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All Journal PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Journal of Information Technology
Ahmad Hasan Faqih Aulia
IPB University
Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

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Perancangan Sistem Layanan Pengaduan Masyarakat Perumnas 2 Kota Bekasi Berbasis Web Ardien Ferdinand Putra Setiawan; Ahmad Hasan Faqih Aulia; Ridho Putra Pratama; Fachrizal Wisnu Pratama; Ahmad Ridha
PROSISKO: Jurnal Pengembangan Riset dan Observasi Sistem Komputer Vol. 12 No. 3 (2025): Prosisko Vol. 12 No. 3 November 2025
Publisher : Pogram Studi Sistem Komputer Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/prosisko.v12i3.10760

Abstract

Pengaduan masyarakat merupakan bagian penting dalam mewujudkan pelayanan publik yang transparan, cepat, dan akuntabel sebagaimana diatur dalam Undang-Undang Nomor 25 Tahun 2009 tentang Pelayanan Publik. Namun, di wilayah Perumnas 2 Kota Bekasi, mekanisme pengaduan masih secara konvensional, sehingga berisiko terhadap tidak terdokumentasinya informasi dan lambatnya tindak lanjut. Penelitian ini bertujuan untuk merancang dan membangun sistem layanan pengaduan masyarakat berbasis web yang dapat memfasilitasi penyampaian aspirasi secara lebih efektif, efisien, dan terdokumentasi. Pengembangan sistem menggunakan metode Waterfall, dengan Laravel sebagai framework utama, MySQL sebagai database, dan Tailwind CSS untuk antarmuka pengguna. Penerapan sistem ini diharapkan mendukung implementasi Sistem Pemerintahan Berbasis Elektronik (SPBE) sesuai Peraturan Presiden Nomor 95 Tahun 2018, serta meningkatkan kualitas layanan publik di lingkungan Perumnas 2 Kota Bekasi. Hasil dari implementasi ini menunjukkan bahwa sistem berhasil memfasilitasi pengaduan secara digital, mempercepat respons, serta mendokumentasikan aduan dengan baik dan terstruktur.
People Counting in Sample Video Footage Using CNN Integrated with YOLOv5 Ahmad Hasan Faqih Aulia; Carissa Fathinah Balti; Keisyah Zahra Anatasya; Gema Parasti Mindara; Endang Purnama Giri
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1933

Abstract

Accurate people counting in dynamic environments remains challenging due to variations in lighting, complex backgrounds, and occlusion. This study proposes a video-based people counting system leveraging a Convolutional Neural Network (CNN) integrated with the YOLOv5 object detection model. The system applies a structured preprocessing pipeline, including frame extraction, normalization, and noise reduction, to enhance data consistency before detection. The model was evaluated using ten real-world campus video sequences to assess detection reliability and counting accuracy. Experimental results demonstrate that the proposed method achieves high precision and recall for real-time detection across diverse scenarios. Performance degradation was observed in frames containing dense crowds or low illumination, indicating limitations under extreme conditions. These findings validate the feasibility of lightweight CNN-based detectors for surveillance and monitoring applications, while highlighting the need for larger datasets and optimized training strategies to improve robustness in more complex environments.
Development of an Integrated Web-Based Academic and Administrative Information System with E-Learning and Student Interest Recommendation Ahmad Hasan Faqih Aulia; Muhammad Raihan Zaldiputra; Ardien Ferdinand Putra Setiawan; Ridho Putra Pratama; Sofiyanti Indriasari; Medhanita Dewi Renanti
J-INTECH ( Journal of Information and Technology) Vol 14 No 02 (2026): Journal of Information and Technology
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v14i02.2323

Abstract

The advancement of information technology in the education sector has driven the need for digital transformation in managing academic and administrative processes, including in Early Childhood Education (ECE) institutions. However, many ECE institutions, such as BKB PAUD Bougenville, still rely on manual and fragmented systems for managing student data, attendance, assessments, and learning materials, which often leads to inefficiencies, data inconsistencies, and delays in information processing. This study aims to develop an integrated web-based academic and administrative information system that incorporates e-learning features and student interest recommendations to support more effective and structured educational management. The system is developed using the Agile methodology, which emphasizes iterative development, continuous user involvement, and adaptive improvements based on user feedback to ensure alignment with real operational needs. The proposed system integrates several core modules, including student data management, attendance recording, e-learning through digital materials (e-books), assessment processing, and a data-driven student interest recommendation feature. The implementation results indicate that the system is able to improve data management efficiency, facilitate real-time access to academic and administrative information, and reduce the risk of data inconsistencies. In addition, the interest recommendation feature provides practical benefits by assisting teachers in identifying student potential and supporting more informed decision-making processes. Therefore, this research contributes to the development of an integrated, user-friendly, and adaptive digital platform that enhances the quality of academic and administrative services in the PAUD environment.
Komparasi Arsitektur Densenet121, MobilenetV2, dan Resnet50 Untuk Klasifikasi Awan Menggunakan Transfer Learning Yashin Al Fauzy Sabara; Carissa Fathinah Balti; Ahmad Hasan Faqih Aulia; Dodik Ariyanto; Faldiena Marcelita; Mayanda Mega Santoni
BETRIK Vol. 17 No. 02 (2026): Jurnal Ilmiah BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : PPPM Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/6hwf0s96

Abstract

Cloud image classification plays an important role in supporting atmospheric analysis and the development of artificial intelligence-based weather prediction systems. However, visual similarities among cloud types and variations in lighting conditions remain major challenges in automatic classification tasks. This study aims to compare the performance of three Convolutional Neural Network (CNN) architectures, namely DenseNet121, MobileNetV2, and ResNet50, for cloud image classification using a transfer learning approach. The dataset used in this study was the TJNU Ground-based Cloud Dataset, consisting of seven cloud classes with a total of 19.000 images; after removing the Mixed Clouds category with the total of 18.047 images; after removing. The research stages included pre-processing, data augmentation using the Color Jitter technique, model training through feature extraction and fine-tuning, and evaluation using Accuracy, Precision, Recall, and F1-Score metrics. Experimental results showed that ResNet50 achieved the best performance with an accuracy of 90,58% and an F1-score of 91,38%, followed by DenseNet121 with 89,53% accuracy and MobileNetV2 with 85,10% accuracy. In addition to obtaining the highest classification performance, ResNet50 also demonstrated good computational efficiency with the fastest training time of 20.9 minutes. These findings indicate that architectures based on residual learning are more effective in capturing the visual characteristics of cloud images compared to the other architectures evaluated in this study.
Co-Authors Ahmad Ridha Ardien Ferdinand Putra Setiawan Carissa Fathinah Balti Dodik Ariyanto Endang Purnama Giri Fachrizal Wisnu Pratama Gema Parasti Mindara Keisyah Zahra Anatasya Marcelita, Faldiena Mayanda Mega Santoni Medhanita Dewi Renanti Muhammad Raihan Zaldiputra Ridho Putra Pratama Sofiyanti Indriasari Yashin Al Fauzy Sabara