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Wahyu Aji Eko Prabowo
Universitas Dian Nuswantoro, Semarang
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Implementasi Deep Neural Network untuk Prediksi Harga Saham PT Bank Central Asia Tbk Muhammad Rakha Almasah; Wahyu Aji Eko Prabowo
JURIKOM (Jurnal Riset Komputer) Vol 12, No 2 (2025): April 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i2.8544

Abstract

Stock price prediction is one of the main challenges in financial market analysis. This study develops a Deep Neural Network (DNN) model using TensorFlow to predict the stock price of PT Bank Central Asia Tbk (BBCA) based on historical stock price data, the Jakarta Composite Index (IHSG), and the USD/IDR exchange rate. The model is optimized through hyperparameter tuning using KerasTuner with the Hyperband method, allowing for more efficient exploration of hyperparameter combinations. The tuning process yielded the best model configuration with a learning rate of 0.001269, four hidden layers, and the application of Batch Normalization, L2 kernel regularization, and dropout in each hidden layer.The model was evaluated using various batch sizes (4, 16, 32, 64, and 128) with Mean Squared Error (MSE) and Mean Absolute Error (MAE) as performance metrics. The results show that batch size 128 provides the best performance, with MSE of 0.0094 and MAE of 0.0490 on the test data, indicating high accuracy and good generalization on unseen data. The best model was then implemented as an API using Flask, deployed to Google Cloud Run, and integrated with a Flutter-based mobile application. Testing confirmed that the API can handle requests quickly using TensorFlow and produce accurate predictions.Thus, this study successfully developed a DNN-based BBCA stock price prediction system that can be applied to data-driven investment decision-making.
Supervised Machine Learning Algorithms untuk Klasifikasi Penyakit Jantung Denny Fajar Riadi; Wahyu Aji Eko Prabowo
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9481

Abstract

Heart disease is one of the leading causes of death worldwide, requiring accurate predictive methods to support early detection and clinical decision making. This study aims to analyze and compare the performance of three supervised machine learning algorithms, namely K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF), in classifying heart disease using the Cleveland Heart Disease dataset consisting of 303 patient records with 13 clinical features. The research stages include data preprocessing, splitting the dataset into 80% training data and 20% testing data, model training, and hyperparameter optimization using GridSearchCV with 5-fold cross-validation. After optimization, prediction was performed using test data followed by performance evaluation to assess generalization ability. Model performance was evaluated using accuracy, precision, recall, F1-score, AUC-ROC, and confusion matrix. The results show that KNN and Random Forest achieved the highest accuracy of 90.16%. The KNN model obtained a recall value of 1.0000, indicating perfect sensitivity in detecting positive cases, while Random Forest demonstrated a more balanced performance between precision and recall with the highest AUC value of 0.9481. Based on these findings, KNN is considered the most suitable model for medical screening purposes, as it successfully detected all positive heart disease patients without producing false negatives. This study is expected to serve as a reference for implementing clinical databased machine learning as a decision support tool for early heart disease detection.
Analisis Perbandingan Kinerja Arsitektur CNN untuk Klasifikasi Penyakit Tuberkulosis pada Citra Rontgen Thoraks Isyeh Rafi Maulana; Wahyu Aji Eko Prabowo
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9482

Abstract

Tuberculosis (TB) is a chronic infectious disease and one of the leading causes of mortality worldwide. Conventional diagnostic processes are often hampered by high costs and technical complexity; consequently, Chest X-Ray (CXR) examinations combined with Artificial Intelligence (AI)-based computer detection systems have emerged as a more efficient alternative. This study aims to comparatively analyze the effectiveness of three fundamental CNN architectures AlexNet, ZFNet, and ResNet18 in detecting TB from chest X-ray images. The research methodology employs the Knowledge Discovery in Databases (KDD) framework on a public CXR dataset. Hyperparameter optimization was implemented using a Grid Search strategy integrated with 5-Fold Cross-Validation to systematically identify the optimal configuration. Experimental results indicate a significant positive correlation between architectural depth and diagnostic performance. Based on the optimal parameters identified through Grid Search specifically a learning rate of 0.0001 and a batch size of 32 the ResNet18 model demonstrated superior performance, achieving 99.28% scores for Accuracy, and 100% for precision, recall, F1-score and AUC-ROC. The superiority of ResNet18 lies in its residual learning mechanism, which effectively addresses the vanishing gradient problem and facilitates the extraction of complex pathological features. The combination of ResNet18 with Grid Search optimization demonstrates that the synergy of modern architecture and systematic tuning yields a highly reliable Computer-Aided Detection (CAD) system, surpassing the results of previous studies
Analisis Komparasi Arsitektur Deep Learning untuk Klasifikasi Penyakit Daun Cabai Bramudya Toguando Sitohang; Wahyu Aji Eko Prabowo
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9483

Abstract

The productivity of chili (Capsicum annuum L.) in Indonesia faces significant challenges due to leaf diseases, which are estimated to reduce harvest yields by up to 35%. Conventional detection methods relying on visual observation often lack accuracy due to the high visual similarity between disease symptoms. This study focuses on a comparative evaluation of three leading Deep Learning architectures VGG16, ResNet50, and InceptionV3 in classifying six types of chili leaf diseases using a public dataset. The research implements a high-resolution image strategy (512 x 512 pixels) to maximize the extraction of disease texture features. The methodology employs a Transfer Learning approach with a standardized hyperparameter tuning scheme. Experimental results indicate that the use of high-resolution images significantly impacts model accuracy. The VGG16 architecture achieved the best performance with a testing accuracy of 99.83% and an F1-Score of 1.00, outperforming ResNet50 (99.75%) and InceptionV3 (84.00%). Confusion Matrix analysis demonstrates that VGG16 possesses superior stability in distinguishing disease classes with high visual similarity, such as Bacterial Spot and Cercospora. The study concludes that architectures preserving deep spatial information, such as VGG16, are more effective for high-resolution image-based plant disease diagnosis compared to more complex architectures that perform aggressive feature compression.
Studi Komparatif Model Machine Learning untuk Klasifikasi Penyakit Jantung dengan SMOTE pada Data Imbalanced Glen Fierre Sijabat; Wahyu Aji Eko Prabowo
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9485

Abstract

This study examines the application of the Synthetic Minority Over-sampling Technique (SMOTE) for heart disease classification using four machine learning algorithms, namely Logistic Regression, Random Forest, LightGBM, and XGBoost, based on the Heart Disease UCI dataset consisting of 920 medical records with 16 clinical features. The original severity labels (0–4) are converted into two classes, namely not sick (0) and sick (1–4), to better align with binary decision-making needs in clinical screening. The experiments are conducted in two scenarios: (1) training models on the original data without handling class imbalance and (2) training models with SMOTE applied only to the training data within a pipeline, accompanied by hyperparameter tuning using k-fold cross-validation. Model performance is evaluated using accuracy, precision, recall, F1-score, AUC-ROC, as well as confusion matrix analysis to examine misclassifications, particularly false negatives in the sick class. In the scenario without SMOTE, the best model, Logistic Regression, achieves an accuracy of 84.78%, recall of 84.31%, F1-score of 86.00%, and AUC-ROC of 91.95%, although the number of false negatives remains relatively high. After applying SMOTE, there is an increase in recall and F1-score for the positive class across all models, with the best performance obtained by Random Forest with SMOTE, which achieves an accuracy of 86.96%, recall of 87.25%, F1-score of 88.12%, and AUC-ROC of 93.34%. These findings indicate that the combination of SMOTE and hyperparameter optimization can produce a more balanced and reliable heart disease classification model that is potentially useful as a clinical decision support system in healthcare services.
Evaluasi Komparatif Random Forest, XGBoost, LightGBM, dan K-Nearest Neighbors untuk Prediksi Cuaca di Kota Semarang Maulana Wahyu Ibrahim; Wahyu Aji Eko Prabowo
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9488

Abstract

Accurate weather predictions play an important role in assisting strategic decisions in various fields, from agriculture to disaster management. However, there is a fundamental challenge in creating automatic prediction models, namely the nature of meteorological datasets, which are often imbalanced in class distribution. This phenomenon causes conventional machine learning algorithms to favor the dominant class and be less capable of detecting the rare class (rain), as seen in the low sensitivity values. This study aims to overcome this bias problem and improve the accuracy of daily rainfall classification using a comparative approach with four algorithms: Random Forest, K-Nearest Neighbor (KNN), LightGBM, and XGBoost. As the main method to overcome data imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied to generate new samples in the underrepresented class. Model performance was evaluated comprehensively using a confusion matrix, One-vs-Rest (OvR) strategy, and conventional evaluation metrics. The results of the experiments on the baseline model showed a failure to detect the minority class with very low Recall and F1-Score values (< 0.30). The application of SMOTE was proven to significantly improve Recall and F1-Score compared to the SMOTE. LightGBM using SMOTE was recorded as the most superior model that successfully balanced all evaluation metrics.
Klasifikasi Penyakit Daun Mangga Menggunakan CNN Berbasis Transfer Learning Dengan Model Arsitektur VGG16, DenseNet121, dan InceptionV3 Zaky Dwi Purnomo; Wahyu Aji Eko Prabowo
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9504

Abstract

Mango is one of the important fruits in Indonesia, but its production is often disrupted by leaf diseases and pests that are difficult to detect early. Manual disease recognition methods usually depend on observers and are not always accurate. This study aims to create an automated system to classify mango leaf diseases, using deep learning techniques based on the Convolutional Neural Network (CNN) algorithm. This study also compares three models, namely VGG16, DenseNet121, and InceptionV3, by applying the transfer learning method. The dataset used consists of 4,000 images divided evenly into 8 categories, consisting of 7 types of diseases (Anthracnose, Bacterial Canker, Cutting Weevil, Die Back, Gall Midge, Powdery Mildew, Sooty Mold) and 1 category of healthy plants. Evaluation was carried out using the 5-Fold Cross-Validation method to ensure valid results. The results show that all three models are able to provide an accuracy of more than 90%. The VGG16 model showed the best and most stable performance, with an accuracy of 93.25%, a Precision of 0.93, a Recall of 0.93, an F1-Score of 0.93, and an AUC-ROC of 0.98. Meanwhile, InceptionV3 achieved an accuracy of 92.38% and DenseNet121 reached 91.25%. Therefore, VGG16 is recommended as the primary model due to its better ability to extract texture features and accurately recognize mango leaf diseases. VGG16 architecture is able to outperform complex models in efficiently extracting mango leaf texture features, making it very potential to be used as a basis for real-time plant disease diagnosis applications for farmers
Analisis Perbandingan Model Machine Learning menggunakan Teknik Stratified K-Fold Cross Validation untuk Klasifikasi Penyakit Jantung Avrilyan Putra Bintang Pratama; Wahyu Aji Eko Prabowo
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 2 (2026): April 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i2.9670

Abstract

Heart disease is one of the leading causes of death worldwide. Conventional approaches still have limitations, such as subjectivity in interpretation and relatively long analysis times. Therefore, this study proposes using machine learning to improve the accuracy of heart disease risk prediction by comparing the performance of Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) algorithms. The research methodology includes data preprocessing, splitting the dataset into training and testing sets, and hyperparameter optimization using Stratified K-Fold Cross Validation with variations of K = 5, 10, 15, and 20. Model evaluation is conducted using accuracy, precision, recall, F1-score, and ROC-AUC metrics to comprehensively and objectively measure classification performance. The results show that the Random Forest algorithm achieves the best performance. At the optimal configuration of K = 15, the model attains an accuracy of 93.17%, a precision of 0.92, a recall of 0.95, an F1-score of 0.94, and an ROC-AUC of 0.97. In addition, this model minimizes classification errors, particularly False Negatives, making it more effective at identifying at-risk patients. The main contribution of this study is demonstrating that the combination of Random Forest and Stratified K-Fold Cross Validation can significantly improve classification performance and produce a model that is accurate, stable, and reliable for implementation in medical decision support systems.
Co-Authors Avrilyan Putra Bintang Pratama Bramudya Toguando Sitohang Denny Fajar Riadi Glen Fierre Sijabat Isyeh Rafi Maulana Maulana Wahyu Ibrahim Muhammad Rakha Almasah Zaky Dwi Purnomo