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Jurnal Masyarakat Informatika
Published by Universitas Diponegoro
ISSN : 20864930     EISSN : 27770648     DOI : https://doi.org/10.14710/jmasif.crossmark
Core Subject : Science,
Computer Science & IT
JURNAL MASYARAKAT INFORMATIKA - JMASIF is a Journal published by the Department of Informatics, Universitas Diponegoro invites lecturers, researchers, students (Bachelor, Master, and Doctoral) as well as practitioners in the field of computer science and informatics to contribute to JMASIF in the form of research articles and review articles. We accept articles in English and Bahasa. Detailed information about the submission process can be read HERE. Authors can also download Templates at HERE. JMASIF Topics include, but are not limited to Applied Computer Science, Artificial Intelligence, Text and Natural Language Processing, Image Processing and Pattern Recognition, Computer Vision, Data Mining, Cryptography, Cybersecurity, Computer Network, Computational Theory and Mathematics, Game Technology, Human and Computer Interaction or UI/UX, Information System, Software Engineering.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 209 Documents
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A Web-Based Tourism Recommendation System for Boyolali Using Content-Based Filtering and Cosine Similarity Muhammad Naufal Arkan; Aris Puji Widodo; Guruh Aryotejo
Jurnal Masyarakat Informatika Vol 17, No 1 (2026): May 2026
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.17.1.72520

Abstract

Tourism is a primary economic driver for Boyolali Regency. However, destination information remains fragmented, lacking a personalized approach to meet diverse visitor preferences. To address this issue, this study developed "BoyTure", a web-based tourism application integrated with a recommendation system. The system development followed the ICONIX Process methodology, selected for its Robustness Analysis phase, which validates system logic before code implementation. The recommendation engine uses Content-Based Filtering with the Cosine Similarity algorithm, applied to a curated dataset of 74 verified destinations sourced from the Youth, Sports, and Tourism Office (Disporapar) of Boyolali Regency. Unlike standard approaches, the TF-IDF feature extraction in this system explicitly concatenates four textual attributes, destination name, category, facilities, and description, to mitigate data sparsity and enrich the semantic context. A comparative analysis justifies the selection of Cosine Similarity over Euclidean Distance or Jaccard Similarity because of its robustness in handling variable-length tourism text descriptions. Testing was conducted using the Black-Box method to ensure functional compliance, and a System Usability Scale (SUS) evaluation yielded an average score of 81.5. This SUS score demonstrates that BoyTure successfully abstracts complex algorithms into a user-friendly interface to provide accurate and personalized tourism recommendations.
Performance Enhancement of Mushroom Species Classification via Modified InceptionV3 Muhammad Khanif Naufal; Christy Atika Sari; Eko Hari Rachmawanto; Musab Iqtait
Jurnal Masyarakat Informatika Vol 17, No 1 (2026): May 2026
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.17.1.73005

Abstract

Mushrooms encompass a very large number of species, and some of them are toxic to humans. It is very difficult to classify mushroom species quickly and accurately, especially for common individuals who often encounter wild mushrooms in nature. To address this problem, this study envisioned an automated mushroom species classification system using deep learning methods and the InceptionV3 model. This model was chosen because it is highly generalizable, performs well with challenging images, and is precise for most image-based classification tasks. The dataset comprises 18 mushroom species and was created from a Kaggle version. Data balancing, preprocessing, data augmentation, and model training constitute the research work. The dataset has been divided into 70% training, 15% validation, and 15% test. The training results show that the model achieves 81.35% accuracy in identifying mushroom species. The study contributes to the development of AI-based image recognition technology that can help humans find mushrooms more rapidly and securely.
Comparative Evaluation of Machine Learning Algorithms with Data Balancing Approach and Hyperparameter Tuning in Predicting Thyroid Disorder Recurrence Darnell Ignasius; Rhyan David Levandra; Ramadhan Rakhmat Sani; Ika Novita Dewi
Jurnal Masyarakat Informatika Vol 16, No 2 (2025): November 2025
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.16.2.75073

Abstract

This research evaluates and compares the performance of five machine learning algorithms (Logistic Regression, K-Nearest Neighbors, Decision Tree, Random Forest, and Gradient Boosting) in predicting thyroid disease recurrence using patient data. The analysis was conducted on the Thyroid Disease Dataset from the UCI Machine Learning Repository. The methodology includes data preprocessing, normalization, and class balancing with the Synthetic Minority Over-sampling Technique (SMOTE). Additionally, hyperparameter tuning was conducted using GridSearchCV to optimize model performance. The results demonstrate that ensemble-based models, specifically Random Forest and Gradient Boosting, consistently outperform the other algorithms in terms of accuracy and robustness. These models achieve 95–96% accuracy across various scenarios.A key finding is that SMOTE significantly improves recall for minority classes, highlighting its value in imbalanced medical datasets.
Structural Correlation Patterns in Regional COVID-19 Surveillance Data and Implications for Epidemiological Monitoring Herfandi Herfandi; Rafat bin Mofidul; Ijaz ahmad Khan
Jurnal Masyarakat Informatika Vol 17, No 1 (2026): May 2026
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.17.1.77361

Abstract

The Covid-19 pandemic has had a significant impact on the health sector in various regions, including Kabupaten Sumbawa. This study aims to analyze relationships among attributes in the Covid-19 dataset using the Correlation Matrix algorithm within the CRISP-DM methodology. The dataset was obtained from the official website of the Government of Kabupaten Sumbawa, comprising 10,573 records, of which 405 were cleaned after the data cleaning process. The analysis was conducted using RapidMiner 9.9 software. The findings indicate a very strong correlation between the attributes KONTAK ERAT-DISCARDE, SUSPEK-DISCARDE, and KONFIRMASI-MENINGGAL DUNIA with the increase in total Covid-19 cases. In addition, a significant negative correlation was observed between the attribute PP-MASIH KARANTINA and the number of deaths. Furthermore, an almost perfect correlation was found between PROBABLE-DISCARDE and PROBABLE-MENINGGAL. Based on these findings, it is recommended that the government prioritize monitoring cases before they are declared discarded and strengthen the quarantine system for travelers. This study provides a data-driven foundation for formulating evidence-based pandemic response policies.
Comparative Analysis of Machine Learning for Stroke Classification Using YOLOv11 Detection and a Radiomics-Based Two-Stage Model Wahyu Ozorah Manurung; Ernawati Ernawati; Widhia KZ Oktoeberza; Desi Andreswari; Endina Putri Purwandari; Rusdi Efendi
Jurnal Masyarakat Informatika Vol 17, No 1 (2026): May 2026
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.
Parameter-Efficient Few-Shot Sentiment Analysis Using LoRA-Enhanced Transformers Nurudeen Jibrin; Gilbert Aimufua; Okorie Sunday Onyedikachi; Alegbe Adesola Anthony; Ugbai Solomon Chukwunwike; Fadila Dantalle Aliyu
Jurnal Masyarakat Informatika Vol 17, No 1 (2026): May 2026
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.17.1.81053

Abstract

Sentiment analysis in low-resource languages is often limited by scarce annotated data and the high computational cost of fine-tuning large language models. This study proposes a parameter-efficient framework that integrates Low-Rank Adaptation (LoRA) with lightweight transformer architectures, including AfriBERTa, DistilBERT, and MiniLMv2, for Hausa sentiment analysis using the NaijaSenti dataset. The framework is designed to address three key challenges: effective few-shot learning, robustness under extreme data scarcity, and mitigation of language-specific linguistic errors. Experimental results demonstrate that AfriBERTa-LoRA achieves 69.0% accuracy, only 4.8 percentage points below a fully fine-tuned XLM-RoBERTa baseline, while utilizing just 1.06% of trainable parameters and reducing GPU memory consumption by approximately 50%. Performance improves consistently with increasing data, indicating strong scalability under few-shot conditions. Linguistic error analysis reveals four dominant Hausa-specific failure modes accounting for 71.5% of misclassifications. Targeted mitigation strategies yield an 8.7 percentage point reduction in error rate (28% relative reduction, p < 0.01), with each individual strategy demonstrating statistical significance. These findings establish LoRA as an effective and efficient paradigm for low-resource natural language processing, providing a scalable and reproducible framework for sentiment analysis in underrepresented African languages.
A Hybrid VGG19-XGBoost Framework with SSIM-Based Feedback for Low-Resource Handwritten Digit Recognition in Digital Learning Systems Monika Mehta; Ashmeet Kaur; Madhulika Bhatia
Jurnal Masyarakat Informatika Vol 17, No 1 (2026): May 2026
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.17.1.78073

Abstract

Handwritten digit recognition is an important component of intelligent educational interfaces, particularly in early learning, digital assessment, and handwriting feedback systems. However, many deep learning-based recognition models rely on large benchmark datasets, whereas practical educational environments may involve limited and heterogeneous learner-generated samples. This study presents a technical feasibility analysis of a hybrid handwritten-digit recognition framework that combines VGG19-based deep feature extraction, XGBoost classification, and Structural Similarity Index Measure (SSIM)-based visual-similarity feedback. A small handwritten-digit dataset was used to simulate a constrained-data setting, with augmentation applied only to the training data to reduce the risk of overfitting. The proposed VGG19-XGBoost pipeline was evaluated against baseline CNN-based models using accuracy, precision, recall, and F1-score, with SSIM used as a supplementary metric to assess the structural similarity between learner input and reference digit forms. The experimental results indicate that the hybrid approach provides stable preliminary classification performance under limited-data conditions and that SSIM can support interpretable visual feedback for handwriting evaluation. However, due to the small number of original samples, the findings should be interpreted as evidence of feasibility rather than as generalizable performance claims. Future work should involve larger real-world datasets, teacher-validated scoring rubrics, and deployment-oriented evaluation in digital learning environments.
A Multi-Layer Privacy-Preserving Federated AI Framework for Smart Vehicle Cybersecurity Salwa Din; Syed Atif Ali
Jurnal Masyarakat Informatika Vol 17, No 2 (2026): November 2026 (Ongoing Issue)
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.17.2.82539

Abstract

Connected and automated vehicles rely on V2X communication, edge devices, cloud services, and onboard sensors, creating large attack surfaces and privacy challenges for conventional centralized intrusion detection systems. This study proposes and evaluates a multi-layer privacy-preserving federated AI framework for smart vehicle cybersecurity. The framework integrates in-vehicle anomaly detection, cloud-based threat correlation, and federated learning to enable collaborative model training without exchanging raw vehicular telemetry data. A hybrid experimental testbed combining NVIDIA Jetson Nano edge nodes, the Flower federated learning framework, PyTorch-based detection models, SUMO mobility simulation, and NS-3 vehicular communication modeling was used to evaluate detection performance, latency, scalability, privacy preservation, attack surface coverage, and adversarial robustness. The results show an F1-score of 0.97, inference latency below 50 ms, 89% robustness under FGSM-based adversarial perturbations, and approximately 14% CPU overhead within the evaluated fleet-size settings. Compared with traditional IDS and cloud-only detection, the proposed framework improves privacy preservation and scalability while maintaining real-time response capability. Blockchain and quantum cryptography are discussed only as potential future research directions and were not experimentally implemented or validated. These findings indicate that federated AI can provide a scalable, privacy-aware, and resilient foundation for securing next-generation smart vehicle environments.
Comparative Evaluation of FinBERT and IndoBERT for LSTM-Based Stock Price Prediction in Indonesia Heri Santosa; Kusrini Kusrini; Rodrígo Martínez-Béjar
Jurnal Masyarakat Informatika Vol 17, No 2 (2026): November 2026 (Ongoing Issue)
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.17.2.82051

Abstract

Stock price prediction remains challenging due to the nonlinear and dynamic behavior of financial markets, where price movements are influenced not only by historical market data but also by investor sentiment reflected in financial news. This study investigates the effectiveness of sentiment integration for stock price prediction by comparing FinBERT, a financial domain-specific language model, and IndoBERT, a language-specific Indonesian language model, within an LSTM-based forecasting framework. Experiments were conducted using daily stock price data and financial news collected for three Indonesian stocks (BBCA, BBRI, and BSDE) over the period 2019–2025. Sentiment scores extracted from financial news were aggregated on a daily basis and incorporated as additional features alongside historical price variables. Model performance was evaluated using MAE, RMSE, MAPE, R², and Directional Accuracy. The results indicate that sentiment-enhanced models do not consistently improve numerical forecasting accuracy compared with the baseline LSTM model. However, sentiment integration does not consistently improve directional prediction, although limited stock-specific differences are observed under certain stock-specific conditions. Comparative analysis further shows that neither FinBERT nor IndoBERT consistently outperforms the other across all datasets and metrics, suggesting that sentiment effectiveness is highly dependent on linguistic context and market characteristics. These findings highlight that sentiment information should be incorporated selectively rather than assumed to universally improve stock forecasting performance in emerging markets.

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