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All Journal CogITo Smart Journal CSRID (Computer Science Research and Its Development Journal) Jurnal Ilmiah Sinus Jurnal Abdimas Mahakam Indonesian Journal of Data and Science International Journal of Social Science, Educational, Economics, Agriculture Research, and Technology (IJSET) Teknik: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Jurnal Ilmiah Dan Karya Mahasiswa International Journal of Artificial Intelligence in Medical Issues Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika
Hamada Zein
Universitas Muhammadiyah Kalimantan TImur
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Public Health Social Sciences

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Journal : Indonesian Journal of Data and Science

 1 
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.
Advancements in Agricultural Automation: SVM Classifier with Hu Moments for Vegetable Identification Waluyo Poetro, Bagus Satrio; Maria, ⁠⁠Eny; Zein, Hamada; Najwaini, Effan; Zulfikar, Dian Hafidh
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.v5i1.123

Abstract

This study investigates the application of Support Vector Machine (SVM) classifiers in conjunction with Hu Moments for the purpose of classifying segmented images of vegetables, specifically Broccoli, Cabbage, and Cauliflower. Utilizing a dataset comprising segmented vegetable images, this research employs the Canny method for image segmentation and Hu Moments for feature extraction to prepare the data for classification. Through the implementation of a 5-fold cross-validation technique, the performance of the SVM classifier was thoroughly evaluated, revealing moderate accuracy, precision, recall, and F1-scores across all folds. The findings highlight the classifier's potential in distinguishing between different vegetable types, albeit with identified areas for improvement. This research contributes to the growing field of agricultural automation by demonstrating the feasibility of using SVM classifiers and image processing techniques for the task of vegetable identification. The moderate performance metrics emphasize the need for further optimization in feature extraction and classifier tuning to enhance classification accuracy. Future recommendations include exploring alternative machine learning algorithms, advanced feature extraction methods, and expanding the dataset to improve the classifier's robustness and applicability in agricultural settings. This study lays a foundation for future advancements in automated vegetable sorting and quality control, offering insights that could lead to more efficient agricultural practices.
Classification of Mushroom Edibility Using K-Nearest Neighbors: A Machine Learning Approach Admojo, Fadhila Tangguh; Radhitya, Made Leo; Zein, Hamada; Naswin, Ahmad
Indonesian Journal of Data and Science Vol. 5 No. 3 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i3.199

Abstract

This study investigates the use of the K-Nearest Neighbors (KNN) algorithm for the binary classification of mushroom edibility using a cleaned version of the UCI Mushroom Dataset. The dataset underwent pre-processing techniques such as modal imputation, one-hot encoding, z-score normalization, and feature selection to ensure data quality. The model was trained on 80% of the dataset and evaluated on the remaining 20%, achieving an overall accuracy of 99%. Evaluation metrics, including precision, recall, and F1-score, confirmed the model's effectiveness in distinguishing between edible and poisonous mushrooms, with minimal misclassification errors. Despite its high performance, the study identified scalability as a limitation due to the computational complexity of KNN, suggesting that future research should explore alternative algorithms for enhanced efficiency. This research underscores the importance of pre-processing and hyperparameter optimization in building reliable classification models for food safety applications.
Co-Authors Abdul Rahim Achmad Farid Adia Lestari Admojo, Fadhila Tangguh Angriani, Husni Any Sawheri Gading Arbansyah Ari Ahmad Dhani Aris Wahyu Murdiyanto Aulia Khofifah Syamsuri Azdy, Rezania Agramanist Azelina Zahra Riadini B, Isdayani Bima Satria Bobi Zinaidin Zidan Bulan Suci Cahayawati Dian Hafidh Zulfikar Effan Najwaini Erfan Tongalu Fajri Harits Muzaki Faldy Alfareza Pambudi Farhan Akbar Febri Ananda Chairi Ferdi Iwanda Fitriyati Agustina Hery Kurniawan Highness Mailani Putri Ilhan Firanda izmy alwiah musdar Jimmy H Moedjahedy Joni Saputra Khusnul Khotimah Kusrini Kusrini Kusrini, Kusrini Lidya Sari Lilis Sagita M. Syukri Mustafa Manda Rela Istiantoko Maria, ⁠⁠Eny Melisa Nur Aini Muhamad Wahyu Tirta Muhammad Aditya Pratama Muhammad Fariz Ijlal Rafi Muhammad Khumaidi Nursyarif Muhammad Taufiq Sumadi Mukminatul Munawaroh Naswin, Ahmad Novia Hidayati Ramadhani Poetro, Bagus Satrio Waluyo Prasetio, Dimaz Arno Priyambada, Galih Radhitya, Made Leo Raenald Syaputra Raihan Nabil Reyka Luna Karalo Ridha Anisa Soldzu Parnga Ririn Wahyuni Rita Yulfani Rudi Setiawan Seftiani Nur Sigiet Haryo Pranoto Sinra, A. Siti Muawwanah Siti Patimah Siti Puspita Hida Sakti MZ Sri Mar’ati Sholikhah Sulistyowati Sulistyowati Supriatin Supriatin Tarigan, Thomas Edyson Taruk, Medi Tri Wahyudi Vito Junivan Rivaldo Widya Sayekti