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Gedung Fakultas Sains dan Teknologi Lt. 4 Jurusan Teknik Informatika Jl. A.H. Nasution No. 105 Cibiru Bandung 40614 Telp. (022) 7800525 / Fax (022) 7803936 Email : jurnal@if.uinsgd.ac.id
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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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Retweet Prediction Using Multi-Layer Perceptron Optimized by The Swarm Intelligence Algorithm Jondri, Jondri; Indwiarti, Indwiarti; Puspandari, Dyas
JOIN (Jurnal Online Informatika) Vol 8 No 2 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i2.1193

Abstract

Retweets are a way to spread information on Twitter. A tweet is affected by several features which determine whether a tweet will be retweeted or not. In this research, we discuss the features that influence the spread of a tweet. These features are user-based, time-based and content-based. User-based features are related to the user who tweeted, time-based features are related to when the tweet was uploaded, while content-based features are features related to the content of the tweet. The classifier used to predict whether a tweet will be retweeted is Multi Layer Perceptron (MLP) and MLP which is optimized by the swarm intelligence algorithm. In this research, data from Indonesian Twitter users with the hashtag FIFA U-20 was used. The results of this research show that the most influential feature in determining whether a tweet will be retweeted or not is the content-based feature. Furthermore, it was found that the MLP optimized with the swarm intelligence algorithm had better performance compared to the MLP.
Optimizing YOLOv8 for Real-Time CCTV Surveillance: A Trade-off Between Speed and Accuracy Sholahuddin, Muhammad Rizqi; Harika, Maisevli; Awaludin, Iwan; Dewi, Yunita Citra; Dhia Fauzan, Fachri; Sudimulya, Bima Putra; Widarta, Vandha Pradiyasma
JOIN (Jurnal Online Informatika) Vol 8 No 2 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i2.1196

Abstract

Real-time video surveillance, especially CCTV systems, requires fast and accurate face detection. Object detection models with slow inference times are ineffective in real-time. This study addresses this challenge by improving the inference speed of the YOLOv8 model, a leading object detection framework known for its accuracy and speed. We focus on pruning the model's architecture, particularly the P5 head section, which detects larger objects. According to Bochkovskiy's 2020 research, this modification enhances the model's performance specifically for medium and small objects in CCTV footage. The standard YOLOv8 model and its modified version were compared for inference time, mean Average Precision (mAP), and model weight. The pruned YOLOv8 model cuts inference time by 15.56%, from 4.5 ms to 3.8 ms, and reduces model weight. The advantages mentioned above are offset by a 1.6% decrease in mean average precision. This research advances object detection technology by demonstrating architectural modifications' efficacy. These changes make the model faster and lighter, making it suitable for real-time surveillance. The accuracy trade-off is slight. The implications of these findings are crucial for implementing efficient object detection systems in CCTV surveillance. These findings also lay the groundwork for future research to improve such systems' speed-accuracy trade-off.
Modeling Face Detection Application Using Convolutional Neural Network and Face-API for Effective and Efficient Online Attendance Tracking Juliandy, Carles; Poi Wong, Ng; Darwin
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.v9i1.1203

Abstract

The pandemic of Covid-19 emergency has ended, but it gives us a new lifestyle every aspect of life and also in the education aspect has changed. At that moment as one of the ways to prevent pandemic infection, many governments give the policy to close the offline class and continue with online classes. The online class system encountered several problems and one of those problems was to track the students’ attendance to ensure all the students were attending the class. The teacher needed extra effort to track it because they needed to call the students one by one which is wasting time and sometimes would miss the presence of the students who attend the class. To make it effective efficient accurate and time-consuming when tracking attendance in online classes for teachers, we proposed the face detection model which combines face-api.js and CNN to detect and recognize the students’ faces to help teachers track attendance by just uploading the screenshot image of the online meeting application. We tested our model with accuracy and speed testing. With 3 images of every student’s face as training data, our model was able to recognize the face with 100% accuracy in just 41,65 seconds which is faster than calling students one by one that need almost 3 to 5 minutes if there are many students. Future research can be done by focusing research on improving the model to detect the students’ faces with different brightness, contrast, and saturation because students may not have the same place and condition when joining an online meeting class.
Classification of Bulughul Maraam Categories: Prohibitions, Recommendations, and Information Using Extreme Learning Machine and Fasttext Handayani, Rissa; Najiyah, Ina; Wisnuwardana, Dirga
JOIN (Jurnal Online Informatika) Vol 8 No 2 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i2.1205

Abstract

Hadith is the second source of Islamic law after the Quran. After the hadiths were compiled, Imam of Hadith created collections of hadiths, one of which is Imam Bukhari who compiled the book Bulughul Maraam, which is considered to have the highest level of authenticity. Digital collections of hadiths can now be found in the form of e-books and web pages, which help in the search for hadiths. The classification of hadiths is necessary to organize them by category, making it easier to search for hadiths based on their categories. Text mining is needed to classify hadiths because it can identify patterns in unstructured text. This research aims to improve the accuracy of classifying recommended, prohibited, and informational hadiths using a dataset of 7008 hadiths, which consists of primary data taken from the book Bulughul Maraam in the Indonesian language. Previously, similar research was conducted in 2017 that classified recommended, prohibited, and obligatory hadiths with an accuracy of 85%, but only for Sahih Bukhari hadiths. In this research, the same classification categories will be examined, proposing a different method, namely the Extreme Learning Machine method and Word2vec Fasttext for text representation with a larger dataset. The results of this research show a model accuracy of 86.31%, 86% precision, and 87% recall, indicating that the proposed model performs well in classifying hadiths.
Deep Learning Based LSTM Model for Predicting the Number of Passengers for Public Transport Bus Operators Siswanto, Joko; Manongga, Danny; Sembiring, Irwan; Wijono, Sutarto
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.v9i1.1245

Abstract

The bus public transportation system has low reliability and ability to predict the number of passengers. The accuracy of predicting the number of passengers by public transport bus operators is still weak, which results in failure to implement solutions by operators. A prediction model with LSTM based on deep learning is proposed to predict passengers for 4 bus public transportation operators (Go Bus, New Zealand Bus, Pavlovich, and Ritchies) which are evaluated by MSLE, MAPE, and SMAPE with variations in epoch, batch size, and neurons. The dataset is a CSV performance report on Auckland Transport (AT) New Zealand metro patronage buses (01/01/2019-07/31/2023). The best prediction model was obtained from the lowest evaluation value and relatively fast time at variations of epoch 60, batch size 16, and neurons 32. The prediction results on training and testing data improved with the suitability of the model tuning. The proposed prediction model performs predictions 12 months later for 4 predictions simultaneously with predicted fluctuations occurring simultaneously. Strong negative correlation on New Zealand Bus-Pavlovich, strong positive correlation on Go Bus with Ritchies and Pavlovich. Predictions that are less closely related and dependent are New Zealand Bus against Go Bus, Pavlovich, and Ritchies. The proposed prediction modeling can be used as a basis for creating operator policies and strategies to deal with passenger fluctuations and for the development of new prediction models.
Analysis of Data and Feature Processing on Stroke Prediction using Wide Range Machine Learning Model Wisesty, Untari Novia; Wirayuda, Tjokorda Agung Budi; Sthevanie, Febryanti; Rismala, Rita
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.v9i1.1249

Abstract

Stroke is a disease which cause the death of brain cells, so that the part of the body controlled by the brain loses its function. If not treated immediately, this disease can cause long-term disability, brain damage, and death. In this research, stroke prediction was carried out on the Stroke dataset acquired from the Kaggle dataset using various machine learning models. Then, data sampling techniques are used to handle data imbalance problems in the stroke dataset, which include Random Undersampling, Random Oversampling, and SMOTE techniques. Pearson Correlation and Principal Component Analysis are also used for dimensional reduction and analyzing the important features that are most influential in predicting stroke. Pearson Correlation produces five attributes that have the highest Pearson coefficient, namely age, hypertension, heart disease, blood sugar level, and marital status. Experimental results have demonstrated that the utilization of RUS, ROS, and SMOTE sampling techniques can significantly boost the F1-Score testing by an impressive 43.44%, 34.44%, and 35.55% respectively, as compared to experiments conducted without implementing any data sampling techniques. The highest F1-Score testing was achieved using the Support Vector Machine and Gaussian Naïve Bayes models, namely 0.83.
Case Study in Network Security System Using Random Port Knocking Method on The Principles of Availability, Confidentiality and Integrity Ernawati, Tati; Idham Kholid; Dahlan; Rohmayani, Dini
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.v9i1.1254

Abstract

Preventing unidentified individuals from misusing their access to information is a major concern when it comes to data security. Network administrators are charged with working harder to be able to secure the computer network they manage. The utilization of right method is a challenge for network administrators to protect computer network from intruders. The RPK method is one of solution to overcome this problem. This research aims to implement RPK method on the principles of availability, confidentiality, and integrity which have not been explored by previous studies. The network system configuration stage involved installing Debian 9, NMAP, Hydra, RPK, cloud server, remote admin, and attacker. The network security system's performance was tested, revealing a 99.97% availability rate and 100% confidentiality. The system's integrity was assessed, with an average response time of 0.22 seconds and 100% blocking accuracy. The test results indicate that the system's network security performance, using the RPK method, capable of protecting server attacks and effectively upholding security stability.
SAER : Comparison of Rule Prediction Algorithms on Constructing a Corpus for Taxation Related Tweet Aspect-Based Sentiment Analysis Sopian, Annisa Mufidah; Ilyas, Ridwan; Kasyidi, Fatan; Hadiana, Asep Id
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.v9i1.1275

Abstract

Twitter is a popular social media in Indonesia, and sentiment analysis on Twitter has an important role in measuring public trust, especially in taxation issues. Aspect extraction is an important task in sentiment analysis. In this research, we propose SAER, a Syntactic Aspect-opinion Extraction and Rule prediction, that used language rule-based approach using syntactic features for aspect and opinion extraction, and we compare several algorithm for rule prediction such as Random Forest Regression, Decision Tree Regression, K-Nearest Neighbor Regression (KNN), Linear Regression, Support Vector Regression (SVR), and Extreme Gradient Boosting Regression (XGBoost) that can generate rules with a tree-based approach. By employing syntactic features and rule prediction, it has been able to explore important features in a sentence. In rule prediction, comparison results show that Support Vector Regression (SVR) was identified as the most effective model for aspects rule prediction, providing the best results with a Mean Squared Error (MSE) of 0.022, Root Mean Squared Error (RMSE) of 0.150, and Mean Absolute Error (MAE) of 0.123. While XGBoost was identified as the most effective model for opinions rule prediction, with MSE of 0.013, RMSE of 0.117, and MAE of 0.075. Since we used syntactic feature-based approaches and rule prediction in this work, it is expected to be implemented for other cases, with other domain datasets.
Data Balancing Techniques Using the PCA-KMeans and ADASYN for Possible Stroke Disease Cases Ungkawa, Uung; Rafi, Muhammad Avilla
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.v9i1.1293

Abstract

Imbalanced data happens when the distribution of classes is not equal between positive and negative classes. In healthcare, the majority class typically consists of healthy patient data, while the minority class contains sick patient data. This condition can cause the minority class prediction to be wrong because the model tends to predict the majority class. In this study, we use a deep neural network algorithm with focal loss that can deal with class imbalance during training. To balance the data, we use the PCA-KMeans combination model to shrink the dataset and the ADASYN model to give the minority class more samples than it needs. In this study, the research problem is how well the two techniques can improve model performance, especially in minority case classification. The mild model is the best without data balancing, resulting in an accuracy value of 84%. The class 0 F1-score has a value of 86%, whereas the class 1 F1-score has a value of 82%. The moderate model is the best model in the case study of PCA-KMeans balancing data, resulting in an accuracy value of 89%; the class 0 F1-score is 91%; and the class 1 F1-score is 85%. The extreme model is the best model in the ADASYN data balancing case study, resulting in an accuracy value of 95%; the value in class 0 gets a F1-score of 96%, while the value in class 1 gets a F1-score of 96%. Of the three test models, the best model is obtained using ADASYN extreme data balancing with an accuracy value of 95%, the value in class 0 with a F1- score of 93%.
Development of a Mobile-Based Application for Classifying Caladium Plants Using the CNN Algorithm Chandra, Rudy; Arifin Prasetyo , Tegar; Lumbangaol, Heni Ernita; Siahaan, Veny; Sianipar, Johan Immanuel
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.v9i1.1296

Abstract

Caladium is a popular ornamental plant and has business potential. However, difficulties in recognizing the type of Caladium often occur because of the similarities in shape, pattern, and color of the leaves between the different kinds of Caladium. To overcome this problem, research will use machine learning with the Convolutional Neural Network (CNN) algorithm to build a mobile application that can accurately classify four types of Caladiums. The data set used is 1200 data with four classes; each class has 300 data. The best model is found with the parameter epoch 100, learning rate 0.001, and batch size 64. The model is then implemented in a mobile application with two menus, "Take a photo" and "Choose an image," which will display the classification output and confidence values of the four types of Caladiums. Testing with 30 test data per class achieves 0.975 accuracy on both menus. On the “Take a photo” menu, precision is 0.974, recall is 0.9725, and f1-score is 0.965. Meanwhile, on the “Choose an image” menu a precision and recall value is 0.975, and f1-score value of 0.97.

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