JUITA : Jurnal Informatika is a science journal focused on the field of informatics, presenting articles that discuss thoughts and research related to the latest developments. JUITA is a peer-reviewed journal with open access. JUITA is published by the Informatics Engineering Study Program, Universitas Muhammadiyah Purwokerto. JUITA invites researchers, lecturers, and practitioners worldwide to exchange and advance knowledge in the field of informatics. JUITA does not accept manuscript submissions in the form of systematic literature reviews (SLR). We encourage authors to submit original research articles or scholarly works that make clear and significant scientific contributions. Documents submitted must be in MsWord format and written according to author guidelines and JUITA template. Starting in 2025, JUITA is published three times a year in March, July, and November. Currently, JUITA has been indexed by DOAJ, Google Scholar, Garuda, Dimensions, BASE, and Crossref and has been accredited by SINTA rank 2 through the Decree of the Director-General of Research and Development Strengthening of the Ministry of Research, Technology, and Higher Education No. 0173/C3/DT.05.00/2025. Journal title : JUITA: Jurnal Informatika Abbreviation: JUITA ISSN : 2579-8901 (online), 2086-9398 (print) DOI Prefix : 10.30595/juita by Type of peer-review : Blind Review Indexing : Sinta 2 and view more Frequency : 3 issues/year (March, July and Nov) Journal History : See Journal history Editors: See Editorial Team Citation analysis: | Google Scholar | Sinta | Dimensions Journal cover: get here Journal template: get here Editorial and Publication Timeline To ensure transparency and maintain high academic standards, our journal follows a structured editorial and peer-review process. The timeline below provides an approximate overview of each stage. Please note that the duration may vary depending on reviewer availability, the quality of the submission, and the extent of revisions required. 1. Initial Editorial Screening (1–2 weeks) All submitted manuscripts are first evaluated by the editorial team to ensure alignment with the journal’s scope, formatting guidelines, and basic methodological quality. Manuscripts that do not meet these requirements may be returned to authors for correction or declined at this stage. 2. Peer Review Process (3–8 weeks) Manuscripts that pass the initial screening are assigned to independent reviewer(s) under a double-blind peer review system. Reviewers evaluate the originality, methodology, clarity, and contribution of the manuscript. Based on their recommendations, the editor will issue one of the following decisions: accept, revision, major revision, resubmit for review, or reject. 3. Revision Stage (2–6 weeks) If revisions are requested, authors are expected to submit a revised manuscript within the specified deadline. Minor revisions are usually expected within 1–3 weeks, while major revisions may require 3–6 weeks. Revised manuscripts may be returned to reviewers for further evaluation if necessary. 4. Final Decision (1–3 weeks) After the revised manuscript is submitted and evaluated, the editor will make a final decision regarding acceptance or rejection. 5. Production and Publication Accepted manuscripts proceed to copyediting, layout editing, and proofreading. Authors will be asked to review proofs before final publication. Once finalized, the article will be scheduled for publication in the upcoming issue. Estimated Total Time The overall process from submission to publication typically takes approximately 2–4 months, although this timeline may vary depending on reviewer response times, revision duration, and the journal’s publication schedule. We appreciate authors’ patience and cooperation throughout the editorial process. Our editorial team is committed to maintaining a fair, timely, and rigorous peer-review system to ensure the quality of all published articles.
Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto
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Cover JUITA Vol. 14 Issue 1, March 2026
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
Editor Preface and Table of Content Vol. 14 Issue 1, March 2026
Combination of VGG19 (Encoder) and U-Net (Decoder) for Colorectal Polyp Segmentation Image
Health involves the proper function of the body and organs, with colon polyps being a common issue. Doctors often face challenges in segmenting medical images, especially endoscopic images for polyp detection. The complexity and variation in the appearance of polyps make accurate identification challenging, and the subjective manual segmentation process can result in misdiagnosis or delayed treatment. This study examines the effectiveness of the combination of U-Net decoder model architecture and VGG19 encoder in segmentation of colon polyp images. This study uses a public dataset, namely Kvasir-Seg with a total of 1000 images of colon polyps. An innovative approach using VGG19 as encoder and U-Net as decoder improves colorectal polyp segmentation, achieving high performance with a Loss of 0.05, Accuracy 0.95, Precision 0.96, Recall 0.92, IoU 0.89, and Dice 0.94. Using optimal parameters such as Nadam Optimizer, 5 Fold Cross Validation, Learning Rate 0.0001, and 25 Epochs significantly improved performance, increasing the Dice Coefficient to 0.92 and IoU to 0.86 compared to previous studies. This study concludes that the proposed architecture is reliable for colon polyp segmentation. Future work should explore attention mechanisms or transformer-based models to enhance accuracy and efficiency.
Copyright (c) 2026 JUITA: Jurnal Informatika https://creativecommons.org/licenses/by/4.0
Classification Brain Tumor in HyperparameterOptimization of VGG-16 Model and Data Augmentation Analysis
This Advancements in computational technology have driven the development of Deep Learning, particularly Convolutional Neural Networks (CNN), in the classification and recognition of digital images. This research focuses on the classification of MRI brain tumor images using the VGG-16 architecture. The primary challenges include gradient vanishing and overfitting due to a small dataset. The objective of the study is to evaluate the performance of the model with various data augmentation techniques and to assess the impact of different dataset compositions (90:10 and 70:30) for training and testing. Two model configurations are used: Model A with 4096 neurons and Model B with 128 and 64 neurons in the first two Dense layers, respectively. The tested augmentation techniques include rotation, flip, Zoom , and their combinations. The results indicate that rotation and Zoom augmentations provide the best performance for both models and dataset compositions. Model A (90:10) achieved an accuracy of 96% with rotation and 92% with Zoom, while Model B (90:10) achieved 94% with rotation and 98% with Zoom. For the 70:30 composition, Model A achieved 94% (rotation) and 90% (Zoom ), while Model B achieved 95% (rotation) and 96% (Zoom ). This research provides valuable insights into optimizing VGG-16 architecture for brain tumor classification using limited datasets.
Copyright (c) 2026 Putu Desiana Wulaning Ayu, I Gede Teguh Satya Dharma, I Wayan Rizky Wijaya, Made Agus Oka Gunawan, Ni Putu Eka Apriyanthi, Civica Moehaimin Dhewanty https://creativecommons.org/licenses/by/4.0
Mobile Forensic Investigation of E-Commerce Fraud Using DFRWS Method and Perceptual Hashing
Social media platforms have enabled real-time communication and broad user interaction, but they are often exploited for cybercrime. One such vulnerable medium is e-commerce applications, which facilitate transactions and store sensitive user data. This study investigates digital evidence in a simulated fraud case involving an e-commerce application by applying mobile forensic techniques guided by the Digital Forensic Research Workshop framework. The investigation focused on recovering user accounts, text messages, images, and videos from an Android smartphone. Two forensic tools Oxygen Forensic Detective and MOBILedit Forensic Express were used for data extraction and analysis. To improve the reliability of visual evidence, the study incorporated perceptual hashing and wavelet hashing techniques to validate compressed image files. The results showed that Oxygen Forensic Detective recovered 71.4% of digital evidence, while MOBILedit achieved 57%. Although both tools successfully recovered multimedia files, Oxygen performed better in extracting text messages. These findings demonstrate the effectiveness of mobile forensic methods in identifying and validating digital evidence in e-commerce fraud cases. Moreover, integrating the DFRWS methodology with perceptual hashing significantly improves the interpretation of manipulated or compressed images, thus enhancing the evidentiary value for legal proceedings.
Copyright (c) 2026 Rizal Prambudi, Imam Riadi, Murinto Murinto https://creativecommons.org/licenses/by/4.0
Interactive 3D Rendering of the Human Heart on Mobile Web Using WebGL and Three.js
The advancement of web-based 3D visualization technology has created new opportunities for interactive medical learning, particularly in anatomy education. The existing rendering techniques for the mobile web still face challenges due to limitations of cellular and mobile device capacity This study focuses on optimizing real-time rendering of an interactive 3D heart model for mobile web platforms using WebGL and Three.js. Several optimization techniques were applied, including Draco compression, polygon reduction, and the GLB file format, to achieve high rendering performance while maintaining anatomical accuracy. Performance testing was conducted on three device tiers—low-, mid-, and high-end—under different network conditions. Key metrics such as frame rate, loading time, and memory usage were systematically measured. The optimized system achieved stable rendering at 58–60 FPS with a reduced loading time from 6.2 seconds to 1.4 seconds, demonstrating strong scalability and responsiveness. From an educational perspective, this interactive 3D heart model enables medical students, trainees, and patients to dynamically explore cardiac anatomy, improving their spatial understanding of complex structures without requiring high-end VR hardware. The novelty of this work lies in its optimization pipeline tailored for mobile web, making real-time anatomical visualization lightweight and accessible. Future research will involve larger user studies to evaluate educational effectiveness.
Copyright (c) 2026 Sunardi Sunardi, Herman Herman, Krisna Astianingrum https://creativecommons.org/licenses/by/4.0
Performance Analysis of SVM and BERT in Predicting the Availability of Stunting Prevention Services in Indonesia
Stunting, defined as being too short for one's age with a Height-for-Age Z-score (HAZ) below –2 SD according to WHO, remains a serious public health problem in Indonesia. This study predicts the availability of stunting prevention services at the village level using machine learning. Data from 25,800 villages were categorized into Complete (9,245), Partial (13,609), and Not Available (2,946), showing class imbalance. Two algorithms were evaluated: Support Vector Machine (SVM) with TF-IDF and SMOTE for class balancing, and Bidirectional Encoder Representations from Transformers (BERT) using IndoBERT with class-weighted loss. Evaluation metrics included accuracy, precision, recall, F1-score, and computation time. Results show BERT achieved 92% accuracy with consistent performance across classes (cross-validation 91.55%, SD 0.0024), effectively capturing contextual meaning in narrative text. SVM reached 83% test accuracy with fast computation (±1 min 42 s) and remained robust for imbalanced data. Both models performed well, but minority-class recognition remains challenging. These findings highlight the complementary strengths of SVM and BERT, providing data-driven insights to support policy decisions and improve targeting of stunting prevention services at the village level.
Copyright (c) 2026 Bayu Anugerah Putra, Fitri Handayani, Yulia Fatma, Jhidan Daelvin Hendra https://creativecommons.org/licenses/by/4.0
Leafy AI: Integrating MobileNetV2 and TensorFlow Lite into a Flutter-Based Application for Real-Time Ornamental Plant Recognition
Operating artificial intelligence on smartphones attracted interest in various applications, but in practice, device capacity limited AI capabilities. Limited processing power, restricted memory capacity, and unstable network connectivity could make AI models difficult to use outside lab environments. In this work, we describe Leafy AI, a mobile application that identifies ornamental plants designed to work fully on the device. The classifier is based on MobileNetV2 and trained with transfer learning using 67,200 images from 112 plant categories. Images were resized to 224 × 224 pixels and normalized before training. After training, the model was converted into TensorFlow Lite format and integrated within a Flutter application. A lightweight service layer manages preprocessing and inference so that the interface remains simple for the user. Evaluation using 13,440 test images achieved a top-one accuracy of 0.89. A smaller field experiment involving 226 photos captured under real-world conditions resulted in lower accuracy, primarily due to variations in lighting and background. Nevertheless, the system remained reliable in offline mode. The findings show that recognition of ornamental plants can be carried out on ordinary smartphones and that further improvements are possible through augmentation, domain adaptation, quantization, and hardware acceleration.
Copyright (c) 2026 Haris Setyawan, Nur Zareen Zulkarnain, Abian Ayatullah Fikri https://creativecommons.org/licenses/by/4.0
Evaluation Municipal E-Health Service Quality: An E-GovQual and IPA Assesment of Surabaya’s Public Healthcare Platform
The Surabaya City E-Health service is one of the digital public services used for online queue registration for community health centers and hospitals owned by the Surabaya City government. Over the past three years, the number of individuals using the service has risen, but during implementation, service users still face several obstacles. The obstacles users encounter certainly affect their experience using the service. Therefore, it is important to evaluate the quality of Surabaya City's E-Health service using the E-GovQual and Importance-Performance Analysis (IPA) methods. Data collection was conducted using questionnaires distributed to users, and 400 respondents who met the research sample criteria were obtained. The analysis using E-GovQual found the lowest gap level for the RL6 indicator at -0.17 and the highest level of conformity at 96%. The quadrant mapping results on the IPA diagram identified six indicators that were highlighted for improvement: TR1, TR2, TR3, TR4, RL2, and CS2. Several improvement recommendations were provided in accordance with the priority improvement indicators, including those related to the security of service users' personal data. In contrast to earlier E-GovQual–IPA research that mainly evaluates general e-government portals, this study delivers an in-depth assessment of a municipal public healthcare digital service and presents targeted strategic suggestions derived from quadrant prioritization. The results emphasize the essential importance of trust-related factors, especially data protection and system dependability, in enhancing public trust in local e-health services
Copyright (c) 2026 Rahadian Bisma, Dewi Rara Prameisty https://creativecommons.org/licenses/by/4.0
Using Text-Based Interactive Games as a Tool for Studying Decision Making Patterns
Decision making is a key factor in student success and wellbeing. Yet little is known about how interactive games foster reflection on daily habits. This study examined the influence of the text-based game A Day at Home on university students’ free-time activities, satisfaction, and intentions to change habits. A qualitative descriptive design was applied with 29 participants aged 18–25 who completed pre- and post-game questionnaires. Data were analyzed thematically and supported by descriptive statistics. The findings show that most students (52 %) reported consistency between their real-life and in-game free-time activities, while 35 % noted differences. Satisfaction with free-time use remained unchanged for 65.5 % of students but decreased for 34.5 %, indicating greater self-awareness and critical evaluation of current routines. Nearly half (48.3 %) expressed new or stronger intentions to adopt healthier behaviors, such as exercising more or reducing gaming. These results suggest that although the game did not alter habits for the majority, it provided a reflective tool that encouraged students to assess their routines and consider alternatives. The study concludes that text-based interactive games offer a promising approach to support reflection, motivate behavioral change, and enhance student wellbeing in higher education.
Copyright (c) 2026 Tony Tan, Tony Wibowo, Riyaldi Walvinson https://creativecommons.org/licenses/by/4.0
Legal Case LLM: An Open-Source Fine-Tuned Model for Indonesian Human Trafficking Jurisprudence
This paper presents Legal-Case LLM, an open-source, fine-tuned language model tailored for Indonesian human-trafficking jurisprudence. General-purpose large language models exhibit high fluency but risk factual hallucination and limited jurisprudential fidelity when applied to legal texts. The objective is to develop a reproducible model that improves factual recall, legal terminology use, and jurisprudential alignment for Indonesian trafficking cases. Methods: We assembled a curated corpus of 400 court decisions from the Direktori Putusan Mahkamah Agung, extracted structured metadata and summaries, and generated question–answer pairs via large models followed by multi-stage cleaning and expert validation. We fine-tuned open models from the LLaMA family variants using parameter-efficient techniques (LoRA), evaluated with automatic metrics (ROUGE, BLEU, BERTScore, BARTScore), and a focused qualitative audit. Results: The fine-tuned model demonstrates marked improvements in content recall and semantic alignment versus zero-shot baselines, produces more jurisprudentially aligned phrasing (accurate use of terms such as amar putusan, Majelis Hakim, and percobaan), and reduces hallucination propensity in statute-related outputs. Conclusion and impact: Legal-Case LLM offers a reproducible, transparent tool to assist legal practitioners and researchers in Indonesia, while emphasising human-in-the-loop verification and citation-matching to ensure legal reliability and ethical deployment.
Copyright (c) 2026 Muhammad Hariz Faizul Anwar, Nizam Avif Anhari, Galih Wasis Wicaksono, Nur Putri Hidayah https://creativecommons.org/licenses/by/4.0
A Comparative Analysis of Transformer-Based Topic Modeling Pipelines for Scientific Literature
The exponential growth of scientific literature poses a significant challenge for manually identifying thematic trends, necessitating automated analysis methods. This study aims to determine an optimal topic modeling pipeline by conducting a comparative analysis to maximize the coherence of topics extracted from scientific research. Three distinct pipelines were implemented and evaluated on a corpus of 20,972 scientific article abstracts. These included a custom pipeline combining SBERT, UMAP, and HDBSCAN; a second configuration using RoBERTa, PCA, and KMeans; and a third using the integrated BERTopic model. Performance evaluation, quantitatively benchmarked using the C_v coherence score, revealed that the integrated BERTopic model achieved the highest score of 0.7012. This result significantly surpassed the custom SBERT-based pipeline and the RoBERTa-based pipeline, which scored 0.6079 and 0.4756, respectively. The findings demonstrate that an integrated, purpose-built model like BERTopic is superior for generating highly coherent and interpretable thematic structures from scientific text. This research provides empirical guidance for researchers, benchmarking how integrated models offer a more robust solution for large-scale literature analysis compared to modular pipeline designs.
Copyright (c) 2026 Farriel Arrianta Akbar Pratama, Muhammad Eka Nur Arief, Vinna Rahmayanti Setyaning Nastiti https://creativecommons.org/licenses/by/4.0
Evaluation of CNN Architectures for Kidney Stone Classification in Ultrasound Image
Kidney stone diagnosis requires fast and reliable evaluation, yet ultrasound interpretation still largely depends on clinical expertise. This study evaluates four Convolutional Neural Network (CNN) architectures, VGG16, ResNet50, MobileNetV2, and EfficientNetB0 for classifying kidney ultrasound images into Normal and Stone categories. Using a public dataset of 9,416 images, the models were assessed in terms of predictive performance and computational efficiency. MobileNetV2 achieved perfect classification performance, recording 100% accuracy, precision, recall, and F1-score, while maintaining the smallest parameter size (≈3.6M) and fastest training time (~44 s/epoch). VGG16 and ResNet50 also delivered near perfect accuracy (99.79% and 99.89%) with full recall for Stone cases. In contrast, EfficientNetB0 failed to generalize, yielding only 51.62% accuracy due to severe misclassification of Normal images. These results demonstrate that MobileNetV2 provides the most reliable and efficient solution for ultrasound based kidney stone classification, highlighting its strong potential for practical clinical deployment.
Copyright (c) 2026 Zuriati Zuriati, Sriyanto Sriyanto, Agiska Ria Supriyatna, Nurul Qomariyah, Dian Ayu Afifah, Zarnelly Zarnelly https://creativecommons.org/licenses/by/4.0
SmartHealth a Web-Based Platform for Adolescent Health Education with User Testing
Adolescents in Indonesia continue to face barriers in accessing accurate, engaging, and relevant health education, while digital health literacy remains uneven. This study aims to develop and evaluate SmartHealth, a web-based adolescent health education platform, using an integrated theoretical framework combining the Technology Acceptance Model, Health Belief Model, Theory of Planned Behavior, and the Digital Health Literacy Model. The research employs a Research and Development approach that integrates the Borg and Gall model with the Waterfall software development method across four stages: need assessment, product design, system development, and limited field testing. A total of 60 high school students participated in usability testing using the System Usability Scale. The results show that SmartHealth achieved a mean usability score of 78.4, indicating good user acceptance, ease of use, and suitability for adolescent needs. The platform successfully provides accessible digital health content, interactive features, and online consultation support. This study concludes that SmartHealth is feasible, usable, and contextually relevant as a digital health education platform for Indonesian adolescents.
Copyright (c) 2026 Rosmalina, Sutiyono Waluyo, Siti Solihat Holida, Khilda Nistrina, Devia Fitri Nopiani5 https://creativecommons.org/licenses/by/4.0
A Multispectral YOLOv8-Based System for Real-Time Object Detection and Distance Estimation in Blind Navigation
Developing reliable real-time navigation systems for visually impaired individuals remains challenging, particularly in dynamic and low-light environments. This study proposes an integrated framework combining YOLOv8, OpenCV-based monocular distance estimation, and RGB–NIR multispectral imaging to enhance detection robustness and distance awareness. A dataset of 1,700 annotated images collected from diverse indoor and outdoor environments was used for training and evaluation using preprocessing techniques such as resizing, normalization, and data augmentation. System performance was evaluated using Precision, Recall, F1-Score, mean Average Precision (mAP), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Frames Per Second (FPS). Experimental results show that YOLOv8x achieved the best performance with an F1-Score of 0.91, mAP@50 of 0.74, MAE of 0.15 m, RMSE of 0.20 m, and a processing speed of 22 FPS. Multispectral RGB–NIR integration further improved low-light performance, increasing the F1-Score from 0.83 to 0.89 and reducing MAE from 0.28 m to 0.19 m with only a minor reduction in speed. These findings demonstrate that the proposed system provides an effective balance between accuracy and real-time performance for assistive navigation applications.
Copyright (c) 2026 Ema Utami, Erwin Syahrudin, Anggit Dwi Hartanto, Suwanto Raharjo https://creativecommons.org/licenses/by/4.0
A Hybrid Case-Based Reasoning Framework Using KNN, Word2Vec, and Cosine Similarity for Employee Attrition Analysis
Employee attrition prediction remains a longstanding challenge in human resource analytics, as organizations increasingly depend on computational decision-support systems that are transparent, consistent, and operationally accountable. Conventional methods that rely solely on numerical attributes are restricted in their ability to accurately capture the structural and contextual relationships inherent in categorical and text-based employee descriptors. To overcome this limitation, the current study investigates a hybrid Case-Based Reasoning (CBR) retrieval framework that combines K-Nearest Neighbors (KNN) with Word2Vec embeddings derived from the dataset's limited textual attributes, specifically Department, Gender, EducationField, MaritalStatus, and OverTime. Eight experimental configurations were assessed to examine the impact of alternative similarity metrics and diverse feature representations. The optimal configuration of KNN, enhanced with Word2Vec embeddings and cosine similarity, attained an accuracy of 0.8526 and a weighted F1-score of 0.8000, thereby exceeding the performance of baseline models based solely on numerical features and those utilizing Manhattan distance. Nonetheless, the improvements in performance remained limited owing to dataset-specific limitations, such as class imbalance and the inherently superficial characteristics of the textual descriptors, which restrict the semantic richness of Word2Vec embeddings. Furthermore, the IBM attrition dataset does not encompass downsizing or termination situations, highlighting conceptual and ethical constraints when utilizing similarity-based predictions for high-stakes HR decisions. Overall, the findings indicate that hybrid similarity representations, particularly the combination of Word2Vec embeddings with cosine distance, can improve the structural expressiveness of CBR, although their predictive effectiveness is still limited by data sparsity and considerations of fairness.
Copyright (c) 2026 Akhmad Arif Faisal Siregar, Ema Utami, Tika Novita Sari https://creativecommons.org/licenses/by/4.0
Enhancing Geospatial House Price Prediction in Greater Jakarta Using XGBoost and ResNet18 Feature Fusion
Precise house price predictions play a vital role in shaping housing policies and informing investment decisions in urban regions such as Greater Jakarta, encompassing Jakarta, Bogor, Depok, Tangerang, and Bekasi. Most models rely exclusively on structured data, ignoring spatial and environmental factors that influence property prices. This study proposes a multimodal machine learning framework that integrates structured property data with Sentinel-2 satellite imagery within a geospatial context. The baseline dataset consists of 17 tabular variables. The ResNet-18 algorithm extracts visual environmental information from satellite imagery. The integration of both modalities through a late fusion strategy results in improved predictive performance. The baseline XGBoost achieved R² scores of 0.81 (log scale) and 0.79 (Rupiah), with an error of about 184 million Rupiah. The image-only model achieved an R² of 0.43, indicating moderate explanatory capability. Late fusion further improved performance, achieving R² values of 0.94 (log scale) and 0.93 (Rupiah), while reducing prediction error by over 40%.
Copyright (c) 2026 Hadi Santoso, Alif Biuti Anastasya https://creativecommons.org/licenses/by/4.0
Dual-Stage Bifurcation With Genetic Algorithm Extraction for Robust Anti-Antiforensic Steganalysis in Grayscale Images
Steganalysis plays a crucial role in digital forensics by detecting and retrieving hidden information embedded within digital media. Traditional statistical methods, such as Chi-Square and RS-Analysis, are computationally efficient but ineffective against adaptive steganography techniques that minimize detectable distortions. While recent deep learning-based approaches, particularly Convolutional Neural Networks (CNNs), have improved detection accuracy, most rely on single-stream architectures and focus solely on classification, neglecting the recovery of concealed payloads. This study proposes a dual-stage steganalysis framework that integrates a bifurcated CNN for enhanced detection with a genetic algorithm-based extraction pipeline for payload recovery. The bifurcation architecture extends GBRAS-Net by enabling parallel feature learning paths to capture diverse noise patterns, while the extraction module employs chromosome key encoding, Hilbert Curve scrambling, and LZMA compression to reconstruct hidden data. Evaluations on BOSSbase 1.01 and BOWS 2 datasets show that the proposed method achieves an average detection accuracy of 92.53%, outperforming the original GBRAS-Net (89.8%) and other CNN-based models by a statistically significant margin (p < 0.01). Furthermore, the extraction module achieves 100% payload recovery with perfect data integrity verification. The results demonstrate that integrating bifurcated feature learning with robust extraction addresses critical gaps in current steganalysis, offering a practical forensic tool for both detection and reconstruction of hidden information. This approach has significant potential for applications in law enforcement, cybersecurity, and intelligence operations.
Copyright (c) 2026 Amadeus Pondera Purnacandra, Yudi Prayudi https://creativecommons.org/licenses/by/4.0
Performance Evaluation of LoRa-Based Wireless Sensor Network for Smart Waste Bank Data Transmission
This study presents the design and performance evaluation of a LoRa-based Wireless Sensor Network (WSN) prototype for data transmission in a Smart Waste Bank system operating in a semi-urban environment. The proposed system adopts a hybrid network architecture, combining multi-hop communication among sensor nodes with a star topology for long-range transmission to a Raspberry Pi–based sink node. An ESP32 microcontroller integrated with a LoRa SX1278 module operating at 433 MHz is used to transmit waste volume and activity data with low power consumption. Unlike prior studies that predominantly focus on generic LoRa performance evaluation, this research provides a contextual and application-driven assessment of LoRa-based WSN communication tailored to community-scale Smart Waste Bank operations under non-line-of-sight conditions. System performance was evaluated using RSSI, SNR, and PDR at transmission distances ranging from 10 m to 1000 m. Experimental results show that RSSI decreases from −56 dB to −112 dB and SNR from 6.3 dB to 1.0 dB as distance increases, while the system maintains reliable communication with an average PDR of 97% up to 800 m. Distinct from existing LoRa performance studies, this work explicitly integrates a hybrid multi-hop and star WSN topology within a real-world smart waste bank deployment, providing empirical communication benchmarks that are directly applicable to community-scale IoT implementations rather than controlled or generic test scenarios.
Copyright (c) 2026 Mila Kusumawardani, Rabbani Yusuf Ghazali, Lis Diana Mustafa, Isa Mahfudi https://creativecommons.org/licenses/by/4.0
Comparing GAN, Diffusion, and Diffusion-GAN for Single-Image Deraining of UAV Imagery
Single-image deraining for Unmanned Aerial Vehicle (UAV) imagery remains challenging due to non-uniform rain patterns, motion blur, and real-time processing requirements. Existing generative paradigms, including Generative Adversarial Networks (GAN), Diffusion, and Diffusion–GAN, each face inherent trade-offs among restoration quality, stability, and efficiency. To address the lack of unified and fair benchmarking across these paradigms, this study presents a systematic and controlled comparative evaluation of three representative models, including TBGAN, WeatherDiff, and SupResDiffGAN, to assess their relative performance in UAV deraining tasks. The models are evaluated on the UAV-Rain1K and Rain100L datasets using PSNR, SSIM, and inference efficiency metrics to support informed selection of paradigms for UAV applications. Experimental results show that WeatherDiff achieves the highest fidelity with 19.99 dB PSNR, 0.8375 SSIM on UAV-Rain1K and 29.51 dB PSNR, 0.9093 SSIM on Rain100L. TBGAN yields sharper details but lower structural consistency, whereas SupResDiffGAN offers balanced performance with 19.03 dB PSNR and 0.7053 SSIM on UAV-Rain1K and 28.51 dB PSNR and 0.8681 SSIM on Rain100L, with faster inference. These findings highlight the practical trade-offs among the three paradigms and demonstrate that diffusion–GAN frameworks provide the most practical solution for UAV deraining, combining diffusion stability with adversarial sharpness for real-time restoration.
Copyright (c) 2026 Salsabilah Aulia Rahman, Laksmita Rahadianti https://creativecommons.org/licenses/by/4.0
Forecasting Post-Patent Time Series Pharmaceutical Sales: A Comparative Study of Statistical and Machine Learning Models
Volatility in the pharmaceutical industry can be caused by expiration of drug patents, leading to a gap between actual and target sales values, which necessitates accurate sales forecasting for pharmaceutical marketers. This study utilizes the sales data from PT. Q, an Indonesian pharmaceutical firm. The comparative performance within the specific context of the post-patent period for pharmaceutical sales remains relatively unexplored. This research aims to compare forecasting models for post-patent pharmaceutical sales. The research method utilized is based on the CRISP-DM data mining framework. The forecasting process is done on a 4.5-year timeframe using forecasting models such as ARIMA, SARIMA, LSTM, and Prophet. The results show that multivariate LSTM works better for forecasting in smaller aggregations in the dataset such as by product type and branch, with a R² score value of up to 0.64 in the aggregation level of Bandung_Sales, and with the smallest error metric values, such as MAE in many aggregation levels, example being regional sales, such as Lampung_Sales with 1.31 and Makassar_Sales with 0.26, which outperforms the other compared models in the majority of cases. This research concludes that multivariate LSTM is a better way to replace outdated methods to set sales targets.
Copyright (c) 2026 Sebastian Miguel, Sfenrianto Sfenrianto https://creativecommons.org/licenses/by/4.0
Performance Evaluation of Tuned and Untuned Machine Learning Models in Speech Emotion Recognition
This analysis takes on a comparative review of three distinct machine learning approaches: Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and Random Forest (RF) to ascertain emotional states in verbal communication by utilizing the RAVDESS resource. In this review, we perform a strategy that unites audio feature extraction, model training with or without tweaks to hyperparameters, and evaluation via metrics including accuracy, precision, recall, and F1-score. The assessment shows that, before any refinement, SVM secured the utmost accuracy of 79%, trailed by MLP at 76% and RF at 71%. Following optimization, only SVM exhibited an enhancement, reaching 80%, whereas MLP and RF displayed negligible or no improvement. An examination of the confusion matrix revealed that SVM produced the most uniformly distributed predictions and effectively reduced misclassification errors, particularly within the emotion categories of “calm” and “happy.” This investigation offers empirical substantiation of SVM as a robust baseline model for speech emotion recognition in localized settings, while simultaneously providing insights into model optimization and development that could inform future implementations in speech-based human–computer interaction.
Copyright (c) 2026 Muhammad Hudzaifah Nasrullah; Dede Cahyadi, Tilly Raycitra Widya, Anggraeni Pratama Indrianto, Lilik Tiara Giantri https://creativecommons.org/licenses/by/4.0
Design and Development of an Augmented Reality Based Storytelling Platform for Interactive Solar System Learning in Primary Education
Concepts in primary science education, such as the Solar System, are often difficult for students to understand when presented through conventional two-dimensional learning media. This study aims to design and develop a storytelling-driven Augmented Reality (AR) learning platform to support interactive Solar System learning in primary education. A Research and Development (R&D) approach was employed, encompassing needs analysis, conceptual design, prototype development, limited trials, and formative evaluation. The developed platform enables learners to visualize three-dimensional planetary models using marker-based AR accompanied by age-appropriate narrative explanations. Limited trials involving ten fourth-grade students and two science teachers were conducted to examine usability and user perceptions. The findings indicate that students and teachers perceived the platform as engaging and supportive for visualizing abstract astronomical concepts. The novelty of this study lies in the integration of structured storytelling with AR visualization tailored for primary learners within an R&D framework. However, the results are based on formative evaluation and user perceptions; therefore, further studies with larger samples and objective learning outcome measures are recommended.
Copyright (c) 2026 I Putu Andika Subagya Putra, Darmayanti Ni Wayan Sri, Pradnyana Putu Beny; Wedayanthi Luh Made Dwi https://creativecommons.org/licenses/by/4.0
Prediction of Potential Fishing Zones Using K-Means Clustering and Random Forest in Batam Waters
Identification of potential fishing zones remains a significant challenge in fisheries management, particularly in coastal and island waters characterized by high spatial and temporal environmental variability. In Batam waters, fishing activities are still dominated by fishermen's experience and heuristic judgment, while existing studies often focus on a single prediction model or limited environmental parameters. This indicates a research gap, namely the lack of an integrated framework that simultaneously captures environmental heterogeneity and improves prediction accuracy using a data-driven approach. To address this gap, this study proposes a hybrid data mining framework that explicitly integrates unsupervised environmental zoning and supervised classification for predicting fishing potential. Weather and oceanographic variables—including sea surface temperature, chlorophyll-a concentration, wind speed, ocean current speed, and salinity—are used in conjunction with historical fish catch data. K-Means clustering is first used to identify homogeneous marine environmental zones, which are then incorporated as contextual features into a Random Forest classification model. Model performance is then evaluated using accuracy, precision, recall, F1 score, and confusion matrix analysis. The results show that the proposed hybrid approach achieves an accuracy of 89.2% and an F1 score of 89.1%, representing a quantitative improvement of approximately 5.6% in accuracy and 5.0% in F1 score compared to the baseline Random Forest model without clustering. This comparison clearly demonstrates that the integration of clustering information significantly improves classification performance. Furthermore, feature importance analysis confirms that sea surface temperature and chlorophyll-a concentration are the most influential predictors, while cluster labels contribute indirectly by improving the model's contextual understanding of complex environmental conditions. The novelty of this research is articulated through the integration of unsupervised marine environmental zoning with supervised machine learning in a local fisheries context, which allows for improved predictive performance and enhanced model interpretability. Unlike conventional approaches that treat environmental variables independently, the proposed framework captures multidimensional environmental interactions in a structured manner. The implications of these findings are profound. The proposed model can support data-driven decision-making for fishermen by reducing search time and operational costs, while providing a scientific basis for fisheries managers for spatial planning and sustainable resource management. Therefore, this research contributes both methodologically and practically to the advancement of intelligent fisheries prediction systems in dynamic coastal environments such as Batam waters.
Copyright (c) 2026 Sarah Astiti, Alvendo Wahyu Aranski, Darmansah https://creativecommons.org/licenses/by/4.0