIS Pro improves evaluation efficiency compared with conventional manual procedures while providing technically reliable recommendations for selecting customer connection locations on Low-Voltage Distribution Networks.
Design and Development of an E-Commerce System for Electronic Goods with Integrated Ordering and Payment using Next.js Lazuardi, Muhammad; Kusuma, Abdi Pandu; Febrinita, Filda
BEST Vol 8 No 2 (2026): BEST
Publisher : Universitas PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/v86qfy34

Abstract

Rizqi Elektronik has been operating conventionally, with its marketing reach still limited to the local area, while inventory tracking is still done manually, meaning customers do not receive real-time stock information and the store risks missing out on sales opportunities. This research aims to design and build a web-based e-commerce system for electronic goods using the Next.js framework, equipped with an integrated ordering and payment system to expand the store’s market reach to the regional level. The approach used is Research and Development (R&D) with the Rapid Application Development (RAD) model. The system was built using TypeScript, the Firebase Firestore database, and the Midtrans payment gateway for digital transaction processing. It implements the Inventory First algorithm to validate stock availability during transactions and the Reorder Point (ROP) method to determine reorder thresholds and timing. System testing was conducted through three phases: Black-box Testing, Expert Validation, and User Acceptance Testing (UAT), yielding results of 97.62%, 95.83%, and 93.33%, respectively—all falling within the “very acceptable” to “very good” categories. Based on these results, the developed system is deemed technically and functionally suitable to support store operations, online transactions, and inventory control at Rizqi Elektronik.  
Design and Development of a Footstep Energy Harvesting Floor using a DC Generator Billah, Mussafa; Rochman, Sagita
BEST Vol 8 No 1 (2026): BEST
Publisher : Universitas PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/pwtn0y63

Abstract

Indonesia’s demand for electrical energy continues to increase, creating a need for small-scale alternative energy sources. One potential source is mechanical energy from human footsteps, which is normally dissipated as waste energy. This study presents the design and development of a floor-based electricity generator that converts footstep energy into electrical energy using a DC generator. The research employed an experimental method consisting of product design, component selection, fabrication, testing, and data analysis. A mechanical transmission consisting of a floor mechanism, gear, shaft, and spring transfers mechanical energy from a footstep to the DC generator. The generated electrical energy is stored in a lithium-ion battery through a solar charge controller and is subsequently used to power an LED lamp. Experimental results showed an average output voltage of 3.33 V and an average current of 11.31 mA. The measured average energy was 0.045 J, with an average footstep duration of 1.14 s. The highest measured voltage and current were 4.8 V and 12.5 mA, respectively, while the highest recorded energy was 0.072 J. The results indicate that the proposed system can be used as a small-scale renewable energy harvesting device, particularly in locations with high pedestrian traffic.
IoT-Based Electrical Power Monitoring and Control using NodeMCU and PZEM-004T for Energy Management in Small-Scale MSMEs julianto, Rizki; Rochman, Sagita
BEST Vol 8 No 1 (2026): BEST
Publisher : Universitas PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/wmdhxw30

Abstract

Efficient monitoring and management of electrical energy are important for small-scale micro, small, and medium enterprises (MSMEs). Conventional prepaid electricity monitoring requires users to inspect the meter directly, which is less practical for continuous monitoring. This study presents the design and implementation of an Internet of Things (IoT)-based electrical power monitoring and control prototype using a NodeMCU/ESP32 controller and PZEM-004T sensor. The system measures voltage, current, power, energy, frequency, and power factor, displays data locally through an I2C LCD, accepts prepaid-energy top-up input through a 4×4 keypad, and controls the electrical load using a relay. Blynk is used for real-time remote monitoring. Top-up values in Indonesian rupiah are converted into kWh using a fixed electricity tariff, while the energy balance is reduced according to measured consumption. Tests were performed by comparing the prototype with a digital watt meter and by evaluating top-up, balance reduction, relay control, and Blynk monitoring functions. The prototype successfully displayed electrical parameters in real time. At 60 min, the prototype measured 234.4 V, 0.2 A, 22 W, and 0.022 kWh, while the reference watt meter measured 232.4 V, 0.175 A, 21 W, and 0.021 kWh. The reported maximum errors were 1.52% for voltage, 14.29% for current, 6.28% for power, 10.00% for energy, 15.69% for power factor, and 0% for frequency. Top-up and relay-control tests were successful, and the five principal functional tests achieved a reported 100% success rate.

Page 17 of 17 | Total Record : 166

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