JOIN (Jurnal Online Informatika) is an international peer-reviewed journal dedicated to advancing the field of Informatics. Published by the Department of Informatics, UIN Sunan Gunung Djati Bandung, Indonesia, JOIN provides a platform for researchers, scientists, and academics worldwide to publish their research findings and share their knowledge with the broader scientific community. Focus Areas: 1. Distributed Computing: Internet of Things (IoT) Technology Cloud Computing Smart System Information Technology (IT) Automation Virtualization Network Security Cryptography Blockchain Ubiquitous Learning Parallel and Distributed Systems 2. Artificial Intelligence: Prompt Engineering Digital Image Processing Multimedia Data Processing Knowledge Representation & Reasoning Deep Learning Natural Language Processing Data Science Artificial Intelligence Driven for IoT Artificial Intelligence Driven for GameTech JOIN welcomes submissions that contribute to these areas of research, fostering collaboration and knowledge exchange among informatics professionals worldwide. JOIN (Jurnal Online Informatika) has been accredited Sinta 2 by Ministry of Research, Technology and Higher Education, Republic of Indonesia as an academic journal (SK Dirjen Dikti No. 72/E/KPT/2024) 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. Thus, we invite Academics, Researchers, and Practitioners to participate in submitting their work to this journal. ISSN : 2528-1682 (Printed) 2527-9165 (Online)
Department of Informatics, UIN Sunan Gunung Djati Bandung
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Comparison of MobilenetV2 and NASNetMobile for Lavender Flower Analysis using Convolutional Neural Network
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.
Copyright (c) 2026 Tito Sugiharto, Iwan Lesmana, Rio Priantama, Munya Saleh Ba Matraf https://creativecommons.org/licenses/by-nd/4.0
Comparative Performance of Fine-Tuned IndoBERT BASE and LARGE Variants for Emotion Detection in Indonesian Tweets
In the digital era, where emotions play a crucial role in shaping human behavior, communication, and decision-making, their expressions are often conveyed through short and informal texts on platforms such as Twitter. This research aims to improve the accuracy of emotion detection in Indonesian text using the IndoBERT-BASE-P2 and IndoBERT-LARGE-P2 transformer models. The dataset consists of 7,080 tweets annotated with six basic emotion categories (anger, fear, joy, love, neutral, and sad). The research methodology included text preprocessing, class balancing using SMOTE, and fine-tuning with optimized training parameters. Evaluation results show that IndoBERT-BASE-P2 achieved an accuracy of 84.43% and a macro F1-score of 84.33%, surpassing previous studies, while the larger IndoBERT-LARGE-P2 model tended to overfit and offered no meaningful improvement. Error analysis showed the neutral class was the most difficult to classify. These findings demonstrate that with effective preprocessing and parameter optimization, a smaller model can be a highly efficient solution for emotion classification in Indonesian text, especially in resource-constrained conditions.
Copyright (c) 2026 Sri Winarno, Ika Novita Dewi, Adhitya Nugraha, Fahri Firdausillah, Maulatus Shaffira Fitri, Talitha Olga Ramadhani, Erna Amalia Widhiyanti, Ainur Rahma Miftakhul Rizqi https://creativecommons.org/licenses/by-nd/4.0
Maleo-Short: An "In-the-Wild" Indonesian Dataset for Speaker Diarization
Speaker diarization (SD), the task of partitioning an audio stream into speaker-homogenous segments, is fundamental for analyzing multi-speaker recordings. Its application to “in-the-wild” data, such as content from the YouTube platform, poses significant challenges, including overlapped speech, ambient noise, and rapid speaker turns, thereby constituting an active research area. While numerous SD datasets are available, they predominantly focus on English and other high-resource languages. A notable scarcity of publicly accessible datasets exists for the Indonesian language, as extant corpora are primarily engineered for Automatic Speech Recognition (ASR). To address this resource deficit, this research introduces Maleo-Short, a new Indonesian multi-speaker dataset derived from YouTube. The dataset comprises 110 short conversational clips, with a total duration of 1 hours 32 minutes. A reliable ground truth was established through a meticulous manual annotation process using ELAN to generate precise speaker segmentation and transcription files. To validate its utility and assess its complexity, the dataset was evaluated using pre-trained baseline models. The empirical results confirm its status as a challenging benchmark, with the most effective models achieving a Diarization Error Rate (DER) of 32.64% and a Word Error Rate (WER) of 33.78%. Maleo-Short is presented as a valuable, publicly accessible resource intended to catalyze advancements in Indonesian speaker diarization research by facilitating the development and rigorous evaluation of SD systems on acoustically complex and realistic conversational data. Maleo-Short is available at https://doi.org/10.57967/hf/7944.
Copyright (c) 2026 Ardi Mardiana, Dinda Desmonda Muslimah, Ade Bastian, Eka Tresna Irawan https://creativecommons.org/licenses/by-nd/4.0
Optimizing Stacking Ensemble Models for Customer Churn Prediction in the Telecommunications Industry
One of the biggest challenges in the telecommunications industry is predicting churn, which is the condition when a customer unsubscribes and switches to another service provider. In an era of competitive market conditions, retaining customers is much more efficient than acquiring new customers. Conventional prediction models are often unable to capture the complexity of customer behavior patterns, resulting in a lower accuracy than optimal. This study aims to optimize customer churn prediction performance by developing a stacking ensemble model that combines several classification algorithms to improve model performance. Fourteen algorithms were tested, and the six algorithms with the best accuracy were selected as base learners, while Logistic Regression was selected as the meta-learner. The stacking model testing was carried out sequentially through a combination of 6 algorithms with the same meta-learner algorithm. Testing was also carried out with and without using the SMOTE data balancing method to evaluate the effect of data balancing on the prediction results. The results of this study show that the combination of the Adaboost, Ridge Classifier, and Logistic Regression algorithms can produce the highest accuracy of 82.97%, which exceeds the prediction performance of a single algorithm. This research contributes to demonstrating an effective stacking ensemble configuration for predicting customer churn in the telecommunications industry and emphasizes that the selection of the right algorithm combination has a greater impact on model performance than the number of algorithms used.
Copyright (c) 2026 Rofik Rofik, Jumanto Unjung, Dwika Ananda Agustina Pertiwi, Much Aziz Muslim https://creativecommons.org/licenses/by-nd/4.0
Denial of Service (DOS) Attack Detection on MQTT Protocol Using the Random Forest Method
The Message Queuing Telemetry Transport (MQTT) protocol serves as a critical lightweight communication infrastructure for Internet of Things (IoT) systems. Still, it remains highly vulnerable to Denial of Service (DoS) attacks that compromise network availability and security. Despite extensive IoT security research, existing MQTT-based intrusion detection systems predominantly employ binary classification approaches and lack comprehensive multi-class attack differentiation capabilities, limiting their practical deployment in real-world scenarios. This study addresses this critical gap by developing a multi-class DoS attack detection system utilizing the Random Forest algorithm to simultaneously classify normal traffic, MQTT flooding attacks, and SYN flood attacks. The methodology encompasses four systematic stages: collecting an MQTT network traffic dataset containing 1,634,286 records across three attack categories through controlled simulations; performing rigorous data preprocessing for cleaning and normalization; strategically extracting 60 MQTT-specific attributes to identify attack signatures; and implementing Random Forest with optimized hyperparameters for multi-class classification. Experimental results demonstrate optimal performance using an 80:20 train-test split with 5-fold cross-validation, achieving 95.27% precision, 95.09% recall, 95.08% F1-score, and 95.09% accuracy. A comprehensive evaluation using macro and micro-averaged metrics confirms the model's ability to autonomously classify MQTT network traffic types with high accuracy and balanced performance across all attack categories, offering a practical security solution for MQTT-enabled IoT infrastructure.
Copyright (c) 2026 Kharisma Monika Dian Pertiwi, Nurul Azizi Hasibuan, Dyah Putri Rahmawati https://creativecommons.org/licenses/by-nd/4.0
Early Fusion of Visual and Ingredient Representations for Multimodal Food Classification
Identifying the most appropriate food dish based on available kitchen ingredients remains a practical yet challenging task in everyday life. To address this, this study specifically aims to develop an intelligent food classification system using a multimodal approach. We propose a multimodal food classification method that performs early fusion by combining visual and textual features extracted using the Contrastive Language–Image Pretraining (CLIP) model. Features from food images and ingredient lists are fused and classified through a two-layer multilayer perceptron. The model is evaluated on the Recipes5k dataset with 4,826 samples across 101 food categories. Results show that the proposed multimodal model achieves 91.32% accuracy, outperforming text-only (85.65%) and image-only (57.26%) baselines. The main contribution of this work lies in demonstrating the effectiveness of early fusion for combining cross-modal representations in food classification. Unlike prior methods, our model supports flexible inference with either text or image input, enabling practical real-world applications. These findings highlight the potential of multimodal learning for food recommendation systems, offering both accuracy and contextual relevance beyond unimodal approaches.
Copyright (c) 2026 Navira Rahma Salsabila, Adela Regita Azzahra, Fitri Utaminingrum, Barlian Henryranu Prasetio https://creativecommons.org/licenses/by-nd/4.0
Optimizing Hypertension Prediction from Electronic Health Records via Hybrid ANN–LSTM with Ant Colony and Bayesian Optimization
Hypertension is a chronic medical condition that, if undetected early, can result in life-threatening complications such as cardiovascular disease and stroke. Despite numerous studies on predictive modeling for hypertension, existing approaches often suffer from limited accuracy due to suboptimal feature selection, inadequate hyperparameter tuning, and imbalanced datasets. This study aims to address these limitations by proposing a hybrid deep learning framework that integrates Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) models with Ant Colony Optimization (ACO) for feature selection and Bayesian Optimization (BO) for hyperparameter tuning. The proposed method is trained on Electronic Health Records (EHR) and employs the Synthetic Minority Over-sampling Technique (SMOTE) to mitigate class imbalance. Experimental results show that the optimized ANN achieves an accuracy of 94.3% and the optimized LSTM reaches 95.1%, outperforming baseline models without optimization. Improvements in precision, recall, and F1-score further demonstrate the model’s robustness in identifying hypertension cases. The main contribution of this research lies in the integration of ACO-based feature optimization and BO-based hyperparameter tuning within a hybrid ANN–LSTM framework, resulting in a clinically applicable model for early hypertension prediction. These findings suggest that the proposed approach has strong potential for deployment in electronic medical record systems to support timely and accurate clinical decision-making.
Copyright (c) 2026 Zulham, Junadhi https://creativecommons.org/licenses/by-nd/4.0
Vehicle Routing Problem: A Performance Comparison of Hybrid Evolutionary Algorithm with Local Search Strategies
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.
Copyright (c) 2026 Thoriq Firdaus Arifin, Muhammad Javier Badruttamam, Maulida Suryaning Aisha, Ibnu Raju Humam, Muhammad Hafiz, Maria Ulfah Siregar, Siti Mutmainah https://creativecommons.org/licenses/by-nd/4.0
Comparative Analysis of Pre-Trained Deep Learning Models for Classifying Tropical Fungal Skin Infections
Tropical fungal skin infections, including Tinea corporis, Tinea versicolor, Tinea pedis, and Tinea nigra, are common health problems in tropical countries such as Indonesia. Although not life-threatening, these diseases can cause discomfort, reduce self-confidence, and interfere with daily activities. Conventional diagnostic methods still rely on subjective visual observation, which is often inaccurate—especially in regions with limited infrastructure and scarce access to specialists. Moreover, existing studies rarely provide a comparative evaluation of deep learning architectures for tropical fungal infections using small and diverse datasets. Therefore, this study aims to address these challenges by conducting a systematic comparative evaluation of three pre-trained models—MobileNetV3, EfficientNet-B2, and SE-ResNet101—to determine the most accurate and computationally efficient architecture for multi-class classification of tropical fungal skin diseases. In this study, a dataset of 660 clinical skin images sourced from the Kaggle repository was used, covering four tropical fungal infection classes. The dataset consisted of 165 images per class, which were divided into training, validation, and testing subsets. Experimental results demonstrated that MobileNetV3 achieved the best performance, with a validation accuracy of 95.08%, a test accuracy of 97.34%, and the shortest training time of 15 minutes, compared to EfficientNet-B2 (16 minutes) and SE-ResNet101 (22 minutes). The main contribution of this study is to provide a systematic comparative evaluation of deep learning models for the classification of tropical fungal skin infections, while recommending MobileNetV3 as the most suitable model for practical implementation of automated image-based diagnosis in primary healthcare services with limited resources.
Copyright (c) 2026 Suhardi Aras, Muhammad RIzwan Darwis, Muhammad Zhaky Arkan https://creativecommons.org/licenses/by-nd/4.0
Improving Imbalanced Data Handling in Intrusion Detection Systems using SMOTE with an Extended Kalman Filter
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.
Copyright (c) 2026 Guntoro Guntoro, Mhd. Nizam Omar, Mohamad Farhan Mohamad Mohsin https://creativecommons.org/licenses/by-nd/4.0
CPSO-LSTM: Chaotic Particle Swarm Optimization improved LSTM Hyperparameters for Air Pollution Prediction
Accurate air pollution predictions are crucial for public health and environmental management, but achieving high prediction accuracy remains a challenge due to the complexity of temporal patterns in pollution data. This study aims to improve performance of Long Short-Term Memory (LSTM) by optimizing hyperparameters tuning based Chaotic Particle Swarm Optimization (CPSO) for air pollution predictions. Hyperparameter optimization included the number of hidden layers, neurons, activation functions, loss functions, optimizers, batch sizes, and epochs. The proposed model LSTM-CPSO compared to other models, baseline LSTM and PSO-LSTM, to predict the concentrations of PM2.5, PM10, NO2, SO2, CO, and O3 in Jakarta based on Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) metrics. The experimental results show that CPSO-LSTM achieves superior performance with MSE 0.012105, MAE 0.086356, RMSE 0.110022, and MAPE 32.31%, outperforming the baseline LSTM by 38.1% on the MSE metric and 11.9% on MAPE. Interestingly, LSTM-CPSO produces better architecture with 2 hidden layers and 91 neurons than LSTM-PSO that requires 7 hidden layers with 51 neurons. Similarly, LSTM-CPSO has shortest 5 training epochs better than LSTM-PSO with 16 epochs. This research demonstrates that chaos-based metaheuristic optimization can select the best hyperparameters to improve the performance of LSTM for air quality forecasting.
Copyright (c) 2026 Tri Andi, Andri Pranolo, Amelia Ritahani Ismail, Candra Juni Cahyo Kusuma https://creativecommons.org/licenses/by-nd/4.0
Convolutional Neural Networks for Measuring Service Satisfaction of Hajj Pilgrims through Facial Expression Analysis
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.
Copyright (c) 2026 Undang Syaripudin, Jumadi Jumadi, Diena Rauda Ramdania, Indah Sri Lestari, Indri Nurfiani, Alfin Yogi Setyawan, Maisevli Harika, Mimin Mintarsih https://creativecommons.org/licenses/by-nd/4.0
Comparative Performance Analysis of Several Python Libraries Utilizing the Least Significant Bit Method
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.
Copyright (c) 2026 Saepudin Nirwan, Rolly Maulana Awangga, Naufal Fachrudin Nirwan https://creativecommons.org/licenses/by-nd/4.0
Leveraging Blockchain for Real‑Time Monitoring and Optimization of Blood Supply Networks
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.
Copyright (c) 2026 Nasurudeen Ahamed N, Tanweer Alam https://creativecommons.org/licenses/by-nd/4.0
A Linear Sequential Model for Cloud-Based ECM: Comparative Analysis with On-Premises ECM
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.
Copyright (c) 2026 Mohd Adan Omar, Ammber Nosheen, Nur Azzah Abu Bakar, Noraziah ChePa, Ijaz Yusuf https://creativecommons.org/licenses/by-nd/4.0
Using Readability Metrics in Estimating the Readability of REpresentational State Transfer State Transfer Uniform Resource Identifiers Schema
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.
Copyright (c) 2026 Fuad Alshraiedeh, Norliza Katuk, Hossam Almahasneh https://creativecommons.org/licenses/by-nd/4.0
An Improved Offline Text-independent Chinese Writer Identification Scheme based on Two-tier Image Retrieval Mechanism
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.
Copyright (c) 2026 Gloria Jennis Tan, Ling Ling Ung, Chi Wee Tan, Zeti Darleena Binti Eri, Norlina Mohd Sabri, Hoshang Kolivand, Ghazali Sulong https://creativecommons.org/licenses/by-nd/4.0
Analysis of Facial Emotion Recognition with Various Techniques
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.
Copyright (c) 2026 Garima Sethi, Krishan Kant Sharma, Mohit Yadav, Khushwant Singh, Fernando Moreira https://creativecommons.org/licenses/by-nd/4.0
Defense Policy Strategy based on Network Centric Warfare (NCW) toward Asymmetric Threats: Case study from an Indonesian Perspective
Implementation of NCW as a core element of defense policy requires a comprehensive strategic framework. Without these basic elements, NCW may remain underutilized or fail to deliver the desired results in the face of asymmetric threats. Therefore, it is important to explore a Network Centric Warfare (NCW)-based defense strategy in addressing asymmetric threats. This paper seeks to analyze the formulation of a defense policy strategy based on the principles of Network Centric Warfare (NCW) to effectively address asymmetric threats by exploring the theoretical foundations of NCW in Indonesia Perspective. The analysis of NCW implementation strategies focuses on information superiority, communication speed, and cross-unit coordination as strategic solutions for detecting and responding to non-linear attack patterns. NCW-based defense strategies require inter-sectoral synergy as part of a holistic national security approach. Given the nature of asymmetric threats, which often involve non-state actors and span multiple domains (physical, digital, social), collaboration between the Indonesian National Armed Forces (TNI), the Indonesian National Police (Polri), the National Cyber and Cryptography Agency (BSSN), the State Intelligence Agency (BIN), and government and private institutions is crucial. NCW provides a technological framework and systems that can integrate these various actors into a national defense strategy that is responsive, adaptive, and collaborative in addressing the complexity of contemporary threats. The primary contribution of this paper is to formulate a defense policy strategy based on Network-Centric Warfare (NCW) principles, which is not only important for assessing NCW in the naval domain but also has potential application in a joint, interagency environment in Indonesia.
Copyright (c) 2026 Nengah Putra, April Kukuh Susilo https://creativecommons.org/licenses/by-nd/4.0
Evaluating RAG Performance on Small Language Models for Low-Resource Devices through Chunking and Retrieval Methods
Retrieval-Augmented Generation (RAG) combines generative capabilities of language models with external document retrieval to answer questions grounded in reference texts. However, deploying RAG on low-resource devices like Android smartphones is challenging because SLMs have limited computational capacity and depend heavily on efficient chunking and retrieval. Although interest in on-device processing is growing, research on RAG configurations for SLMs under strict resource constraints especially for domain-specific tasks remains limited. This study therefore investigates which combinations of chunking technique, chunk size, overlap, and retrieval strategy best balance accuracy and speed on low-resource devices. The evaluation uses 148 Indonesian questions sourced from an official Hajj guidebook. The study consists of two phases retrieval and generation. Retrieval is evaluated using BLEU, ROUGE-L, MRR, MAP, and Hit@k, while answer quality is measured with BERTScore. The experiments compare different chunking methods (fixed-size or semantic), chunk sizes (128 or 256 tokens), overlaps (25, 50 and 100 tokens), and retrieval methods (dense, sparse, or hybrid). Results show that sparse retrieval with 256-token chunks and 100-token overlap yields the best answer quality (F1 = 0.726). However, 128-token chunks with the same overlap provide the fastest generation time (69.737 seconds). The main contribution of this study is a systematic evaluation of RAG configurations for fully on-device SLMs using a domain-specific Hajj and Umrah dataset not explored in prior research. The findings provide practical guidance for designing efficient and accurate RAG-based question-answering systems on low-resource devices.
Copyright (c) 2026 Amelia Dewi Agustiani, Salsabila Maharani Putri, Jonner Hutahaean, Muhammad Rizqi Sholahuddin, Muhammad Riza Alifi, Ade Hodijah https://creativecommons.org/licenses/by-nd/4.0