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All Journal Jurnal Masyarakat Informatika Rekursif: Jurnal Informatika Sinkron : Jurnal dan Penelitian Teknik Informatika Communications in Science and Technology Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Medika Teknika : Jurnal Teknik Elektromedik Indonesia
Oktoeberza, Widhia KZ
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Comparative Study of Deep Learning Models to Classify of Multi-Class Skin Cancer on Imbalanced Data Oktoeberza, Widhia KZ; Rahman, Muhammad Farchan Al; Vasiguhamiaz, Azvadennys; Huda, Widya Nurul; Mainil, Afdhal Kurniawan; Sari, Julia Purnama
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 2 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i2.85544

Abstract

Skin cancer diagnosis faces challenges in efficiency and accuracy. This research addresses the need for improved non-invasive diagnostic tools by leveraging deep learning for multi-class skin cancer classification from dermoscopic images. A key focus is overcoming the limitations of imbalanced datasets, common in medical imaging, which can hinder model performance. We propose an optimal strategy utilizing a Convolutional Neural Network (CNN) transfer learning methodology. The process involves CNN-based segmentation to isolate relevant regions, followed by feature extraction and classification. We comparatively evaluated three pre-trained transfer learning techniques: DenseNet201, ResNet50, and VGG16, using the HAM10000 dataset (10,015 images across seven skin cancer classes). To mitigate severe class imbalance, Random Oversampling was employed, chosen for its simplicity and effectiveness in balancing the dataset and enhancing model generalization. Model performance was rigorously evaluated using accuracy, precision, recall, and F1-score. DenseNet201 consistently achieved superior performance, with an accuracy of 97% post-oversampling. It also exhibited the highest precision, recall, and F1-score across all models, confirming its effectiveness in classifying both majority and minority classes. Compared to previous studies on HAM10000, our DenseNet201 model's test accuracy of 96.52% is competitive or superior to reported accuracy of 90-92%. This highlights the synergistic effect of DenseNet201's efficient feature reuse and robust data balancing. This research provides a robust framework for advanced methodologies in skin cancer classification, particularly for imbalanced medical image datasets.
Efficient CNN-Based Classification of SARS-CoV-2 Spike Gene Sequences Using Alignment-Free Encoding Anggarah, Rengga; Ernawati, Ernawati; Oktoeberza, Widhia KZ
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15691

Abstract

The COVID-19 pandemic caused by SARS-CoV-2 continues to challenge the global health system through the emergence of various variants with genetic characteristics that affect vaccine transmission and effectiveness. Conventional identification methods such as Whole-Genome Sequencing (WGS) have high accuracy but are constrained by significant cost and time. Most classification studies today still rely on complex hybrid architectures such as CNN-LSTM or image-based representations that increase computational load. This study aims to develop  an  efficient and lightweight pure Convolutional Neural Network model based on alignment-free encoding to classify five Variant of Concern (VOC) variants of SARS-CoV-2 (Alpha, Beta, Delta, Gamma, and Omicron) with an exclusive focus on the Spike gene sequence. The dataset consists of 5,000 Spike gene sequences that are represented using integer encoding and standardized with zero-padding. CNN  proposed Lightweight architecture  consists of four 1D convolution layers with a total of approximately 1.6 million parameters. The test results show that the model achieves excellent performance with an overall accuracy of 98.93%. The precision, recall, and F1-score values averaged 0.99, while the analysis of the ROC curve showed AUC values above 0.99 for all variants. This approach has proven to be efficient and effective, offering a fast, scalable, and resource-efficient solution to support real-time genomic surveillance systems in future pandemic mitigation.
Implementasi YOLOv11 Untuk Deteksi Penyakit Tanaman Padi Berdasarkan Citra Daun Alifyandra Akbar, Farrel; Sari, Julia Purnama; Oktoeberza, Widhia KZ
Rekursif: Jurnal Informatika Vol 13 No 2 (2025): Volume 13 Nomor 2 November 2025
Publisher : Universitas Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/rekursif.v13i2.43876

Abstract

Rice (Oryza sativa) is a strategic commodity for food security in Indonesia, yet it is highly vulnerable to diseases such as bacterial leaf blight (blight), blast, and tungro, which can significantly reduce productivity. Early detection of these diseases through manual observation by farmers is often inaccurate and slow. This study aims to implement the YOLOv11 algorithm, a deep learning-based approach, to detect rice plant diseases from leaf images with high accuracy. The research method follows the CRISP-DM (Cross Industry Standard Process for Data Mining) framework, encompassing business understanding, data collection, data preparation, modeling, and evaluation. The dataset consists of 500 rice leaf images classified into three disease categories. The data was processed through augmentation and resizing to balance class distribution and standardize image dimensions. The YOLOv11 model was trained with parameters set at 100 epochs, an image size of 224x224 pixels, and a batch size of 32. Evaluation results demonstrate that the model achieved 95% accuracy, with average precision and recall exceeding 95%. The confusion matrix revealed excellent classification performance, particularly for tungro disease (100% accuracy). The model also proved efficient in prediction, with an inference time of 8.2 milliseconds per image. In conclusion, this research confirms the effectiveness of YOLOv11 for rice disease detection based on leaf images. Recommendations for future development include expanding dataset diversity, integrating the model into mobile applications, and conducting field tests to validate real-world performance. Keywords: YOLOv11, rice disease detection, deep learning, leaf image, computer vision.
Pengembangan Sistem Deteksi Dini Mahasiswa Berisiko Menggunakan Machine Learning Berbasis Data Learning Management System: Studi Kasus: rumahilmu.org Syahputra, Wahyu; Purwandari, Endina Putri; Oktoeberza, Widhia KZ
Rekursif: Jurnal Informatika Vol 13 No 2 (2025): Volume 13 Nomor 2 November 2025
Publisher : Universitas Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/rekursif.v13i2.43948

Abstract

Abstract: This research aims to develop an early detection system for at-risk students using machine learning based on data from the Learning Management System (LMS) rumahilmu.org. The system was designed for the Information Systems Study Programs at the University of Bengkulu, analyzing data from 459 student enrollments across five courses. A total of 37–76 features were extracted from LMS activities to predict students likely to score below the 30th percentile at three strategic time points (25%, 50%, and 75% of the semester). This study implemented a per-class optimization approach, testing 11 algorithms to find the best model for each course. The results showed that no single algorithm was universally superior; the most effective models varied for each course, with Gaussian Process, Logistic Regression, and Voting Classifier being the most frequently chosen. However, evaluation on the test data revealed significant challenges: despite high cross-validation scores (F1-score > 0.80), overfitting and performance degradation occurred. The most critical finding was the model's low capability in detecting the 'At-Risk' minority class, with the Recall (At-Risk) metric reaching 0.00 in 8 out of 15 scenarios. The best detection performance was achieved in the Statistics & Probability course with a Recall of 0.50. The implemented system, featuring a 3-tier architecture (FastAPI and React), provides an interactive dashboard, but its predictive effectiveness for early detection is limited by small and imbalanced datasets.
Comparative Analysis of Machine Learning for Stroke Classification Using YOLOv11 Detection and a Radiomics-Based Two-Stage Model Manurung, Wahyu Ozorah; Ernawati, Ernawati; Oktoeberza, Widhia KZ; Andreswari, Desi; Purwandari, Endina Putri; Efendi, Rusdi
Jurnal Masyarakat Informatika Vol 17, No 1 (2026): May 2026 (Ongoing)
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.17.1.78464

Abstract

Stroke is a leading cause of disability and death worldwide, including in Indonesia. Rapid and accurate diagnosis is crucial, especially during the golden period (3–4.5 hours). CT scans are the primary imaging modality, but manual interpretation is often limited by time, subjectivity, and radiologist availability. This study proposes a two-stage model integrating YOLOv11 for lesion detection and machine learning for classification, using radiomics for feature extraction. In the first stage, YOLOv11 detects lesions and generates bounding boxes, which serve as Regions of Interest (ROIs). In the second stage, radiomics features are extracted and classified using Naïve Bayes, Support Vector Machine (SVM), and Random Forest. Results show YOLOv11 achieved an overall mAP@50 of 0.732, with the highest performance in hemorrhagic stroke (0.741). Radiomics-based classification further improves stability, achieving accuracies of 0.97–0.99 and precision, recall, and F1 scores≥0.94. Among classifiers, SVM performed best, with a test accuracy of 0.97, a false positive rate of 1.23%, total error 0.0218, generalization gap -0.0117, variance 0.0002, standard deviation 0.003635, confidence interval 0.9708 (+/-0.0073), and consistent fold accuracy between 96.5–97.5%, indicating stability without overfitting. These findings confirm that the combination of the YOLOv11 two-stage model, radiomics, and SVM provides a robust approach to support stroke diagnosis.
Co-Authors Aan Erlansari Afdhal Kurniawan Mainil Aliana, Mareta Alifyandra Akbar, Farrel Anggarah, Rengga Butar Butar, Federika Dartiko, Fedryanto Desi Andreswari Dwi Jayanto, Paksi Endina Putri Purwandari Ernawati Ernawati Funny Farady Coastera Gita Fitria, Nabila Hanung Adi Nugroho Hijrayanti, Hikmah Hotama, Christianus Frederick Huda, Widya Nurul Igi Ardiyanto Ikhsan, Arif Rahmat Indah Soesanti Julia Purnama Sari Julia Purnama Sari Listyalina, Latifah Luthfi, Irfan Manurung, Wahyu Ozorah Muthi, Marsa Hulwa Indri Pradana, Refki Jorgi Rahman, Muhammad Farchan Al Rusdi Efendi Saputri, Dian Ardiyanti Sari, Renti Epana Sunu Wibirama Syahputra, Wahyu Vasiguhamiaz, Azvadennys Yulyanti, Vesi Zarah Juaita, Aisyah Amelia