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All Journal Jurnal Ilmu Komputer dan Sistem Informasi
Rizky Barus
Universitas Islam Negeri Sumatera Utara
Computer Science & IT Library & Information Science

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Penggunaan Algoritma Komputasi untuk Analisis Sederhana Data DNA dalam Studi Bioinformatika Ishlahiyah Nur Rizky; Rosa Prahasti; Natria Selina; Rizky Barus
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 4 No. 1 (2025): Januari 2025
Publisher : LKP Unity Academy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70340/jirsi.v4i1.163

Abstract

Advances in computational technology and bioinformatics have enabled efficient and accurate DNA (Deoxyribo Nucleic Acid) data analysis. This study explores computational algorithm utilization for simple DNA data analysis in bioinformatics. We implemented basic algorithms (sequence sequence, pattern matching, clustering) using Python and Biopython library to analyze 500 DNA sequence samples from various model organisms. Results show computational algorithms accelerate analysis by 70% compared to manual methods, achieving 95% accuracy in identifying sequence patterns and structural similarities. Performance analysis reveals dynamic programming-based sequence sequence has O(mn) time complexity, while hierarchical clustering requires O(n²) computational time. This study highlights optimization needs for large-scale Dataset s and parameter adjustments for specific cases. Computational algorithms prove effective in supporting simple DNA data analysis, paving the way for developing complex bioinformatics tools.
Analisis Sentimen Publik Terhadap Revisi UU TNI 2025 Menggunakan Algoritma Naïve Bayes Rizky Barus; Rakhmat Kurniawan
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 5 No. 2 (2025): Mei 2026
Publisher : LKP Unity Academy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70340/jirsi.v5i2.380

Abstract

The development of public opinion regarding the revision of the 2025 Indonesian National Armed Forces Law (UU TNI) on social media has generated various responses that are difficult to analyze manually due to the large and unstructured amount of data. This condition requires a computational approach that is able to systematically identify public sentiment trends. This study aims to analyze public sentiment towards the revision of the 2025 TNI Law using the TF-IDF-based Naïve Bayes algorithm and evaluate the performance of the classification model used. The research data was obtained through crawling techniques from YouTube user comments related to the revision of the 2025 TNI Law. The data processing stages include cleaning, case folding, tokenizing, normalization, stopword removal, and stemming before TF-IDF weighting and the classification process using Naïve Bayes. The results of the study of 1826 data show that public sentiment is dominated by the neutral category at 79.8%, while positive sentiment is 13.1% and negative sentiment is 7.0%. The model evaluation yielded an accuracy of 77.11%, but the model showed a bias toward the majority class, resulting in suboptimal classification of positive and negative sentiments. Based on these results, the Naïve Bayes method is quite effective as an initial approach in sentiment analysis, but it still has limitations in handling imbalanced datasets and the complex characteristics of social media language. Therefore, the development of more adaptive methods is needed to improve the quality of sentiment classification results.
Co-Authors Ishlahiyah Nur Rizky Natria selina Rakhmat Kurniawan R Rosa Prahasti