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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 Daun Mangga Menggunakan CNN Berbasis Transfer Learning Dengan Model Arsitektur VGG16, DenseNet121, dan InceptionV3 Purnomo, Zaky Dwi; 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.v13i1.9504

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
Quantum Machine Learning Models, Limitations, and Opportunities in the NISQ Era: A Review Akrom, Muhamad; Safitri, Aprilyani Nur; Hidayat, Novianto Nur; Prabowo, Wahyu Aji Eko; Budi, Setyo
Journal of Multiscale Materials Informatics Vol. 3 No. 1 (2026): April (In Progress)
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jimat.v3i1.15955

Abstract

Quantum machine learning (QML) has emerged as a promising interdisciplinary field that integrates principles of quantum computing with machine learning techniques to address complex computational challenges. By leveraging quantum phenomena such as superposition and entanglement, QML aims to enhance learning efficiency, improve model performance, and enable the exploration of high-dimensional feature spaces that are intractable for classical methods. This paper presents a comprehensive review of recent developments in QML, covering fundamental concepts, algorithmic taxonomies, data encoding techniques, implementation challenges, and real-world applications. Key approaches, including quantum support vector machines (QSVM), variational quantum circuits (VQC), and quantum neural networks (QNN), are systematically analyzed. Furthermore, critical challenges, including noisy intermediate-scale quantum (NISQ) limitations, barren plateaus, data encoding bottlenecks, and the lack of demonstrated quantum advantage, are discussed in detail. The review also highlights emerging applications in material informatics, energy systems, healthcare, and optimization problems. Finally, future research directions are outlined, emphasizing the need for advancements in quantum hardware, scalable algorithms, hybrid frameworks, and standardized benchmarking. This work aims to provide a structured perspective on the current state of QML and to identify opportunities in deploy it effectively in solve real-world problems.
Analisis Perbandingan Model Machine Learning menggunakan Teknik Stratified K-Fold Cross Validation untuk Klasifikasi Penyakit Jantung Pratama, Avrilyan Putra Bintang; Prabowo, Wahyu Aji Eko
JURNAL RISET KOMPUTER (JURIKOM) 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 Achmad Wahid Kurniawan Adi, Ilham Arif Kuncoro Adriansyah, Vicky Puja Ajisaputra, Faris Prasetya 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 Pratama, Avrilyan Putra Bintang 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