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Applied Information Technology and Computer Science (AICOMS)
Published by Politeknik Negeri Ketapang
ISSN : -     EISSN : 29647703     DOI : https://doi.org/10.58466/aicoms
Core Subject : Science,
Computer Science & IT
Applied Information Technology and Computer Science (AICOMS) is an online version of national journal in Bahasa Indonesia and English, published by Department of Informatics Engineering, Politeknik Negeri Ketapang. AICOMS also has a print version. AICOMS also invites academics and researchers in the field of information technology, particularly from informatics engineering and information systems research to submit their articles. The articles to be published is an original work and has never been published. Incoming articles will be reviewed by a team of reviewers from internal and external sources.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 73 Documents
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Penerapan Metode Branch and Bound untuk Optimalisasi Rute Wisata Terdekat di Kota Palembang Jaysen Stephanus; Felix Gunawan; Yohannes Yohannes
Applied Information Technology and Computer Science (AICOMS) Vol 5 No 1 (2026): AICOMS
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/eqadem96

Abstract

This study discusses the application of the Branch and Bound method to optimize the nearest tourist route in Palembang City using the Traveling Salesman Problem (TSP) approach. The problem raised is how to determine the most efficient tourist route from several tourist destinations with minimum travel distance. The study utilizes geographic coordinate data of tourist destinations obtained through OpenStreetMap, then the distance between locations is calculated using the Haversine Formula to obtain an accurate distance estimate based on latitude and longitude. Furthermore, the Branch and Bound Algorithm is used to find the optimal route solution through the process of branching, bounding, and pruning so that the solution search becomes more efficient than the brute force method. The results show that the system successfully produces an optimal circular tourist route with a total minimum distance of 40.47 km and an execution time of 12.84 seconds. The integration of the Haversine Formula and Branch and Bound is proven to be able to provide efficient, accurate, and adaptive tourist route recommendations to help tourists save travel time and transportation costs in Palembang City.
Perbandingan Algoritma Greedy dan Dynamic Programming Pada Optimasi Playlist Spotify Untuk Jogging Fadhel Muhammad; Muhammad Radja Juang Jamemiko; Yohannes Yohannes
Applied Information Technology and Computer Science (AICOMS) Vol 5 No 1 (2026): AICOMS
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/1htfcz49

Abstract

Spotify provides audio metadata that can be utilized to support physical activities such as jogging. This study compares the performance of Greedy and Dynamic Programming algorithms for Spotify playlist optimization modeled as a 0/1 Knapsack Problem. Song duration is treated as weight, while a score derived from popularity and energy is used as value. The dataset was obtained from Spotify Wrapped 2025 Top 50 Songs and Spotify All-Time Top 100 Songs, resulting in 31 candidate songs after preprocessing and filtering. Experiments were conducted on playlist durations of 30, 45, 60, 75, and 90 minutes. The results show that Dynamic Programming consistently achieved higher total scores than Greedy across all scenarios. For the 60-minute playlist, Dynamic Programming obtained a total score of 1897 compared to 1894 achieved by Greedy. However, Greedy required a lower execution time (4.244 ms) than Dynamic Programming (16.196 ms). The average optimality gap between the two methods was 1.89%, indicating that Greedy produced solutions that were close to the optimal solutions generated by Dynamic Programming while requiring less computation time.
Perbandingan Kinerja Algoritma Greedy dan Dynamic Programming dalam Optimasi Diskon Keranjang Belanja E-Commerce Menggunakan Dataset Online Retail UCI Jonathan Tanujaya; Daffa Yudha Musyaffa; Yohannes Yohannes
Applied Information Technology and Computer Science (AICOMS) Vol 5 No 1 (2026): AICOMS
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/f0fdve41

Abstract

E-commerce platforms heavily rely on automated promotional strategies, such as tiered discounts, to enhance customer loyalty. Therefore, this study aims to analyze the performance of computational algorithms in determining item priorities within a shopping cart under promotional budget constraints. The 0/1 Knapsack Problem was addressed by comparing two computational approaches: Dynamic Programming (DP) and the Greedy Algorithm. Transaction data from the UCI Online Retail dataset were cleaned and aggregated into 3,746 unique product catalogs, then simulated using a promotional budget limit of £499.40 with a 10% discount policy. Computational experiments revealed contrasting trade-off characteristics between the two approaches. The DP algorithm guaranteed an absolute optimal solution with a total profit of £2,725,575.77 but required 28.10 seconds of computation time. In contrast, the Greedy algorithm completed the selection process in a fraction of a second (0.17 seconds) while incurring only a marginal profit deficit of 0.01%. The Greedy heuristic approach proved to be highly practical and efficient for integration into real-time user interface systems, whereas the superior accuracy of DP makes it more suitable for offline database processing and inventory analytics research.

Page 8 of 8 | Total Record : 73

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