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All Journal BERKALA FISIKA Jurnal Harpodon Borneo JURNAL FISIKA Sainteknol : Jurnal Sains dan Teknologi Jurnal Online Mahasiswa (JOM) Bidang Teknik dan Sains International Journal of Advances in Intelligent Informatics Jurnal Pendidikan Biologi Indonesia Civis: Jurnal Ilmiah Ilmu Sosial dan Pendidikan ETNOSIA : Jurnal Etnografi Indonesia Chemistry in Education JOIV : International Journal on Informatics Visualization Pi: Mathematics Education Journal Jurnal Rekayasa Material, Manufaktur & Energi Jurnal Organisasi Dan Manajemen ILKOM Jurnal Ilmiah Prosiding Seminar Nasional Sains dan Teknologi Terapan IKRA-ITH ABDIMAS Journal of Information Systems and Informatics Jurnal Pendidikan dan Kewirausahaan International Journal of Electrical, Energy and Power System Engineering (IJEEPSE) Jurnal Penelitian Fisika dan Terapannya (JUPITER) Indonesian Journal of Data and Science Dinasti International Journal of Economics, Finance & Accounting (DIJEFA) International Journal of Engineering, Science and Information Technology Best : Journal of Applied Electrical, Science and Technology Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (Senastindo) Brilliance: Research of Artificial Intelligence JURNAL PENDIDIKAN INDONESIA: Teori, Penelitian, dan Inovasi Sapientia Humana : Jurnal Sosial Humaniora Pi: Mathematics Education Journal Room of Civil Society Development Jurnal Ilmiah Ekonomi, Manajemen dan Syariah Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS Ramatekno Jurnal Penelitian Pendidikan Indonesia International Journal of Artificial Intelligence in Medical Issues Journal Of Public Policy (Social Politics) Sciences (Polisci) TeknoKreatif: Jurnal Pengabdian kepada Masyarakat PASAI : Jurnal Pengabdian kepada Masyarakat Room of Civil Social Development SIMPUL: Jurnal Ilmu Politik dan Hukum Jurnal Ilmu Kesehatan
Rudi Setiawan
Magister Ilmu Fisika,Universitas Diponegoro
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Journal : Indonesian Journal of Data and Science

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Rice Leaf Disease Classification with Machine Learning: An Approach Using Nu-SVM Setiawan, Rudi; Zein, Hamada; Azdy, Rezania Agramanist; Sulistyowati, Sulistyowati
Indonesian Journal of Data and Science Vol. 4 No. 3 (2023): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v4i3.114

Abstract

This study explores the application of machine learning for classifying rice leaf diseases, employing the Nu-Support Vector Machine (Nu-SVM) algorithm, analyzed through a 5-fold cross-validation approach. The research focuses on distinguishing between healthy leaves and those affected by BrownSpot and LeafBlast diseases. The dataset, comprising segmented rice leaf images processed using Sobel edge detection and Hu Moments feature extraction, is utilized to train and test the model. Results indicate a moderate level of accuracy (52.12% to 53.81%) across the cross-validation folds, with precision and recall metrics demonstrating variability and highlighting the challenges in precise disease classification. Despite this, the model maintains a consistent ability to identify true positives. The study contributes to the field of precision agriculture by showcasing the potential and limitations of using machine learning for plant disease diagnosis. It underscores the need for advanced image processing techniques and diverse feature extraction methods to enhance model performance. The findings are pivotal for developing more effective, automated diagnostic tools in agriculture, thereby aiding in better disease management and potentially improving crop yields. This research serves as a foundational step towards integrating machine learning in agricultural disease detection, emphasizing its importance in sustainable farming practices.
Classification of Rice Grain Varieties Using Ensemble Learning and Image Analysis Techniques Setiawan, Rudi; Hayatou Oumarou
Indonesian Journal of Data and Science Vol. 5 No. 1 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i1.129

Abstract

This research explored the efficacy of machine learning techniques, specifically the Bagging meta-estimator, in the classification of rice grain images. Utilizing a dataset composed of 45,000 images of Arborio, Basmati, and Jasmine rice varieties, a 5-fold cross-validation was employed to evaluate the model's performance. The results were highly promising, with the model consistently achieving over 96% in accuracy, precision, recall, and F1-score across all folds, indicating its robustness and reliability. The study confirmed that ensemble learning techniques could significantly improve the classification accuracy over single classifier systems in agricultural applications. The findings offer a significant contribution to automated agricultural processes, suggesting that machine learning can greatly enhance the efficiency and precision of rice variety classification. These results pave the way for further research into the integration of such models into agricultural quality control and provide a foundation for the exploration of advanced image processing and deep learning techniques for improved performance. Future research directions include expanding the model to encompass a wider variety of crops and integrating additional data modalities to refine classification accuracy further. Practical applications should explore the incorporation of this technology into existing agricultural systems to maximize the benefits of automation in quality control.
Co-Authors Adi Pamungkas Agustiany, Fifin Arifiani Aisyah Rahmawati Andreas N, R Djoko Ani Haryani, Ani Aprianti, Penti Arif Setiawan , Andi Aris Wahyu Murdiyanto Aritonang, Sovian Asep Ahmad Sopandi Asnani Asnani Aulia Safitri, Aulia Azdy, Rezania Agramanist Azzahra, Haliza Bartolomeus Samho Bartoven Vivit Nurdin Bernard Dehaan, Yeovan Naufal Chairunnisa, Devina Azzahra Cristofer, Fernando Daulay, Okky Freeza Dede Hertina Dedet Erawati Dermawijaya, Boyke Iskandarsyah Dewi, Fitria Dewi, Manda Iska Eddy, Syaiful Elsa Efrina Elvi Yenie, Elvi Enggar Alfianto Etty Puji Lestari Fahruzi, Akhmad Fatmawati, Fatmawati Grahita Kusumastuti, Grahita Hakim Santoso, Abdul Hamada Zein Hari Agus Sujono Hartono, Ahmat Dwi Hayatou Oumarou Heri Sutanto Herninda, Amalia Revy I Ketut Sutapa Ida Zahrina Imam, Asep Indiriani, Dini Irawan, Ghani Rafif Irwansyah, Defi Iskandar Mirza Istiqomah, Hilmi Aulia Janizal Jarwinda, Jarwinda Jayawarsa, A.A. Ketut Juhana, Riyadi Kadir Parewe, Andi Maulidinnawati Abdul Kholida, Putri Kisman, Zainul Laksono, Muharram Budi Latipah, Asslia Johar Madi, Madi Margianto, Tofik Marlina Marlina Mashud, Mustain Mega Iswari Nuhan, Hudan Khalish Nurhasan Nugroho Nurwijayanti Oscar Yasunari Pambudi, Wisnu Setyo Pratama, Gerald Untirtha Priyanto, Rachmat Puji Astuti, Nur Rochmah Dyah Purwoko, Agus Putra, Fajri Profesio Putri, Sherlyana Hardiyandeffi Rahmadi, Isnaini Rahmahtrisilvia Rahmahtrisilvia Rahman, Titik Khawa Abdul Ramayanti, Rizka Rio Febrianto Arifendi, Rio Febrianto Ronny Durrotun Nasihien, Ronny Durrotun Rozak, Abd Rumondang, Rumondang Sabar Sabar Sdarmin Sudarmin, Sdarmin Setiawan, Cahya Heru Siagian, Apriansyah Dharmawan Siburian, Marsudi siregar, umaiyu Siti Khumayah Sparingga, Daniel Sri Ernawati Sulistyowati Sulistyowati Sumarmi Sunarno Sunarno Supriyadi Supriyadi Supriyanto, Adolf Asih Susilo Susilo Sutikno Sutikno Syahputra, Ahmad Reynaldi Syahrizal Nasution Syaripudin Syaripudin Thahyo Subroto, Thahyo Umar Al Faruq Wahyuningtyas, Amalia Wibowo, Nanang Roni Widodo YOSAN, R BAGUS Yoseptry, Ricky Yulianti, Wayan Rifa Zaenal Arifin Zulmiyetri Zulmiyetri