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All Journal Jurnal Informatika Al-Ashr : Jurnal Pendidikan dan Pembelajaran Dasar
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EFEKTIFITAS MODEL PEMBELAJARAN TUTORIAL DALAM BACA KITAB KUNING: Studi Kasus di Pondok Pesantren Al-Bidayah Tegal Besar-Kaliwates-Jember N
Al-Ashr : Jurnal Pendidikan dan Pembelajaran Dasar Vol 3 No 2 (2018): September
Publisher : PGMI Fakultas Tarbiyah Universitas Islam Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (364.946 KB)

Abstract

Sampai saat ini, dalam kegiatan pembelajarannya, Pesantren dan kitab kuning adalah dua sisi yang tak terpisahkan dalam keping pendidikan Islam di Indonesia, bahkan tanpa keberadaan danpengajaran kitab kuning, suatu lembaga pendidikan tak absah disebut pesantren. Untuk menjadikan pesantren tetap sebagai pusat kajian keislaman, pemeliharaan dan kitab kuning tetap dijadikan ciriutamanya. Oleh karena itu begitu pentingnya efektifitas pembelajaran tutorial ini diterapkan di pondok pesantren AL-Bidayah Tegal BesarJember. sebagai wujud dan upaya untuk bagaimana supaya santrisantrinya dengan model tutorial ini mampu dan bisa untuk menguasai dan memahami kitab kuning yang sudah ada yang selayaknya sudah di karang oleh para ulama’ salafun sholeh
Model Pelacakan Objek Berbasis YOLO-OCSort Untuk Estimasi Kepadatan Lalu Lintas Linda, Deppi; N; Agus, Isnandar
Jurnal Informatika Vol 25 No 1 (2025): Jurnal Informatika
Publisher : Institut Informatika Dan Bisnis Darmajaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30873/jurnalinformatika.v25i3

Abstract

Accurate traffic density estimation is a crucial component in intelligent transportation systems for optimizing urban trafficmanagement. This research develops an object tracking model based on YOLOv8 and Online Clustering SORT (OCSort) integration for realtime trafficdensity estimation. YOLOv8 is used as a highaccuracy object detector, while OCSort implements a tracking algorithm with observationcentric association approach to overcome conventional tracking limitations in handling nonlinear motion and temporal occlusion. Comprehensive evaluation was conducted using MOT17FRCNN benchmark Dataset for tracking performance measurement and UADETRAC Dataset subset for computational performance analysis. The system was tested on Intel Core i7 Generation 13 hardware, 32 GB RAM, and NVIDIA GeForce RTX 3060 12 GB VRAM. Evaluation results show MOTA of 0.4910, MOTP 0.9072, and IDF1 0.6702 with very low identity switches (7 occurrences) and 65 fragmentations. Comparison with stateoftheart methods using 2001 frames demonstrates significant YOLOOCSort superiority in computational efficiency with best processing time of 118.01 seconds, outperforming ByteTrack (146.98s), BoTSORT (161.91s), and DeepSORT (152.33s). The system achieves endtoend FPS of 17.0, model processing time 33.8 ms per frame, and detects average 20.1 objects per frame with confidence score 0.608. Statistical analysis with 95% confidence interval confirms consistency and reliability of evaluation results. The contribution of this research is empirical validation of YOLOv8OCSort integration effectiveness that produces optimal balance between tracking accuracy and computational efficiency for realtime traffic cmonitoring applications.
Co-Authors Agus, Isnandar Deppi Linda