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All Journal METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Jurnal Publikasi Manajemen Informatika
Hasibun Asikin
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Computer Science & IT

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SISTEM PENDUKUNG KEPUTUSAN PENILAIN KINERJA GURU MENGGUNAKAN METODE MOORA PADA SD KATOLIK KRISTUS RAJA BAUN Batista Farani Derang; Meliyana Oktavia Meo; Hasibun Asikin
Jurnal Publikasi Manajemen Informatika Vol. 5 No. 1 (2026): JURNAL PUBLIKASI MANAJEMEN INFORMATIKA
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jupumi.v5i1.4647

Abstract

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IMPLEMENTASI TRANSFER LEARNING DENGAN FINE-TUNING PADA DETEKSI OBJEK MULTI-KELAS MENGGUNAKAN YOLO (STUDI KASUS CAR FREE DAY JALAN EL TARI KOTA KUPANG) Hendrikus Samuel Ola Sogen; Erna Rosani Nubatonis; Hasibun Asikin
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 12 No. 2 (2026): Volume 12 Nomor 2 Tahun 2026
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v12i2.5923

Abstract

Object detection is a computer vision technology used to recognize and determine the location of objects in images or videos. This study aims to implement the transfer learning method with fine-tuning on the YOLOv8m model to detect multi-class objects consisting of persons, vehicles, and umbrellas in the Car Free Day environment on El Tari Street, Kupang City, as well as to develop a web-based object detection system capable of automatically detecting objects in images and videos. The research dataset consisted of 315 images obtained through field documentation and annotated using Roboflow, which was then increased to 757 images through the augmentation process before being divided into training, validation, and testing datasets. The YOLOv8m pretrained model based on the COCO dataset was trained using Google Colab for 100 epochs with the transfer learning and fine-tuning approach. The results showed that the model achieved a precision of 0.903, a recall of 0.801, an mAP50 of 0.868, and an mAP50-95 of 0.625. In addition, the developed web-based system was able to automatically detect objects in images and videos and display bounding boxes, confidence scores, object counts, detection result graphs, and model evaluation metrics. The results of the study indicate that the application of fine-tuning to YOLOv8m is capable of improving the model's adaptability to the characteristics of the local Car Free Day environment, thereby potentially supporting more effective public activity monitoring.
Co-Authors Batista Farani Derang Erna Rosani Nubatonis Hendrikus Samuel Ola Sogen Meliyana Oktavia Meo