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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 10 Documents
 1 
Search results for , issue "Vol. 11 No. 3 (2025): September" : 10 Documents clear
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.
Design and Optimization of a Programmable Logic Controller-Based Monitoring System for Machine Parameters in a Frame Assembly Line Prasetyani, Lin; Ramadhani, Ahmad Fadlan; Setiyadi , Surawan; Hidayat, Muhammad
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.v11i3.31040

Abstract

This research addresses the challenge of machine parameter monitoring in the frame assembly line of a motorcycle manufacturing facility. In this process, several machines operate in parallel to integrate key motorcycle components such as engine units, wheels, and body frames. A critical issue identified was that seven machines used standalone controllers that lacked Ethernet communication, limiting real-time monitoring and data acquisition of vital parameters such as status, downtime, and cycle time. To solve this problem, additional programmable controllers were installed using ladder logic programming to retrofit the non-networked machines. These upgrades enabled continuous data transmission and real-time parameter monitoring via a centralized system. The method involved developing a structured architecture capable of collecting, processing, and visualizing machine condition indicators. The research contribution is (1) the successful integration of isolated machines into a unified monitoring network using low-cost PLC retrofitting and (2) the deployment of a register-based monitoring system that supports predictive maintenance through real-time alerts. The system was tested in a live production environment, where more than 80 parameter registers were monitored successfully. Results demonstrated improvements in machine utilization and maintenance response time, with reductions in unplanned downtime and better traceability of machine conditions. The new system improved operational transparency and supported data-driven decision-making for maintenance planning. In conclusion, the implementation of this monitoring system optimized industry 4.0 on the supervision of machine parameters and significantly enhanced production reliability and efficiency in the frame assembly line.
TekenToken Development: IoT based Cartesian Robot System for Prepaid Electricity Token Recharging Automation Puji, Muhammad Nurul; Eduard, Christoper; Fico
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.v11i3.30332

Abstract

Manual input of the prepaid token on the electricity meter is prone to several drawbacks, such as the possibility of entering incorrect numbers or dealing with an electricity meter that is hard to reach, which makes the process inefficient and reduces precision. This may result in billing issues or an interrupted power supply. Automating the electricity recharging process will eliminate the need for manual input, making it much more efficient and significantly improving accuracy. TekenToken is an innovation in the field of robotics designed to automate the process of electricity recharging through prepaid tokens. This system utilizes a Cartesian robot, which enables precise movement along the X, Y, and Z axes to press the buttons on the electricity meter. TekenToken also incorporates an IoT system using an online database like Firebase, connecting the system to a mobile application that allows for long-distance remote control. By implementing a flexible system such as a Cartesian robot and IoT, TekenToken can also be modified for application in many other fields.
Color Image Denoising Methods: A Laconic Review Younis, Zainab; Mohd Rahim, Mohd Shafry; Mohamed , Farhan Bin
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.v11i3.31200

Abstract

Color image denoising is an essential process in image processing, intending to remove noise from images while preserving the important image details, for instance, edges, resolution, and accuracy. This paper presents an experiment-based review of the recent methods of color image denoising algorithms, focusing on their strengths, limitations, complexity, findings, accuracy, and comparative performance. Therefore, eight methods in color image denoising with different concepts were reviewed and evaluated under a reliable experimental environment. The evaluation was conducted using a dataset collected from three different sources, such as a professional DSLR camera, various mobile devices, and the MIT-Adobe database, tested under different real-world noise conditions. The reviewed methods are assessed by three preceding metrics were selected as no-reference metrics to evaluate real color images where clean reference images are unavailable: fast image sharpness estimation (FISH), no-reference structure similarity (NRSS), sparsity, and dominant-orientation quality index (SDQI), objectively, along with subjective visual analysis. The results demonstrate that the Total Variation with Split Bregman (TVSB) algorithm achieved the highest performance and exceeded the other methods. Reviewed methods showed competitive results in fine structure, details, and preserving edges.  Additionally, the study discusses future recommendations for improving the effectiveness of these algorithms. Finally, this research is carried out systematically and empirically and focuses on the merits and demerits of their performance. It provides stepwise guidance on how to systematically target a particular approach in the color image denoising process, which highlights the practical and theoretical disparity. Moreover, it serves as a rich and source for scholars intending to develop algorithms in this domain.  

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