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JOIN (Jurnal Online Informatika)
Published by Universitas Islam Negeri Sunan Gunung Djati Bandung
ISSN : 25281682     EISSN : 25279165     DOI : 10.15575/join
Core Subject : Science,
Computer Science & IT
JOIN (Jurnal Online Informatika) is a scientific journal published by the Department of Informatics UIN Sunan Gunung Djati Bandung. This journal contains scientific papers from Academics, Researchers, and Practitioners about research on informatics. JOIN (Jurnal Online Informatika) is published twice a year in June and December. The paper is an original script and has a research base on Informatics.
Arjuna Subject : -
Articles 15 Documents
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Search results for , issue "Vol 9 No 2 (2024)" : 15 Documents clear
Comparison of EfficientNetB0 and EfficientNetB7 Models in Classifying Malaria Based on Blood Cells Kiki, Muhammad Rizkiansyah Maulana; Sari, Zamah; Rizki, Didih
JOIN (Jurnal Online Informatika) Vol 9 No 2 (2024)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v9i2.1195

Abstract

Malaria is a disease caused by the bite of malaria mosquitoes, which spreads through blood. Malaria mosquitoes will spread the Plasmodium parasite through their bites. Early malaria identification is essential so the disease can be prevented immediately. Through data science, which utilizes the CNN model, the classification of blood infected with parasites can be predicted accurately. This research uses data obtained from Kaggle website with 27,558 image samples. The data is divided into two classes, parasite-infected and uninfected, which are then divided again into two types. The first class is training data divided into 80% of the total data and the other 20% as validation data. This research used two test scenarios to obtain a more effective classification model. The first scenario uses Hyperparameter Tuning and the EfficientNetB0 model with classification results of 95%. Meanwhile, the classification achievement for scenario two was 99% by utilizing EfficientNetB7.
Predictive Performance Evaluation of ARIMA and Hybrid ARIMA-LSTM Models for Particulate Matter Concentration Kurniawan, Johanes Dian; Parhusip, Hanna Arini; Trihandaru, Suryasatria
JOIN (Jurnal Online Informatika) Vol 9 No 2 (2024)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v9i2.1318

Abstract

This study provides an objective evaluation of prediction performance models for particulate matter policy for industrial stakeholders by comparing the ARIMA and Hybrid ARIMA-LSTM models for predicting air quality data from the industrial environment. In the case of PM 1.0 concentration, we have an RMSE value of 8.29 and an error ratio of 0.45 for the ARIMA model and an RMSE value of 3.54 and an error ratio of 0.22 for the hybrid ARIMA-LSTM model. Meanwhile, for PM 2.5 concentration, we obtain an RMSE value of 6.61, an error ratio of 0.66 for the ARIMA model, an RMSE value of 2.68, and an error ratio of 0.19 for the hybrid ARIMA-LSTM model. According to this study, the ARIMA model, which is found in autoarima and represents the best model, is (2,0,1) for PM1.0 and (1,0,1) for PM2.5. The hybrid ARIMA-LSTM model outperforms the ARIMA model in terms of prediction accuracy, as evidenced by the RMSE and error ratio values, which are improved by approximately 57.30% and 51.11% for PM1.0 and 59.46% and 71.21% for PM2.5, respectively, since the hybrid ARIMA-LSTM model can accommodate variable-length sequences and capture long-term relationships to become noise-resistant, which makes higher prediction accuracy possible.
Enhancing Remote Sensing Image Quality through Data Fusion and Synthetic Aperture Radar (SAR): A Comparative Analysis of CNN, Lightweight ConvNet, and VGG16 Models Anggreyni, Desynike Puspa; Indriatmoko; Arymurthy, Aniati Murni; Setiyoko, Andie
JOIN (Jurnal Online Informatika) Vol 9 No 2 (2024)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v9i2.1321

Abstract

Remote sensing technology benefits many parties, especially for carrying out land surveillance with comprehensive coverage without needing to move the equipment close to photograph the area. However, this technology needs to improve: the image quality depends on natural conditions, so objects such as fog, clouds, and smoke can interfere with the image results. This study uses data fusion techniques to enhance the quality of remote-sensing images affected by natural conditions. The method involves using Synthetic Aperture Radar (SAR) to combine adjacent satellite images from different viewpoints, thereby improving image coverage. Three image classification models were evaluated to process the fused data: Convolutional Neural Network (CNN), Lightweight ConvNet, and Visual Geometry Group 16 (VGG16). The results indicate that all three models achieve similar accuracy and execution speed, namely 0.925, with VGG16 demonstrating a slight superiority over the others, namely 0.90.
File Integrity Monitoring as a Method for Detecting and Preventing Web Defacement Attacks Kurniawan, Candra; Triayudi, Agung
JOIN (Jurnal Online Informatika) Vol 9 No 2 (2024)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v9i2.1326

Abstract

The cybersecurity landscape in Indonesia recorded an increase in cyberattacks in 2022. One of the types of attacks observed was web defacement attacks targeting government websites. In 2022, there were a total of 2,348 web defacement attacks in Indonesia, with the majority occurring in the governmental sector. In proactive efforts to monitor and prevent web defacement attacks, this study implemented the open-source tool Wazuh and activated the file integrity monitoring module to detect file changes in the system. Testing was conducted with two types of attacks: brute force attacks to gain system access and web defacement attacks involving script insertion to trigger alerts from the file integrity monitoring. The results of the testing show that the implementation of Wazuh and the file integrity monitoring module can real-time detect malicious activities and file additions, so that it can be used to mitigate cyberattacks.
Evaluating Readiness and Acceptance of Artificial Intelligence Adoption Among Elementary School Teachers Darmawan, Erlan; Rahman, Titik Khawa Abdul; Thamrin, Nani Ronsani
JOIN (Jurnal Online Informatika) Vol 9 No 2 (2024)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v9i2.1385

Abstract

Artificial Intelligence (AI) is a computer system that mimics the human brain's ability to process information and make decisions. AI technology is used to learn patterns in data and make predictions or decisions based on that learning. Despite the potential benefits of AI in education, elementary school teachers face significant challenges in adopting AI technology due to limited training, lack of resources, and resistance to change. This research aims to identify the factors influencing the adoption of AI technology among primary school teachers in West Java, Indonesia. The study involved 384 participants and employed a quantitative approach. Specific factors influencing AI adoption were identified by developing a model for AI-based teaching and learning and assessing readiness factors. The results identified optimism, innovativeness, insecurity, discomfort, perceived validity, trust, usefulness, and ease of use as critical factors for successful AI adoption among primary school teachers in West Java. The customized adoption model provides a practical roadmap for integrating AI into teaching and learning processes, addressing regional specificities while remaining relevant to similar educational challenges worldwide. The assessment of readiness factors offers actionable insights for fostering a supportive environment for technology integration. The study concludes with recommendations for future research and implications for educators, administrators, and policymakers.

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