cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
jurnal@if.uinsgd.ac.id
Editorial Address
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
Location
Kota bandung,
Jawa barat
INDONESIA
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
 44   45   46   47   48 
Comparison Airport Traffic Prediction Performance Using BiGRU and CNN-BiGRU Models Riyadi, Willy; Jasmir; Sika, Xaverius
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.v10i1.1362

Abstract

COVID-19 pandemic has significantly disrupted the aviation industry, highlighting the critical need for accurate airport traffic predictions. This study compares the performance of BiGRU and CNN-BiGRU models to enhance airport traffic forecasting accuracy models from March to December 2020. Data preprocessing was performed using Python's Pandas library. This involved filtering, scaling using min-max normalization, and splitting the data into 80:20 training-testing split using Python's Pandas library. Various optimization techniques—RMSProp, Adam, Nadam, Adamax, AdamW, and Lion—were applied, along with ReduceLROnPlateau, to optimize model performance. The models were evaluated using Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Mean Squared Error (MSE). The best predictive performance was observed in the United States using the CNN-BiGRU model with the Adam optimizer, achieving the lowest MAE of 0.0580, MSE of 0.0097, and MAPE of 0.0979. The use of a balanced dataset, representing each airport's traffic as a percentage of a baseline period, significantly improved prediction accuracy. This research provides valuable insights for stakeholders seeking effective airport traffic prediction methods during unprecedented times.
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.
A Comparison of YOLOv8 Series Performance in Student Facial Expressions Detection on Online Learning Tresnawati, Dewi; Nurhidayanti, Shopi; Lestari, Nina
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.v10i1.1390

Abstract

Student engagement in online learning is an important factor that can affect learning outcomes. One indicator of engagement is facial expression. However, research on facial expression detection in online learning environments is still limited, especially in the use of the YOLOv8 algorithm. This study aims to compare the performance of several YOLOv8 variants, namely YOLOv8x, YOLOv8m, YOLOv8s, YOLOv8n, and YOLOv8l in recognizing six facial expressions: happy, sad, angry, surprised, afraid, and neutral. Student facial expression data was collected through the Moodle platform every 15 seconds during the learning process. All models were trained using 640x640 pixel images for 100 epochs to improve facial expression detection capabilities. The main contribution of this study is to provide a comprehensive analysis of the effectiveness of YOLOv8 in detecting student facial expressions, which can be used to improve the online learning experience. The evaluation results show that the YOLOv8s model has the best performance with the highest mAP of 0.840 and the fastest inference speed of 2.4 ms per image. YOLOv8m and YOLOv8x also performed well with mAP of 0.816 and 0.815, respectively. Although YOLOv8x had the slowest inference speed, it was superior in detecting fear, happiness, and sadness expressions with mAP above 0.9. YOLOv8n had mAP of 0.636, while YOLOv8l achieved mAP of 0.813 with an inference speed of 9.1 ms per image. This study shows that the YOLOv8 algorithm, especially YOLOv8s, can be an effective solution to analyze student engagement based on their facial expressions during online learning.
Synergistic Disruption: Harnessing AI and Blockchain for Enhanced Privacy and Security in Federated Learning Sandi Rahmadika; Winda Agustiarmi; Ryan Fikri; Kweka, Bruno Joachim
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.v10i1.1392

Abstract

Combining blockchain technology with artificial intelligence (AI) offers revolutionary possibilities for developing strong solutions that capitalize on each technology's own advantages. Blockchain technology makes self-executing agreements possible by enabling smart contracts, which reduce the need for middlemen and increase efficiency by precisely encoding contractual terms in code. By using AI oracles, these contracts can communicate with outside data sources and make well-informed decisions based on actual occurrences. Additionally, there is a lot of potential for improving machine learning and data interchange in terms of privacy, security, and transparency through the integration of blockchain with federated learning. In order to provide accountability and transparency, the blockchain's immutable ledger can painstakingly record every transaction that takes place during the federated learning process, from data submissions to model modifications and remuneration. Participants in federated learning networks also develop trust because of blockchain's transparency and resistance to tampering. Strong participant verification procedures are put in place to strengthen data integrity and model updates, which raises the system's overall reliability. In the end, this chapter examines novel research avenues for combining blockchain technology with federated learning, providing practical methods and strategies to improve transaction security and privacy and opening the door to a new era of reliable and effective machine learning applications.
The Impact of Online Reviews to Predict The Number of International Tourists Vashellya, Zhasa; Nurmawati, Erna; Sugiyarto, Teguh
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.v10i1.1409

Abstract

The tourism sector is a potential resource for advancing the Indonesian economy. The development of the tourism industry is represented by the number of international tourist arrivals. Therefore, this indicator becomes an objective in development programs. To accomplish this goal and assess the demand aspect of the tourism sector, it is a must to have a precise forecast of the number of international visitors. This research attempts to develop precise methods and models for estimating the number of international tourists based on this premise. This study chooses Bali Province as its focus since nearly half, or 47%, of the tourists who visit Indonesia arrive through the entry point in Bali Province. This research uses the LSTM method and big data online reviews in building prediction models. The results of this study show that sentiment analysis of tourist attractions in Bali using the BERT model has an accuracy of 75%. The results also depict that reviews by visitors about tourist attractions in Bali Province during the period 2012-2023 contain more positive sentiments. Furthermore, the best model to predict the number of international tourists, with the smallest RMSE and MAPE values (39,470.64 and 11.25%, respectively), includes inflation, rupiah exchange rates, TPK, monthly sentiment scores, and the number of reviews as dependent variables. The prediction model also show that the review variables (sentiment score and number of reviews) can improve prediction accuracy.
Application of Self-Organizing Map and K-Means to Cluster Bandwidth Usage Patterns in Campus Environment Miftahuddin, Yusup; Ridwan, Abdur Rafi Syach
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.v10i1.1438

Abstract

Unequal bandwidth distribution in campus environments often stems from a lack of understanding of WiFi usage patterns, as seen at Itenas Bandung. Here, bandwidth is allocated equally across all buildings, ignoring differences in demand, leading to inefficiencies in high-usage areas and poor money management due to unnecessary allocation of resources to low-demand buildings. This study aims to optimize bandwidth allocation by analyzing usage patterns using a combination of Self-Organizing Map (SOM) and K-Means clustering methods. SOM is used to group buildings into low, medium, and high bandwidth usage categories, while K-Means refines these clusters to enhance accuracy. The proposed approach demonstrated significant improvements in clustering quality, with the Silhouette Index increasing from 0.321 to 0.773 and the Davies-Bouldin Index dropping from 0.896 to 0.623 in the first test. Similar enhancements were observed in subsequent tests, highlighting the effectiveness of this method in addressing unequal bandwidth distribution. This research offers a practical solution for more efficient network and financial management in educational institutions.
Study of the Application of Text Augmentation with Paraphrasing to Overcome Imbalanced Data in Indonesian Text Classification Sari, Mutiara Indryan; Suadaa, Lya Hulliyyatus
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.v10i1.1472

Abstract

Data imbalance in text classification often leads to poor recognition of minority classes, as classifiers tend to favor majority categories. This study addresses the data imbalance issue in Indonesian text classification by proposing a novel text augmentation approach using fine-tuned pre-trained models: IndoGPT2, IndoBART-v2, and mBART50. Unlike back-translation, which struggles with informal text, text augmentation using pre-trained models significantly improves the F1 score of minority labels, with fine-tuned mBART50 outperforming back translation and other models by balancing semantic preservation and lexical diversity. However, the approach faces limitations, including the risk of overfitting due to synthetic text's lack of natural variations, restricted generalizability from reliance on datasets such as ParaCotta, and the high computational costs associated with fine-tuning large models like mBART50. Future research should explore hybrid methods that integrate synthetic and real-world data to enhance text quality and diversity, as well as develop smaller, more efficient models to reduce computational demands. The findings underscore the potential of pre-trained models for text augmentation while emphasizing the importance of considering dataset characteristics, language style, and augmentation volume to achieve optimal results.
Road Damage Detection Using YOLOv7 with Cluster Weighted Distance-IoU NMS Rachman, Rudy; Suciati, Nanik; Hidayati, Shintami Chusnul
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.v10i1.1481

Abstract

Road damage can occur everywhere. Potholes are one of the most common types of road damage. Previous research that used images as input for pothole detection used the Faster Regional Convolutional Neural Network (R-CNN) method. It has a large inference time because it is a two-stage detection method. The object detection method requires post-processing for its detection results to save only the best prediction from the method, namely, non-maximum suppression (NMS). However, the original NMS could not properly detect small, far, and two objects close to each other. Therefore, this research uses the YoloV7 method as the object detection method because it has better mean Average Precision (mAP) results and a lower inference time than other object detection methods; with an improved NMS method, namely Cluster Weighted Distance Intersection over Union (DIoU) NMS (CWD-NMS), to solve small or close potholes. When training YoloV7, we combined a new, independently collected pothole dataset, with previous public research datasets, where the detection results of the YoloV7 method were better than those of Faster R-CNN. The YoloV7 method was trained using various scenarios. The best scenario during training is using the best checkpoint without using a scheduler. The mAP.5 and mAP.5-.95 value of CWD-NMS was 89.20% and 63.30% with 10.30 millisecond per image for inference time.
LLM-Based Information Retrieval for Disease Detection Using Semantic Similarity Muhammad Adrinta Abdurrazzaq; Edwin Lesmana Tjiong; Kent Algren Wanady
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.v10i1.1486

Abstract

Information retrieval systems are vital for disease prediction, but traditional methods like TF-IDF struggle with word meanings and produce long, complex vectors. This research uses Large Language Models (LLMs) and follows the CRISP-DM methodology to improve accuracy. Using health forum discussions labeled with specific diseases, we split the data into queries and a corpus. Semantic similarity is used to retrieve the most relevant text from the corpus. After preprocessing, we compare LLMs and TF-IDF, with LLMs achieving an accuracy of 0.911 (Top-K=30), outperforming TF-IDF. LLMs excel by creating shorter, meaningful vectors that preserve context, enabling precise semantic matching. These results demonstrate LLMs' potential to enhance healthcare information retrieval, offering more accurate and context-aware solutions. This research highlights how advanced AI can overcome traditional methods' limitations, opening new possibilities for medical informatics.
Modality-based Modeling with Data Balancing and Dimensionality Reduction for Early Stunting Detection Setiawan, Yohanes; Al Faroby, Mohammad Hamim Zajuli; Ma’ady, Mochamad Nizar Palefi; Sanjaya, I Made Wisnu Adi; Ramadhani, Cisa Valentino Cahya
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.v10i1.1495

Abstract

In Indonesia, the stunting rate has reached 36%, significantly higher than the World Health Organization's (WHO) standard of 20%. This high prevalence underscores the urgent need for effective early detection methods. Traditional data mining approaches for stunting detection have primarily focused on unimodal data, either tabular or image data alone, limiting the comprehensiveness and accuracy of the detection models. Modality-based modeling, which integrates image and tabular data, can provide a more holistic view and improve detection accuracy. This research aims to analyze modality-based modeling for the early detection of stunting. Two modalities, unimodal and multimodal, are used in this study. The main contributions of this research are the development of a comprehensive framework for modality-based analysis, the application of advanced data preprocessing techniques, and the comparison of various machine learning algorithms to identify the best model for stunting detection. The dataset, comprising images and tabular data, is sourced from Posyandu in Sidoarjo, Indonesia. Image data undergoes preprocessing, including background segmentation and feature extraction using the Gray Level Co-occurrence Matrix (GLCM), while tabular data is processed through categorical encoding. The Synthetic Minority Oversampling Technique (SMOTE) addresses class imbalance, and Principal Component Analysis (PCA) is used for dimensionality reduction. Unimodal modeling uses tabular or image data alone, while multimodal modeling combines both before classification. The study achieves the best F1 scores of 0.96, 0.91, and 0.90 for tabular-only, image-only, and image-tabular modalities, respectively, demonstrating the effectiveness of data balancing and dimensionality reduction techniques.

Page 46 of 49 | Total Record : 490

 44   45   46   47   48 

Filter by Year

2016 2025


Reset
Filter By Issues
All Issue Vol 10 No 2 (2025) Vol 10 No 1 (2025) Vol 9 No 2 (2024) Vol 9 No 1 (2024) Vol 8 No 2 (2023) Vol 8 No 1 (2023) Vol 7 No 2 (2022) Vol 7 No 1 (2022) Vol 6 No 2 (2021) Vol 6 No 1 (2021) Vol. 5 No 2 (2020) Vol 5 No 1 (2020) Vol 5, No 1 (2020) Vol 4 No 2 (2019) Vol 4 No 1 (2019) Vol 3 No 2 (2018) Vol 3 No 1 (2018) Vol 2 No 2 (2017) Vol 2 No 1 (2017) Vol 1 No 2 (2016) Vol 1 No 1 (2016) More Issue