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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Rosanti, Yerika Puspa
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Penerapan Algoritma Genetika Untuk Mencari Optimasi Kasus TSP Pada 20 Gerai Indomart Rosanti, Yerika Puspa; Triana, Iwel; Pancahayani, Sigit
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 3 (2024): Edisi Juli
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i3.423

Abstract

In the delivery of a package, goods, and conducting business, location is a crucial factor to manage. A common issue is the late arrival of packages because delivery couriers cannot find the fastest or most efficient route. This study aims to apply a genetic algorithm to optimize the traveling salesman problem (TSP) for the distribution of goods to 20 Indomaret outlets in the Dago area of Bandung City. TSP is a classic optimization problem that seeks to find the shortest route that visits each city once and returns to the origin city. The genetic algorithm, as a population-based search and optimization method, is used due to its capability to find near-optimal solutions for complex and large problems. This algorithm leverages natural selection mechanisms such as selection, crossover, and mutation to develop solutions from one generation to the next. Initial parameters were set with a population of 100 and a maximum of 500 generations to increase the variety of solutions without taking too much time. The fitness value was obtained by taking the negative of the total distance traveled, and after the iteration process, an optimal result with a fitness value of -0.10 was achieved. It only took 50 seconds to run 500 generations for selecting the distribution route of 20 Indomaret outlets.
Exploring Energy Data through Clustering: A Hyperparameter Approach to Mapping Indonesia's Primary Energy Supply Windarto, Agus Perdana; Rosanti, Yerika Puspa; Mesran, Mesran
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 2 (2024): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i2.29032

Abstract

The rapid economic growth and population development in Indonesia have significantly increased the demand for energy, presenting complex challenges in managing the primary energy supply due to geographical variability and dispersed natural resources. This study addresses these challenges by applying clustering techniques with a hyperparameter approach to explore and map Indonesia's primary energy supply. The research contributes to the field by offering an effective method for analyzing energy data patterns and optimizing energy management. Secondary data on energy production, consumption, and distribution from reliable sources such as the Ministry of Energy and Mineral Resources were collected and analyzed. Various clustering algorithms, including K-Means, Fast K-Means, X-Means, and K-Medoids, were applied to identify energy supply patterns across different regions. The Davies-Bouldin Index was used to evaluate the effectiveness of the clustering algorithms. The results indicate that distance measures such as Euclidean Distance and Chebychev Distance consistently show excellent clustering performance. The study found that the choice of distance measure significantly impacts the clustering quality. The insights gained from this analysis provide valuable information for stakeholders involved in energy planning and policy-making, enhancing the efficiency and sustainability of energy management in Indonesia. This research establishes a foundation for further detailed and holistic energy data analysis, supporting better decision-making in energy planning and development.
Co-Authors Agus Perdana Windarto Mesran, Mesran Pancahayani, Sigit Triana, Iwel