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SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan
Published by RAM PUBLISHER
ISSN : -     EISSN : 30323991     DOI : -
Core Subject : Humanities, Science, Engineering,
Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan or in English the publication title Information Systems, Engineering and Applied Technology is an open access journal committed to publishing high quality research articles in the fields of Information Systems, Informatics, Digital Communication Information Technology, Tourism Technology, Transportation Technology, Agricultural Technology, Plantations, Fisheries, Marine, Environmental Technology, Artificial Intelligence, Mechanical Engineering, Electrical Engineering, Industrial Engineering and Civil Engineering. Published 4 X (Times) a year in January, April, July, and October. SITEKNIK accepts and selects quality articles and focuses on providing the best service for writers. SITEKNIK is committed to being a leading platform for researchers to share their innovative findings. We also provide a fast and transparent review process to ensure the quality and originality of each published article.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 44 Documents
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Water Quality Analysis and Consumption Feasibility Using Support Vector Machine and CatBoosting with Hyperparameter Tuning Rahayu, Christa Putri; Kusnawi
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 2 No. 4 (2025): October
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.17342085

Abstract

Water quality analysis plays an important role in determining the suitability of water for human consumption. This study aims to build a machine learning model that is able to classify water quality based on several parameters such as pH, hardness, solids content, chloramines, sulfate, conductivity, organic carbon, trihalomethanes, and turbidity. The dataset used comes from Kaggle with a total of 3,276 sample data. The two main algorithms applied in this study are Support Vector Machine (SVM) and CatBoost. The research process includes data preprocessing, data balancing using SMOTE, modeling, and model performance evaluation. Hyperparameter tuning is applied to both algorithms to improve performance. The results show that CatBoost has the best performance with an accuracy of 95.8% after hyperparameter tuning, compared to SVM which achieved an accuracy of 77.9%. In addition, CatBoost excels in all evaluation metrics, including precision, recall, and F1-score.
Integrating Augmented Reality (AR) in Education in the Era of Society 5.0: A Systematic Literature Review Asyam, Muhammad Rizq Dzaki
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 2 No. 4 (2025): October
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.17386571

Abstract

The advent of the term Society 5.0 brings in a new concept of human-centered technology integration, education included. Augmented Reality (AR) provides an interactive and immersive learning experience. The objective of this research is to systematically examine the trends, advantages, challenges, and relevance of the use of AR in education within the context of Society 5.0. Through the application of the Systematic Literature Review (SLR) method, 46 research articles published on ScienceDirect between 2020 and 2025 were filtered through inclusion and exclusion criteria. The findings indicate that AR adoption is on the rise, specifically in higher education and in medical and engineering disciplines. Most studies cite the function of AR in aiding enhanced learning outcomes, student motivation, and interactive simulations. However, the use of AR must also navigate infrastructural limitations, teacher preparedness, and budget constraints. This study charts the deployment of AR in a human-centered education system and offers direction to subsequent research and development
Chili Leaf Disease Classification Using Transfer Learning with VGG16 and MobileNetV2 Combined with Random Search Hyperparameter Tuning Aryawijaya; Kusnawi
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 2 No. 4 (2025): October
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.17383224

Abstract

Chili is one of the main food commodities in Indonesia with considerable economic value. Frequent climate changes have made chili plants more vulnerable to pest and disease attacks. Early identification of these diseases is crucial, as delays can lead to crop failure. However, this process presents its own challenges, as it requires specific expertise and considerable time. This study employs the transfer learning method using the VGG16 and MobileNetV2 architectures to build a model capable of classifying diseases in chili plants based on leaf images, along with the use of Random Search hyperparameter tuning to improve model accuracy. The results show that the use of transfer learning for disease classification achieved high accuracy, with MobileNetV2 reaching an accuracy score of 88% without tuning. Meanwhile, the application of Random Search hyperparameter tuning proved effective in improving model accuracy, particularly with the VGG16 architecture, which saw a significant accuracy increase from 51% to 89%. It can be concluded that the transfer learning method is well-suited for identifying diseases in chili plants based on leaf images with high accuracy, and that the application of Random Search hyperparameter tuning successfully enhanced the model’s performance.
Performance Study of 13.56 Mhz Full-Bridge Inverter on Wireless Power Transfer System for Electric Vehicle Charging MT, Meky Taba Orlando; Hery Sudaryanto; Iqbal Ahmad Dahlan; Aam Muharam
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 2 No. 4 (2025): October
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.17448266

Abstract

This research developed a series of GaN MOSFET-based full bridge inverters (TP90H050) with UCC27524 drivers for Wireless Power Transfer (WPT) applications of military vehicles, which are targeted to operate at the ISM frequency of 13.56 MHz. The LTspice simulation showed a potential for near-sinusoidal waves (THD<1%) and a power efficiency of ≈2.13 kW at 50Ω. However, the PCB prototype was only capable of stable operation up to 666.66 kHz with a clean box wave output, and separate tests on fourth-order LC–Butterworth filters achieved a sinusoidal signal with an efficiency of ≈75%. Failure analysis attributed MOSFET damage to switching path length, parasitic effects, and protection limitations. Significant differences were found between the simulation and the implementation at 666.66 kHz, where the hardware RMS voltage was only ≈33% of the simulation. Improvements going forward include the use of precision oscillators/DDSs, drivers with protective features (UVLO and active Miller-clamp),  calibrated snubber, and closed controls

Page 5 of 5 | Total Record : 44

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