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All Journal Journal of Innovation Information Technology and Application (JINITA)
Juanda Hakim Lubis
Universitas Pembinaan Masyarakat Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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Evaluating ERD Models and RAID-Based Storage for Query Performance Optimization in Relational Databases Juanda Hakim Lubis; Sri Handayani; Herman Mawengkang; Yuliska
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 1 (2025): JINITA, June 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i1.2707

Abstract

The amount of data stored in magnetic disks (e.g., floppy disks) increases by 100% each year for each department in a company, necessitating efforts to maintain an optimal database system. Designing a database is the initial step in creating a system with optimal performance. However, database design alone is not sufficient to enhance performance. One approach to improving data transaction speed is by optimizing query processing. This research evaluates different relational database models using varying amounts of data. Query costs are analyzed using the Cost-Based Optimizer method and access time measurements. The results of this study provide insights for database administrators in designing relational database models effectively and selecting appropriate query structures to optimize database performance. The findings indicate that: (1) database design can be optimized by separating entities based on specialized usage, and (2) factors such as record count, attribute size, query type, use of unique or primary keys, order-by clauses, index sequences, and SQL function usage significantly impact query cost and overall performance.
Shortest Path Calculation for Bandung Tourist Destinations Using Ant Colony Optimization Juanda Hakim Lubis; Sri Handayani; Akhyar Lubis; Fajrul Malik Aminullah Napitupulu
Journal of Innovation Information Technology and Application (JINITA) Vol 8 No 1 (2026): JINITA, June 2026
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v8i1.2839

Abstract

Efficient route planning is essential in modern tourism, particularly for multi-destination travel in complex urban environments. This study proposes a tourism route optimization model based on Ant Colony Optimization (ACO) integrated with real-world geographic distance data. The problem is modeled as a weighted graph, where tourist destinations are represented as nodes and edge weights are derived from distances obtained via the Google Maps Directions API. The objective is to determine an optimal route that minimizes total travel distance across multiple destinations. The ACO algorithm employs a probabilistic search mechanism based on pheromone trails and heuristic visibility to iteratively construct candidate routes. Experimental results show that the model consistently converges to the optimal route sequence 1 → 4 → 2 → 5 with a total distance of 8323 meters under appropriate parameter settings. The findings also indicate that heuristic influence plays a critical role in ensuring convergence and solution quality. Compared to a deterministic baseline based on Dijkstra’s algorithm, ACO demonstrates greater flexibility in exploring multiple route combinations, making it more suitable for multi-destination routing. The integration of real-world data enhances the practical applicability of the model, although it introduces a higher computational cost.
Co-Authors Akhyar Lubis Fajrul Malik Aminullah Napitupulu Herman Mawengkang Sri Handayani Yuliska