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Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer
Published by Politeknik Negeri Banjarmasin
ISSN : 25983245     EISSN : 25983288     DOI : -
Core Subject : Science, Engineering,
Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
We are the Editor of Jurnal ELTIKOM, invites Mr. / Ms Lecturer, researcher and practitioner to be able to publish your paper on topics covering Electrical Engineering, Electronics Engineering, Telecommunications Engineering, Computer Engineering, Information Technology.
Arjuna Subject : -
Articles 233 Documents
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Wound Depth Measurement System in Forensic Cases using Image Processing and Machine Learning Wahyuni, Elvira Sukma; Ahnaf, Kern Cesarean; Firdaus, Firdaus; Abdul-Kadir, Nurul Ashikin; Zakaria, Nor Aini; Wiraagni, Idha Arfianti; Kadarmo, Diwangkoro Aji
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 9 No. 2 (2025)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v9i2.1636

Abstract

Accurate evaluation of wound depth is crucial in forensic investigations, as it significantly affects case assessments and outcomes. This study introduces a method for classifying wound depth using a Support Vector Machine (SVM) model and compares its performance with Decision Tree and Logistic Regression models. The classification was based on color features extracted from HSV and LAB color spaces. The da-taset consisted of 76 images categorized into three stages: stage 2 (36 images), stage 3 (12 images), and stage 4 (28 images). Model performance was evaluated using confusion matrices, precision, recall, and F1-score. The SVM model achieved an overall accuracy of 85%, demonstrating higher precision and re-call across all stages compared to the Decision Tree and Logistic Regression models, which achieved 50% and 70%, respectively. The results indicate that the SVM model performed particularly well in distinguish-ing stage 2 wounds, although differentiating between stages 3 and 4 remained challenging. Overall, the proposed system shows potential to enhance the accuracy and efficiency of forensic wound evaluation by providing a rapid and objective classification tool. However, as the system was tested on a limited dataset under controlled conditions, further research should expand the dataset, incorporate additional features, and explore other machine learning algorithms to improve robustness and applicability in real forensic contexts.
The Synergy of Blockchain and Cybersecurity: Building Trust in Digital Environments Zangana, Hewa Majeed; Sallow, Zina Bibo; Mustafa, Firas Mahmood; Husain, Mamo Muhamad
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 9 No. 2 (2025)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v9i2.1701

Abstract

The rapid expansion of digital ecosystems has intensified concerns about data security, privacy, and trust. Blockchain technology, characterized by its decentralized, immutable, and transparent nature, offers a transformative approach to strengthening cybersecurity. This paper examines the synergy between blockchain and cybersecurity, emphasizing how blockchain’s cryptographic foundations, consensus mechanisms, and smart contracts can mitigate cyber threats, enhance authentication, and ensure data integrity. By analyzing emerging trends, challenges, and real-world applications, this study underscores the potential of blockchain to reinforce digital trust and resilience across diverse sectors. The findings contribute to the ongoing discourse on secure digital environments by proposing an integrated framework for blockchain-based cybersecurity solutions
Optimization Model for Fake Account Detection on Twitter (X) Social Media using Feature Engineering and Machine Learning Approaches Perimawati, Ni Nyoman Eny; Huizen, Roy Rudolf; Hostiadi, Dandy Pramana
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 9 No. 2 (2025)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v9i2.1727

Abstract

Twitter (X) has become an important platform for community interaction, but this also creates serious challenges due to the proliferation of fake accounts that can harm users and undermine credibility. Previous studies have proposed detection methods but often lacked forensic analysis based on extracted feature information. This study utilizes labeled datasets and supervised evaluation metrics (precision, recall, and F1-score) to validate model performance. Extracting behavioral information from features is crucial for achieving accurate and reliable detection results. The study introduces a novelty in the form of engineered behavioral features that significantly enhance detection accuracy, achieving up to 99.94% using AdaBoost. The proposed approach detects fake accounts on Twitter (X) by extracting key feature information and developing an optimal detection method through machine learning algorithms, including Random Forest, SVM, and AdaBoost. Furthermore, the model is optimized using feature engineering techniques. The novelty of this work lies in the development of engineered features through distribution analysis based on data characteristics and the improvement of classification performance through feature engineering optimization. The initial experiment without feature engineering shows that Random Forest achieved the highest accuracy of 98.77%, followed by AdaBoost at 98.57% and SVM at 95.90%. After applying feature engineering, performance improved, with AdaBoost reaching 99.94%, Random Forest 99.69%, and SVM 99.32%. The proposed model can assist system analysts in detecting fake accounts and contribute to solving forensic cybercrime challenges, particularly in identifying fake social media profiles.

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