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All Journal JURNAL PENJAKORA International Journal Software Engineering and Computer Science (IJSECS)
Fikri, M
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Computer Science & IT Education

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MINAT SISWA TERHADAP PEMBELAJARAN PENDIDIKAN JASMANI Nazirun, Novia; Gazali, Novri; Fikri, M
JURNAL PENJAKORA Vol 6, No 2 (2019): September 2019
Publisher : Undiksha Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/penjakora.v6i2.20898

Abstract

Tujuan penelitian ini adalah untuk mengetahui minat siswa terhadap pembelajaran Pendidikan Jasmani diSMPYLPI Pekanbaru. Jenis penelitian ini adalah deskriptif kuantitatif. Populasi penelitian ini siswa putriSMPYLPI Pekanbaru yang berjumlah 65 siswa.Teknik sampel adalah sampel jenuh dengan jumlah sampel 65 siswa. Indikator minat belajar dalam angket terbagai menjadi 4 indikator yaitu : 1) Perasaan senang, 2) ketertarikan siswa, 3) Perhatian, 4) keterlibatan siswa. Jumlah pernyataanawal dirancang 40 butir pernyataan dan setelah uji validitas menjadi 32 butir pernyataan. Hasil penelitian ini adalah minat siswa terhadap pembelajaran Pendidikan Jasmani diSMPYLPI Pekanbaru dengan rata-rata 76% dengan kategori kuat.
Image Segmentation of East OKU Script Using the Bounding Box Method for Cultural Heritage Digitization Fikri, M; Yadi, Ilman Zuhri; Kunang, Yesi Novaria; Abdillah, Leon Andretti
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 2 (2025): AUGUST 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i2.4045

Abstract

East Ogan Komering Ulu (OKU) is distinguished by its cultural heritage, which encompasses historical artifacts such as traditional houses, crafts, and ceremonial dances. Among the most significant cultural assets are relics inscribed with ancient scripts, including Pallawa and Ulu, which offer valuable insight into the region’s historical literacy. The present study addresses the segmentation of OKU Timur script images through the Bounding Box method. This approach was selected based on its practicality and efficiency, particularly in the context of datasets where script characters exhibit straightforward forms and the overall data volume remains manageable. The segmentation process utilizes Python within the Google Colaboratory platform, ensuring accessible and reproducible workflows. Accurate segmentation is essential to support ongoing digitization and preservation of cultural scripts. The methodology involves gathering data from local artifacts, converting images to binary format, and isolating characters using Bounding Boxes. The results demonstrate that the method effectively separates individual script characters, laying the groundwork for dataset development and subsequent image classification tasks.
Co-Authors Gazali, Novri Ilman Zuhri Yadi Leon Andretti Abdillah Nazirun, Novia Yesi Novaria Kunang