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All Journal Speed - Sentra Penelitian Engineering dan Edukasi Jurnal Teknologi Informasi dan Ilmu Komputer JIKO (Jurnal Informatika dan Komputer) Jurnal TAM (Technology Acceptance Model) Abdimas: Jurnal Pengabdian Masyarakat Universitas Merdeka Malang International Journal of Engineering, Technology and Natural Sciences (IJETS) Jurnal Berdaya Mandiri Indonesian Journal of Data and Science SPEED - Sentra Penelitian Engineering dan Edukasi Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Fahma : Jurnal Informatika Komputer, Bisnis dan Manajemen International Journal of Artificial Intelligence in Medical Issues Jurnal TAM (Technology Acceptance Model)
Edi Faizal
Prodi Rekayasa Perangkat Lunak Aplikasi, FTI, Universitas Teknologi Digital Indonesia
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Journal : Indonesian Journal of Data and Science

 1 
A Machine Learning Perspective on Daisy and Dandelion Classification: Gaussian Naive Bayes with Sobel Suhendra, Christian Dwi; Najwaini, Effan; Maria, Eny; Faizal, Edi
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.v4i3.112

Abstract

This study explores the classification of Daisy and Dandelion flowers using a Gaussian Naive Bayes classifier, enhanced by Sobel segmentation and Hu moment feature extraction. The research adopted a quantitative approach, utilizing a balanced dataset of Daisy and Dandelion images. The Sobel operator was employed for image segmentation, accentuating the floral features crucial for classification. Hu moments, known for their invariance to image transformations, were extracted as features. The Gaussian Naive Bayes algorithm was then applied, with its performance evaluated through a 5-fold cross-validation process. The results exhibited moderate accuracy, with the highest recorded at 60%, and precision peaking at 62.60%. These findings indicate a reasonable level of effectiveness in distinguishing between the two species, though variations in performance metrics suggested room for improvement. The study contributes to the field of botanical image classification by demonstrating the potential of integrating image processing techniques with machine learning for flower classification. However, it also highlights the limitations of the Gaussian Naive Bayes approach in handling complex image data. Future research directions include exploring more advanced machine learning algorithms and expanding the feature set to enhance classification accuracy. The practical implications of this research extend to ecological monitoring and agricultural studies, where efficient and accurate plant classification is vital
Evaluating the Performance of Voting Classifier in Multiclass Classification of Dry Bean Varieties Adi Pratama, I Putu; Jullev Atmadji, Ery Setiyawan; Purnamasar, Dwi Amalia; Faizal, Edi
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.v5i1.124

Abstract

This study explores the application of a voting classifier, integrating Decision Tree, Logistic Regression, and Gaussian Naive Bayes models, for the multiclass classification of dry bean varieties. Utilizing a dataset comprising 13,611 images of dry bean grains, captured through a high-resolution computer vision system, we extracted 16 features to train and test the classifier. Through a rigorous 5-fold cross-validation process, we assessed the model's performance, focusing on accuracy, precision, recall, and F1-score metrics. The results demonstrated significant variability in the classifier's performance across different data subsets, with accuracy rates fluctuating between 31.23% and 96.73%. This variability highlights the classifier's potential under specific conditions while also indicating areas for improvement. The research contributes to the agricultural informatics field by showcasing the effectiveness and challenges of using ensemble learning methods for crop variety classification, a crucial task for enhancing agricultural productivity and food security. Recommendations for future research include exploring additional features to improve model generalization, extending the dataset for broader applicability, and comparing the voting classifier's performance with other ensemble methods or advanced machine learning models. This study underscores the importance of machine learning in advancing agricultural classification tasks, paving the way for more efficient and accurate crop sorting and grading processes.
An Optimization Strategy for Reducing CO₂ in Livestock Farming with IoT Integration and Decision Support System Approach Using Linear Programming Shimbun, Annisa Fikria; Alfian, Muhammad Arif; Jati, Agam Saka; Faizal, Edi
Indonesian Journal of Data and Science Vol. 6 No. 1 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i1.204

Abstract

Introduction: Livestock waste mismanagement contributes significantly to CO₂ emissions, adversely affecting animal health and environmental sustainability. This study aims to develop an optimization strategy for reducing CO₂ levels in livestock environments through the integration of Internet of Things (IoT) technology and a Decision Support System (DSS) using Linear Programming. Methods: IoT sensors were deployed to monitor environmental parameters such as CO₂ levels, temperature, and humidity in real time. A Linear Programming (LP) model was formulated to determine the optimal frequency of two CO₂-reducing actions: spraying Effective Microorganisms (EM4) and performing waste dredging. The objective was to maximize CO₂ reduction under cost and time constraints. The model iteratively updated its parameters based on sensor data feedback, ensuring dynamic and adaptive optimization. Results: Simulation results indicated that the LP model successfully identified optimal actions within predefined constraints. The optimal strategy was spraying EM4 eight times over eight days, achieving a CO₂ reduction of 800 ppm with a total cost of Rp 400,000—within the Rp 500,000 budget limit and 8-hour duration constraint. Validation through simulation confirmed the model’s accuracy, with prediction deviations consistently falling within an acceptable threshold (±20 ppm). Conclusions: The integration of IoT with an LP-based DSS offers a practical and efficient solution for CO₂ reduction in livestock farming. This system enhances decision-making for environmental management, demonstrating potential for scalable application in sustainable agriculture. Future work should incorporate more environmental variables and broader validation to improve model generalizability and precision.
Implementation of Support Vector Machine Algorithm for Classification of Study Period and Graduation Predicate of Students Sumiyatun; Cahyadi, Yagus; Faizal, Edi
Indonesian Journal of Data and Science Vol. 6 No. 1 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i1.214

Abstract

Introduction: Accurately predicting the duration of study and graduation predicates in higher education is essential for improving academic outcomes and decision-making. This study aims to classify students’ study period and graduation predicates in the Information Systems program at UTDI using the Support Vector Machine (SVM) algorithm. Methods: A dataset of 500 student records containing academic and demographic variables—including GPA, age, semesters, and graduation predicates—was processed through data cleaning, normalization, and feature selection. Study duration was categorized into three classes: short (≤4 years), medium (4–6 years), and long (>6 years). An SVM with a linear kernel was applied, and the model was evaluated using accuracy, precision, recall, and F1-score. Results: The SVM model achieved perfect classification for study duration, with 100% accuracy, precision, recall, and F1-score across all categories. For graduation predicate classification, the model attained 95.18% accuracy. While it performed well overall, it faced some difficulty distinguishing between "Cum Laude" and "Very Satisfactory" due to overlapping GPA ranges. The analysis identified GPA as the most influential feature in both classification tasks, while age and the number of semesters played supporting roles. Conclusions: The SVM model demonstrates strong capability in classifying study duration and graduation predicates, offering valuable insights for academic management. Although performance was high, especially for study period prediction, further refinement is suggested to enhance classification in overlapping categories. Future work may benefit from larger, more balanced datasets and exploration of advanced models to increase prediction reliability.
Co-Authors Alfian, Muhammad Arif Andhina Ika Sunardi Andhina Ika Sunardi Christian Dwi Suhendra, Christian Dwi Dedi Rosadi Edi Iskandar, Edi Effan Najwaini Gusti Ahmad Syaripudin Hera Wasiati I Putu Adi Pratama Iskandar, Edi Jati, Agam Saka Jullev Atmadji, Ery Setiyawan Khabib Mustofa Kurniyati, Nany Noor Maria, Eny Pulut Suryati Purnamasar, Dwi Amalia Rezania Agramanisti Azdy, Rezania Agramanisti Shimbun, Annisa Fikria Siti Khomsah, Siti Sri Redjeki Sri Setyowati Sudarmanto Sumiyatun Sumiyatun Syam, Rahmat Fuadi Thomas Edyson Tarigan Totok Suprawoto Totok Suprawoto Yagus Cahyadi Yasinta Marto Enga