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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 18 Documents
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Search results for , issue "Vol 11 No 1 (2026)" : 18 Documents clear
Vehicle Routing Problem: A Performance Comparison of Hybrid Evolutionary Algorithm with Local Search Strategies Maria Ulfah Siregar; Thoriq Firdaus Arifin; Muhammad Javier Badruttamam; Maulida Suryaning Aisha; Ibnu Raju Humam; Muhammad Hafiz; Siti Mutmainah
JOIN (Jurnal Online Informatika) Vol 11 No 1 (2026)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v11i1.1539

Abstract

The Vehicle Routing Problem (VRP), one of the most challenging problems in logistics and transport, has been an area of optimization solutions to minimize costs and optimize the operational process. This study examines a hybrid of metaheuristic algorithms that are combinations of the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Local Search (LS) to tackle various complexities of VRP. The hybrid approach offered better exploration and exploitation by integrating global explorations with GA and PSO and local refinement with SA and LS. The performance was performed using real datasets and generated randomly with problem sizes ranging from 9 to 100 customers. PSO-LS and GA-LS are LS-based hybrids that produce lower standard deviations, showing a stable and consistent result for small to medium problems. For example, PSO-LS computed 3.31 for 9 customers and 5.76 for 50 customers. However, SA-based hybrids, such as PSO-SA and GA-SA, presented more variability, with SA-GA reaching 100 customers as much as 7.83. These findings highlight key trade-offs while optimizing VRP between stability, efficiency, and problem scale.
Leveraging Blockchain for Real‑Time Monitoring and Optimization of Blood Supply Networks Ahamed N, Nasurudeen; Alam, Tanweer; Benaida, Mohamed
JOIN (Jurnal Online Informatika) Vol 11 No 1 (2026)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v11i1.1620

Abstract

Blood is one of the most vital fluids in the human body. Many existing blood donation systems lack sufficient time, reliable data, effective tracking, data integrity, visibility, monitoring, anonymity, and privacy. Centralized systems are also prone to failures due to their dependence on fixed locations. As a result, the availability of blood continues to decline while the demand steadily increases. Furthermore, current blood management frameworks face challenges due to the need for detailed data collection and the lack of consistent data transparency. Healthcare systems worldwide continue to struggle with ensuring the timely availability of safe blood, largely due to fragmented supply chains, limited visibility, and inefficient inventory management. This paper proposes a novel blood supply chain framework based on blockchain technology to address these challenges. Blockchain is an emerging technology gaining popularity across various domains, including voting systems, smart cities, and healthcare. This study explores how blockchain can enable real-time monitoring and optimization of blood supply networks by providing a decentralized and tamper-proof ledger for tracking donations, storage conditions, transportation, and transfusions. The proposed framework enhances data accessibility by incorporating generalized blood supply information into the shared ledger. The framework utilizes a permissioned blockchain, specifically Hyperledger Fabric, to ensure secure and efficient transaction management. This approach eliminates intermediaries and reduces the risk of illegal blood trade. Smart contracts are implemented within the permissioned network using Go and Java language to enforce data integrity and prevent unauthorized modifications. In the proposed blood cold chain framework, data cannot be altered once recorded, ensuring transparency and reliability, while continuously captured data is maintained in a simplified and structured manner. The effectiveness and value of the proposed solution are validated through a comprehensive evaluation process.
A Linear Sequential Model for Cloud-Based ECM: Comparative Analysis with On-Premises ECM Omar, Mohd Adan; Nosheen, Ammber; Abu Bakar, Nur Azzah; ChePa, Noraziah; Yusuf, Ijaz
JOIN (Jurnal Online Informatika) Vol 11 No 1 (2026)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v11i1.1635

Abstract

The rapid growth of digital information has made effective enterprise content management (ECM) essential for modern organizations. While traditional on-premises ECM systems have long supported information management needs, the shift toward cloud-based ECM offers enhanced scalability, accessibility, and cost-efficiency. Decision-makers now have the option to use cloud computing and migrate ECM systems to the cloud. Having a cloud solution can provide a significant competitive edge. For instance, it can guarantee quicker ECM deployment and lower fixed IT department costs. This paper focuses on the evolution from on-premises to cloud-based systems and proposes a linear sequential model of cloud-based ECM. It presents a conceptual framework addressing key concerns, including stages of the linear sequential model that arise in cloud-based ECM adoption. A structured literature review was conducted using major databases to support the development of the proposed model. Furthermore, the paper highlights the comparative advantages of cloud-based ECM over traditional systems, including enhanced business efficiency, real-time collaboration, and improved resource utilization. By analyzing these aspects, the paper underscores how cloud-based ECM systems transform information management, providing organizations with the tools to drive innovation and maintain a competitive edge. It is crucial to comprehend all options and activities throughout the installation of ECM in the cloud to reap the most excellent possible benefits. This study proposes and describes a broad model for cloud-based ECM implementation.
Using Readability Metrics in Estimating the Readability of REpresentational State Transfer State Transfer Uniform Resource Identifiers Schema Alshraiedeh, Fuad; Katuk, Norliza; Almahasneh, Hossam
JOIN (Jurnal Online Informatika) Vol 11 No 1 (2026)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v11i1.1653

Abstract

Uniform Resource Identifiers (URIs) may have a direct impact on the understanding of REpresentational State Transfer State Transfer (RESTful) functionality, and thus, on the discovery of final RESTful product. RESTful Web Services (WS)/Application Programming Interfaces (APIs) are designed to expose data and functionality through resources accessed by dedicated URIs over HyperText Transfer Protocol (HTTP), which recently represents the direct descriptions schema of what functions does the concerned RESTful WS/API present. Furthermore, the discovery of suitable RESTful is heavily rely on the simplicity of understanding their URI schemas, which recently suffer from critical issues in how to measure their readability. For that, WS/APIs developers aspire to measure the readability of RESTful URI schemas before exposing them over the Internet to estimate their usability. Consequently, this research proposes four readability metrics for the stated purpose namely: Flesch-Kincaid (F-K), Flesch Reading Ease (FRES), Simple Measure of Gobbledygook (SMOG), and Coleman Liau Index (CLI). The research identifies the variables required to calculate the readability metric and formulate the equations for them. Four experts in linguistics were asked to validate the proposed metrics and their identified variables. The research successfully conducted empirical research on 8 well-known RESTful WSs/APIs of the dataset, and the proposed metrics were implemented on 6952 URIs schemas. The average values for the aforementioned metrics were 7.41%, 59.63%, 6.73%, and 17.55% respectively, where in certain metrics, a low average value signifies easy readability, but in others, it signifies hard readability, and vice versa.
Comparison of MobilenetV2 and NASNetMobile for Lavender Flower Analysis using Convolutional Neural Network Sugiharto, Tito; Lesmana, Iwan; Priantama, Rio; Saleh Ba Matraf, Munya
JOIN (Jurnal Online Informatika) Vol 11 No 1 (2026)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v11i1.1654

Abstract

The identification of lavender flower varieties is a critical challenge in botany and agriculture, primarily due to the high morphological similarity among different varieties and the influence of environmental conditions on their appearance. Traditional methods of identifying lavender varieties, which often rely on manual observation, face significant limitations. These methods are time-consuming, prone to subjective error, and may not account for subtle environmental variations that affect flower morphology. The specific goal of this research is to develop an automated classification model using Deep Learning techniques, specifically Convolutional Neural Networks (CNN), to improve the accuracy and efficiency of lavender variety identification. The study leverages a dataset from Kaggle, which contains images of three lavender varieties—Lavandula angustifolia, Lavandula viridis, and Lavandula multifida. By applying data augmentation techniques to address dataset variability, the research compares two advanced CNN architectures, MobileNetV2 and NASNetMobile, for their classification performance. The key contribution of this work is demonstrating that NASNetMobile achieves superior performance, with 91.87% accuracy and a lower loss value, compared to MobileNetV2, which reaches 81.67% accuracy. This study highlights the novelty of using CNN models for lavender classification, offering a significant advancement over traditional methods by enhancing the identification process's accuracy and reducing reliance on manual and inefficient approaches. The findings have broad implications for botanical research, agricultural practices, and plant conservation efforts, showing that CNNs can significantly improve the efficiency of plant species identification.  
An Improved Offline Text-independent Chinese Writer Identification Scheme based on Two-tier Image Retrieval Mechanism Tan, Gloria Jennis; Ung, Ling Ling; Tan, Chi Wee; Eri, Zeti Darleena Binti; Mohd Sabri, Norlina; Hoshang Kolivand; Ghazali Sulong
JOIN (Jurnal Online Informatika) Vol 11 No 1 (2026)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v11i1.1666

Abstract

Research in writer identification has received significant interest in recent years due to its forensic applicability.  Undoubtedly, many achievements have been carried out on the traditional method which is without retrieval and only focused on inconsistent and lead ambiguous identification performance.  A major problem with this kind of traditional method is searching and retrieval of a document from large image repositories is currently a big issue.   In this paper, the focus aim is to determine the effectiveness and reliability of integrating retrieval mechanisms compared to the best and up-to-date techniques for writer identification without retrieval mechanism in offline text-independent Chinese writer identification.  Experiments were conducted on an open HIT-MW database which is widely used for performance evaluation and employed the same standard dataset for benchmarking. The proposed method incorporates a combination of selected features—Statistical Local Ternary Local Binary Pattern (SLT-LBP), Histogram of Contour (HC), and Gray Level Difference Method (GLDM)—integrated with a Euclidean distance-based classification framework. Experimental evaluations conducted on the publicly available HIT-MW dataset demonstrate that the proposed approach achieves an identification accuracy of 96.68%.  These results indicate the potential of the proposed method to perform competitively with existing state-of-the-art techniques, while also offering improvements in scalability and interpretability for writer identification tasks.  Integration method with two-tier image retrieval for reducing search space in interpretability of results by forensic experts when large databases are involved and improving identification rates, yet remarkable accuracy.  This area, however, still has a large room for research which can be taken by upcoming researchers.  
Analysis of Facial Emotion Recognition with Various Techniques Sethi, Garima; Sharma, Krishan Kant; Yadav, Mohit; Singh, Khushwant; Moreira, Fernando
JOIN (Jurnal Online Informatika) Vol 11 No 1 (2026)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v11i1.1674

Abstract

Facial emotion recognition (FER) is a prominent investigation area in computer vision and affective computing. It involves the automatic detection and analysis of human emotions based on facial expressions. The current work offers a broad analysis of the present state-of-the-art approaches, methodologies, and challenges in facial emotion recognition. The paper explores the various components involved in FER, including face detection, feature extraction, classification algorithms, and datasets. Additionally, it discusses the applications, limitations, and future directions of FER research. The aim of this research is to utilize Facial Emotion Recognition (FER) as an advancing technique with considerable ramifications across multiple sectors. Contemporary facial emotion recognition (FER) research extensively employs deep learning techniques, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). To enhance the performance of the FER system, attempt various feature extraction strategies, model designs, and hyper-parameter setups. Advancements in deep learning and computer vision techniques have considerably enhanced the precision and efficacy of FER systems, allowing for the accurate detection and classification of emotions from facial expressions. Facial Emotion Recognition has advanced considerably in the precise identification and interpretation of emotions conveyed through facial expressions. Ongoing research and innovation in FER could transform multiple fields, including human-computer interface, healthcare diagnostics, market research, and beyond.
Convolutional Neural Networks for Measuring Service Satisfaction of Hajj Pilgrims through Facial Expression Analysis Syaripudin, Undang; Jumadi, Jumadi; Ramdania, Diena Rauda; Lestari, Indah Sri; Nurfiani, Indri; Setyawan, Alfin Yogi; Harika, Maisevli; Mintarsih, Mimin
JOIN (Jurnal Online Informatika) Vol 11 No 1 (2026)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v11i1.1677

Abstract

Facial expressions serve as important non-verbal indicators of human emotions and can be leveraged to assess satisfaction levels in service environments. In the context of Hajj and Umrah, where verbal feedback may be limited due to language barriers or cultural factors, facial expression recognition offers a non-intrusive method to evaluate service quality. This study proposes a Convolutional Neural Network (CNN)-based model to detect emotional states such as happiness and dissatisfaction through facial expressions of pilgrims. A quantitative approach was adopted, employing preprocessing techniques including normalization, augmentation, and image resizing. The CNN architecture comprised multiple convolutional, pooling, and fully connected layers. The model was evaluated using accuracy, precision, recall, and F1-score metrics. Experiments with varying batch sizes (32, 64, 128, 256) across 50 epochs revealed that the optimal performance was achieved with a batch size of 64, resulting in an accuracy of 63%, precision of 66%, recall of 60%, and F1-score of 62%. During deployment, the model correctly classified 12 out of 16 real-world images, achieving a real-time accuracy of 78%. Therefore, the deployment results should be considered preliminary. Future studies will involve larger deployment samples, n-fold stratified cross-validation to obtain statistically reliable model performance, and subgroup analyses (e.g., lighting, facial pose, age, and gender) to better understand model behavior under diverse real-world conditions. All deployment images were collected with participant consent and processed without storing biometric data. These findings suggest that CNN-based emotion recognition can support real-time service evaluation and enhance the quality of pilgrim services during the Hajj and Umrah.
Improving Imbalanced Data Handling in Intrusion Detection Systems using SMOTE with an Extended Kalman Filter Guntoro, Guntoro; Omar, Mohd. Nizam; Mohsin, Mohamad Farhan Mohamad
JOIN (Jurnal Online Informatika) Vol 11 No 1 (2026)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v11i1.1687

Abstract

Class imbalance is a major hurdle when building intrusion detection systems (IDS). Most network traffic is normal, while certain types of attacks are very rare. This uneven distribution makes it hard for machine learning models to perform well—they often focus on the common traffic and miss the less frequent but critical attacks, like Remote to Local (R2L) and User to Root (U2R). To tackle this problem, this study proposes an improved oversampling method called SMOTE-EKF. It combines the Synthetic Minority Oversampling Technique (SMOTE) with the Extended Kalman Filter (EKF). By treating the creation of synthetic data as a nonlinear estimation problem, the EKF helps refine the generated samples, making them more accurate and reducing noise or overly broad boundaries. The method was tested on the NSL-KDD dataset using a Random Forest classifier, with performance evaluated through metrics like Accuracy, Precision, Recall, F1-score, G-Mean, and AUC-ROC, along with runtime analysis and cross-validation. The results show that SMOTE-EKF outperforms the baseline approaches, achieving impressive scores: 99.70% accuracy, 98.33% precision, 98.38% recall, 98.35% F1-score, a G-Mean of 98.29%, and an AUC-ROC of 0.993. Importantly, it also improves detection of rare attacks, with F1-scores of 96.76% for R2L and 93.65% for U2R. The SMOTE-EKF model proves to be more balanced in detecting all attack classes, without succumbing to overfitting. This study also suggests that incorporating predictive methods into the oversampling process can serve as a valuable strategy for improving the performance of machine learning-based intrusion detection systems.
Comparative Performance Analysis of Several Python Libraries Utilizing the Least Significant Bit Method Nirwan, Saepudin; Awangga, Rolly Maulana; Nirwan, Naufal Fachrudin
JOIN (Jurnal Online Informatika) Vol 11 No 1 (2026)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v11i1.1688

Abstract

Steganography serves as a critical information security technique for concealing data within digital media. While the spatial-domain Least Significant Bit (LSB) method is widely adopted due to its embedding effectiveness and straightforward implementation, this study addresses a crucial gap: the lack of implementation-level comparisons of deployable Python LSB libraries utilizing dual-metric evaluation and standardized robustness testing. We present a systematic comparative performance analysis of three distinct Python-based implementations: classical LSB sequential, LSB randomized, and Discrete Cosine Transform (DCT)-based LSB embedding. Image quality and fidelity were rigorously quantified through Peak Signal-to-Noise Ratio (PSNR) and the Structural Similarity Index Measure (SSIM). Under ideal conditions, baseline evaluations demonstrated high imperceptibility across all methods, yielding PSNR values ranging from 43.1 to 48.2 dB and SSIM scores between 0.85 and 0.95. However, standardized robustness testing by encompassing Gaussian noise, spatial cropping, and rotational manipulations exposed significant vulnerabilities. Post-manipulation image quality assessments revealed severe structural degradations, with PSNR values dropping to a range of 6.81 dB to 22.79 dB and SSIM scores falling between 0.6454 and 0.8781, depending on the attack type. Consequently, classical LSB methods exhibited Bit Error Rates (BER) of 44-54% for color images and 45-50% for grayscale images. Notably, the DCT-based method demonstrated superior resilience against geometric transformations, significantly reducing the BER to 25.37% under rotational attacks for grayscale images, compared to 50% for classical LSB. These findings provide vital empirical guidance for selecting appropriate Python implementations based on specific application requirements, effectively balancing embedding capacity, imperceptibility, and robustness against attacks.

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