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All Journal Techno.Com: Jurnal Teknologi Informasi JAIS (Journal of Applied Intelligent System) JURIKOM (Jurnal Riset Komputer) JE (Journal of Empowerment) Abdimasku : Jurnal Pengabdian Masyarakat DEDIKASI PKM Jurnal Inovasi Penelitian dan Pengabdian Masyarakat Journal of Multiscale Materials Informatics
Prabowo, Wahyu Aji Eko
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Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Social Sciences Other

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Journal : JURIKOM (Jurnal Riset Komputer)

 1 
Klasifikasi Penyakit Diabetes Menggunakan Pendekatan Pembelajaran Mesin dengan Model Non-linier Adi, Ilham Arif Kuncoro; Prabowo, Wahyu Aji Eko
JURIKOM (Jurnal Riset Komputer) Vol 12, No 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8586

Abstract

The increasing prevalence of diabetes mellitus highlights the need for accurate early detection methods. This study proposes a classification model for diabetes prediction using non-linear machine learning algorithms, namely Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbors (K-NN). The dataset, obtained from Kaggle, includes clinical features such as glucose levels, BMI, blood pressure, and insulin. The methodology comprises data preprocessing, partitioning the data into training and testing sets, and evaluating the model’s using accuracy, precision, recall, and F1-score. Experimental results indicate that the Random Forest algorithm achieved the highest performance, followed by SVM and K-NN. We attribute Random Forest’s superior performance to its robustness in handling complex patterns and minimizing overfitting. We expect this research to contribute to developing practical early detection tools for diabetes, thereby supporting timely and data-driven medical decision-making.
Pemanfaatan Model Linier dalam Klasifikasi Penyakit Diabetes Berbasis Machine Learning Ajisaputra, Faris Prasetya; Prabowo, Wahyu Aji Eko
JURIKOM (Jurnal Riset Komputer) Vol 12, No 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8587

Abstract

Diabetes is a chronic disease that may lead to serious health complications if not detected and treated early. Early detection plays a crucial role in minimizing long-term risks. This study aims to classify diabetes cases using a machine-learning approach based on linear models. The models applied in this research include logistic regression, linear discriminant analysis (LDA), ridge classifier, and support vector machine (SVM) with a linear kernel. We preprocessed the dataset to ensure quality and consistency. We evaluated each model’s performance using accuracy, precision, recall, F1-score, and AUC-ROC. Experimental results show that the ridge classifier achieved the highest performance, followed by LDA and linear SVM, with comparable results. Logistic regression also performed reasonably well, albeit with slightly lower metrics. These findings indicate that the linear model can provide accurate and reliable classification in the task of predicting diabetes, contributing to the proof that this model can serve as the basis for a decision support system for early diabetes diagnosis in the healthcare sector.
Analisis Performansi Model Machine Learning dalam Klasifikasi Penyakit Diabetes Tipe 2 Hidayatulloh, Ryan; Prabowo, Wahyu Aji Eko
JURIKOM (Jurnal Riset Komputer) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8747

Abstract

Type 2 Diabetes Mellitus is a chronic disease that develops gradually and can lead to serious complications—such as heart disease, kidney failure, and blindness—if not detected early. This study aims to evaluate and compare the performance of four machine learning algorithms—Logistic Regression, Random Forest, Multilayer Perceptron, and Deep Neural Network—in predicting the risk of type 2 diabetes based on medical data. The analysis uses the Pima Indians Diabetes dataset, which contains 9.538 patient records and 16 predictor variables. We split the data into training and testing sets using an 80:20 ratio. During training, we performed hyperparameter tuning using Grid Search combined with cross-validation. To evaluate model performance, we applied several metrics, including accuracy, precision, recall, F1-score, Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R², and an analysis of overfitting. The results indicate that the Random Forest model outperformed the others, achieving 100% accuracy, zero classification errors, near-zero prediction error values, and no signs of overfitting. Logistic Regression also performed well, though slightly below the Random Forest. In contrast, the Multilayer Perceptron and Deep Neural Network models showed mild overfitting and higher false negative rates. Based on these findings, we recommend the Random Forest model as the most reliable option for early prediction systems in type 2 diabetes mellitus.
Implementasi Deep Neural Network untuk Prediksi Harga Saham PT Bank Central Asia Tbk Almasah, Muhammad Rakha; Prabowo, Wahyu Aji Eko
JURNAL RISET KOMPUTER (JURIKOM) 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.
Klasifikasi Penyakit Diabetes Menggunakan Pendekatan Pembelajaran Mesin dengan Model Non-linier Adi, Ilham Arif Kuncoro; Prabowo, Wahyu Aji Eko
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8586

Abstract

The increasing prevalence of diabetes mellitus highlights the need for accurate early detection methods. This study proposes a classification model for diabetes prediction using non-linear machine learning algorithms, namely Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbors (K-NN). The dataset, obtained from Kaggle, includes clinical features such as glucose levels, BMI, blood pressure, and insulin. The methodology comprises data preprocessing, partitioning the data into training and testing sets, and evaluating the model’s using accuracy, precision, recall, and F1-score. Experimental results indicate that the Random Forest algorithm achieved the highest performance, followed by SVM and K-NN. We attribute Random Forest’s superior performance to its robustness in handling complex patterns and minimizing overfitting. We expect this research to contribute to developing practical early detection tools for diabetes, thereby supporting timely and data-driven medical decision-making.
Pemanfaatan Model Linier dalam Klasifikasi Penyakit Diabetes Berbasis Machine Learning Ajisaputra, Faris Prasetya; Prabowo, Wahyu Aji Eko
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8587

Abstract

Diabetes is a chronic disease that may lead to serious health complications if not detected and treated early. Early detection plays a crucial role in minimizing long-term risks. This study aims to classify diabetes cases using a machine-learning approach based on linear models. The models applied in this research include logistic regression, linear discriminant analysis (LDA), ridge classifier, and support vector machine (SVM) with a linear kernel. We preprocessed the dataset to ensure quality and consistency. We evaluated each model’s performance using accuracy, precision, recall, F1-score, and AUC-ROC. Experimental results show that the ridge classifier achieved the highest performance, followed by LDA and linear SVM, with comparable results. Logistic regression also performed reasonably well, albeit with slightly lower metrics. These findings indicate that the linear model can provide accurate and reliable classification in the task of predicting diabetes, contributing to the proof that this model can serve as the basis for a decision support system for early diabetes diagnosis in the healthcare sector.
Analisis Performansi Model Machine Learning dalam Klasifikasi Penyakit Diabetes Tipe 2 Hidayatulloh, Ryan; Prabowo, Wahyu Aji Eko
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8747

Abstract

Type 2 Diabetes Mellitus is a chronic disease that develops gradually and can lead to serious complications—such as heart disease, kidney failure, and blindness—if not detected early. This study aims to evaluate and compare the performance of four machine learning algorithms—Logistic Regression, Random Forest, Multilayer Perceptron, and Deep Neural Network—in predicting the risk of type 2 diabetes based on medical data. The analysis uses the Pima Indians Diabetes dataset, which contains 9.538 patient records and 16 predictor variables. We split the data into training and testing sets using an 80:20 ratio. During training, we performed hyperparameter tuning using Grid Search combined with cross-validation. To evaluate model performance, we applied several metrics, including accuracy, precision, recall, F1-score, Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R², and an analysis of overfitting. The results indicate that the Random Forest model outperformed the others, achieving 100% accuracy, zero classification errors, near-zero prediction error values, and no signs of overfitting. Logistic Regression also performed well, though slightly below the Random Forest. In contrast, the Multilayer Perceptron and Deep Neural Network models showed mild overfitting and higher false negative rates. Based on these findings, we recommend the Random Forest model as the most reliable option for early prediction systems in type 2 diabetes mellitus.
Co-Authors Achmad Wahid Kurniawan Adi, Ilham Arif Kuncoro Adriansyah, Vicky Puja Ajisaputra, Faris Prasetya Akrom, Muhamad Almasah, Muhammad Rakha Ayasy, Ahmad Yahya Ayu Pertiwi Budi, Setyo Harun Al Azies Herowati, Wise Hidayat, Novianto Nur Hidayatulloh, Ryan Moch Anjas Aprihartha Muhammad Naufal, Muhammad Nadia Itona Siregar, Nadia Itona Noor Ageng Setiyanto, Noor Ageng Nugraini, Siti Hadiati Putrawan, Zulhandi Safitri, Aprilyani Nur Sudibyo, Usman Trisnapradika, Gustina Alfa Wibawa, Tangkas Surya Wibawa, Vincentius Hadianta Zulfiningrum, Rahmawati