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All Journal Jurnal Biologi Tropis Journal of Community Development Malcom: Indonesian Journal of Machine Learning and Computer Science
Tisha Melia
Universitas Riau
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Immunology & microbiology Nursing Public Health Social Sciences

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DNA Barcoding Analysis of Betok Fish (Anabas testudineus) from Kampar, Riau Based on Cytochrome Oxidase Subunit I (COI) Rahmah, Miftahul; Hasibuan, Aldy Riau Wansyah; Melia, Tisha; Al Khairi, Hapiz; Roslim, Dewi Indriyani
Jurnal Biologi Tropis Vol. 24 No. 2 (2024): April - Juni
Publisher : Biology Education Study Program, Faculty of Teacher Training and Education, University of Mataram, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jbt.v24i2.7121

Abstract

Ikan betok (Anabas testudineus, Bloch 1792) is one of fishes which is a member of family Anabantidae. Betok is an endemic fish in Riau Province. The scientific study about DNA barcoding of betok fish is still rarely. This research aims to analyze the DNA barcode sequence of cytochrome c oxidase I (COI) on betok fish. Methods were used in this study such as sampling, total DNA extraction, PCR, electrophoresis, sequencing and data analysis. The COI sequence studied had size of 694 bp. The BLASTn analysis showed that the betok fish had the highest similarity of 99.7% with A. testudineus and the lowest 93.00% with A. cobojinus. There was one nucleotide that characterize the betok fish based on the COI sequence namely nucleotide number 309. This study may enrich the DNA barcode database of betok fish in GeneBank.
Manual ke Digital: Revitalisasi Pelayanan Publik di Kelurahan Sungai Pakning Al Aminuddin; Sukamto Sukamto; Ibnu Daqiqil; Tisha Melia; Sonya Meitarice; Rahmat Hidayat
Journal of Community Development Vol. 5 No. 3 (2025): April
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/comdev.v5i3.1469

Abstract

Sungai Pakning Village Office has 12 village officials who are tasked with providing services to the people of Sungai Pakning Village, Bengkalis Regency. Public services carried out by the Sungai Pakning Village Government are currently still manual. The Sungai Pakning Village assistance office has not utilized information and communication technology in providing services to the community. The absence of the application of information and communication technology is a weakness that must be addressed by the Sungai Pakning Village Government. The purpose of this community service activity is to digitize village administration services carried out in Sungai Pakning Village using OpenSID which is developed openly (open source) by the Opendesa Community. This activity begins with a location survey, problem identification, implementation, socialization, and ends with an evaluation. This community service activity produces a website that can be used by the Sungai Pakning Village Apparatus in providing digital-based services to the community. The survey results produced socialization activities related to the suitability of the website, namely 37% very appropriate/clear, 60% appropriate/clear, 2% hesitant, and 1% quite appropriate/clear. This shows that participants can accept the community service activities that have been carried out.
Residential and Commercial Building Classification Based on Street-Level Images Using Deep Learning Atsari, Najmi Fadhila; Melia, Tisha; Sihombing, Aland Polma Naek; Fatayat, Fatayat
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 3 (2026): MALCOM July 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i4.2676

Abstract

The classification of buildings into residential and commercial categories is essential for computer vision applications that support urban analysis and automated decision-making. Accurate identification of building functions from visual data enables downstream applications, such as service differentiation and resource allocation. However, manual image-based classification is inefficient and subjective, particularly when applied to large-scale datasets. This study proposes a deep learning approach to classifying residential and commercial buildings using street-level images. A dataset comprising 882 images was collected from publicly available sources and categorized based on visual characteristics. The dataset was divided into training, validation, and test sets in a 60:20:20 ratio. The preprocessing stages included data cleaning, labeling, cropping, and resizing. Two models were evaluated: a conventional Convolutional Neural Network (CNN) and MobileNetV2 with transfer learning. Model performance was optimized by tuning the batch size and learning rate. The experimental results show that MobileNetV2 achieved the best performance with a batch size of 32 and a learning rate of 0.0001, attaining an accuracy of 92.05% and precision, recall, and F1-score values of 91.46%. Evaluation using a confusion matrix indicated low misclassification rates both building categories. These results demonstrate that deep learning models can effectively classify buildings based on visual data.
Co-Authors Al Aminuddin Al Khairi, Hapiz Aland Polma Naek Sihombing Atsari, Najmi Fadhila Dewi Indriyani Roslim Fatayat, Fatayat Hasibuan, Aldy Riau Wansyah Ibnu Daqiqil Miftahul Rahmah Rahmat Hidayat Sonya Meitarice Sukamto, Sukamto