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All Journal JOIV : International Journal on Informatics Visualization Jurnal Pilar Nusa Mandiri Eduvest - Journal of Universal Studies
Murtako, Amir
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Aerospace Engineering Computer Science & IT Health Professions Neuroscience Social Sciences

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Development of Mobile and Spatial Based Smart Community Applications to Improve the Community Economy Veritawati, Ionia; Pribadi, Adi Wahyu; Murtako, Amir; Riono, Bambang
Eduvest - Journal of Universal Studies Vol. 5 No. 2 (2025): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v5i2.1734

Abstract

The economy in Depok Community is a problem, where the community needs support from the surrounding community and stakeholders in developing its economy. In planning, Depok is planned to become a smart city. Smart community is part of a smart city with a concept that includes technology-people-innovation in developing society. For this reason, this research developed the Smart Community concept which uses mobile and spatial-based technology to help improve the economy of its citizens, including MSME business actors in the sub-district area, especially in Cilodong Sub-district in this case study. The application development method uses the water fall methodology and an object-oriented approach. The results obtained, the application developed can be used by citizens to share information and communicate to help the citizens' economy, one of which is online marketing, and assisting stakeholders in monitoring and providing support in economic matters.
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.
IDENTIFICATION OF FOOD DIVERSIFICATION ON JAVA ISLAND USING ARCGIS Murtako, Amir; Hanifa, Faiqa Hadya; Effatha, Eidelwise Gloria; Nursari, Sri Rezeki Candra; Maspiyanti, Febri
Jurnal Pilar Nusa Mandiri Vol. 21 No. 2 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v21i2.6570

Abstract

Indonesia is addressing the challenges of food security and consumer preference also known as Food diversification. The research aims to analyze the potential of various local food sources as alternatives to rice, which is the dominant staple food in Indonesia, with a particular focus on geographic implications. Although local carbohydrate sources like corn, potatoes, and tubers are available, their adoption is limited and understudied in relation to geographic distribution and consumer behavior. This study integrates survey data and GIS-based spatial analysis to evaluate local food diversification potential. Findings show that while 100% of respondents consume rice, 48.7% have tried alternatives, with limited availability (41.03%) and higher costs (17.95%) as key barriers. With 94.7% expressing willingness to adopt new staples, the results suggest GIS-based decision support systems can guide effective, region-specific food policy interventions.
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 Effatha, Eidelwise Gloria Erick Fernando B311087192 Gatc, Jullend Hanifa, Faiqa Hadya Ionia Veritawati Maspiyanti, Febri Mastra, Riadika Pribadi, Adi Wahyu Riono, Bambang