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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 505 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 Kestane, Bahadir Besir; Guney, Emin; Bayilmis, Cuneyt
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 Gunawan, Resky Nuralisa; Putri, Desy Dwi; Ojochegbe, Ameh Timothy; Olugbade, Damola; Zulhaq, Briliant Dwi
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 Sari, Sekar; Rachmawati, Oktavia Citra Resmi
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 Saputra, Dimas Chaerul Ekty; Oktavia, Vessa Rizky; Futri, Irianna; Pertiwi, Affifah Mutiara
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.

Page 51 of 51 | Total Record : 505

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