cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Prajanto Wahyu Adi
Contact Email
jmasif@live.undip.ac.id
Phone
+6281222260833
Journal Mail Official
jmasif@live.undip.ac.id
Editorial Address
Ruang E305 Ged. E Lt.3 Jurusan Ilmu Komputer / Informatika Fakultas Sains dan Matematika Universitas Diponegoro Jl. Prof. Soedarto, S.H Tembalang, Semarang, 50275 Telp. 024-7474754 ext. 5001
Location
Kota semarang,
Jawa tengah
INDONESIA
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 205 Documents
 17   18   19   20   21 
A Web-Based Tourism Recommendation System for Boyolali Using Content-Based Filtering and Cosine Similarity Arkan, Muhammad Naufal; Widodo, Aris Puji; Aryotejo, Guruh
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.17.1.72520

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 Naufal, Muhammad Khanif; Sari, Christy Atika; Rachmawanto, Eko Hari; Iqtait, Musab
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.17.1.73005

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; Mofidul, Rafat bin; Khan, Ijaz ahmad
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.17.1.77361

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 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.

Page 21 of 21 | Total Record : 205

 17   18   19   20   21 

Filter by Year

2010 2026


Reset
Filter By Issues
All Issue Vol 17, No 1 (2026): May 2026 (Ongoing) Vol 16, No 2 (2025): November 2025 Vol 16, No 1 (2025): May 2025 Vol 15, No 2 (2024): November 2024 Vol 15, No 1 (2024): May 2024 Vol 14, No 2 (2023): JURNAL MASYARAKAT INFORMATIKA Vol 14, No 1 (2023): JURNAL MASYARAKAT INFORMATIKA Vol 13, No 2 (2022): JURNAL MASYARAKAT INFORMATIKA Vol 13, No 1 (2022): JURNAL MASYARAKAT INFORMATIKA Vol 12, No 2 (2021): JURNAL MASYARAKAT INFORMATIKA Vol 12, No 1 (2021): JURNAL MASYARAKAT INFORMATIKA Vol 11, No 2 (2020): JURNAL MASYARAKAT INFORMATIKA Vol 11, No 1 (2020): JURNAL MASYARAKAT INFORMATIKA Vol 10, No 2 (2019): JURNAL MASYARAKAT INFORMATIKA Vol 10, No 1 (2019): JURNAL MASYARAKAT INFORMATIKA Vol 9, No 2 (2018): JURNAL MASYARAKAT INFORMATIKA Vol 9, No 1 (2018): JURNAL MASYARAKAT INFORMATIKA Vol 8, No 2 (2017): JURNAL MASYARAKAT INFORMATIKA Vol 8, No 1 (2017): JURNAL MASYARAKAT INFORMATIKA Vol 7, No 2 (2016): JURNAL MASYARAKAT INFORMATIKA Vol 7, No 1 (2016): JURNAL MASYARAKAT INFORMATIKA Vol 6, No 12 (2015): JURNAL MASYARAKAT INFORMATIKA Vol 6, No 11 (2015): JURNAL MASYARAKAT INFORMATIKA Vol 5, No 10 (2014): JURNAL MASYARAKAT INFORMATIKA Vol 5, No 9 (2014): JURNAL MASYARAKAT INFORMATIKA Vol 4, No 8 (2013): JURNAL MASYARAKAT INFORMATIKA Vol 4, No 7 (2013): JURNAL MASYARAKAT INFORMATIKA Vol 3, No 6 (2012): JURNAL MASYARAKAT INFORMATIKA Vol 3, No 5 (2012): JURNAL MASYARAKAT INFORMATIKA Vol 2, No 4 (2011): Jurnal Masyarakat Informatika Vol 2, No 3 (2011): Jurnal Masyarakat Informatika Vol 1, No 2 (2010): Jurnal Masyarakat Informatika Vol 1, No 1 (2010): Jurnal Masyarakat Informatika More Issue