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All Journal VOX EDUKASI: Jurnal Ilmiah Ilmu Pendidikan Jurnal Abdi Insani Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Abdimas Bina Bangsa Kohesi: Jurnal Sains dan Teknologi SmartComp Jurnal Informatika Polinema (JIP)
Octariadi, Barry Ceasar
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences Other

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Journal : Jurnal Computer Science and Information Technology (CoSciTech)

 1 
Implementasi Ant Colony Optimization Untuk Rute Terpendek Pada Pengiriman Barang J&T Cahyo, Rahandya; Siregar, Alda Cendekia; Octariadi, Barry Ceasar
Computer Science and Information Technology Vol 6 No 3 (2025): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v6i3.10467

Abstract

Parcel delivery is a logistics service that requires speed and efficiency, especially in determining delivery routes. The choice of this topic is based on the problem faced by J&T delivery in Kubu Raya, particularly Desa Kapur, where long travel distances often result in inefficiency. This study applies the Ant Colony Optimization (ACO) algorithm to identify the shortest route for parcel delivery. ACO mimics the behavior of ants in finding optimal paths based on pheromone intensity. Location data were obtained using coordinates from the Google Maps API and modeled into a weighted graph, where nodes represent delivery points and edges represent distances. The optimization process was carried out by simulating the movement of ant agents to evaluate alternative routes, followed by pheromone updates on the more efficient paths. The results indicate that ACO successfully generated more efficient delivery routes compared to conventional methods, achieving a distance reduction of 28.29%, equivalent to approximately 10.68 km saved. This efficiency contributes to reduced travel time and operational costs. The optimized routes were also visualized through an interactive map using Leaflet.js to facilitate analysis and interpretation. Therefore, ACO is proven to be effective in optimizing delivery routes and has strong potential for real-world application in courier services.
Analisis Penerapan Algoritma Random Forest Dalam Klasifikasi Prakiraan Cuaca Saputra, Deny Saputra; Pangestika, Menur Wahyu; Octariadi, Barry Ceasar
Computer Science and Information Technology Vol 6 No 3 (2025): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v6i3.10846

Abstract

Weather plays an important role in various aspects of life, such as agriculture and transportation. However, weather prediction remains challenging because it is influenced by many complex factors. Extreme weather events, such as storms and floods, can cause significant losses, making accurate weather forecast classification systems essential. This study applies the Random Forest algorithm to improve prediction accuracy and optimizes it using Grid Search Cross Validation. The method used is CRISP-DM, consisting of six main stages. The data were obtained from the Meteorological, Climatological, and Geophysical Agency (BMKG), containing features such as temperature, humidity, wind speed, cloud cover, visibility, and wind direction, with the labels Weather Condition and Region Name serving as indicators of the classified weather category and location. The final evaluation uses a confusion matrix, yielding an accuracy of 98.84% on the training data and 95.33% on the testing data, demonstrating stable performance and strong generalization capability.
Co-Authors Alinafiah, Andika Muhammad azwar, agim aljanata Cahyo, Rahandya Chatarina Umbul Wahyuni Deni, Deni Deni Febrilian, Uray Menur Wahyu Pangestika, Menur Wahyu Muhammad Agus Muljanto Putri, Alya Amanda Repan Saputra, Deny Saputra Siregar, Alda Cendekia Sucipto Sucipto Sucipto Utami, Putri Yuli Yanti, Dewi