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INDONESIA
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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Volcanic Object Identification in Volcano Images Using Deep Learning–Based Instance Segmentation Resa Zulfikar; Arrie Kurniawardhani
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.v10i1.16144

Abstract

Indonesia is home to over 120 active volcanoes, many of which pose significant risks to surrounding communities and global atmospheric conditions. The rapid and accurate identification of volcanic objects remains a challenging task for effective volcano monitoring and disaster mitigation. To enable rapid and accurate automatic identification of volcanic objects, this study leverages two Deep Learning models focused on instance segmentation. The two instance segmentation models, You Only Look Once version 7 (YOLOv7) and Mask Region-Based Convolutional Neural Network (Mask R-CNN), were evaluated for their effectiveness in segmenting volcanic objects, namely lava flows, volcanic ash, mountains, and vegetation, from volcanic imagery and videos. A dataset of 140 labeled volcanic images was used for training and validation, while video data were employed to assess model performance under real-time conditions. Both models were trained using transfer learning with pre-trained MS COCO weights and their performance was measured using mean Average Precision (mAP). The results indicate that YOLOv7 achieves higher accuracy (mAP50 = 0.872) along with faster training and inference times, while Mask R-CNN obtains lower accuracy (mAP50 = 0.535) but consistently produces higher confidence scores (>0.90). These findings suggest that YOLOv7 is more suitable for real-time volcano monitoring, while Mask R-CNN is preferable in applications where high detection confidence is prioritized. The results imply that deep learning–based instance segmentation can support automated volcano monitoring systems and enhance disaster mitigation efforts through rapid identification of critical volcanic features.
Development of an Interactive Android Vocabulary Game ‘FLC’s LevelUp’ Using the ADDIE Model for Beginner Learners at FLC Zaenul Amri; Aris Sudianto; Muhammad Sulhan Habibi; Muhammad Zamroni Uska
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.v10i1.16420

Abstract

Vocabulary mastery is a crucial foundation for Level 1 students at the Foreign Language Center (FLC). However,conventional methods such as whiteboard writing and rote memorization pose challenges, specifically studentboredom and decreased learning motivation. To address this issue, this study aims to design and develop an interactiveAndroid-based educational game entitled “FLC’s LevelUp!”. The research methodology employed is Research andDevelopment (R&D), adapted into four main stages: architectural research, material collection, data measurement,and data analysis. The results of the functionality testing (Black-Box Testing) achieved a 100% success rate, while theinterface feasibility test using Maze obtained a score of 87/100. These results demonstrate that the game operatesoptimally, is user-friendly, and is capable of fostering a more interactive learning atmosphere. Based on these findings,it can be concluded that the FLC LevelUp! game effectively facilitates students in retaining English vocabulary throughvisual representations and direct in-game interactions. Furthermore, the implications of this study indicate that theutilization of educational games not only assists students but also significantly supports tutors in their instructionalduties, simultaneously serving as a sustainable digital learning media innovation within the FLC environment.
Content-Based Book Recommendation System Using TF-IDF and Cosine Similarity Gede Satyamahinsa Prastita Uttama; Dharma Wiguna Limmarga; Gerrard Sebastian; Alqis Rausanfita
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.v10i1.17131

Abstract

The growth of digital platforms offering a wide variety of content or products often leads to information overload, making it difficult for users to find items that match their preferences. This research aims to design and implement a content-based recommendation system capable of providing personalized recommendations based on the similarity of item characteristics. The methods employed include data pre-processing (case folding and text cleaning), text representation using Term Frequency–Inverse Document Frequency (TF-IDF), and the measurement of similarity between objects using Cosine Similarity. The dataset contains 133,102 book titles, with descriptive attributes converted into numerical vectors to form the basis of the recommendation process. The quality of the recommendations was evaluated using two complementary approaches: intrinsic metrics (average Cosine Similarity and Average Intra-List Similarity) and user-based validation via a questionnaire completed by 31 respondents who assessed 20 sample books, measured using Precision@5, Recall@5 and F1@5 under a leave-one-out protocol. The research results show that the system generates recommendations that are relevant to the reference objects and are confirmed by the preferences of real users. This approach is effective when applied in situations where there is limited user interaction data (cold-start).
Sentiment Analysis Of The Clash Of Champions Event By Ruangguru With The Naïve Bayes Method Yuniar Farida; Rofina Muti’atun Khasanah Khasanah
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.v10i1.17241

Abstract

The rapid development of information technology has encouraged people to actively express their opinions through social media platforms, including X (formerly Twitter). One topic that has attracted considerable public attention is Clash of Champions, an academic competition organized by Ruangguru. This study aims to analyze X users’ sentiments toward the Clash of Champions event and evaluate the performance of the Naïve Bayes method in classifying these sentiments. The dataset consisted of 5,121 tweets collected through a crawling process using Google Colab from June to November 2024. The data were subjected to preprocessing and represented using Term Frequency–Inverse Document Frequency (TF-IDF) weighting before being classified into positive and negative sentiment categories using the Naïve Bayes algorithm. The results indicate that most tweets expressed positive sentiment, suggesting that Clash of Champions received favorable public responses. The classification model achieved an accuracy of 89.74%, a precision of 91.94%, a recall of 86.91%, and an F1-score of 89.34%. These findings demonstrate that the Naïve Bayes method is effective for analyzing public sentiment on social media. This study contributes empirical evidence regarding public perceptions of educational competition programs and supports the application of Naïve Bayes for sentiment classification in Indonesian social media data.
Design and Optimization of Hydraulic Control Systems for Geothermal Flow Control Valve Stability Alfian Rangga Pradhivatama; Syah Alam
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.v10i1.17315

Abstract

Operational instability in Flow Control Valves (FCV) within Geothermal Power Plant (GPP) Circulating Water Systems (CWS) presents a significant challenge, leading to control hunting, elevated condenser levels, and degraded vacuum pressure, thereby compromising overall thermal efficiency. This study presents a systematic optimization of the FCV's Hydraulic Power Unit (HPU) control system, integrating Programmable Logic Controller (PLC) and Distributed Control System (DCS) frameworks. The methodology encompassed a comprehensive intervention involving: (1) meticulous tuning of PI control parameters, (2) mechanical and hydraulic refurbishment of the HPU, and (3) precise calibration of the 4-20 mA instrumentation signal. Post-implementation analysis revealed a profound enhancement in system stability. FCV deviation variance was drastically reduced from 0.87 to 0.20. This stabilization directly correlated with improved plant performance, evidenced by a 16% reduction in condenser level variance and an enhancement of the mean condenser vacuum from -0.73 to -0.74 bar. The findings demonstrate that this integrated PLC/DCS-based optimization provides a robust and effective solution for mitigating hydraulic control instability, enhancing CWS reliability, and improving the thermal efficiency of geothermal power generation
Recognition Image Text Using Faster Region-Based Convolutional Neural Network with Optical Character Recognition Arie Satia Dharma; Arlinta Christy Barus; Samuel Herlinton Sibuea; Nanchy Monika Siadari
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.v10i1.17700

Abstract

Business cards serve as a form of identification that facilitates communication, but managing large amounts of contact information on business cards can often be challenging. To address this issue, this study developed an end-to-end architecture model to automatically extract information from business cards image. This model utilizes Optical Character Recognition and the Faster Region-Based Convolutional Neural Network method. This model allows users to extract contact information from business cards. Using a dataset of 450 business card images, we conducted experiments to evaluate their impact on the task of detecting text in images. We used an image batch size of 500 with 50, 100, and 500 epochs as hyper experiment parameters. The highest accuracy achieved was 0.8342 with mAP was 0.8513. For the character recognition task, Optical Character Recognition produced results with a Character Error Rate (CER) less than 0.08. These findings suggest that the integration of Faster R-CNN and OCR is effective in detecting and extracting textual content from diverse business card layouts. In conclusion, the proposed approach provides a reliable and efficient solution for automated business card digitization and shows strong potential for practical applications in contact information management systems.
Machine Learning for Classifying Priority Areas for School Infrastructure Improvement Funding Aryanti Aryanti; Nurul Mardhiyah; Aulia Syafitri; Muhammad Ghalib; Nabil Alrofi
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.v10i1.17789

Abstract

Equitable funding for school infrastructure is often hampered by subjective evaluation methods that lead to inappropriate prioritization. This study develops a data-driven approach that uses machine learning to objectively classify funding priority areas. Using the CRISP-DM (Cross-Industry Standard Process for Data Mining) framework and data from the Indonesian Ministry of Education, a support vector regression (SVR) model with an RBF kernel is developed. This model integrates key facility indicators including laboratory availability, sanitation, and access to utilities to predict infrastructure adequacy. Separate models were built for public and private secondary schools with a 70:15:15 data split. The results demonstrated excellent predictive accuracy, with an R² of 0.9938 for public schools and 0.9969 for private schools, at a minimal error rate (MAE <0.20). By grouping the regression results into priority categories, the model successfully identified twenty high-priority areas that require immediate intervention by 2024. These results demonstrate that the SVR-based framework provides a robust decision support system, enabling policymakers to allocate infrastructure funds more transparently, equitably, and in direct alignment with empirical realities on the ground.
Public Sentiment on Indonesia’s MBG Budget Allocation: Twitter/X Analysis Using IndoBERT Model Muhammad Zamroni Uska; Rasyid Hardi Wirasasmita; Wilda Susanti; Yosi Nur Kholisho
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.v10i1.17882

Abstract

The increasing use of social media has created opportunities to examine public opinion regarding government policies through sentiment analysis approaches. This study analyzes public sentiment toward the budget allocation of the Makan Bergizi Gratis (MBG) program in Indonesia using Twitter/X data and the IndoBERT model. Data were collected through keyword-based crawling techniques, resulting in 24,836 tweets, which were reduced to 18,742 tweets after preprocessing. The dataset was manually annotated into positive, negative, and neutral sentiment categories before being classified using IndoBERT. The evaluation results indicate that the proposed model achieved strong performance, with an accuracy of 92.41%, precision of 91.86%, recall of 92.03%, F1-score of 91.94%, and ROC-AUC value of 0.961. The findings reveal that negative sentiment dominated public discourse, primarily associated with concerns regarding fiscal sustainability, transparency, and policy priorities. Positive sentiment mainly emphasized nutritional benefits and educational welfare impacts. The study demonstrates that transformer-based NLP models are effective for analyzing Indonesian social media discourse and provides practical insights for evidence-based policymaking and digital governance evaluation.
Improving Liver Disease Diagnosis Accuracy Using Synthetic Minority Oversampling Technique and Particle Swarm Optimization Harsih Rianto; Amrin amrin
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.v10i1.17943

Abstract

This study aims to improve liver disease diagnosis accuracy using the Synthetic Minority Oversampling Technique (SMOTE) and Particle Swarm Optimization (PSO). The dataset used in this study is the Indian Liver Patient Dataset (ILPD), which consists of 583 patient records with 10 input attributes and one class attribute. The main problem in this dataset is the imbalance of class distribution between liver and non-liver patients, which may affect the performance of classification models. The research stages include data preprocessing, missing value imputation, stratified 70:30 train-test splitting, applying SMOTE to the training data, feature selection using PSO, and evaluating several classification models, namely Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB), and Logistic Regression (LR). The experimental results show that SMOTE-RF achieved the best overall performance with an AUC of 0.8610, accuracy of 0.7784, F1-score of 0.7783, and MCC of 0.5570. Meanwhile, SMOTE-PSO-DT improved the Tree model by selecting five important attributes, namely Direct Bilirubin, SGOT, Total Proteins, Albumin, and A/G Ratio. These results indicate that SMOTE effectively improves classification performance, while PSO helps simplify the model through feature selection.
K-Nearest Neighbor Classification of Fish Catch Species in Tanjungbalai Fahmi; Abdul Halim Hasugian
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.v10i1.17997

Abstract

Fish catch identification is still frequently performed manually, which may lead to recording errors, particularly when fish species have similar shapes, colors, and textures. This study aims to apply the K-Nearest Neighbor (KNN) algorithm to classify fish catch images in Tanjung Balai City. The research object is limited to three fish species: tuna, mackerel, and Spanish mackerel. The dataset consists of 627 images, divided into 534 training data and 93 testing data. The research stages include image preprocessing using resize with padding at 224 x 224 pixels, color feature extraction using HSV color space, texture feature extraction using Gray Level Co-occurrence Matrix (GLCM), KNN parameter optimization using Grid Search Cross Validation, and evaluation using a confusion matrix. The testing results show an accuracy of 61.29%, precision of 68.65%, recall of 61.29%, and F1-score of 62.83%. The tuna class achieved the best performance, while the Spanish mackerel class was most frequently misclassified. These results indicate that KNN can be used as an initial method for digital image-based fish classification.

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