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All Journal International Journal of Electrical and Computer Engineering Sistemasi: Jurnal Sistem Informasi JURNAL ILMIAH MATEMATIKA DAN TERAPAN JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vygotsky: Jurnal Pendidikan Matematika dan Matematika Jurnal Pengabdian Masyarakat Bhinneka Transcendent Journal of Mathematics and Applications Jurnal Pengabdian Farmasi dan Sains
Abdul Mahatir Najar
Tadulako University
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Immunology & microbiology Mathematics Medicine & Pharmacology Physics Public Health

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Journal : Transcendent Journal of Mathematics and Applications

 1 
Implementasi Pengolahan Citra dan Machine Learning Untuk Klasifikiasi Jenis Penyakit Pada Daun Padi Najar, Abdul Mahatir; Abu, Maulidyani; Resnawati, Resnawati; Syahrullah, Syahrullah
Transcendent Journal of Mathematics and Applications Vol 3, No 1 (2024)
Publisher : Syiah Kuala University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/tjoma.v3i1.38642

Abstract

Identification of diseases from images of plants is one of the interesting research areas in the agriculture field, for which machine learning concepts from the computer science field can be applied. This article presents a prototype system for the detection and classification of rice diseases based on images of infected rice plants. This prototype system was developed after detailed experimental analysis of various techniques used in image processing operations. We consider three rice plant diseases: Bacterial Leaf Blight, Blast, and Tungro. We used the Otsu method to remove the background. To enable accurate extraction of features, we combined Gabor and Sobel techniques. In the classification process, we used five machine learning techniques: Random Forest (RF), Support Vector Machine (SVM), Nave Bayes (NB), and Quadratic Discriminant Analysis (QDA). We empirically evaluated these methods, achieving 77%, 50%, 60%, and 37% accuracy, respectively.
Identification of Maleo (Macrocephalon Maleo) and Gosong Kaki Merah (Megapodius Reindwardt) DNA Similarity Level Using Needleman-Wunsch Algorithm Rusmi, Rusmi; Ratianingsih, Rina; Najar, Abdul Mahatir
Transcendent Journal of Mathematics and Applications Vol 2, No 2 (2023)
Publisher : Syiah Kuala University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/tjoma.v2i2.34313

Abstract

Sequence Alignment is used to find out the similarity of the sequence of two DNA. One of the alignment algorithms is the Needleman-Wunsch algorithm which is a global alignment algorithm that uses the entire length of the DNA sequence. In this research, the algorithm is applied to a website-based application system to determine the level of similarity of the DNA sequence of Maleo (Macrocephalon Maleo) which is an endemic animal to Sulawesi, with a comparison of Gosong Kaki Merah (Megapodius Reinwardt) which has a wide global distribution. The results of the alignment of Maleo (Macrocephalon Maleo) and Kaki Merah (Megapodius Reinwardt) DNA sequences on a website-based application system have an average similarity level of 83.39% and an average gap value of 8.42%.
Co-Authors Aan Bin Mardi Agus Indra Jaya Agusman Sahari Alfiani Andri Andri Deny Wiria Nugraha Dwi Shinta Angreni Dwimanhendra, Muhammad Rifaldi Fadjryani, Fadjryani Gamayanti, Nurul Fiskia Hajar Hajra Rasmita Ngemba Hayyan Ahmad Ulul Albab I Wayan Sudarsana Iwan Setiawan Juni Wijayanti Puspita Maulidyani Abu Maulidyani Abu Ningrum, Desy Riani Sukma Nouval Trezandy Lapatta Nurul Ikrima RACHMAT HIDAYAT Resnawati Rina Ratianingsih Rosiyadi, Didi Rusmi, Rusmi Ryfial Azhar, Ryfial Sultan Andhika Syahrullah Syahrullah Syaiful Hendra Yuri Yudhaswana Joefrie