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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 490 Documents
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A Mathematical Modelling and Behaviour Simulation of a Smart Grid Cyber-Physical System Tamakloe, Elvis; Griffith, Klogo Selorm; Kommey, Benjamin
JOIN (Jurnal Online Informatika) Vol 9 No 1 (2024)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v9i1.1344

Abstract

The significant contributions of information and communication technology (ICT) and other operational technologies (OTs) or cyber networks have had a tremendous impact on the real-time monitoring, management, and control of the power or energy system facilities. Thus, the integration of these technologies into the energy grid system created a smart, complex, and interdependent system. This system is established and referred to as a smart grid cyber physical power system (SGCPPS). The performances of cyber physical systems are achieved via computation and communication and are imperatively based on a real-time feedback mechanism. In reference to the energy system, monitoring and control of the grid systems is extremely essential in ensuring efficient power supply, quality, reliability, stability and resilience among other determinants. However, their interdependence and integrated nature exposes the grid to disturbances subsequently leading to faults in the grid. Hence, failure to know the grid conditions at a particular period subjugates it to complete system collapse. This paper focused on the development of a mathematical model for a smart gird cyber physical system. Additionally, simulations were performed to study the behaviour of the Smart grid cyber-physical power system (SGCPPS) with regards to monitoring and controlling the physical systems using MATLAB Simulink tool to facilitate system awareness.
Reviewing the Framework of Blockchain in Fake News Detection Alam, Tanweer; Gupta, Ruchi
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.v9i2.1349

Abstract

In the social media environment, fake news is a significant issue. It might be online or offline, depending on the field of journalism. Concerns have been expressed by media and publishing houses, who are looking for solutions to the problem. One of the solutions the industry has to offer in this area is Blockchain. It could be digital security trading, source or identity verification, or quotes following a certain news piece, photo, or video. It's miles of shared document generation to deliver timely files, and it's done with the help of a specific article, video, or image that has been addressed. This will no longer assist the fact abuser in verifying the details. This will help the fact abuser confirm the details, but it will also offer documentation of metadata generated at all phases. It allows you to cut the expense of disseminating false information by forwarding and explicit disclosure to persons who have first-hand knowledge of the subject. The proposed structure for acquiring fake news is supported by the blockchain age, which allows news organizations to deliver their content to their subscribers transparently. This framework was created for journalists and can be integrated into any current platform to publish a news piece and include asset statistics.
Identification of Inpari HDB 32 Superior Rice Seeds based on Android in Realtime with Artificial Neural Network Akram, Rizalul; Atmaja, Teuku Hadi Wibowo; Novianda
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.v9i2.941

Abstract

Rice is a staple food for humans living in East Asia. Rice is a crystal fruit. The Latin name for rice is Oryza Sativa. Rice plants are 110-120 days old. The selection of quality rice seeds by farmers is seen from the bright yellow color of the rice without black/brown spots, its large size and rounder. Rice seeds that are not of good quality are dark brown in color, have black/brown spots, and are flat in shape. The absence of superior rice recognition technology that is not Android-based in real time is the main reason for this research. The focus of this research is to identify superior and non-superior rice in Inpari HDB 32 rice with a high recognition accuracy rate of more than 70 percent with a viewing angle of 0-180 degrees using the real-time ANN method. The training data used in this research was 1000 datasets consisting of 350 superior rice datasets and 650 non-superior datasets. The smart model for classifying rice seeds that has been built in this research is generally able to run well where the classification accuracy obtained is quite good. The classification accuracy of the ANN model during training of the neural network model was able to classify rice seeds with an accuracy of 70-100% with the confidence value of the real-time classification results ranging from 65-98%. Real-time classification of rice grains with maximum accuracy of 96% and many grains 73%.
Detection of Drowsiness in Drivers Using Image Processing and Support Vector Machine (SVM) Classification Sugeng; Praminiarto, Hendri
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.v9i2.1076

Abstract

Accidents can be caused by external factors on the road, vehicle conditions, or internal factors such as drowsiness. Drowsiness while driving poses risks to the driver and others. An early detection system is crucial to alert drivers to stop or rest if they show signs of drowsiness. Physical signs of drowsiness include a lethargic facial expression, frequent eye blinking, continuous yawning, or nodding off. A detection system utilizing image processing and machine learning can observe these signs by detecting facial landmarks and analyzing activities such as eye blinking, yawning, and head tilt. This study aims to classify the drowsiness condition based on these three factors. The classification process is conducted using machine learning with the Support Vector Machine (SVM) method to determine whether a person is drowsy or not. The dataset consists of the number of eye blinks, head tilts, and yawns. Conditions are classified into two classes, drowsy and not drowsy. In this study, the SVM classification method can predict drowsiness with an accuracy of up to 77% in the conducted tests.
Prediction of Solar Radiation Data for Garlic Production in Magelang Regency Using Long Short-Term Memory Safrudin, Muhammad Safrul; Sitanggang, Imas Sukaesih; Adrianto, Hari Agung; Aini, Syarifah
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.v9i2.1113

Abstract

Garlic importation in Indonesia is frequently carried out to meet the high domestic market demand. To reduce dependency on imports, the development of local garlic production is crucial. This study aims to determine the optimal solar radiation for garlic growth using the Long Short-Term Memory (LSTM) algorithm. This algorithm was selected due to its ability to analyze time-series data and predict long-term patterns. The LSTM model was trained with the Adam optimizer, using a configuration of 1000 epochs, a batch size of 6, and a dropout rate of 2.0 to prevent overfitting. The model evaluation results show an indicating good accuracy with a RMSE of 0.1020, a Mean Squared Error (MSE) of 0.0104, and a correlation coefficient of 0.740, although it still has limitations in capturing extreme data fluctuations. The study found that in Magelang Regency especially in the sub-districts of Windusari, Grabag, Ngablak, Pakis, Dukun, Kaliangkrik, and Kajoran have optimal solar radiation for garlic cultivation between March and May, with a radiation range of 380 W/m² to 440 W/m². These findings provide valuable guidance for farmers in determining the optimal planting period, potentially enhancing local garlic production and reducing import dependency.
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.
Variational Quantum Circuit-Based Quantum Machine Learning Approach for Predicting Corrosion Inhibition Efficiency of Expired Pharmaceuticals Akrom, Muhamad; Rosyid, Muhammad Reesa; Mawaddah, Lubna; Santosa, Akbar Priyo
JOIN (Jurnal Online Informatika) Vol 10 No 1 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i1.1333

Abstract

This study examines the potential of quantum machine learning (QML) to predict the corrosion inhibition capacity of expired pharmaceutical compounds. The investigation employs a QSPR model, using features generated from density functional theory (DFT) calculations as input. At the same time, corrosion inhibition efficiency (CIE) values obtained from experimental data serve as the target output. The VQC model demonstrates varied performance across evaluation metrics, especially with encoding and ansatz design. The model achieves fine scores in evaluation metrics, with root mean square error (RMSE) of 6.15, mean absolute error (MAE) of 5.63, and mean absolute deviation (MAD) of 5.50. The research underscores the significance of larger datasets for enhancing predictive accuracy and points to QML's potential in exploring anti-corrosion materials. Although there are some limitations, this study provides a foundational framework for using QML to predict anti-corrosive properties.

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