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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
 1   2 
Search results for , issue "Vol 9 No 2 (2024)" : 15 Documents clear
Cassava Diseases Classification using EfficientNet Model with Imbalance Data Handling Ngesthi, Stephany Octaviani; Wulandhari, Lili Ayu
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.v9i2.1300

Abstract

This research highlights the urgent need for classifying cassava diseases into five classes, such as Cassava Bacterial Blight (CBB), Cassava Brown Streak Disease (CBSD), Cassava Green Mottle (CGM), and Cassava Mosaic Disease (CMD), and Healthy. The study proposes the utilization of the EfficientNet model, a lightweight deep learning architecture, for classifying cassava diseases based on leaf images. However, the datasets available for this classification task are all unbalanced, made it difficult for researchers to perform. To tackle this imbalance issue, the authors compared several imbalance data handling methods commonly used for image classification, including SMOTE (Synthetic Minority Oversampling Technique), basic augmentation, and neural style transfer, to be applied before fed into EfficientNet. Initially, EfficientNet model without addressing dataset imbalances, the F1-Score stands at 78%, with most images misclassified into the majority class. Integration with SMOTE notably boosts the F1-Score to 82%, showcasing the efficacy of oversampling methods in enhancing model performance. Conversely, employing data augmentation, both basic and deep learning-based, lowers the F1-Score to 74% and 65% respectively, yet it results in a more balanced distribution of true positives across disease classes. The findings suggest that SMOTE surpasses the other methods in handling imbalanced data.
Strengthening the Authentication Mechanism of Blockchain-Based E-Voting System Using Post-Quantum Cryptography Laia, Sonitema; Barmawi, Ari Moesriami
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.v9i2.1305

Abstract

Election systems often face severe challenges regarding security and trust. Threats such as vote falsification and lack of transparency in vote counting have shaken the integrity of elections in various countries. The use of blockchain technology in e-voting has been proposed as an attractive solution to overcome this problem. Several studies use blockchain for the security of electronic voting systems. The existing methods are not resistant against impersonation attacks and man-in-the-middle attacks. This research proposes a new scheme to strengthen a blockchain-based e-voting system. The blockchain used in the proposed method is Ethereum. The proposed scheme uses the modified framework and The Goldreich-Goldwasser-Halevi (GGH) signature scheme. Digital signatures generated using Goldreich-Goldwasser-Halevi (GGH) can strengthen the identity of the message sender so that enemies cannot imitate someone. In this research, the Voter's public key and anonymous ID are used by the Voter to maintain the Voter's anonymity. Based on the experimental results, it can be concluded that the proposed scheme is stronger than the previous scheme because the probability of success in impersonating the sender with the proposed scheme using an impersonation attack and man-in-the-middle attack is small.
CatBoost Optimization Using Recursive Feature Elimination Hadianto, Agus; Utomo, Wiranto Herry
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.v9i2.1324

Abstract

CatBoost is a powerful machine learning algorithm capable of classification and regression application. There are many studies focusing on its application but are still lacking on how to enhance its performance, especially when using RFE as a feature selection. This study examines the CatBoost optimization for regression tasks by using Recursive Feature Elimination (RFE) for feature selection in combination with several regression algorithm. Furthermore, an Isolation Forest algorithm is employed at preprocessing to identify and eliminate outliers from the dataset. The experiment is conducted by comparing the CatBoost regression model's performances with and without the use of RFE feature selection. The outcomes of the experiments indicate that CatBoost with RFE, which selects features using Random Forests, performs better than the baseline model without feature selection. CatBoost-RFE outperformed the baseline with notable gains of over 48.6% in training time, 8.2% in RMSE score, and 1.3% in R2 score. Furthermore, compared to AdaBoost, Gradient Boosting, XGBoost, and artificial neural networks (ANN), it demonstrated better prediction accuracy. The CatBoost improvement has a substantial implication for predicting the exhaust temperature in a coal-fired power plant.
The Effect of the Number of Nodes on Data Communication Performance in Nomad Clusters Using the Gossip Protocol Hadikusuma, Ridwan Satrio; Mahyastuty, Veronica Windha; Lukas; Epril Moh Rizaludin
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.v9i2.1327

Abstract

This research aims to understand the effect of the number of nodes on the performance of data communication in Nomad clusters using the gossip protocol. Through a series of tests, it can be concluded that data communication performance is greatly affected by the number of nodes in the cluster. Tests were conducted using two clusters, where one cluster consists of three nodes. The results show that when using a cluster with three nodes, no packet loss occurs in all data transmissions performed, indicating a reliable communication system. The average latency in one data communication cycle varied in each test, but generally remained within the acceptable range of below 100ms based on data communication quality of service parameters. CPU and disc usage remained relatively stable throughout the experiment. Although there were slight differences in throughput between clusters, the throughput generally remained above 100 Mbps, which is still in the good category according to the research parameters. These results show the importance of taking into account the number of nodes in the cluster in designing and managing data communication systems in a Nomad cluster environment with the gossip protocol.
Machine Learning Monitoring Model for Fertilization and Irrigation to Support Sustainable Cassava Production: Systematic Literature Review Chusyairi, Ahmad; Herdiyeni, Yeni; Sukoco, Heru; Santosa, Edi
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.v9i2.1328

Abstract

The manual and time-consuming nature of current agronomic technology monitoring of fertilizer and irrigation requirements, the possibility of overusing fertilizer and water, the size of cassava plantations, and the scarcity of human resources are among its drawbacks. Efforts to increase the yield of cassava plants > 40 tons per ha include monitoring fertilization approach or treatment, as well as water stress or drought using UAVs and deep learning. The novel aspect of this research is the creation of a monitoring model for the irrigation and fertilizer to support sustainable cassava production. This study emphasizes the use of Unnamed Aerial Vehicle (UAV) imagery for evaluating the irrigation and fertilization status of cassava crops. The UAV is processed by building an orthomosaic, labeling, extracting features, and Convolutional Neural Network (CNN) modeling. The outcomes are then analyzed to determine the requirements for air pressure and fertilization. Important new information on the application of UAV technology, multispectral imaging, thermal imaging, among the vegetation indices are the Soil-Adjusted Vegetation Index (SAVI), Leaf Color Index (LCI), Leaf Area Index (LAI), Normalized Difference Water Index (NDWI), Normalized Difference Red Edge Index (NDRE), and Green Normalized Difference Vegetation Index (GNDVI).
Wave Downscaling Approach with TCN model, Case Study in Bengkulu, Indonesia Navialdy, Dio; Adytia, Didit
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.v9i2.1329

Abstract

When conducting marine operations that rely on wave conditions, such as maritime trade, the fishing industry, and ocean energy, accurate wave downscaling is important, especially in coastal locations with complicated geometries. Traditional approaches for wave downscaling are usually obtained by performing nested simulations on a high-resolution local grid from global grid information. However, this approach requires high computation resources. In this paper, to downscale global wave height data into a high-resolution local wave height with less computation resources, we propose a machine learning-based approach to downscaling using the Temporal Convolutional Network (TCN) model. To train the model, we obtain the wave dataset using the SWAN model in a local domain. The global datasets are taken from the ECMWF Reanalysis (ERA-5) and used to train the model. We choose the coastal area of Bengkulu, Indonesia, as a case study. The  results of TCN are also compared with other models such as LSTM and Transformers. It showed that TCN demonstrated superior performance with a CC of 0.984, RMSE of 0.077, and MAPE of 4.638, outperforming the other models in terms of accuracy and computational efficiency. It proves that our TCN model can be alternative model to downscale in Bengkulu’s coastal area.
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.

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