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All Journal Indonesia Chimica Acta Al-Khwarizmi: Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam Jurnal Informatika Global Jurnal Studia Akuntansi dan Bisnis (The Indonesian Journal of Management & Accounting) Jurnal Alwatzikhoebillah : Kajian Islam, Pendidikan, Ekonomi, Humaniora Jurnal Ekonomi Bisnis, Manajemen dan Akuntansi (JEBMA) JABKO: Jurnal Akuntansi dan Bisnis Kontemporer Konsentrasi: Jurnal Manajemen dan Bisnis Journal of Computer Science Application and Engineering Jurnal Sistem Informasi dan Teknik Informatika (JAFOTIK) Jurnal Pengabdian Masyarakat ISEI Jurnal Pengabdian Masyarakat Jurnal Pengabdian Masyarakat Bumi Anoa Masyarakat: Jurnal Pengabdian Jurnal MediLab Mandala Waluya Cleanliness: Journal of Health Sciences and Medical Research Jurnal Abdimas Cendekiawan Indonesia Journal of Golden Generation Economic
Amirah
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Religion Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Library & Information Science Mathematics Nursing Public Health Social Sciences Other

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Journal : Journal of Computer Science Application and Engineering

 1 
First Step for Vehicle License Plate Identification Using Machine Learning Approach Amirah; Sanmorino, Ahmad
Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 1 No. 1 (2023): JOSAPEN - January
Publisher : PT. Lentera Ilmu Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70356/josapen.v1i1.6

Abstract

Automated vehicle license plate identification, critical in modern transportation systems, finds application in traffic monitoring, law enforcement, and transportation optimization. This study explores machine learning's potential to enhance accuracy and efficiency in this domain. Leveraging neural networks and pattern recognition, it aims to build an automated system robust across diverse conditions. Addressing limitations in traditional methods, it focuses on adapting to lighting, angles, and image quality variations. The societal impact includes streamlining law enforcement and optimizing traffic flow, revolutionizing transportation and surveillance. Methodologies cover data collection, ethical considerations, preprocessing, feature extraction, model selection, and iterative refinement. Ethical data handling ensures privacy compliance. Feature extraction methods like HOG, LBP, CNNs, and color histograms capture crucial aspects for identification. Model selection spans SVMs, CNNs, decision trees, and ensemble methods, considering task complexity and dataset characteristics. This study evaluates machine learning's potential for revolutionizing license plate identification systems.
From Algorithms to Cures: AI's Impact on Drug Discovery Karimah, Fitrah; Amirah
Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 1 No. 2 (2023): JOSAPEN - July
Publisher : PT. Lentera Ilmu Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70356/josapen.v1i2.12

Abstract

This study explores the paradigm-shifting fusion of artificial intelligence (AI) and pharmaceuticals, heralding a new era of innovation in drug development. AI's transformative potential revolutionizes the traditionally arduous drug discovery process by seamlessly assimilating vast data volumes encompassing molecular structures, genetics, and disease pathways. This synergy expedites the identification of potential drug candidates with heightened precision and efficiency, propelling breakthrough treatments. The exploration navigates through AI-driven computational models, showcasing their role in expediting drug validation and optimization. AI's iterative learning enhances predictive capabilities, forecasting medication efficacy and safety profiles, thereby minimizing clinical trial risks and boosting success rates. Beyond acceleration, AI reshapes drug development strategies toward personalized medicine. Analyzing expansive patient datasets, AI tailors treatments based on genetic variations and disease characteristics, promising optimized therapeutic outcomes and minimized adverse effects, marking a departure from traditional healthcare approaches. The methodology employed various research techniques, including literature reviews, data collection, surveys, case studies, synthesis, and recommendations, offering comprehensive insights into AI's impact on drug discovery. In conclusion, the study emphasized AI's transformative potential in revolutionizing drug discovery, advocating for continued exploration and integration to optimize pharmaceutical research and development practices.
Editorial: Smart Parking Management System Using Artificial Intelligence Amirah
Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 2 No. 1 (2024): JOSAPEN - January
Publisher : PT. Lentera Ilmu Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70356/josapen.v2i1.25

Abstract

The escalating challenges of urban parking due to increasing urbanization and rising vehicle numbers have spurred the integration of Artificial Intelligence (AI) into parking management. This article explores the potential of a Smart Parking Management System (SPMS) driven by AI to revolutionize urban parking infrastructure. The SPMS leverages AI technologies, including advanced algorithms, machine learning models, and real-time data analytics, to intelligently monitor, allocate, and optimize parking spaces. Beyond addressing immediate concerns such as congestion and parking availability, the system aligns with broader urban development goals of sustainability and improved quality of life. The SPMS offers benefits beyond convenience, contributing to a more sustainable and eco-friendly urban environment. By optimizing traffic flow and reducing time spent searching for parking, the system aims to decrease fuel consumption, emissions, and overall environmental impact. The emergence of Internet of Things (IoT) technologies plays a crucial role, with sensors in parking spaces providing real-time occupancy information, and enabling dynamic system responses. Mobile applications and smart devices further empower users with real-time information, fostering smart and sustainable transportation habits. While the promise of AI-driven SPMS is considerable, challenges such as data privacy, security, and seamless integration into existing urban infrastructure must be addressed.
Machine Learning-Based Route Optimization for Smart Urban Transportation Systems Anson, Adriel Moses; Amirah
Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 3 No. 2 (2025): JOSAPEN - July
Publisher : PT. Lentera Ilmu Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70356/josapen.v3i2.65

Abstract

Urban transportation systems face increasing challenges due to rapid population growth, traffic congestion, and unpredictable road conditions. Traditional routing algorithms like Dijkstra and A* are limited in their ability to respond to real-time events such as accidents, roadwork, or weather disruptions. This study aims to develop a smarter, more adaptive route optimization system using machine learning techniques. The goal is to enhance travel time accuracy, reduce congestion, and improve commuter satisfaction through intelligent, data-driven decision-making. The proposed method integrates supervised learning for travel time prediction and reinforcement learning for real-time route selection, using data from GPS trajectories, traffic flow, weather reports, and user behaviors. A grid-based environment is used for reinforcement learning simulations, while OpenStreetMap data supports city-level route optimization. Results show that the machine learning-enhanced model significantly outperforms traditional algorithms in terms of adaptability, responsiveness, and reliability. In particular, reinforcement learning proved effective in dynamic environments, learning optimal routes over time and adjusting to disruptions. This research contributes to the development of intelligent transportation systems and supports the broader vision of smart cities, where mobility is safer, faster, and more efficient through the power of AI and real-time data integration.
Co-Authors Ahmad Sanmorino Anson, Adriel Moses Ariyadi Wijaya Arlitha Deka Yana Catur Wahyudi, Catur Desyani Ariza Dewi, Risma Arlia Sinta Efrianto Eko Maulana Syaputra Fadilah, Dina Jihan Faizal, Achmad Fajar Fitriyani, Anisa Gilbreth Pamean Indah, Ni Luh Dian Sundari Janah, Nur Lutfiatul Juli Riyanto Tri Wijaya Karimah, Fitrah Kasmuddin Darmo Ka’ba Paharu Kusuma, I Made Ari Mapparessa, Nurlaela Mei Rani Amalia Mohammad Arridho Nur Amin Muh Sainal Abidin Muttaqin, Ibnu Najmalia, Risa Nurun Parwati, Ni Made Suwitri Putri Salsabila, Nabila Romadhon, Istiqomah Salsabila, Amelia Azhar Santi Santi Sarinah Basri K Sri Murdiati Sri Murdiati Sri, Diyana sukhriyatun fitriyah Susilawati, Agnes Dwita Syaputra, Eko Maulana Tabrani Tanra, Andi Ainil Mufidah Titi Purnama Totanan, Chalarce Tri Sulistyani Tubety, Ari Yanasari, Dita Yudistira Yuni Utami Yuniarti Herwinarni Zahra, Y