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All Journal Building of Informatics, Technology and Science TIN: TERAPAN INFORMATIKA NUSANTARA Journal of Applied Data Sciences Jurnal Komtika (Komputasi dan Informatika)
Santoso, Cahyono Budy
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Enhancing Apple Leaf Disease Detection with Deep Learning: From Model Training to Android App Integration Santoso, Cahyono Budy; Singadji, Marcello; Purnama, Denny Ganjar; Abdel, Saimam; Kharismawardani, Aqila
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.507

Abstract

This study presents an innovative approach to enhance apple leaf disease detection using deep learning by comparing three models: ReXNet-150, EfficientNet, and Conventional CNN (ResNet-18). The objective is to identify the most accurate and efficient model for real-world deployment in resource-constrained environments. Utilizing a dataset of 1,730 high-quality images, the models were trained using transfer learning, achieving significant results. ReXNet-150 outperformed other models with an F1-score of 0.988, precision of 0.989, and recall of 0.989. EfficientNet and ResNet-18 demonstrated competitive performances with F1-scores of 0.966 and 0.977, respectively. The integration of the ReXNet-150 model into a TensorFlow Lite-based Android application ensures real-time detection, enabling farmers and researchers to capture or upload images for immediate classification. The findings highlight ReXNet-150's robustness, achieving a test accuracy of 98.9% and minimal misclassification, making it ideal for practical agricultural applications. The novelty lies in bridging advanced deep learning with mobile deployment, addressing real-world constraints. Future work could extend this framework to multi-crop disease detection and real-time video analysis, providing scalable solutions for precision agriculture.
Implementasi Convolutional Neural Network dengan SMOTE+ENN untuk Klasifikasi Kualitas Udara Berdasarkan Data Deret Waktu Polutan Santoso, Cahyono Budy; Kesya Makarena, Maria Rachel
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.8057

Abstract

The degradation of air quality in metropolitan areas, such as Jakarta, constitutes a significant environmental and public health challenge, contributing directly to an elevated risk of various diseases. The primary objective of this study is to develop and evaluate the effectiveness of an air quality classification model based on a Convolutional Neural Network (CNN), with a specific focus on addressing class imbalance using the hybrid resampling technique SMOTE+ENN. Utilizing a historical dataset from the HI Jakarta Station spanning 2010-2021, the model leverages key pollutant parameters (PM10, SO₂, CO, O₃, and NO₂) to classify air quality according to the Indonesian Air Quality Index (ISPU) standard. To mitigate the inherent challenge of class imbalance within the dataset, this study conducts a comparative analysis between a baseline CNN model and an optimized model enhanced with the hybrid resampling technique, Synthetic Minority Over-sampling Technique and Edited Nearest Neighbours (SMOTE + ENN). The dataset was partitioned into an 80% training set and a 20% testing set. Empirical results demonstrate that the application of SMOTE + ENN yields a substantial improvement in performance. The final optimized model achieves a superior accuracy of 98.98%, significantly outperforming the baseline model. This outcome confirms that integrating CNN with the SMOTE + ENN strategy produces a highly effective and robust framework for air quality classification in Jakarta. Nonetheless, subsequent validation on more diverse datasets is recommended to ascertain the model's generalization capabilities and long-term reliability.
Klasifikasi Aritmia Berbasis Model Hybrid Convolutional Neural Network dan Transformer dengan Implementasi Berbasis Web Arief, Jibril Muhammad; Santoso, Cahyono Budy
TIN: Terapan Informatika Nusantara Vol 6 No 7 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i7.8777

Abstract

Cardiac arrhythmia is a heart rhythm disorder that can trigger serious cardiovascular conditions and significantly increase the risk of sudden cardiac death. Conventional arrhythmia detection processes still rely on the manual interpretation of electrocardiogram (ECG) signals by medical experts, which necessitates high precision and is time-consuming. Advancements in artificial intelligence, particularly in Deep Learning, have paved the way for the development of faster and more consistent automated detection systems. This study proposes an arrhythmia classification model based on a hybrid architecture combining Convolutional Neural Networks (CNN) and Transformers. The CNN is utilized to extract spatial features from ECG signals, while the Transformer functions to capture temporal patterns within the signal sequences. The MIT-BIH Arrhythmia Database was employed for training and validation, encompassing five heartbeat classes: Normal, Supraventricular, Ventricular, Fusion, and Unknown beats. Experimental results demonstrate that the model achieved a validation accuracy of 98%, accompanied by balanced precision, recall, and F1-scores for the majority class. Furthermore, the model demonstrated robustness in detecting critical minority classes, achieving a sensitivity (recall) of 93.7% for Ventricular beats and 57.1% for Supraventricular beats. This model was subsequently implemented into a web-based system to enable real-time and flexible ECG signal analysis. These findings indicate that the integration of CNN and Transformer effectively enhances arrhythmia detection accuracy, not only for majority classes but also for rare pathological classes.
Perbandingan XGBoost, Random Forest, dan MLP untuk Klasifikasi Kesiapan Atlet Wardana, Bintang Putra; Santoso, Cahyono Budy
Jurnal Komtika (Komputasi dan Informatika) Vol. 10 No. 1 (2026)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v10.i1.16850

Abstract

Athlete readiness classification is critical for optimizing training load and preventing overtraining-related injuries. This study develops and compares three machine learning algorithms XGBoost, Random Forest, and MLP Neural Network to classify athlete readiness into three ordinal categories: Lower, Middle, and Upper. The dataset comprises 1153 instances with nine multidimensional features encompassing physiological parameters (training duration, intensity, interval days, points, recovery) and psychological indicators (mental score, athlete category, consistency). Data preprocessing involved label encoding for categorical variables and standard scaling for numerical features, followed by a stratified 80:20 train-test split. Model performance was evaluated using weighted precision, recall, F1-score, confusion matrix, and one-vs-rest ROC-AUC curves with 5-fold cross-validation. Results indicate that XGBoost achieved the highest predictive performance (F1-score: 0.93, AUC: 0.99), followed by Random Forest (F1-score: 0.91, AUC: 0.98) and MLP Neural Network (F1-score: 0.85, AUC: 0.94). Feature importance analysis revealed that mental score, training intensity, and consistency were the strongest predictors of readiness status. The proposed framework offers a robust, data-driven decision support tool for sports practitioners, enabling objective readiness monitoring and dynamic training adjustments. Future work will focus on real-time wearable integration and automated hyperparameter optimization
Perbandingan Kinerja Algoritma Machine Learning pada Sentimen #KaburAjaDulu dengan Penanganan Ketidakseimbangan Data Menggunakan SMOTE Sumahesa, Alisha; Santoso, Cahyono Budy
Jurnal Komtika (Komputasi dan Informatika) Vol. 10 No. 1 (2026)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v10.i1.16851

Abstract

The phenomenon of labor migration abroad has become a widely discussed social issue on social media, particularly through the hashtag #KaburAjaDulu on the X platform. This study aims to analyze public sentiment toward the phenomenon using machine learning classification methods with the implementation of Synthetic Minority Over-sampling Technique (SMOTE) to address data imbalance. The research was conducted using 1,750 data collected from the X platform through several stages, including data collection, text preprocessing, sentiment labeling, TF-IDF weighting, SMOTE implementation, and classification using Support Vector Machine (SVM), Naive Bayes, Random Forest, and Logistic Regression algorithms. Model evaluation was carried out using accuracy, precision, recall, and f1-score metrics. The results show that the implementation of SMOTE significantly improved classification performance. Logistic Regression achieved the best performance with an accuracy of 91.36%, followed by Random Forest at 90.30%, Support Vector Machine at 88.89%, and Naive Bayes at 82.89%. These findings indicate that Logistic Regression has the best capability in recognizing sentiment patterns within unstructured social media data. This study proves that data balancing using SMOTE plays an important role in improving sentiment classification performance and in understanding public opinion regarding social phenomena developing in digital media.
Co-Authors Abdel, Saimam Arief, Jibril Muhammad Kesya Makarena, Maria Rachel Kharismawardani, Aqila Marcello Singadji Purnama, Denny Ganjar Sumahesa, Alisha Wardana, Bintang Putra