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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 601 Documents
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Genetic Algorithm and GloVe for Information Credibility Detection Using Recurrent Neural Networks on Social Media Twitter (X) Ramadhani, Andi Nailul Izzah; Setiawan, Erwin Budi
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 2 (2024): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i2.29185

Abstract

Social media, especially X, has become a key source of information for many individuals, but the level of trust in the information spread on these platforms is a critical issue. To overcome this problem, this research proposed an information credibility detection system using a Recurrent Neural Network (RNN) with the utilization of TF-IDF feature extraction, GloVe feature expansion, BERT word embedding, and Genetic Algorithm (GA) optimization. This research contributes to assessing the credibility of tweets related to the 2024 Indonesian election by integrating TF-IDF to identify important words, GloVe to enhance word context, BERT for deeper understanding, and GA is present to optimize RNN performance. The main focus is to provide maximum accuracy by integrating these methods. In this research, the dataset used consists of 54,766 tweets relating to the 2024 Indonesia election and includes relatively equal numbers of credible and non-credible labels. The corpus construction utilized source X with a total of 40,466 data, IndoNews with a total of 131,580, and a combination of both with a total of 150,943. This research conducted six experimental scenarios, namely optimal data split, max features, N-grams, Top-N rank similarity corpus, BERT and GA application. Through these scenarios, the model achieved a significant accuracy improvement of 1.81% over the baseline, reaching an accuracy of 90.60%. This result demonstrates the effectiveness of the proposed system by presenting a higher quality of accuracy compared to the baseline model. Moreover, this research underscores the significant contribution of increasing the accuracy of information credibility detection.
Real-time Recyclable Waste Detection Using YOLOv8 for Reverse Vending Machines Bahadir Besir Kestane; Emin Guney; Cuneyt Bayilmis
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 2 (2024): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i2.29208

Abstract

Increasing challenges in waste management necessitate optimizing the efficiency of recycling systems. Reverse Vending Machines (RVMs) offer a promising solution by incentivizing recycling through user rewards. However, inaccurate waste detection methods hinder the effectiveness of RVMs. This study explores the potential of the YOLOv8 deep learning algorithm to enhance real-time waste classification accuracy in RVMs. We propose a YOLOv8-based framework for real-time detection of seven key recyclable materials. The model is trained on a combined dataset comprising the public TrashNet dataset and a study-specific dataset tailored to materials and variations encountered in RVMs. Performance evaluation metrics include F1-score, precision, recall, and PR curves.Results demonstrate the superior performance of the YOLOv8-based approach compared to other popular deep learning algorithms, including YOLOv5, YOLOv7, and YOLOv9. The YOLOv8 model achieves an accuracy rate of over 97%, significantly outperforming other algorithms. This improvement translates into enhanced recycling efficiency and reduced misclassification errors in RVMs. This research contributes to the development of more sustainable waste management systems by improving the efficiency and accuracy of RVMs. The YOLOv8-based framework presents a promising solution for real-time waste detection in RVMs, paving the way for more effective recycling practices and reduced environmental impact.
Unveiling the Growth and Development of Electrical, Computer, and Informatics Engineering Education: A Bibliometric Perspective Resky Nuralisa Gunawan; Desy Dwi Putri; Ameh Timothy Ojochegbe; Damola Olugbade; Briliant Dwi Zulhaq
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 4 (2025): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i4.31454

Abstract

This study presents a bibliometric analysis of research trends in Electrical, Computer, and Informatics Engineering Education from 2015 to 2023, focusing on the integration of emerging technologies such as AI, IoT, and e-learning platforms. Data was extracted from the Scopus database, and analysis was conducted using co-occurrence analysis and citation network mapping. The study identifies key research themes, such as the shift towards active learning methodologies (e.g., problem-based learning and gamification) and the growing emphasis on technology-driven curricula. Findings show a significant rise in research output, particularly during the COVID-19 pandemic, with IEEE journals dominating publications in the field. The results highlight the transformative role of digital tools in engineering education and the challenges of balancing technological integration with traditional teaching methods. This research offers insights into the evolving landscape of engineering education and provides recommendations for future research directions.
Exploratory Data Analysis for Monitoring The Environment Variables of Sugarcane Growth Sekar Sari; Oktavia Citra Resmi Rachmawati
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 4 (2025): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i4.31360

Abstract

Sugarcane is vital to the national sugar industry and food security; however, its productivity is significantly affected by environmental factors, including temperature, light intensity, soil moisture, and pH. Fluctuations in these variables frequently lead to erratic yields and diminished sugar quality. Data obtained from IoT-based monitoring systems is often affected by noise, absent values, and outliers, complicating analysis. This research employs exploratory data analysis (EDA) on IoT-based sensor data to obtain comprehensive insights into environmental factors influencing sugarcane growth. The dataset contains 1,811 non-null entries from sensors that measure temperature, light, soil moisture, and pH. Data preparation encompassed cleansing, addressing missing values, and eliminating outliers. Univariate and multivariate analyses were conducted to evaluate variable distributions and correlations. The findings indicated that eliminating outliers improved data consistency and showed that temperature and pH had near-normal distributions, whereas light and soil moisture were skewed. A correlation study revealed moderate associations between light and pH, while regression analysis confirmed a favorable relationship between light intensity and pH. This research emphasizes enhancing the dependability and interpretability of IoT-based monitoring data through EDA, providing significant insights for precision agriculture. Future research may concentrate on predictive modeling and real-time decision-support systems to enhance farming operations.
An Extreme Gradient Boosting for Blood Disease Classification Using Hematological Parameters: A Comparative Evaluation with Ensemble and Non-Ensemble Models Dimas Chaerul Ekty Saputra; Vessa Rizky Oktavia; Irianna Futri; Affifah Mutiara Pertiwi
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 4 (2025): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i4.31659

Abstract

The early detection of hematological disorders remains challenging because many conditions share similar clinical characteristics and show substantial variation in laboratory measurements. Existing machine learning systems often struggle to maintain consistent accuracy in multi-class settings with imbalanced data. The research contribution is a multi-class diagnostic framework that identifies nine hematological disease categories using only routine laboratory parameters, supported by a leakage-free evaluation protocol and a comprehensive comparison across baseline classifiers. The proposed solution uses an extreme gradient boosting model as the primary classifier and evaluates it against support vector machine, random forest, and extra trees. The method includes data cleaning and numerical standardization, and class balancing with the Synthetic Minority Oversampling Technique applied only to the training subset within each fold of ten-fold cross-validation to prevent optimistic bias. Model performance is assessed using accuracy, precision, recall, and F1-score, together with computational efficiency measured through processing time and memory usage. The results show that the extreme gradient boosting model achieves the best overall performance, with an average accuracy of 98.67%, precision of 98.80%, recall of 98.67%, and an F1-score of 98.66%. It also demonstrates efficient memory usage and shorter processing time compared with the other tested methods. The competing models perform adequately but exhibit higher variability and weaker recognition for minority classes. In conclusion, these findings indicate that extreme gradient boosting provides an accurate and efficient approach for hematology-based multi-class disease classification when evaluated under a strict, leakage-free resampling protocol.
Game Recommendation System Using Transformer with Remastered Feature I Made Suwija Putra; Made Jiyestha Arturito; Anak Agung Kompiang Oka Sudana; Ni Wayan Emmy Rosiana Dewi
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 4 (2025): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i4.31345

Abstract

The rapidly growing game industry makes it difficult for players to find games that match their preferences. Conventional recommendation methods are often unsatisfactory due to a lack of personalization. This study aims to design and build a web-based game recommendation system using the content-based filtering method by leveraging a fine-tuned Transformer embedding model, all-MPNet-base-v2, to deeply analyze the textual content of games. The research methodology included data collection from the Steam API (43,900 games), text preprocessing with TF-IDF for keyword extraction, and significantly, fine-tuning the all-MPNet-base-v2 model using the Knowledge Distillation method with jina-embedding-v3 as the teacher model. A novel game series identification feature using fuzzy string matching was also implemented. The resulting embedding vectors were indexed using LanceDB and deployed in a Flask web application. The research contributions are the successful domain-specific adaptation of MPNet via Knowledge Distillation and the implementation of the series identification feature. Quantitative evaluation demonstrated the fine-tuned model's superiority, achieving substantial improvements over the baseline in MRR@10 (0.5857), MAP@10 (0.5149), and Hit Rate@3 (0.90). User Acceptance Testing (UAT) with 15 respondents showed high acceptance (92.89%). Limitations include the Steam-only dataset, potential information loss from TF-IDF, and the small UAT sample size. This study confirms that fine-tuned Transformer embeddings within a content-based framework, enhanced by Knowledge Distillation, can produce effective, accurate, and well-received game recommendations, further improved by context-aware features like series identification.
Lightweight Hybrid Linformer-Mamba U-Net for Efficient Retinal Microaneurysm Segmentation Arif Setia Sandi Ariyanto; Deny Nugroho Triwibowo; Agriby Diandra Chaniago; Indah Trivilia; Annastasya Nabila Elsa Wulandari
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 4 (2025): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i4.31598

Abstract

Diabetic retinopathy is a major microvascular complication of diabetes and a leading cause of vision loss among the working-age population. Microaneurysms (MAs), as the earliest clinical indicators of disease progression, remain challenging to segment due to their small size, low contrast, and extreme class imbalance. This study proposes a lightweight hybrid U-Net architecture for microaneurysm segmentation in retinal fundus images, designed to balance detection sensitivity and computational efficiency for deployment in resource-constrained environments. The proposed architecture integrates depthwise separable convolutions for efficient local feature extraction, a Transformer-Lite bottleneck based on Linformer self-attention for global contextual modeling, and a Mamba State Space Model (SSM)–based decoder to enhance feature propagation and spatial continuity.  The research contribution of this work is threefold: the introduction of an efficient hybrid U-Net combining Linformer and Mamba SSM for microaneurysm segmentation; a deployment-oriented evaluation protocol that explicitly distinguishes patch-level learning behavior from full-image reconstruction performance; and a transparent analysis of false positive behavior under extreme background dominance.  Experiments were conducted on the IDRiD dataset, consisting of 81 retinal images, using patient-level data splitting prior to patch extraction to prevent data leakage.  The results indicate that while patch-level evaluation demonstrates effective lesion-centric learning, deployment-realistic full-image evaluation reveals a notable performance degradation caused by false positive accumulation in extensive background regions. Nevertheless, the model maintains high recall, indicating preserved lesion sensitivity. These findings suggest that lightweight architectural design can deliver meaningful performance and is well suited for screening-oriented decision-support systems that prioritize efficiency and sensitivity.
Success Factors in the Implementation of IoT-Enabled Predictive Maintenance Technology in Industrial Electrical Applications: A Systematic Literature Review Daniel Ngolu Jiledo Maringga; Muhamad Ali; Ridho Azahar
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 12 No. 1 (2026): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v12i1.31651

Abstract

The development of the Internet of Things (IoT) in predictive maintenance (IoT-enabled Predictive Maintenance) for industrial electrical equipment offers significant potential to enhance system efficiency and reliability; however, its implementation is constrained by challenges related to sensor data integration, communication infrastructure quality, and security issues. This study addresses a gap in the literature by describing patterns of successful IoT-enabled Predictive Maintenance implementation in industrial electrical applications. The contribution of this research lies in providing a systematic synthesis of leading technologies and key success factors in the adoption of IoT-enabled Predictive Maintenance.  The method employed is a Systematic Literature Review (SLR) using the PRISMA approach, which resulted in 16 relevant articles. The findings indicate that the combination of IoT technologies, sensors, wireless networks, and edge-cloud architecture represents an appropriate technological configuration for building an effective Predictive Maintenance chain. These implementations are predominantly found in the manufacturing, energy, and transportation sectors, with the main success factors determined by data quality and network sustainability. These findings offer practical solutions for industry practitioners in improving the efficiency and sustainability of their systems. In conclusion, the successful implementation of IoT-enabled Predictive Maintenance in industrial electrical systems is highly dependent on the suitability of technological infrastructure, data governance, and service-based business models, while also opening opportunities for further research and the expansion of applications into other sectors.
Reproducible Biomedical NER and Proxy Relation Extraction for Drug–Adverse Event Analysis in Breast Cancer Deny Nugroho Triwibowo; Hadi Jayusman; Rachman Hidayat; Anisya; Annastasya Nabila Elsa Wulandari
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 12 No. 1 (2026): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v12i1.31594

Abstract

Pharmacovigilance requires automated systems to extract biomedical entities and their relationships from text, as manual processes are inefficient and prone to error. This study develops a reproducible pipeline for Named Entity Recognition (NER) and pattern-based proxy relation formation, focusing on drug side effects related to breast cancer. The research contribution is twofold: a domain-specific annotated dataset for pharmacovigilance NER, and a reproducible pipeline for proxy-based relation analysis. The experimental setup combines MobileBERT, DistilBERT, TinyBERT, and ALBERT. Evaluation is conducted using accuracy, precision, recall, F1-score, ROC AUC, and computational efficiency metrics. The results show that ALBERT achieves the highest NER performance (F1-score = 0.9261), while DistilBERT attains the best ROC AUC (0.9037). TinyBERT is the most efficient model, with 4.57 million parameters, 4.68 G FLOPs, and an average training time of 45.8 seconds per scenario. The proposed pipeline demonstrates a trade-off between accuracy and computational efficiency under the evaluated setting. The generated relations act as sentence-level proxy indicators of potential drug–adverse event associations and serve as a preliminary triage layer requiring expert validation rather than a high-precision system. However, the approach does not account for negation, uncertainty, or cross-sentence context, which may introduce false positive associations. Despite these limitations, the pipeline provides a reproducible baseline for exploratory pharmacovigilance analysis.
Development of an Automated Jar Testing System Based on the Internet of Things (IoT) with 3D Web Visualization Muhaimin Toh Arlim; Sritrusta Sukaridhoto; M. Udin Harun Al Rasyid; Evianita Dewi Fajrianti; Faris Saifullah; Wahyu Nur Hidayat
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 12 No. 1 (2026): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v12i1.32077

Abstract

Manual Jar Testing for coagulant dosage determination in water treatment is labor-intensive, time-consuming, and susceptible to operator bias, limiting the ability of Indonesian Regional Water Utilities (PDAM) to respond in real-time to dynamic raw water quality changes. The research contribution of this study is: (1) an ESP32-based automated Jar Testing platform with closed-loop DC motor control and a multi-parameter sensor array (turbidity, TDS, pH) for objective, repeatable coagulation-flocculation evaluation; and (2) a real-time 3D cloud visualization framework using MQTT and Three.js that provides remote monitoring with sub-1.5-second latency. The system integrates a SEN0189 turbidity sensor, a TDS conductivity sensor, and a pH-4502C sensor, each calibrated against laboratory-grade reference instruments using polynomial calibration equations derived from experimental data. Encoder-based closed-loop feedback regulates DC motor speed across a 0–100 RPM range, while all sensor telemetry is transmitted via the MQTT publish-subscribe protocol to a cloud database and rendered by a Three.js-based 3D digital visualization interface. Sensor validation yielded Mean Absolute Errors (MAE) of 0.15 NTU for turbidity, 5.33 ppm for TDS, and 0.04 pH units, all within the respective sensor tolerance bounds. DC motor control achieved MAE of 0.05–0.30 RPM across the 10–100 RPM setpoint range. Six discrete alum dosage trials on raw water with initial turbidity of 6.6 NTU identified 70 mg/L as the optimal concentration, achieving 90.15% turbidity removal with a residual turbidity of 0.65 NTU, below the 1 NTU threshold for potable water quality. The 3D visualization layer-maintained data synchronization latency below 1.5 seconds under laboratory network conditions. The proposed system substantially reduces operator workload and eliminates visual observation bias compared to conventional manual Jar Testing, offering a scalable and low-cost platform for data-driven coagulant dosing optimization in modern water treatment facilities.

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