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JUITA : Jurnal Informatika
Published by Universitas Muhammadiyah Purwokerto
ISSN : 20869398     EISSN : 25798901     DOI : 10.30595/JUITA
Core Subject : Science,
Computer Science & IT
UITA: Jurnal Informatika is a science journal and informatics field application that presents articles on thoughts and research of the latest developments. JUITA is a journal peer reviewed and 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. Documents submitted must be in Ms format. Word and written according to author guideline. JUITA is published twice a year in May and November. Currently, JUITA has been indexed by Google Scholar, IPI, DOAJ, 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. 36/E/KPT/2019. JUITA is intended as a media for informatics research among academics, practitioners, and society in general. JUITA covers the following topics of informatics research: Software engineering Artificial Intelligence Data Mining Computer network Multimedia Management Information System Digital forensics Game
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 338 Documents
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Enhancing Geospatial House Price Prediction in Greater Jakarta Using XGBoost and ResNet18 Feature Fusion Santoso, Hadi; Anastasya, Alif Biuti
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 1, March 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

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Abstract

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%.
Dual-Stage Bifurcation With Genetic Algorithm Extraction for Robust Anti-Antiforensic Steganalysis in Grayscale Images Purnacandra, Amadeus Pondera; Prayudi, Yudi
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 1, March 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

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Abstract

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.  
Evaluasi Kinerja Jaringan Sensor Nirkabel Berbasis Lora untuk Transmisi Data Bank Sampah Cerdas Kusumawardani, Mila; Ghazali, Rabbani Yusuf; Mustafa, Lis Diana; Mahfudi, Isa
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 1, March 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

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Abstract

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.
Comparing GAN, Diffusion, and Diffusion-GAN for Single-Image Deraining of UAV Imagery Rahman, Salsabilah Aulia; Rahadianti, Laksmita
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 1, March 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

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Abstract

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.
Peramalan Penjualan Obat Pasca Masa Paten: Studi Perbandingan Model Statistik dan Pembelajaran Mesin Miguel, Sebastian; Sfenrianto, Sfenrianto
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 1, March 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

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Abstract

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.
Performance Evaluation of Tuned and Untuned Machine Learning Models in Speech Emotion Recognition Nasrullah, Muhammad Hudzaifah; Cahyadi, Dede; Widya, Tilly Raycitra; Suciana, Ewin; Giantri, Lilik Tiara
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 1, March 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

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Abstract

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.
Design and Development of an Augmented Reality Based Storytelling Platform for Interactive Solar System Learning in Primary Education Putra, I Putu Andika Subagya; Darmayanti, Ni Wayan Sri; Pradnyana, Putu Beny; Wedayanthi, Luh Made Dwi
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 1, March 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

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Abstract

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.
Prediction of Potential Fishing Zones Using K-Means Clustering and Random Forest in Batam Waters Astiti, Sarah; Aranski, Alvendo Wahyu; Darmansah
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 1, March 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

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Abstract

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.

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