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All Journal JBIO: jurnal biosains (the journal of biosciences) Jurnal Pelita Pendidikan Jurnal Pendidikan Fisika JOURNAL OF APPLIED INFORMATICS AND COMPUTING Pendas : Jurnah Ilmiah Pendidikan Dasar
Tri Harsono
EEPIS ITS
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Education Immunology & microbiology Medicine & Pharmacology Physics Social Sciences Other

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Deteksi Microaneurysm Pada Mata Sebagai Langkah Awal Untuk Penentuan Diabetic Retinophaty Menggunakan Pengolahan Citra Digital Anisa Habsari; Tri Harsono; Heny Yuniarti; Rita Tjandra
Journal of Applied Informatics and Computing Vol 5 No 2 (2021): December 2021
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v5i2.3302

Abstract

Diabetic Retinopathy is a microvascular complication of diabetes mellitus. According to WHO (World Health Organization), there are more than 347 billion people who suffer from diabetes. This disease will become the seventh leading cause of death in the world in 2030. Based on research in Indonesia, it is estimated that there are 42.6% of diabetic retinopathy. Therefore, this final project plans a system to assist doctors in identifying diabetic retinopathy through its characteristics, namely microaneurysm. This system begins with an input retinal image from the fundus camera. Then the input will be processed in preprocessing to increase the contrast using the green channel. The next stage is segmentation. This is used to detect candidates from blood vessels and microaneurysms that use morphology operations. The next step is feature extraction, where it uses the features of glcm and white pixels detected in the image resulting from segmentation. The value of the white pixels and the values in the glcm feature are used as parameters in determining whether the classification process will be used as a determination of a Diabetic Retinopathy image or not. The success rate of the system using the SVM (Support Vector Machine) method is 88.4%.
Analisis Miskonsepsi Siswa Kelas X pada Materi Virus Menggunakan Tes Diagnostik Multiple Reasoned Choice di Smas Nurul Hasanah Tembung Renata Devi Pardede; Tri Harsono
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 11 No. 03 (2026): Volume 11 Nomor 03, September 2026 Completed
Publisher : Program Studi Pendidikan Guru Sekolah Dasar FKIP Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/jp.v11i03.63305

Abstract

This study aimed to analyze Grade X students’ misconceptions on virus material using a Multiple Reasoned Choice diagnostic test at SMAS Nurul Hasanah Tembung in the 2025/2026 academic year. The study employed a quantitative descriptive method involving 36 students as the research sample. Data were collected through a Multiple Reasoned Choice diagnostic test, questionnaires on misconception factors, and documentation. Data analysis was conducted by calculating the percentage of students’ conceptual understanding categories, namely understanding concepts, misconceptions, and lack of understanding. The results showed that the average percentage of students who understood the concepts was 84%, while 8% experienced misconceptions and 8% did not understand the concepts. The highest misconceptions were found in indicators related to analyzing lytic and lysogenic cycles, identifying virus structures, and analyzing stages of virus assembly. The major factors causing misconceptions were teaching methods at 84.44%, contextual errors at 81.11%, student factors at 79.31%, textbooks at 72.78%, and teachers at 54.44%. The study concluded that most students had understood virus material well, although misconceptions still occurred in several important concepts. Therefore, more innovative and interactive learning strategies are needed to minimize students’ misconceptions.
Co-Authors Anggun Hutagaol Anisa Habsari Anwar Indrawati Berutu Carolina Savitry Hutagaol Darusalim Darusalim Derlina , Duma Duma Heni Nurdianti Pratama Heny Yuniarti Herawati Herawati Joke Pratilastiarso Kharina areeisty Masdiana Masdiana Renata Devi Pardede Rifkah Nafthali Tarigan Rita Tjandra Rully Soelaiman Sabani . Suci Anggraeni Ummi Rapikah Yusriati Yusriati