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PROtek : Jurnal Ilmiah Teknik Elektro
Published by Universitas Khairun
ISSN : 23548924     EISSN : 25279572     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy
PROtek adalah jurnal ilmiah teknik elektro yang pertama kali dipublikasikan pada September 2013. Jurnal PROtek berada di bawah asuhan Program Studi Teknik Elektro Fakultas Teknik Universitas Khairun, yang merupakan wadah ilmiah untuk menyebarluaskan hasil-hasil penelitian dan kajian analisis yang berkaitan dengan bidang keilmuan sistem tenaga listrik, teknik kendali, telekomunikasi, elektronika, dan teknologi informasi.
Arjuna Subject : -
Articles 8 Documents
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Search results for , issue "Vol 12, No 1 (2025): Protek : Jurnal Ilmiah Teknik Elektro" : 8 Documents clear
Fire Detection Design Based on Gas Leakage Accompanied by fire Location Point Using ESP32 Based on IoT Wibowo, Nanda; Zarory, Hilman; Mursyitah, Dian; Ullah, Aulia
PROtek : Jurnal Ilmiah Teknik Elektro Vol 12, No 1 (2025): Protek : Jurnal Ilmiah Teknik Elektro
Publisher : Program Studi Teknik Elektro Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/protk.v12i1.7744

Abstract

Type B fires are fires that occur due to the burning of fuel in the form of gas, this type of fire occurs quite often where in DKI Jakarta this type of fire occurs in up to 180 cases and one of the causes of this fire is due to LPG gas leaks then at this time the fire extinguisher fire only has 1 method to find the location of the fire, therefore in this research a tool was created that can detect gas leaks and fires which can send danger warning messages to users, namely building owners and firefighters via the telegram application, from research carried out by the tool created to successfully detect the presence of gas leaks when the PPM value of LPG gas exceeds 100 PPM as well as fires when the presence of fire is detected, the carbon dioxide value in smoke exceeds 100 PPM, and also high temperatures, the tool will identify dangerous conditions and send a danger warning message to the user along with the coordinates and Google Maps link for the location of the equipment when a fire occurs.
Implementing the TOPSIS Method for Book Update and Procurement Priority Tamsir, Nurlindasari; Magfirah, Magfirah; Rosida, Vivi; H. Umar, St. Amina; Widyawati, Widyawati
PROtek : Jurnal Ilmiah Teknik Elektro Vol 12, No 1 (2025): Protek : Jurnal Ilmiah Teknik Elektro
Publisher : Program Studi Teknik Elektro Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/protk.v12i1.8126

Abstract

A library is a building or room designed for the purpose of storing and organizing various types of library items, including books, monographs, serial publications, brochures, and non-library materials. In the Hasanuddin University (Unhas) library, there are at least 516,000 volumes of literature. One of the important things is how to ensure that the contents of the library remain relevant to the special literacy needs of this modern era. Considering that books are static, of course it is necessary to update and procure the newest and most up-to-date types of books. The goal of this research is to use the TOPSIS algorithm to determine the procurement and updating of books in the UNHAS library, particularly at the Faculty of Nursing. Based on the regular year data criteria (2008–2023), it is known that books borrowed (5–25≤ ) and book stocks (5–45 copies) are based on the publication every five years. It is known that if the preference value (v) 0.60 is included in the procurement category, preference (v) 0.30 is included in the updating category, and preference (v) 0.31–0.59 is not included in the procurement and update categories. The ranking results of 6 (six) book samples reveal that 2 (two) categorized into the procurement category with a preference value (v) of 0.64, 2 (two) categorized into the updating category with a preference value (v) of 0.2 and 0.3, and 2 (two) categorized into the category where book updates and procurement do not occur, with preference values (v) of 0.36 and 0.5.
Feasibility Study of Hybrid Photovoltaic (PV)-Generator Set Power Plant at Palm Oil Mill Saputra, Ramadian; Jelita, Marhama
PROtek : Jurnal Ilmiah Teknik Elektro Vol 12, No 1 (2025): Protek : Jurnal Ilmiah Teknik Elektro
Publisher : Program Studi Teknik Elektro Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/protk.v12i1.7720

Abstract

Kuko Palm Oil Mill, currently not interconnected with PLN electricity, relies on diesel-fueled generators of 1100 kVA and 650 kVA to meet its electrical needs. To reduce diesel consumption, a Hybrid Power Plant (PLTH) system comprising generators, batteries, and Photovoltaic (PV) panels is proposed. Optimization studies, evaluating technical and economic feasibility, are conducted using HOMER software. Optimal PLTH configuration includes 8,727 kW PV, 500 kW generator, 1,000 kW generator, 25,708 battery units, and 1,481 kW converter. PLTH generates 14,339,116 kWh/year, fulfilling PKS electricity requirements. Economic analysis yields NPC of Rp356,000,000,000, initial investment of Rp77,400,000,000, operation and maintenance costs of Rp21,600,000,000, and LCOE of Rp3,147/kWh. IRR stands at 22%, NPV is positive at Rp124,000,000,000, with a 3.5-year capital return. These results indicate PLTH feasibility both technically and economically
Implementation Of Convolutional Neural Network (Cnn) Based On Mobile Application For Rice Quality Determination Altim, Muhammad Zainal; Basalamah, Abdullah; kasman, kasman
PROtek : Jurnal Ilmiah Teknik Elektro Vol 12, No 1 (2025): Protek : Jurnal Ilmiah Teknik Elektro
Publisher : Program Studi Teknik Elektro Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/protk.v12i1.9396

Abstract

The purpose of this study is to design and build a CNN deep learning program modeling for a mobile application for rice quality classification and analyze the performance of a mobile application-based classification program as a means of halal information in real time. The method applied is an experimental method that utilizes machine learning technology by using many rice images that are used as datasets. The data of these images is classified by their shape, color and background. This image is used as a reference for the training dataset. After the CNN training model is formed, it is then set up in a web editor p5.js then an interface is created to connect to a server such as Google Cloud using FastAPI, which can be accessed using a mobile application or a web server such as Chrome. In the mobile application, create an interface to connect with the camera system and data base on the cloud server. The results of the study were obtained that CNN deep learning modeling can be used in real time. In web browser usage, the data shown is also affected by lighting. The accuracy level of the built model reached above 99.8 percent with a validation accuracy rate of 99.7 percent in the data training process. When testing, the average accuracy of the data was around 99.9 percent. This clearly proves that CNNs can be used to classify objects properly and accurately.
Increasing the Soil Resistance Value in the 20 kV Medium Voltage Distribution Network using the Soil Treatment method Taryo, Taryo; Fauzi, Andri Ahmad; Rosidi, Rosidi
PROtek : Jurnal Ilmiah Teknik Elektro Vol 12, No 1 (2025): Protek : Jurnal Ilmiah Teknik Elektro
Publisher : Program Studi Teknik Elektro Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/protk.v12i1.8238

Abstract

This study aims to enhance soil resistivity in a 20 kV electrical distribution network using a mixture of rice husk ash and mangrove wood charcoal. Rice husk ash is an additive known to reduce grounding resistance due to its water-absorbing properties, maintaining soil moisture. Effective grounding system is crucial to ensure fault currents can safely dissipate into the ground, protecting equipment and maintaining electrical distribution continuity. PUIL 2000 standards mandate soil resistivity below 5 Ω. The study method involved initial soil resistivity measurements using a Digital Earth Tester 4015 A, adding various mixtures of rice husk ash and mangrove wood charcoal around electrode planting areas, and periodic resistivity measurements over five days after four mixture additions. Scheme 1, 70% mangrove wood charcoal and 30% rice husk ash. Scheme 2, 30% mangrove wood charcoal and 70% rice husk ash. Scheme 3, 100% rice husk ash. Scheme 4, 100% mangrove wood charcoal. Results showed significant resistivity reduction, with the 30% rice husk ash and 70% mangrove wood charcoal mixture lowering resistivity to an average of 3.78 Ω, reflecting a 43.5% decrease from the initial value. Adding rice husk ash and mangrove wood charcoal to field soil enhanced soil conductivity, enabling resistance reduction to meet safety standards. This study recommends this blend as an effective and economical alternative to reduce grounding resistance in 20 kV electrical distribution systems.
Advanced in Islanding Detection and Fault Classification for Grid-Connected Distributed Generation using Deep Learning Neural Network Qatrunnada, Rusvaira; Novizon, Novizon; Hasanah, Mardini; Angraini, Tuti; Anton, Anton
PROtek : Jurnal Ilmiah Teknik Elektro Vol 12, No 1 (2025): Protek : Jurnal Ilmiah Teknik Elektro
Publisher : Program Studi Teknik Elektro Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/protk.v12i1.7573

Abstract

Nowadays, the use of renewable energy is increasing, especially distributed power generation (DG) connected to the power grid. There are several problems when DG is connected to the grid. The principal obstacle pertains to the detachment of Distributed Generation (DG) from the grid, a phenomenon well known as islanding. Islanding detection is an important task that should be completed in no more than two seconds. Earlier studies have shown several approaches to islanding detection. The use of an Artificial Neural Network (ANN) based on the learning vector quantization (LVQ) technique is proposed in this paper for fault classification and islanding detection in grid-connected distributed generators. The method consists of discrete wavelet transform (DWT), which extracts some features from the fault signal. Then, LVQ is used to classify the disturbance and detect islanding events. Power, entropy, and total harmonic distortion (THD) are used to obtain the total harmonic value. All features become inputs for LVQ, and system disturbances, lightning, and islanding disturbances are used as LVQ outputs. There are 600 datasets consisting of 200 datasets for each fault as training data. To test the LVQ training results, 120 datasets consisting of 40 datasets for each disturbance are used. The training error is made at 0.1 percent to get good testing results. The test results from 120 datasets showed that the test data achieved 99.10% accuracy. In other words, the test results are very effective because there are only 0.9% errors, and there are 2 test data that do not match the actual situation.
ESC PWM Fullbridge Motor DC Berbasis Arduino jati, budi pramono; Hapsari, Jenny Putri
PROtek : Jurnal Ilmiah Teknik Elektro Vol 12, No 1 (2025): Protek : Jurnal Ilmiah Teknik Elektro
Publisher : Program Studi Teknik Elektro Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/protk.v12i1.5374

Abstract

The problem with DC motors is speed regulation, adjusted to the needs in the field. The speed of a DC motor can be changed by adjusting the motor voltage. To overcome this, the motor voltage is regulated using Pulse Wide Modulation (PWM). PWM works by changing the duty cycle of the voltage in the form of pulses so that the average voltage can be changed. The solution is to use an Electronic Speed Control (ESC) controller where the pulse voltage duty cycle is realized using a variable PWM generator using an Arduino which is fed to a fullbridge MOSFET inverter. This paper discusses a DC motor ESC using PWM with a full bridge which is used for the inverter process, the aim is to prove the effect of voltage changes on the speed of a DC motor. The model is specified as a DC motor controlled with a fullbridge PWM inverter. PWM ESC consists of three main parts, namely AC to DC converter, fullbridge mosfet inverter, PWM variable signal source. The parameters determined are: change in duty cycle of the square wave signal. The method used is fullbridge PWM in the form of manufacturing design (hardware and software) and testing of DC motor speed controllers with PWM. The ESC parts consist of: fullbridge switching inverter, PWM signal generator, Arduino, Arduino sketch software, SMPS power supply. The ESC is built using 8 130A 200V MOSFETs, with a duty cycle varying from 0 -100%. 
Implementation of Fuzzy Logic in the Monitoring and Controlling System for Temperature and pH of Fry Aquarium Water Betta Fish Based on the Internet of Things Wahyudi, Rico; Ullah, Aulia; Zarory, Hilman; Faizal, Ahmad
PROtek : Jurnal Ilmiah Teknik Elektro Vol 12, No 1 (2025): Protek : Jurnal Ilmiah Teknik Elektro
Publisher : Program Studi Teknik Elektro Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/protk.v12i1.7619

Abstract

A problem faced by betta fish farmers is the difficulty in monitoring and controlling the temperature and pH of the water in betta fish fry ponds. This condition causes many deaths of Betta fish fry which results in a reduction in the supply of Betta fish seeds. To overcome this problem, a system based on the Internet of Things was developed(IoT) which can monitor in real time and control the temperature and pH of the water in the Betta fish fry pond. This system is implemented in an aquarium equipped with artificial intelligence in decision making which aims to keep the temperature and pH of the aquarium water stable. The components used in this system include ESP32, DS18B20 temperature sensor, water pH sensor, Thermo Electric Cooler (TEC), heater, DC pump, and fuzzy logic implementation. The results of system testing for 14 days showed that the system was able to monitor and control the temperature and pH of the aquarium water, maintaining ideal conditions for Betta fish fry with an average temperature of 28.79°C and an average water pH of 7.45. The system also succeeded in reducing the mortality rate of Betta fish fry, as proven in comparative tests between aquariums without system implementation and aquariums with system implementation. In this trial, each aquarium was filled with 30 betta fish fry. The results showed that the aquarium with system implementation was able to reduce the death rate of Betta fish fry by 5 or 16.67% from a total of 30 fish. Meanwhile, aquariums without system implementation had a death rate of 12 betta fish fry or 40% of the total of 30 betta fish fry.

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