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INDONESIA
JOURNAL OF APPLIED INFORMATICS AND COMPUTING
Published by Politeknik Negeri Batam
ISSN : -     EISSN : 25486861     DOI : 10.3087
Core Subject : Science,
Computer Science & IT
Journal of Applied Informatics and Computing (JAIC) Volume 2, Nomor 1, Juli 2018. Berisi tulisan yang diangkat dari hasil penelitian di bidang Teknologi Informatika dan Komputer Terapan dengan e-ISSN: 2548-9828. Terdapat 3 artikel yang telah ditelaah secara substansial oleh tim editorial dan reviewer.
Arjuna Subject : -
Articles 1,006 Documents
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Prediction of Remaining Productive Life of Oil Palm Plantation Soil Using Support Vector Regression with Permutation-Based Feature Importance Analysis Zara Yunizar Zainal; Nurdin Nurdin; Zharif Athaya Andarfi
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13229

Abstract

Oil palm (Elaeis guineensis Jacq.) is a strategically vital crop in Southeast Asia, yet progressive soil degradation driven by prolonged monoculture, pathogen pressure, and intensive land use poses a critical threat to long-term plantation sustainability. Existing soil assessment methods deliver static fertility classifications without quantifying the remaining productive lifespan of a given plot. This study introduces Remaining Productive Life (RPL) as a novel regression target defined as the estimated number of years before plantation soil productivity falls below a critical economic threshold. A Support Vector Regression (SVR) model with Radial Basis Function (RBF) kernel, formulated as K(xi, xj) = exp(−γ‖xi − xj‖²) with γ = 0.0303 (= 1/n_features) and regularization parameter λ = 0.5, was applied to a realistic synthetic multi-year dataset comprising 14,400 observations across 800 plantation plots spanning five soil types (Ultisol, Inceptisol, Alfisol, Peat, Oxisol) and the period 2008–2025. Thirty-three soil physicochemical, biological, management, and economic indicators constituted the input feature set. The SVR model achieved R² = 0.9141, MAE = 0.5122 years, RMSE = 1.1026 years, and MAPE = 11.883% on the independent test set, with 92.0% of predictions yielding an absolute error below one year. Permutation-based feature importance analysis identified Degradation Rate (ΔMAE = 0.2864), Plantation Age (0.2560), and Soil Productivity Index (0.2530) as the three dominant predictors, while Ganoderma Risk Index ranked fourth (0.2138), revealing the pivotal contribution of biological soil health to long-term productivity prediction. These findings establish SVR-based RPL estimation as an effective, interpretable framework for precision plantation management and proactive soil sustainability planning.
Hybrid Federated Social Networking Sites for eLearning Beauty Mugoniwa; Enerst Ketcha Ngassam; Shawren Singh
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13232

Abstract

This study explores the feasibility and development of hybrid federated social networking site (HFSNS) architecture for enhancing teaching and learning activities in higher education. The study addresses the increasing limitations of centralised social networking sites. The main objective is to develop an HFSNS architecture that can support scalability, interoperability and privacy-preserving educational collaborations across SNS platforms. The research is grounded in design science research (DSR), while Dubin’s theory building methodology guides the systematic identification and construction of architectural components and their attributes. The study pursued a mixed-method research design, based on a pragmatist philosophy, which therefore provided a basis for an exploratory case study of one university. Forty-five respondents were purposively selected for unstructured interviews, while 748 contributed to an online survey. Qualitative data were analysed thematically and quantitative data was analysed using regression and correlation statistical to examine the relationships between HFSNS adoption and perceived eLearning opportunities. Findings indicate high institutional readiness for HFSNS adoption, since most participants are already using SNSs for teaching and learning activities. Statistical results shows a significant relationship between HFSNS implementation and improved eLearning opportunities, platform independence, privacy protection, improved collaboration and timely academic feedback. However, limitations such as interoperability and infrastructural challenges were also identified. The study proposes a novel HFSNS for eLearning and it extends DSR in education by integrating federated social networking (FSN) principles with eLearning systems. This study also brings in empirical evidence on the readiness and opportunities of FSN in higher education.
Application of the Rule-Based Pattern Matching Method to Detection of Types of Mujarrad and Mazid Verbs in the Qur'an Based on Arabic Morphology Patterns Suci Khairani; Rini Meiyanti; Nunsina Nunsina
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13235

Abstract

Arabic has a complex morphological system, particularly in the formation of fi’il (verbs), which in sharaf are classified into fi’il mujarrad and fi’il mazid. Fi’il mujarrad refers to basic verbs without additional letters, while fi’il mazid involves added letters that form specific wazan (patterns). The main problem addressed in this study is the similarity of morphological patterns between fi’il and non-fi’il, which affects classification accuracy. This study aims to identify fi’il in Surah Al-Baqarah and classify them into mujarrad and mazid, along with their wazan patterns, automatically using a rule-based pattern matching approach. The method applies rules based on Arabic morphological patterns, such as fi’il mudhari’ prefixes, word length, and the presence of additional letters in accordance with sharaf principles.The data consist of Qur’anic text processed through preprocessing stages, including load data and tokenization. The system detected 916 candidate fi’il, of which 468 data points were used for evaluation by comparison with manual annotations using a confusion matrix.The results show that the system achieved an accuracy of 75% for fi’il type classification, with precision, recall, and F1-score of 0.77, 0.75, and 0.75, respectively. For wazan classification, the system achieved an accuracy of 69.23%, with weighted average precision of 0.66, recall of 0.69, and F1-score of 0.65. These findings indicate that the rule-based approach is sufficiently effective in detecting fi’il mujarrad and mazid, although performance for certain wazan patterns remains limited due to structural similarities. Therefore, further development of more specific rules and integration with machine learning methods are recommended to improve system accuracy.  
Development of a Configurable IoT-Based Adaptive Thermal Comfort Monitoring Platform Using Scrum and DevOps Practices Sri Atikah
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13238

Abstract

Effective indoor thermal comfort management in multi-room buildings requires the integration of environmental sensor data and occupant feedback while remaining adaptable to different building configurations. However, many existing occupant-centric thermal comfort systems are tightly coupled to specific building environments, which limits their adaptability to new buildings and different room configurations. To address this limitation, this study develops and evaluates an IoT-based adaptive thermal comfort monitoring platform designed for configurable multi-building deployment. The platform integrates environmental sensor data with occupant feedback collected through room-specific QR code-accessible web forms and supports two complementary recommendation mechanisms: sensor-driven threshold alerts and feedback-driven recommendations based on aggregated occupant responses. The platform was developed using the Scrum methodology over seven one-week sprint cycles, supported by a DevOps CI/CD pipeline that automated application building, testing, artifact management, and deployment. Evaluation included unit testing, integration testing, Selenium-based user interface testing, and Apache JMeter performance testing, with all automated test scenarios passing successfully. Performance testing yielded APDEX scores of 0.997 and 0.960 for the General User and Administrator workflows, respectively, with zero errors across all tested endpoints. Flexible building and room expansion was achieved through an entity-based architecture that models Building, Room, and Sensor as independent entities, enabling the incorporation of new spaces without structural modifications. These results demonstrate that the proposed platform provides a configurable and extensible software solution for adaptive thermal comfort monitoring in multi-room building environments.
Risk Management and Sharia Compliance in Crypto Staking: A Systematic Literature Review Maulana Asykari Muhammad; Fitroh Fitroh; Rinda Hesti Kusumaningtyas
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13241

Abstract

The adoption of Decentralized Finance (DeFi) introduces multidimensional risks that challenge institutional stability and Sharia jurisprudential standards. Existing studies often isolate technical security measures from theological compliance, creating a gap in governing digital assets under Sharia principles. This study aims to systematically review the intersection of technical risk mitigation and Sharia compliance in crypto-asset mechanisms. A Systematic Literature Review (SLR) was conducted following the PRISMA guidelines, analyzing 32 peer-reviewed articles published between 2023 and 2025. The synthesis reveals that technological and operational vulnerabilities in smart contracts induce elements of Gharar or uncertainty and Maysir or speculation, which conflict with the Maqasid al-Shariah objective of Hifzul Mal or protection of wealth. To bridge the gap between cryptographic protocols and Fiqh Muamalah or Islamic commercial law, this review identifies Enterprise Architecture (EA), specifically the TOGAF Architecture Development Method (ADM), as a structural integration framework. EA functions as a governance framework that systematically maps Sharia constraints directly into executable IT control layers. Specifically, the Business Architecture layer enforces contract validity, the Information Systems Architecture layer standardizes auditable data flows, and the Technology Architecture layer secures the consensus mechanics. The findings conclude that future research must transition from conceptual reviews to empirical implementations, particularly by designing Sharia-compliant enterprise architectures for post-Merge Ethereum staking protocols.
Comparison of LSTM and Random Forest for Hydrogen Production Prediction in Alkaline Water Electrolysis Ulil Amri Ulil; Ardi Pujiyanta; Sunardi Sunardi
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13254

Abstract

Hydrogen has emerged as a promising clean energy carrier capable of supporting the transition toward sustainable energy systems. In alkaline water electrolysis systems, accurate prediction of hydrogen production is essential for improving system monitoring, operational efficiency, and future control strategies. This study aims to compare the performance of Random Forest (RF) and Long Short-Term Memory (LSTM) algorithms in predicting hydrogen production based on operational parameters of an alkaline water electrolysis system. The dataset used in this study consists of more than 100,000 operational data samples collected from laboratory-scale electrolysis experiments, including voltage, current, temperature, and hydrogen gas pressure measurements. Hydrogen production was calculated using the ideal gas law and used as the target variable for model development. Prior to model training, the dataset underwent preprocessing, including data cleaning, normalization, and train-test splitting. The predictive performance of both models was evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and coefficient of determination (R²). Experimental results show that the Random Forest model achieved superior performance with an MAE of 0.0085, RMSE of 0.0106, and R² of 0.9905, while the LSTM model obtained an MAE of 0.0302, RMSE of 0.0361, and R² of 0.7907. The findings indicate that Random Forest is more effective than LSTM in modeling the relationship between operational parameters and hydrogen production in the investigated alkaline water electrolysis system. This study demonstrates the potential of machine learning approaches for accurate hydrogen production prediction and provides insights into the suitability of different predictive models for electrolysis-based hydrogen generation systems.
PerceptionGuard: Privacy-Aware Split Inference for Multimodal Mobile Applications Sridhar Muthineni
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13255

Abstract

Multimodal mobile AI applications increasingly rely on cloud-based large language models (LLMs) for complex reasoning over visual inputs, but raw-image cloud upload creates substantial privacy exposure, high inference costs, and unacceptable latency for interactive use cases. This paper proposes PerceptionGuard, a four-layer split-inference architecture where on-device vision-language models (VLMs) handle privacy-sensitive perception and adaptive routing, sending only compact, privacy-preserving representations to cloud LLMs for higher-order reasoning. Three representation modes are defined and evaluated: dense embeddings (Mode A), structured scene graphs (Mode B), and redacted natural-language captions (Mode C). The architecture incorporates information-bottleneck filtering and calibrated differential-privacy noise to resist membership inference and embedding-inversion attacks. Experimental evaluation across three representative mobile workloads, accessibility visual question answering on VizWiz, augmented reality scene understanding, and visual document search on DocVQA, demonstrates that Mode B achieves task accuracy within approximately 5 to 7 percentage points of cloud-only baselines while reducing cloud token cost by over 60 percent and achieving meaningful reductions in membership-inference attack success. A learned adaptive router outperforms confidence-threshold cascade baselines on cost-accuracy Pareto frontiers. PerceptionGuard is implemented as open-source Android and iOS libraries and contributes design heuristics for practitioners building privacy-respecting multimodal mobile applications at scale.
Comparison of U-Net and Attention U-Net for Binary Flood Segmentation from UAV Imagery Fariida Aini; Muhammad Akrom; Gustina Alfa
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13263

Abstract

Flood disasters consistently cause massive damage every year, making rapid mapping of affected areas crucial for coordinating emergency aid. The use of unmanned aerial vehicles (UAVs) offers a practical solution to obtain high-resolution aerial imagery, but manually identifying flood areas from hundreds of images remains time-consuming. This study analyzes and compares two deep learning segmentation architectures, U-Net and Attention U-Net, for automatic flood area detection from UAV RGB images. Both models were trained using 290 image-mask pairs from a public dataset, with a split of 70% for training, 10% for validation, and 20% for testing. Images were processed at a resolution of 256×256 pixels, normalized to the range [0,1], and augmented with horizontal flipping, brightness adjustment, and affine transformations. Attention U-Net enhances the standard U-Net structure by adding attention gates to all skip connections in the decoder to suppress irrelevant background features. Both models were evaluated across five independent training runs using different random seeds to assess result robustness. Across these runs, Attention U-Net achieved a marginally higher mean IoU (77.11% ± 0.76) and Dice/F1 (87.07% ± 0.49) compared to the U-Net baseline (IoU: 76.97% ± 0.54; Dice/F1: 86.98% ± 0.34), but a paired t-test revealed that these differences were not statistically significant (IoU: p = 0.77; Dice/F1: p = 0.77). These results suggest that, on this dataset, attention gates do not provide a measurable advantage over the standard U-Net architecture, establishing both as comparable practical baselines for future flood mapping research.
Comparative Analysis of Machine Learning Algorithms with SMOTE for Imbalanced Sentiment Classification of IndiHome on Platform X Rizky Adisaputra; Muhammad Arifin; Soni Adiyono
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13271

Abstract

Sentiment analysis of IndiHome users on social media X faces a severe class imbalance, with negative tweets dominating 88.26% of the dataset. This study compares four machine learning algorithms, Support Vector Machine (SVM), Naive Bayes, Decision Tree, and Random Forest, for sentiment classification using SMOTE to address the imbalance. Initially, 20,001 Indonesian tweets were scraped using Tweet Harvest with the keyword "indihome". After duplicate removal and preprocessing, 7,199 tweets were retained. Each tweet was manually annotated into positive, negative, and neutral categories. TF-IDF was applied for feature extraction, and Stratified 5-Fold Cross Validation was used for evaluation. Algorithms were tested under two conditions: without and with SMOTE. Before SMOTE, SVM achieved the highest accuracy (94.55%) and F1-score (94.05%). After SMOTE, Random Forest outperformed others with 94.14% accuracy and 93.83% F1-score, as the only algorithm showing consistent improvement across all metrics, including balanced accuracy and MCC. Although Wilcoxon tests showed no statistically significant differences between algorithms, Random Forest demonstrated the most stable and consistent performance. These findings confirm that Random Forest with SMOTE is the most effective strategy for imbalanced sentiment classification in this context.
A Comparison of Classical Machine Learning and IndoBERT on Sentiment Analysis of Danantara Program in X Silvan Pradana; Etika Kartikadarma
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13280

Abstract

The rapid growth of social media has made it a primary channel for the public to express opinions on national strategic economic policies, including the establishment of the Danantara entity. This study aims to map public sentiment on Platform X and compare the performance of classical frequency-based architectures with transformer-based models. A common research gap in previous studies is the reliance on Bag-of-Words models, which fail to capture local context and sarcasm in informal text. A total of 9,525 tweets from the period January–May 2025 were collected via crawling and labeled using a hybrid approach combining InSet Lexicon and manual validation by experts (Cohen’s Kappa = 0.81). To address significant class imbalance (66.5% negative), SMOTE was applied to classical models. Experimental results reveal a significant performance gap: the classical TF-IDF + SVM model achieved a positive-class F1-score of only 59% due to feature distortion caused by SMOTE in the TF-IDF space, while the fine-tuned IndoBERT model substantially outperformed it with a global accuracy of 95.80% and a positive-class F1-score of 81%. These findings demonstrate that the deep transformer approach is far more robust in extracting semantics from informal Indonesian social media text, with practical implications for public policy decision-making.

Page 94 of 101 | Total Record : 1006

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