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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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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.
PENGENALAN PERANGKAT LUNAK LaTeX SEBAGAI MEDIA ALTERNATIF PENULISAN BUKU AJAR BAGI GURU Herowati, Wise; Budi, Setyo; Wibawa, Tangkas Surya; Prabowo, Wahyu Aji Eko
JE (Journal of Empowerment) Vol 3, No 2 (2022): DESEMBER
Publisher : Universitas Suryakancana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/je.v3i2.2740

Abstract

ABSTRAK Penulisan dokumen secara digital merupakan kemampuan yang harus dimiliki di era digitalisasi sekarang ini. Secara khusus pada bidang pendidikan, tenaga pendidik wajib memiliki ketrampilan dalam penulisan teks secara digital. Beberapa tahun yang lalu, penulisan dokumen dilakukan menggunakan mesin tik. Kemudian beralih ke era digital,  diperkenalkan perangkat lunak berbasis komputer, salah satunya adalah Microsoft Word. Meskipun penggunaannya yang mudah, namun kurang dapat menampilkan visualisasi persamaan matematika dengan indah. Perangkat lunak lain yang dapat digunakan untuk membuat formulasi matematika lebih rapi dan indah adalah LaTeX. Tujuan dari kegiatan pengabdian kepada masyarakat (PKM) ini adalah untuk memperkenalkan dan memberi pelatihan pengggunaan LaTeX kepada tenaga pendidik di Yayasan Hidayatullah Gunung Pati, Kota Semarang. Metode yang digunakan pada PKM ini adalah dengan melakukan pengenalan dan pelatihan penggunaan perangkat lunak LaTeX. Luaran PKM ini adalah softskill guru meningkat sehingga menunjang proses penulisan buku ataupun media ajar yang lain sehingga kualitas SDM dan Yayasan menjadi lebih baik.ABSTRACTNowadays, the ability to document writing is important. In particular, in the field of education, educators are required to have the ability and soft skills in digital text writing. A few decades ago, writing documents was done using a typewriter. In this digital era, computer-based software was introduced, and one of the software was Microsoft Word. Even though easy to use, it is not able to visualize the mathematical formula beautifully. Another software that can produce the mathematical formula beautifully is LaTeX. The purpose of this PKM is to introduce and train on the use of latex to educators at the Hidayatullah Foundations, Gunung Pati, Semarang City. The method used in this PKM is to introduce and train the use of LaTeX software. The output of this PKM is increasing the educator’s soft skills to support the production of teaching media so that the quality of human resources becomes better. 
Peningkatan Brand UMKM Melalui Pendekatan Konten Animasi Prabowo, Wahyu Aji Eko; Zulfiningrum, Rahmawati; Siregar, Nadia Itona; Nugraini, Siti Hadiati; Ayasy, Ahmad Yahya; Adriansyah, Vicky Puja; Putrawan, Zulhandi
DEDIKASI PKM Vol. 5 No. 3 (2024): DEDIKASI PKM UNPAM
Publisher : Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/dkp.v5i3.40174

Abstract

Potensi wisata terhadap UMKM akan meningkatkan pendapatan wilayah. Salah satu pengembangan daerah wisata yang berpotensi besar dalam meningkatkan perekonomian negara adalah destinasi wisata Karimunjawa. Kegiatan pengabdian masyarakat ini bertujuan untuk meningkatkan brand UMKM melalui pendekatan konten animasi sebagai ikon UMKM di Karimunjawa, bekerjasama dengan BUMDES Sejahtera Bahari. Kegiatan ini mengusung pentingnya ikon bagi UMKM lokal dalam mempromosikan identitas produk mereka. Program ini dirancang untuk memanfaatkan elemen budaya unik yang ada di Karimunjawa, meningkatkan kemampuan lokal dalam pembuatan konten animasi, dan memperkuat pemasaran UMKM. Metode yang digunakan adalah Asset Based Community Development (ABCD) untuk membantu masyarakat dalam melihat potensi yang dimiliki dan mengarahkan untuk peningkatan. Pelatihan dilaksanalan dalam bentuk workshop yang bertujuan untuk merancang pembuatan video animasi sebagai implementasi alat pemasaran yang efektif. Manfaat utama kegiatan ini adalah peningkatan pengetahuan dan keterampilan lokal dalam mengeksplorasi potensi daerah, dengan harapan meningkatkan keterlibatan dan daya ingat konsumen terhadap produk lokal. Melalui video animasi diharapkan dapat membantu promosi produk UMKM Karimunjawa, peningkatan ekonomi bagi masyarakat lokal dan menambah daya tarik wisatawan.
Supervised Machine Learning Algorithms untuk Klasifikasi Penyakit Jantung Riadi, Denny Fajar; Prabowo, Wahyu Aji Eko
JURNAL RISET KOMPUTER (JURIKOM) 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 Maulana, Isyeh Rafi; Prabowo, Wahyu Aji Eko
JURNAL RISET KOMPUTER (JURIKOM) 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 Sitohang, Bramudya Toguando; Prabowo, Wahyu Aji Eko
JURNAL RISET KOMPUTER (JURIKOM) 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 Sijabat, Glen Fierre; Prabowo, Wahyu Aji Eko
JURNAL RISET KOMPUTER (JURIKOM) 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; Prabowo, Wahyu Aji Eko
JURNAL RISET KOMPUTER (JURIKOM) 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.
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 Maulana Wahyu Ibrahim Maulana, Isyeh Rafi Moch Anjas Aprihartha Muhammad Naufal Nadia Itona Siregar, Nadia Itona Noor Ageng Setiyanto, Noor Ageng Nugraini, Siti Hadiati Purnomo, Zaky Dwi Putrawan, Zulhandi Riadi, Denny Fajar Safitri, Aprilyani Nur Sijabat, Glen Fierre Sitohang, Bramudya Toguando Sudibyo, Usman Trisnapradika, Gustina Alfa Wibawa, Tangkas Surya Wibawa, Vincentius Hadianta Zulfiningrum, Rahmawati