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All Journal bit-Tech Indonesian Journal of Education And Computer Science
Abdul Muin Nasution
Randwick International Research and Analysis Institute
Computer Science & IT Education

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Comparative Analysis of Dijkstra and A* Algorithms for Determining the Shortest Route Ardiansyah Ardiansyah; Abdul Muin Nasution; Muhammad Iqbal
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3474

Abstract

This study presents a comparative analysis of Dijkstra and A* algorithms for determining the shortest route in an urban road network scenario, specifically from SMKN 9 Medan to Gramedia Gajah Mada, Medan. The road network is modeled as a weighted graph, where nodes represent key locations, and edges represent inter-node distances derived from Google Maps. Three alternative routes are evaluated based on inter-node distances and direct heuristic distances to the destination. Dijkstra’s algorithm, an uninformed search method, guarantees optimality by exhaustively exploring all possible paths with non-negative weights. In contrast, the A* algorithm incorporates a heuristic function that estimates the remaining distance to the goal, enhancing search efficiency by focusing on the most promising paths. Both algorithms are applied to the same graph data for a fair comparison, with performance metrics including total route distance, number of nodes explored, and computational efficiency. The results show that while both algorithms identify the same optimal route (A–B–E–G, 5.7 km), A* outperforms Dijkstra in terms of computational efficiency, exploring fewer nodes and requiring less computation time. These findings suggest that while Dijkstra remains reliable for smaller networks, A* is better suited for real-world navigation applications where efficiency and scalability are critical. This study provides empirical evidence supporting the use of heuristic-based algorithms in urban route planning systems.
Implementasi Kecerdasan Buatan dalam Deteksi Cybercrime: Komparasi Model Naive Bayes dan SVM pada Pola Komentar Judi Online Ardiansyah Ardiansyah; Abdul Muin Nasution; Muhammad Syahputra Novelan
Indonesian Journal of Education And Computer Science Vol. 3 No. 3 (2025): INDOTECH - December 2025
Publisher : PT. INOVASI TEKNOLOGI KOMPUTER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60076/indotech.v3i3.1762

Abstract

Maraknya promosi judi online di platform media sosial seperti YouTube telah menjadi ancaman serius dalam kategori cybercrime di Indonesia. Pola komentar yang bervariasi dan penggunaan bahasa non-formal menyulitkan identifikasi konten secara manual. Penelitian ini bertujuan untuk mengimplementasikan teknologi Kecerdasan Buatan (AI) melalui pendekatan Machine Learning untuk mendeteksi secara otomatis pola komentar judi online. Dua algoritma populer, yaitu Naive Bayes (NB) dan Support Vector Machine (SVM), digunakan dan dibandingkan kinerjanya untuk menentukan model klasifikasi terbaik. Data penelitian diekstraksi dari komentar YouTube berbahasa Indonesia, yang kemudian melewati tahap pra-pemrosesan teks meliputi case folding, tokenization, stopword removal, dan stemming. Fitur teks ditransformasikan menjadi bentuk numerik menggunakan metode Term Frequency-Inverse Document Frequency (TF-IDF). Hasil penelitian menunjukkan perbandingan kinerja kedua algoritma berdasarkan metrik akurasi, precision, recall, dan f1-score. Temuan ini diharapkan dapat memberikan kontribusi bagi pengembangan sistem filtrasi konten negatif otomatis guna memperkuat keamanan siber di ekosistem digital Indonesia.
Co-Authors Ardiansyah Ardiansyah Muhammad Iqbal Muhammad Syahputra Novelan