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Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI)
Published by Universitas Ahmad Dahlan
ISSN : 23383070     EISSN : 23383062     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
JITEKI (Jurnal Ilmiah Teknik Elektro Komputer dan Informatika) is a peer-reviewed, scientific journal published by Universitas Ahmad Dahlan (UAD) in collaboration with Institute of Advanced Engineering and Science (IAES). The aim of this journal scope is 1) Control and Automation, 2) Electrical (power), 3) Signal Processing, 4) Computing and Informatics, generally or on specific issues, etc.
Arjuna Subject : -
Articles 403 Documents
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Optimizing K-Nearest Neighbors with Particle Swarm Optimization for Improved Classification Accuracy Dafid, Ach.; Sudianto, Achmad Imam; Thinakaran, Rajermani; Umam, Faikul; Adiputra, Firmansyah; Izzuddin; Sitepu Debora , Ribka
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 2 (2025): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i2.30775

Abstract

This study aims to improve the performance of the K-Nearest Neighbors (KNN) algorithm in classifying public reviews of Batik Madura through optimizing the K value using the Particle Swarm Optimization (PSO) algorithm. Public reviews collected from the Google Maps platform are used as a dataset, with positive, negative, and neutral sentiment categories. Optimization of the K value is carried out to overcome the constraints of KNN performance, which is highly dependent on the K parameter, with PSO providing a more efficient approach than the grid search method. However, PSO also presents challenges such as sensitivity to parameter tuning and potential computational overhead. This study has succeeded in developing a web-based system using the Python Streamlit framework, which makes it easy for users to access sentiment analysis results. Testing shows that optimizing the K value with PSO increases the accuracy of KNN to 88.5% with an optimal K value of 19. However, this accuracy is not compared to other optimization techniques, leaving its relative advantage unverified. The results are expected to help Batik Madura entrepreneurs in evaluating public perception and guiding strategic innovations. Research outputs include a prototype, intellectual property registration, and journal publication, although the role of deep learning models is only briefly noted without further development.
Quantitative Assessment of Blacklist-Based Malicious Domain Filtering for ISP Security: Balancing Protection and Performance Subiyantoro, Muhti; Setiawan, Mukhammad Andri
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 2 (2025): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i2.30815

Abstract

The growing dependence on internet connectivity has heightened cybersecurity threats through malicious domains that facilitate malware, phishing, and botnet operations. These threats significantly impact individuals and organizations, particularly in Internet Service Provider (ISP) settings. Domain filtering on firewalls is a common defensive strategy, yet its effectiveness remains underestimated in large-scale ISP settings. Previous studies have not focused specifically on security systems commonly employed by ISPs, impeding practical adoption. The research contributions are: (1) developing a cost-effective malicious domain filtering approach specifically designed for ISP environments requiring minimal infrastructure investment, and (2) providing quantitative evidence of how blacklist-based filtering impacts both security effectiveness and network performance. The methodology employs alternating firewall states over four time periods to collect metrics including connection flow, bandwidth utilization, and packet rate. Results demonstrate that malicious domain filtering improves security while causing a 2.49% increase in total connection flow due to retry mechanisms. This process yields a 24.5% reduction in total bytes transferred, 10.5% decrease in packets sent, 22.58% reduction in bandwidth, and 8.81% decrease in packet rate. The study identified 1,919 malicious IP addresses blocked from 1,090 user attempts to access harmful domains. These findings confirm blacklist-based domain filtering strengthens security and enhances bandwidth efficiency by mitigating unwanted traffic. This approach is particularly relevant for ISPs, providing a cost-effective solution that balances cybersecurity with optimized network performance, allowing organizations to protect users while maintaining operational effectiveness.
Enhancing Refactoring Prediction at the Method-Level Using Stacking and Boosting Models Khaleel, Shahbaa I.; Ahmed, Rasha
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 2 (2025): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i2.30839

Abstract

Refactoring software code is crucial for developers since it enhances code maintainability and decreases technical complexity. The existing manual approach to refactoring demonstrates restricted scalability because of its requirement for substantial human intervention and big training information. A method-level refactoring prediction technique based on meta-learning uses classifier stacking and boosting and Lion Optimization Algorithm (LOA) for feature selection. The evaluation of the proposed model used four Java open source projects namely JUnit, McMMO, MapDB, and ANTLR4 showing exceptional predictive results. The technique successfully decreased training data necessities by 30% yet generated better prediction results by 10–15% above typical models to deliver 100% accuracy and F1 scores on DTS3 and DTS4 datasets. The system decreased incorrect refactoring alert counts by 40% which lowered the amount of needed developer examination.
Diabetes Mellitus Classification Using CNN-Based Plantar Thermogram Analysis Rihamzah, Muhamad; Pradipta, Gede Angga; Huizen, Roy Rudolf
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i3.30640

Abstract

Diabetes Mellitus (DM) is a chronic metabolic disorder that often causes serious complications, including neuropathy and lower extremity disorders, which impact the quality of life of patients. Early detection of DM is a major challenge due to limited data and the complexity of image analysis. This study proposes a plantar thermogram image-based approach to support non-invasive diagnosis of DM through the development of a Convolutional Neural Network (CNN)-based model and machine learning techniques. This model integrates data augmentation techniques, such as rotation, flip, and zoom, to improve image variation and model robustness. Two CNN architectures, InceptionV3 and ResNet-50, are used in the training process, followed by feature selection using the Chi-Square method and classification using the Random Forest algorithm. The results showed that the proposed model achieved the best performance with accuracy, F1-score, precision, recall, and AUC (Area Under Curve) of 99.6% each. This approach makes a significant contribution by showing improvement compared to previous methods, while opening up opportunities for the development of more efficient clinical applications in early detection and monitoring of DM.
Fine-tuning GloVe Embedding with Contextual Information for Synthetic Batik Pattern Generation Khalida, Rakhmi; Madenda, Sarifuddin; Harmanto, Suryadi; Wiryana , I Made
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i3.30738

Abstract

Batik is an Indonesian cultural heritage that is rich in philosophical and symbolic meanings. To support preservation and innovation, this research examines the use of Generative Adversarial Networks (GAN) in generating synthetic batik patterns based on natural language description. The main challenge lies in the interpretation of semantically complex cultural texts. This research proposes a fine-tuning approach of the GloVe word insertion model with a batik domain-specific corpus. The dataset consists of 3,100 batik images of Parang and Kawung motifs, each accompanied by 10 textual descriptions. Two approaches were evaluated: GloVe generalized pre-training and GloVe enhanced. The GAN architecture combines multimodal input and up sampling techniques to generate images from text. Intrinsic evaluation results showed that the customized GloVe model improved the average cosine similarity value to 0.99. A paired t-test between the general model and the refined results yielded p < 0.01, indicating a statistically significant improvement. Extrinsic evaluation using Fréchet Inception Distance (FID) and Inception Score (IS) showed an improvement in visual quality: FID decreased from 64.5 to 48.1, and IS increased from 2.37 to 3.23. These findings demonstrate the effectiveness of semantic enhancement for improving the synthesis of culturally meaningful visuals. In addition to the technical contribution, this study demonstrates the potential of AI in the preservation of Indonesia's cultural heritage through.
A Hybrid SMOTE-PSO-LSTM-GRU Model for Enhanced Android Malware Detection Efendi, Rissal; R. Widiasari, Indrastanti
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i3.30774

Abstract

As the use of Android devices increases, malware threats are becoming increasingly critical and often undetected by conventional methods due to data imbalance and dynamic behavior in network traffic and application activities. This study aims to answer the question of whether a hybrid deep learning model equipped with optimization and data balancing techniques can significantly improve the performance of malware detection. We propose a novel architecture that integrates SMOTE to balance the class distribution by oversampling minority malware samples, an LSTM-GRU network to learn sequential behavioral patterns, and Particle Swarm Optimization (PSO) to optimize model hyperparameters. The model is trained using a real-world dataset that includes labeled network and application activity logs. Compared with baseline models such as standard LSTM and GRU, our approach shows significant performance improvements, with an F1 score of 98.3%, an accuracy of 98.8%, a precision of 98.1%, and a recall of 98.5%. These results indicate that the proposed model not only addresses the major challenges in Android malware detection but also has strong potential for application in real-world mobile security systems.
Online Handwritten Recognition for Learning to Balinese Script Santiari, Ni Putu Linda; Rahayuda, I Gede Surya
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i3.30951

Abstract

The Balinese script is a valuable cultural heritage. However, in recent decades, the use of Balinese script has significantly declined, especially among the younger generation. This is due to several factors, including the influence of technology and the lack of effective educational resources. The development of information and communication technology has supported the younger generation to communicate with each other online. Therefore, the younger generations are more interested in communicating by using technology instead of handwriting. However, it is possible to integrate handwriting and technology using handwriting recognition. The purpose of this study is to develop handwriting recognition in writing Balinese especially for young learners. Handwriting recognition in writing Balinese brings many advantages including reducing paper waste. Handwriting recognition also motivates young learners to practice handwriting using digital writing instruments on mobile devices, laptops, or desktops. This study developed handwriting recognition in a web-based application using the Bootstrap framework and employing the Jaccard Similarity and Pearson Correlation Coefficient algorithms. The sample data used are the Balinese script characters ha, na, ca, ra, ka, da, ta, and sa, collected from 10 young learners. The accuracy comparison results of the two algorithms are JS algorithm = 0% and PCC algorithm = 79%. The contributions of this research are: (1) the design of a web-based Balinese script learning system integrated with handwriting recognition technology; and (2) the application of Jaccard Similarity and Pearson Correlation Coefficient algorithms for handwriting recognition evaluation.
Predicting Early Lease Termination Risk in Jakarta Shopping Malls Using a SMOTE-Enhanced SVM Model for Financial Loss Prevention Syarifuddin Abdullah , Andi; Rusdah
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i3.30980

Abstract

The high incidence of early lease termination in shopping malls poses significant challenges to revenue generation, unit utilization, and the operational stability of commercial properties. The limitations of traditional management practices in identifying high-risk tenants early often result in financial losses and suboptimal asset allocation. To address this issue, this study developed a data-driven predictive model designed to identify the likelihood of early lease termination. The approach integrates the Support Vector Machine (SVM) algorithm with the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance within the dataset. The model development followed the CRISP-DM methodology and utilized a historical dataset comprising 795 lease records from a major shopping mall in Jakarta, spanning the years 2015 to 2022. Through systematic data preprocessing, feature selection, and model optimization using grid search and cross-validation, the model achieved excellent classification performance: 93.10% accuracy, 90.50% precision, 96.40% recall, 93.30% F1-score, and 97.30% AUC. The findings demonstrate that the SMOTE–SVM combination consistently outperforms in detecting minority-class cases. A prototype system was also developed, enabling mall managers to predict tenant risk in real-time through an intuitive user interface. The contributions of this research are twofold. First, it presents a novel application of the SMOTE–SVM approach for addressing data imbalance in early lease termination prediction within the Indonesian commercial property sector an area that remains underexplored. Second, the study delivers a practical and deployable prototype system that enables real-time risk assessment for mall management, thereby bridging the gap between predictive modeling and operational decision-making. Overall, the proposed model offers a reliable and scalable predictive solution that can be adapted for risk management in other commercial property contexts, supporting a data-driven and proactive decision-making approach. However, it is important to note that the applicability of the proposed SMOTE–SVM model may face certain challenges when deployed in different commercial property contexts. Variations in tenant characteristics, market dynamics, economic conditions, and data availability across regions could impact model generalizability and performance. Moreover, the reliance on historical lease data assumes consistency in tenant behavior patterns, which may not hold true in rapidly evolving retail environments or for properties with distinct operational models such as coworking spaces or mixed-use developments. These factors should be carefully considered when adapting the model to ensure its validity and effectiveness outside the original study setting.
Enhancing Smart Grid Efficiency Through Big Data Analytics: A TOE-Based Framework for Renewable Energy Integration Jamil, Mira Amielia; Abu Bakar, Nur Azaliah Abu; Yahya, Farashazillah
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i3.30986

Abstract

The increasing penetration of variable renewable energy sources, such as solar and wind, presents challenges in real-time data processing and system coordination within smart grids. This paper addresses the need for a systematic approach to integrate big data analytics (BDA) into renewable energy management. Anchored in the Technology–Organisation–Environment (TOE) framework, the research contributes a structured model to support digital transformation in the energy sector. The research contribution is a validated conceptual framework that defines the enablers and constraints of BDA adoption in smart grid systems. The study applied a four-phase qualitative methodology: (i) a narrative review of 65 peer-reviewed publications to extract relevant constructs; (ii) thematic synthesis to identify technological, organizational, and environmental dimensions; (iii) development of a TOE-aligned conceptual model; and (iv) expert validation involving seven industry specialists. The results indicate the framework’s robustness in supporting: (a) scalable data ingestion from distributed sources; (b) seamless integration with existing energy information systems; (c) enhanced compliance with sectoral data regulations; and (d) organisational readiness through skill and infrastructure alignment. Key refinements from expert feedback led to clearer indicators and architecture pathways. The final model offers practical guidance for energy utilities and policymakers to deploy BDA capabilities that improve resilience, operational efficiency, and sustainability. This study advances conceptual understanding and supports future empirical research on smart energy data ecosystems.
Air Quality and COVID-19 Patient Conditions in Jakarta: A Comparative Analysis of Classification Algorithms Wowor, Alz Danny; Jesajas, Marthen Billy; Dimara, Indri; Salama, Aditya; Pakereng, Magdalena Ariance Ineke
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i3.30826

Abstract

The COVID-19 pandemic has become a global challenge, with environmental factors such as air quality contributing to disease severity. This study analyzes the relationship between air pollution parameters (PM2.5, PM10, NO2, SO2, CO, and O3) and COVID-19 patient conditions in Jakarta, categorized into three groups: positive, recovered, and deceased. A comparative evaluation was conducted using five classification algorithms: Na¨ıve Bayes, Random Forest, k-Nearest Neighbors (kNN), Decision Tree, and Support Vector Machine (SVM). The results show that kNN achieved the highest accuracy of 80.71%, while Na¨ıve Bayes obtained the highest recall of 91.83% and a precision of 80.75%. This study contributes by evaluating the effectiveness of classification techniques in mapping the impact of air quality on patient conditions and by identifying the most accurate predictive model. The findings suggest that classification methods can serve as reliable predictive tools to assess the health impacts of air pollution on the population.

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