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INDONESIA
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 22 Documents
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Search results for , issue "Vol. 10 No. 2 (2024): June" : 22 Documents clear
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.

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