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All Journal INTECOMS: Journal of Information Technology and Computer Science Jurnal Sains dan Teknologi Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) INFORMASI (Jurnal Informatika dan Sistem Informasi) Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Indonesia : Manajemen Informatika dan Komunikasi Jurnal Pengabdian Nasional (JPN) Indonesia Journal Innovations Computer Science
Poerwandono, Edhy
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Physics Social Sciences Other

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Journal : Journal Innovations Computer Science

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Optimization of SMOTE Application for Classification Accuracy of Heart Disease Risk Using Artificial Neural Network Ibnu Sarky, Fauzan; Poerwandono, Edhy
Journal Innovations Computer Science Vol. 4 No. 2 (2025): November
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i2.302

Abstract

Heart disease remains a leading cause of mortality worldwide, including in Indonesia, and is often difficult to detect at an early stage. One of the main challenges in the Indonesian healthcare system is the lack of fully digitalized data management and the issue of imbalanced patient datasets, which reduce classification accuracy. This study developed a web-based information system designed to manage patient records and automatically classify heart disease risk. The system was implemented using the CodeIgniter framework with a MySQL database, and applied an Artificial Neural Network (ANN) in combination with the Synthetic Minority Over-sampling Technique (SMOTE) to address data imbalance. A total of 60 secondary patient records were processed through preprocessing, data balancing, model training, and cross-validation. Experimental results demonstrated that the application of SMOTE improved model sensitivity, with performance metrics of 87.4% accuracy, 85.2% precision, 88.6% recall, and an AUC-ROC of 0.94. These findings confirm that integrating ANN and SMOTE into a web-based system enhances classification reliability and supports faster medical decision-making. However, the study also acknowledges certain limitations, including the restricted dataset size and the absence of validation in real clinical environments. Future work should expand the dataset, test the system in healthcare facilities, and compare performance with other algorithms such as Random Forest or SVM to identify the most optimal predictive model.
Mobile-Based Real-Time Ornamental Rose Classification System Using YOLOv8 Algorithm on Digital Imagery Achmad Fahrezi, Irgy; Poerwandono, Edhy
Journal Innovations Computer Science Vol. 4 No. 2 (2025): November
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i2.339

Abstract

This research introduces a mobile-based system for real-time identification of ornamental rose varieties using the YOLOv8 deep learning algorithm. Motivated by the growing interest in ornamental plants during the COVID-19 pandemic and the high penetration of smartphone users in Indonesia, the study aims to create an efficient and accessible flower recognition tool. A dataset of 813 labeled rose images—red, white, yellow, orange, and pink—was collected from the Roboflow platform and processed using data augmentation techniques to improve model generalization. The YOLOv8 model was trained with 100 epochs, a batch size of 16, and the SGD optimizer, then converted to TensorFlow Lite for mobile deployment through the Flutter framework. Experimental results achieved a mean average precision (mAP50–95) of 0.581, with strong detection performance across most classes. The system successfully operated offline, delivering real-time classification accuracy despite dataset imbalance, particularly in the orange rose class. These findings demonstrate that YOLOv8 can be effectively adapted for mobile horticultural applications, offering practical benefits for flower sorting, crop management, and educational use. Future studies are recommended to expand dataset diversity, enhance environmental testing, and explore cloud-based integration for scalable deployment.
Optimization of SMOTE Application for Classification Accuracy of Heart Disease Risk Using Artificial Neural Network Ibnu Sarky, Fauzan; Poerwandono, Edhy
Journal Innovations Computer Science Vol. 4 No. 2 (2025): November
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i2.302

Abstract

Heart disease remains a leading cause of mortality worldwide, including in Indonesia, and is often difficult to detect at an early stage. One of the main challenges in the Indonesian healthcare system is the lack of fully digitalized data management and the issue of imbalanced patient datasets, which reduce classification accuracy. This study developed a web-based information system designed to manage patient records and automatically classify heart disease risk. The system was implemented using the CodeIgniter framework with a MySQL database, and applied an Artificial Neural Network (ANN) in combination with the Synthetic Minority Over-sampling Technique (SMOTE) to address data imbalance. A total of 60 secondary patient records were processed through preprocessing, data balancing, model training, and cross-validation. Experimental results demonstrated that the application of SMOTE improved model sensitivity, with performance metrics of 87.4% accuracy, 85.2% precision, 88.6% recall, and an AUC-ROC of 0.94. These findings confirm that integrating ANN and SMOTE into a web-based system enhances classification reliability and supports faster medical decision-making. However, the study also acknowledges certain limitations, including the restricted dataset size and the absence of validation in real clinical environments. Future work should expand the dataset, test the system in healthcare facilities, and compare performance with other algorithms such as Random Forest or SVM to identify the most optimal predictive model.
Mobile-Based Real-Time Ornamental Rose Classification System Using YOLOv8 Algorithm on Digital Imagery Achmad Fahrezi, Irgy; Poerwandono, Edhy
Journal Innovations Computer Science Vol. 4 No. 2 (2025): November
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i2.339

Abstract

This research introduces a mobile-based system for real-time identification of ornamental rose varieties using the YOLOv8 deep learning algorithm. Motivated by the growing interest in ornamental plants during the COVID-19 pandemic and the high penetration of smartphone users in Indonesia, the study aims to create an efficient and accessible flower recognition tool. A dataset of 813 labeled rose images—red, white, yellow, orange, and pink—was collected from the Roboflow platform and processed using data augmentation techniques to improve model generalization. The YOLOv8 model was trained with 100 epochs, a batch size of 16, and the SGD optimizer, then converted to TensorFlow Lite for mobile deployment through the Flutter framework. Experimental results achieved a mean average precision (mAP50–95) of 0.581, with strong detection performance across most classes. The system successfully operated offline, delivering real-time classification accuracy despite dataset imbalance, particularly in the orange rose class. These findings demonstrate that YOLOv8 can be effectively adapted for mobile horticultural applications, offering practical benefits for flower sorting, crop management, and educational use. Future studies are recommended to expand dataset diversity, enhance environmental testing, and explore cloud-based integration for scalable deployment.
Co-Authors Achmad Fahrezi, Irgy Akbar, Yuma Anwar, Anggit Saepul Barronzoeputra, Gaoeng Qalbun Damayanti, Yulia Heryani, Anissa Pramudyah Hidayat, Aditya Zakaria Ibnu Sarky, Fauzan Maharani, Delia Mutia, Selvi Nabilah, Laila Perwitosari, Faizal Joko Ramadhan, Muhammad Arya Rohmansyah, Fadillah Ali Salfa Dhiyaa Azzizah, Putri Sultan Faqih Fiddin Taufik, M. Endang Tri Wahyudi