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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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4 Levels of IoT Architecture for Smart Irrigation Rice Fields Hardyanto, R Hafid; Ciptadi, Prahenusa Wahyu; Setiawan, Endri
JOIN (Jurnal Online Informatika) Vol 10 No 2 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i2.1252

Abstract

Water is one of the main components in the agricultural sector. Traditional irrigation systems are often inefficient and ineffective, which can lead to water wastage and require huge resources. Intelligent irrigation systems based on the Internet of Things (IoT) offer a solution to overcome these problems. The purpose of this research is to create a 4 Layer IoT architecture for smart irrigation in Gadon village. The method used in this research uses research and development methods, starting from literature study, field survey, design, assembly, and testing. Design of Internet of Things (IoT) architecture using ESP8266 for irrigation of rice fields in Gadon village Dlingo, Bantul. The design of this system aims to facilitate irrigation. This system utilizes IoT technology in its implementation. This system consists of four IoT layers, namely the Smart Things layer which consists of a water level sensor, water ph sensor, with control using ESP8266. Networks and Gateways layer, which consists of a router to connect smart things with the internet, Middleware layer, and Application layer which consists of an android application for the system interface. This system contributes directly to the form of convenience for farmers to manage irrigation of rice fields using ESP8266-based IoT applications. In addition, this system also provides water level information to facilitate farmers in the irrigation process.
Comparative Analysis of IndoBERT and LSTM for Multi-Label Text Classification of Indonesian Motivation Letter Setiawan, Yosep; Lili Ayu Wulandhari
JOIN (Jurnal Online Informatika) Vol 10 No 2 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i2.1499

Abstract

The evaluation of motivation letters is a crucial step in the student admission process for one of vocational institutions in Indonesia. However, the current manual assessment method is prone to subjectivity and inconsistency, making it less reliable for fair student selection. This research presents a comparative analysis of two deep learning models, IndoBERT and Long Short-Term Memory (LSTM), for multi-label text classification of motivation letters written in Indonesian. Using a dataset of 676 motivation letters labeled with nine predefined categories, we evaluate the models based on their classification performance. The results indicate that IndoBERT outperforms LSTM, achieving an F1-score of 81%, compared to 76% for LSTM. This research provides insights into the effectiveness of IndoBERT for multi-label classification tasks in the Indonesian language and serves as a benchmark for future research in automating motivation letter evaluations.
Performance Evaluation of NAS Parallel and High-Performance Conjugate Gradient Benchmarks in Mahameru Wirahman, Taufiq; Latifah, Arnida L; Muttaqien, Furqon Hensan; Swardiana, I Wayan Aditya; Arisal, Andria; Iryanto, Syam Budi; Sadikin, Rifki
JOIN (Jurnal Online Informatika) Vol 10 No 2 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i2.1557

Abstract

High-Performance Computing (HPC) plays a crucial role in accelerating scientific advancement across numerous fields of research and in effectively implementing various complex scientific applications. Mahameru is one of the largest national HPC systems in Indonesia and has been utilized by many sectors. However, it has not undergone proper benchmarking evaluation, which is vital for identifying issues related to hardware and software configurations and confirming system reliability. Therefore, this study aims to evaluate the performance, efficiency, and capabilities of Mahameru. We present a benchmarking system on Mahameru utilizing two benchmark suites: the NAS Parallel Benchmarks (NPB) and the high-performance conjugate gradient (HPCG) benchmark. Our results indicate that the NPB exhibits a lower speedup in Message Passing Interface (MPI) compared to OpenMP, which can be attributed to the communication overhead and the nature of the computational tasks. Additionally, the HPCG benchmark demonstrates that Mahameru performance can compete with the lower tiers of the Top 500 supercomputers. When operating at full capacity, Mahameru can achieve approximately 2.5% of its theoretical peak performance. While the system generally performs reliably with parallel algorithms, it may not fully leverage hyperthreading with certain algorithms. This benchmark result can serve as a basis for decision-making regarding potential upgrades or changes to a system.
Performance Evaluation of Vehicular Ad Hoc Networks Considering Malicious Node Impact on Quality of Services Metrics Alfarizi, Naufal Faiz; Nuruzzaman, Muhammad Taufiq; Uyun, Shofwatul; Sugiantoro, Bambang; Abdullah, Mohd. Fikri Azli bin
JOIN (Jurnal Online Informatika) Vol 10 No 2 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i2.1568

Abstract

Vehicular Ad Hoc Networks (VANETs), a subset of mobile ad hoc networks (MANETs), is essential for enabling communication between vehicles in intelligent transportation systems. However, their dynamic and decentralized nature exposes them to significant security threats, particularly from malicious nodes. Attacks such as black holes and wormholes can severely degrade network performance by causing packet loss and increasing end-to-end delays. This paper aims to evaluate the impact of malicious node behavior on VANET performance using key Quality of Service (QoS) parameters, including throughput, end-to-end delay, jitter, packet delivery ratio (PDR), and packet loss ratio (PLR). The specific objective is to analyze how black hole and wormhole attacks affect communication efficiency in VANET environments. The main contribution of this work lies in the integration of Simulation of Urban Mobility (SUMO) for realistic traffic scenario generation with Network Simulator 3 (NS-3) for detailed network performance evaluation. This approach enables comprehensive simulation of VANET behavior under attack conditions. The findings provide valuable insights into the vulnerabilities of VANETs and form a basis for the design of more robust and secure vehicular communication systems.
Optimizing Machine Learning Models for Graduation on Time Prediction: A Comparative Study with Resampling and Hyperparameter Tuning Bakri, Rizal; Alam, Syamsu; Astuti, Niken Probondani; Bakhtiar, Muhammad Ilham
JOIN (Jurnal Online Informatika) Vol 10 No 2 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i2.1590

Abstract

Timely graduation prediction is a crucial issue in higher education, especially when academic, demographic, and behavioral factors interact in complex ways. However, many previous studies rely on default machine learning (ML) parameters and fail to consider the class imbalance problem, leading to suboptimal predictions. This study aims to build a comprehensive framework to evaluate the effectiveness of seven ML algorithms, which are AdaBoost, K-Nearest Neighbors, Naïve Bayes, Neural Network, Random Forest, SVM-RBF, and XGBoost, for predicting graduation on time by incorporating five resampling techniques and hyperparameter tuning. Resampling methods include Random Undersampling (RUS), Random Oversampling (ROS), SMOTENC, and two hybrid approaches (RUS-ROS and SMOTENC-RUS). Hyperparameter tuning was conducted using Grid Search, and model performance was evaluated through cross-validation and hold-out methods. The results show that Random Forest combined with RUS-ROS achieved the best performance, with an average metric score of 0.948. Statistical analysis using PERMANOVA (p = 0.009) and Bonferroni's post-hoc pairwise tests confirmed significant differences between certain models. This study contributes to the educational data mining literature by demonstrating that combining resampling and hyperparameter tuning improves classification performance in imbalanced educational datasets.
Forensic Analysis of Web Scraping Documents on Carding Forums and Shops using Latent Dirichlet Allocation: Profiling Forensic and NLP Approaches for Cybercrime Investigation Adristi, Fikri Irfan; Prayudi, Yudi
JOIN (Jurnal Online Informatika) Vol 10 No 2 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i2.1603

Abstract

This research is based on the massive cybercrime activity in carding forums and carding shops. Based on the many victims and losses from these activities a cybercrime investigation action is needed by a digital forensic investigator. The purpose of this study is to develop a forensic carding investigation framework based on document analysis of web scraping results on carding forums and carding shops, which applies forensic profiling analysis methods and natural language processing based on the latent dirichlet allocation (LDA) algorithm. The tools used for web scraping in this study are WebHarvy Version 7.3.0.222. The tools used for data processing in this study are Microsoft Excel and Orange Data Mining. The conclusion of this study shows that the application of web scraping investigation techniques on carding forums and carding shops based on an carding investigation framework has been effective in collecting relevant data and analyzing the activities of cybercriminal appropriately. Overall, this study has succeeded in developing a more organized and data-driven approach to dealing with crimes in carding forums and carding shops, which can be a reference for further research and application in the field of digital forensic investigation.
A Comparison Analysis Between ResNET50 and XCeption for Handwritten Hangeul Character using Transfer Learning Kurniadi, Dede; Nurhaliza, Nabila Putri; Balilo Jr, Benedicto B.; Aulawi, Hilmi; Mulyani, Asri
JOIN (Jurnal Online Informatika) Vol 10 No 2 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i2.1606

Abstract

The enthusiasm for Korean pop culture in Indonesia has contributed to a growing interest in learning the Korean language, including its writing system, Hangeul, which currently ranks as the 6th most studied language. Hangeul has a unique structure, where each character is arranged in syllabic blocks of consonants and vowel combinations. The main challenge in Korean character classification lies in the similarity between characters and the complex structure, making it more difficult for models to recognize. This study aims to compare two deep convolutional neural networks are ResNet50 and Xception, using transfer learning for handwritten Hangeul character classification. While previous studies have examined CNN-based character recognition, this study highlights the effectiveness of deeper architectures with limited yet augmented data. Unlike earlier works, it incorporates Grad-CAM visualizations, transfer learning with partial fine-tuning, and multiple train-test ratios to analyze model behavior. A total of 1,920 images across 24 classes were evaluated using 5-fold cross-validation, with extensive augmentation and preprocessing to simulate variation. The Machine Learning Life Cycle (MLLC) framework assessed model performance through accuracy, precision, recall, F1-score, and AUC. Both models achieved high performance, with ResNet50 consistently outperforming Xception in most folds, especially in precision and F1-score. ResNet50 achieved perfect scores (100%) across all metrics, while Xception also performed strongly with up to 99.74% accuracy. These results indicate that ResNet50 is more effective in classifying Korean letters on the dataset used in this study. For future research, a robustness evaluation can be applied using data that was not included in previous training or testing.
Land Cover Classification in Mountainous Regions Using Multi-Scale Fusion and Convolutional Neural Networks: A Case Study on Mount Slamet Yulis Rijal Fauzan; 'Uyun, Shofwatul
JOIN (Jurnal Online Informatika) Vol 10 No 2 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i2.1612

Abstract

Mount Slamet, located in Central Java, Indonesia, is a high-risk volcanic region where accurate land cover classification is essential for disaster mitigation and sustainable land management. However, satellite imagery in this area often suffers from haze and cloud cover, posing challenges to reliable classification. This study aims to develop an effective land cover classification model using Sentinel-2 imagery by addressing these visual distortions. The specific goal is to classify land cover into five classes—Forest, Settlements, Summit, RiceField, and River—using enhanced satellite images. A total of 1101 labeled images were processed through dehazing with Multi-Scale Fusion (MSF) and smoothing using a Guided Filter to improve image quality. The classification was performed using three Convolutional Neural Network (CNN) architectures: VGG-16, MobileNetV2, and DenseNet121. The main contribution of this study is the integration of a tailored preprocessing pipeline with CNN-based modeling for haze-affected mountainous satellite imagery. Among the models tested, MobileNetV2 achieved the highest accuracy of 85.4%, outperforming DenseNet121 (83.8%) and VGG-16 (82.3%). The results demonstrate the effectiveness of combining image enhancement techniques with lightweight CNN architectures for land cover classification in challenging environments with limited and imbalanced dataset.
Developing an AI-Enhanced Enterprise Architecture Model for Strategic Decision-Making in Malaysia’s Railway Industry Jayakrishnan, Mailasan; Subri, Nor Fatiha; Asri, Lyana Izzati Mohd
JOIN (Jurnal Online Informatika) Vol 10 No 2 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i2.1170

Abstract

Most developing nations, including Malaysia, still lack a model for the decision-making process that is comprehensive enough to account for a wide variety of potential effects and failures. The implementation of this investigation is crucial for Enterprise Architecture (EA) parameters for Railway Industry (RI) supplier performance that emphasize strategic decision-making processes to help the organizations become more competitive. In response to this need, the research integrates Artificial Intelligence (AI) as an enabler within the EA model to support intelligent and data-driven decision-making. This research has implemented a strategic decision-making process in the RI context and conducted it from a developing country perspective. The study identifies several elements of the decision-making process faced and experienced by the RI and the potential gaps for further observations in adopting the EA model. As a result, a fresh conceptual model enhanced with AI-driven analytics and intelligent decision support was created and assessed. By fulfilling the aims of the study, this research makes important contributions to the RI in terms of the use of EA, aligned with the worldwide standard of the four fundamental EA criteria, and explores the transformative potential of AI integration to accelerate EA adoption. The study's findings will impact both theory and practice, providing a pathway for developing nations to harness AI for strategic advantage and digital maturity.
Plant Disease Detection Using Digital Image Processing: Opportunities and Challenges Fitriani, Leni; Fatimah, Dini Destiani Siti; Tresnawati, Dewi
JOIN (Jurnal Online Informatika) Vol 10 No 2 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i2.1330

Abstract

Diseases in plants affect the yield of the plant itself. Agriculture is essential in human life, and if plant conditions are left unchecked, it will result in crop failure, which can affect the economy. Many researchers have developed methods to detect plant diseases, ranging from expert systems to deep learning algorithms. Machine learning is particularly effective for this task as it relies on datasets composed of plant images, making image processing crucial for the identification process. This article reviews the current literature and identifies several research gaps, opportunities, and challenges that must be addressed. Specifically, the article outlines potential avenues for future research in detecting plant diseases using image processing techniques. A significant opportunity exists to develop more effective algorithmic models for detecting plant diseases.

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