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JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING
Published by Universitas Medan Area
ISSN : 25496247     EISSN : 25496255     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Engineering
JURNAL TEKNIK INFORMATIKA, JITE (Journal of Informatics and Telecommunication Engineering) is a journal that contains articles / publications and research results of scientific work related to the field of science of Informatics Engineering such as Software Engineering, Database, Data Mining, Network, Telecommunication and Artificial Intelligence which published and managed by the Faculty of Informatics Engineering at the University of Medan Area .
Arjuna Subject : -
Articles 464 Documents
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Performance Analysis Of Smooth Variable Structure Filter (SVSF) For Noise Reduction In Voltage, Current, And Power Estimation Of Photovoltaic (PV) Systems Elly Hastiningtyas; Ike Yuni Wulandari; Heru Suwoyo
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 10 No. 1 (2026): Issues July 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v10i1.18421

Abstract

Photovoltaic (PV) systems are renewable energy sources whose performance is affected by changes in irradiation and temperature, so that noise interference in voltage and current signals can degrade the quality of power processing. This study proposes the use of a Smooth Variable Structure Filter (SVSF) as a signal processing stage before being used by the MPPT algorithm, because SVSF has robust properties against model uncertainty and measurement interference. Testing was carried out through Matlab-based simulations with input voltage and current signals and Gaussian noise injection to represent dynamic PV operating conditions. The simulation results showed that the SVSF was able to reduce power fluctuations by 83.92%, with an MAE value of 3.06 W and an RMSE of 4.3 W. Thus, the proposed method is able to produce a filtered power signal that is smoother, has lower ripple, and is closer to the actual PV power characteristics
Comparative Study of Machine Learning Algorithms for Student Performance Prediction in Islamic Boarding Schools Ari Adaninggar; Muhammad Arifin; Diana Laily Fithri
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 10 No. 1 (2026): Issues July 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v10i1.18673

Abstract

Al-Manshurin Ar-Rikzan Jepara Islamic Boarding School faces challenges in the early detection of potential declines in student achievement because the evaluation process still relies on manual recording. This study aims to design an interactive website-based Early Warning System to predict the final performance predicate of students by integrating academic data and non-academic behavior (mutabaah yaumiyah). The methodology used refers to the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework. This study compares three classification algorithms: Decision Tree (C4.5), Naïve Bayes, and K-Nearest Neighbor (K-NN). To address class imbalance in the 210 original data records, the Synthetic Minority Over-sampling Technique (SMOTE) was applied to generate 450 balanced records, which were then evaluated using 10-Fold Cross Validation. The test results show that the Decision Tree (C4.5) algorithm produces the most superior performance with an Accuracy of 97.62%, Precision of 97.96%, and Recall of 97.62%. This superiority is driven by the decision tree structure's ability to map non-linear conditional rules relevant to the pesantren's absolute rules. As an applicative output, the best model is extracted into a Streamlit-based dashboard equipped with expert recommendations (feature importances). This system automatically highlights the variables contributing the highest risk, enabling administrators to formulate accurate intervention and mentoring steps before the semester evaluation ends.
Performance Evaluation of ESP32-Based IoT Communication latency in an Assembly line Model Mohammad Fauzan Aulialdi; Vina Sari Yosephine; Muhammad Amir Fajri; Haikal Kamil
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 10 No. 1 (2026): Issues July 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v10i1.18715

Abstract

The development of Internet of Things (IoT) technology provides opportunities to support the digitalization of Micro, Small, and Medium Enterprises (MSMEs), particularly in monitoring systems and automated operational data recording. However, the performance of IoT-based monitoring systems is highly influenced by communication delays between edge devices and cloud services, making communication performance evaluation essential to ensure system reliability prior to practical deployment. This study aims to evaluate the communication performance of an ESP32-based IoT system connected to a cloud spreadsheet by analyzing data transmission delays in a four-station assembly line model representing MSME-scale production processes. The method used is an experimental approach with 30 trials conducted at each station equipped with infrared sensors, and time measurement using a stopwatch as a visual reference. The collected data were analyzed using average values, standard deviation, and the Three Sigma method to evaluate communication stability. The results show that the average data transmission delay is approximately 3 seconds, with variations remaining within the control limits, indicating that the system operates under stable (in-control) conditions. Differences in delay among the stations indicate the influence of network communication characteristics, including Wi-Fi connection quality, network traffic, and the HTTP communication protocol used for cloud transmission. These findings demonstrate that the proposed system provides sufficiently stable communication performance to support operational monitoring in MSMEs and can serve as a baseline for the future development of cloud-based monitoring systems toward digital twin implementation.
Comparative Evaluation of ROC-MOORA, ROC-TOPSIS, and ROC-WASPAS for Group Internship Assessment Using Robustness, Ranking Stability, and Rank Correlation Analyses Arnol Hamonangan Simbolon; Syaiful Zuhri Harahap; Ibnu Rasyid Munthe; Budianto Bangun
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 10 No. 1 (2026): Issues July 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v10i1.18781

Abstract

Evaluating student internship (Praktik Kerja Lapangan, PKL) groups requires an objective ranking procedure because it involves multiple assessment criteria. Previous comparative studies on Multi-Criteria Decision Making (MCDM) methods have commonly employed different weighting schemes for each method and rarely evaluated robustness, ranking stability, and rank correlation simultaneously. This study compares the performance of ROC-MOORA, ROC-TOPSIS, and ROC-WASPAS using the same Rank Order Centroid (ROC) weighting scheme for internship group evaluation. A total of 23 internship groups were assessed based on five criteria and ranked using the three methods. Performance was further evaluated through one-at-a-time weight perturbation and Monte Carlo–based robustness analysis, ranking stability analysis, and Spearman's and Kendall's rank correlation tests. The results show that the three methods produced nearly identical rankings, with Spearman correlation coefficients ranging from 0.999 to 1.000 and Kendall's Tau from 0.992 to 1.000. Weight perturbations of 10–30% resulted in a maximum average rank shift of only 0.017 positions without changing the top-ranked group. Monte Carlo simulations across 1,000 scenarios indicated that ROC-TOPSIS was slightly more stable than ROC-MOORA and ROC-WASPAS. Overall, all three methods demonstrated robust, stable, and consistent performance under identical ROC weighting.

Page 47 of 47 | Total Record : 464

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