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All Journal JOIV : International Journal on Informatics Visualization Jurnal Pilar Nusa Mandiri Jurnal Teknologi Terpadu Jurnal Komunikasi, Sains dan Teknologi (JKST) Journal of Informatics and Advanced Computing Journal of Applied Research In Computer Science and Information Systems Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital
Maspiyanti, Febri
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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Journal : JOIV : International Journal on Informatics Visualization

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Comparison of Classification Algorithms in Bamboo Distribution Mapping for Identification of Industrial Supporting Raw Materials Veritawati, Ionia; Maspiyanti, Febri; Mastra, Riadika; Fernando, Erick; Murtako, Amir
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3072

Abstract

This study aims to address the challenges in the widespread supply of bamboo raw materials and the lack of coordination between bamboo-producing regions, as well as to conduct a comprehensive inventory and mapping of bamboo resources. In addition, this study also explores the factors that influence the distribution and growth characteristics of bamboo, such as soil type, altitude, and rainfall. The main problems faced in the bamboo industry are the uneven distribution of raw materials and the lack of coordination between regions, which hinder the development of a strong and sustainable bamboo industry value chain. The lack of in-depth information on the ecological factors that influence bamboo growth also exacerbates this situation. The method used in this study involves mapping bamboo potential through aerial photography data collection, which is then analyzed using machine learning technology. The three algorithms used in the classification process are K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest. The study was conducted in an area rich in bamboo vegetation, especially Bojongmangu District in Bekasi, West Java, Indonesia. From the analysis results, the SVM algorithm showed the best performance with a classification accuracy ranging from 80% to 90%. These results indicate that this method is very effective in mapping bamboo vegetation areas with high precision. This study also identified other variables, such as soil type and altitude, that play a role in bamboo distribution. With this more holistic approach, the study is expected to provide deeper insights into bamboo ecology and improve sustainable bamboo resource management.
Course Timetabling using Genetic Algorithm and Fuzzy Cross-Over Maspiyanti, Febri; Gatc, Jullend; Nursari, Sri Rezeki Candra; Murtako, Amir
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3196

Abstract

Course timetabling at universities presents a complex problem due to the limited timeframe to create schedules that avoid conflicts between activities. This issue becomes more challenging as the number of activities increases while the available rooms remain constant. Numerous studies have attempted to automate the scheduling process, but their success is often limited to specific cases, meaning their effectiveness may not be applicable in different institutions. One method that has shown potential in solving timetable problems is the genetic algorithm, either as a standalone approach or combined with other techniques. Despite criticisms regarding computational time complexity, genetic algorithms serve as practical global optimization tools, making them suitable for timetabling when computational time constraints are manageable. A hybrid Genetic Algorithm combined with Fuzzy Partitioning is essential for determining the crossover point, one of the key operators in genetic algorithms. In this study, we use a hybrid genetic algorithm with fuzzy crossover to address the course timetabling problem at Pancasila University, focusing on two departments, Informatics and Electro, which share classrooms on the same floor. In this study, we use data from 31 courses; our experiment achieved convergence at generation 78, with a fitness function score of zero, indicating the complete elimination of scheduling conflicts. For further improvement, adjustments could be made to the fitness function to penalize inefficient room usage, reducing the total number of generations to decrease execution time without compromising solution quality, and reducing the mutation rate to enhance solution stability.
Co-Authors Adam Nugroho Adrianty, Syahrani Andini Putri, Andini Aniati Murni Arymurthy Deni Septiadi Dona Ines, Monika Effatha, Eidelwise Gloria Erick Fernando B311087192 Fanany, M. Ivan Gatc, Jullend Hadiyanti, Nadya Hanifa, Faiqa Hadya Ionia Veritawati Mastra, Riadika Murtako, Amir Ninuk Wiliani Shabrina Putri