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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 805 Documents
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Enhanced Multi-Objective Green Vehicle Routing with a New Fuzzy Speed-Driven Fuel Consumption Model Kayij Nawej, Emile; Mangongo, Yves Tinda; Kafunda, Pierre Katalay; Kampempe, Justin-Dupar Busili
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11794

Abstract

Today, decision-makers begun to prioritize the concept of green logistics, which is based on strategies aimed to promote more environmentally sustainable practices during vehicle routing. Among key factors influencing fuel consumption in such problems, vehicle speed plays a crucial role. This article adapts the Comprehensive Modal Emission Model (CMEM) for fuel consumption by treating vehicle speed as a fuzzy variable. This enhanced version, referred as Fuzzy-CMEM, enables the formulation of a more realistic fuzzy multi-objective Green Vehicle Routing Problem (GVRP). The proposed methodology follows four main steps. First, we formulate the problem considering the vehicle speed as a fuzzy variable. Second the initial fuzzy problem is defuzzified using the interval approximation approach. Third, a sequential approach is adopted where the sweep heuristic is used to construct feasible routes, and the BicriterionAnt metaheuristic is employed to generate optimal Pareto-front solutions of the resulting deterministic problem. Finally, a numerical simulation is addressed, followed by a comparative analysis of results and discussion.
Classification Analysis of Single Tuition Fees Using the Random Forest Method with K-Fold Cross Validation Khaidar, Al; Nurdin, Nurdin; Fajriana, Fajriana; Taufiq, Taufiq; Hamdhana, Defry
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11798

Abstract

Classification is the process of grouping data into specific categories based on their characteristics or features, which plays a crucial role in the analysis, decision-making, and prediction of new data. In academic settings, classification is used to determine the Single Tuition Fee to place students according to their economic ability. Lhokseumawe State Polytechnic has implemented the UKT system since 2020 with eight categories, but some students are still placed in UKT groups that do not match the results of the manual process, which has limited accuracy. This study uses the Random Forest method as a technology-based solution to improve the accuracy and objectivity of UKT classification. The dataset used consists of 10,000 student data with 10 variables, covering economic and social information. The research process includes data preprocessing, Random Forest model training, performance evaluation using accuracy, precision, recall, and F1-score, and model stability testing through 10-fold K-Fold Cross Validation. The results show that Random Forest is able to classify most UKT classes well, especially classes 0–5 and 7. Class 6 has lower performance with a recall of 0.39 and an F1-score of 0.56 due to the limited number of samples. The overall accuracy of the model reaches 96%, while K-Fold Cross Validation produces an average accuracy of 95.50% with a standard deviation of 0.66%, indicating the model is stable and able to generalize to new data. This study proves that Random Forest is effective in UKT classification, producing an objective, fair, and efficient system. This implementation model supports data-driven decision-making in higher education and increases transparency in UKT determination.
Analysis of Deep Learning Implementation Using Xception for Rice Leaf Disease Classification Puspitaningrum, Niken; Rahardi, Majid
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11800

Abstract

Identifying rice leaf diseases plays a crucial role in maintaining agricultural productivity and preventing massive losses. In recent years, deep learning models have shown very promising performance in plant disease classification tasks. This study proposes a Rice Leaf Disease Detection System based on the Xception model from Keras Applications, an architecture that is still relatively unexplored for rice plant disease cases. Through preprocessing, data augmentation, and model refinement, the developed system achieved a training accuracy of 93% and a testing accuracy of 89% in classifying rice leaf conditions. In addition, metric evaluation showed precision, recall, and F1-score values of 89%, reflecting the model's ability to make consistent and balanced predictions. The trained model was then integrated into a web-based application to facilitate real-time disease diagnosis through image uploads. The results of this study prove the effectiveness of the Xception architecture in extracting agricultural image features and its potential for application in artificial intelligence-based smart farming systems.
Implementing Defense-in-Depth Framework on Orange Pi NAS Using Host-Based Security and ZFS Hady, Muhammad Fatih; Pratama, Hafiyyan Putra
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11801

Abstract

Network-Attached Storage (NAS) based on low cost Single Board Computers (SBC) offers an affordable alternative to commercial storage systems, yet its exposure to network-based threats requires a robust and layered security approach. This research implements the Defense-in-Depth (DiD) framework on an Orange Pi based NAS running Debian 12, integrating host-based security mechanisms and the ZFS file system to enhance data integrity, availability, and system resilience. The security layers include firewall restrictions, intrusion prevention with Fail2Ban, integrity monitoring using AIDE and rkhunter, system auditing with Lynis, and log analysis with Logwatch. Additionally, ZFS snapshots and the Sanoid retention policy are applied to provide rapid data recovery with minimal storage overhead. Experimental results show that all defense layers function effectively under testing scenarios such as brute-force attempts, unauthorized port access, file modification, and data deletion. ZFS snapshots successfully restore deleted or altered files, ensuring minimal Recovery Point Objective (RPO) of one hour. System performance remained stable, with CPU usage averaging only 7.9% and memory usage at 33%, indicating that the DiD model is feasible even on low-resource SBC hardware. These findings demonstrate that a cost-efficient SBC-based NAS can achieve strong resilience against common cyber threats through layered security design and modern file system capabilities.
Application of SARIMA, GRU, and Prophet for Capturing Seasonal Patterns in Consumer Price Inflation Mualifah, Laily Nissa Atul
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11802

Abstract

Seasonal dynamics make inflation forecasting challenging in emerging economies where holiday effects, regulated prices, and supply shocks interact. This study models Indonesia’s monthly consumer price inflation (CPI) using official data from Statistics Indonesia (May 2006–April 2025) and evaluates three forecasting paradigms: a classical seasonal baseline (SARIMA), a decomposable model with trend–seasonality components (Prophet), and a neural sequence learner (GRU). A 10-fold sliding window design is employed to preserve temporal order. Performance is assessed with RMSE, MAE, and MASE, summarized across folds with boxplots and statistical descriptives (means, standard deviations, and 95% confidence intervals). Across folds and metrics, Prophet consistently achieves the lowest error and the tightest dispersion, GRU ranks second with competitive accuracy and stable variance, and SARIMA remains a transparent yet weaker benchmark. MASE values below one for Prophet (and generally for GRU) indicate improvements over a naïve baseline. Practically, Prophet’s decompositions support policy communication by linking forecast movements to interpretable components (e.g., Ramadan/Eid and year-end effects), while GRU is useful during more nonlinear or volatile periods; SARIMA remains valuable for diagnostics in stable regimes.
From Speech to Summary: A Pipeline-Based Evaluation of Whisper and Transformer Models for Indonesian Dialogue Summarization Manullang, Martin Clinton Tosima; Yulita, Winda; Kartagama, Fathan Andi; Putra, A. Edwin Krisandika
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11826

Abstract

The rapid increase in online meetings has produced massive amounts of undocumented spoken content, creating a practical need for automatic summarization. For Indonesian, this task is hindered by a dual-faceted resource scarcity and a lack of foundational benchmarks for pipeline components. This paper addresses this gap by creating a new synthetic conversational dataset for Indonesian and conducting two systematic, discrete benchmarks to identify the optimal components for an end-to-end pipeline. First, we evaluated six Whisper ASR model variants (from tiny to turbo) and found a clear, non-obvious winner: the turbo (distil-large-v2) model was not only the most accurate (7.97% WER) but also one of the fastest (1.25s inference), breaking the expected cost-accuracy trade-off. Second, we benchmarked 13 zero-shot summarization models on gold-standard transcripts, which revealed a critical divergence between lexical and semantic performance. Indonesian-specific models excelled at lexical overlap (ROUGE-1: 17.09 for cahya/t5-base...), while the multilingual google/long-t5-tglobal-base model was the clear semantic winner (BERTScore F1: 67.09).
Outperforming DNN Using MLP in Water Quality Assessment for Aquaculture Anshori, Mochammad; Musthofa, Mufid
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11835

Abstract

Aquaculture production relies heavily on stable water quality conditions, requiring accurate and efficient assessment methods to support early environmental monitoring and sustainable management. Although deep neural network models have been widely applied to water quality classification, their high computational complexity often limits their applicability in real-time and resource-constrained aquaculture systems. This study aims to evaluate whether a systematically optimized Multilayer Perceptron can outperform a reported deep neural network benchmark in aquaculture water quality assessment while maintaining computational efficiency. The study adopts a structured methodology involving dataset characterization, extreme outlier removal, feature normalization, and stratified data partitioning. A single-hidden-layer Multilayer Perceptron is trained using a feedforward backpropagation learning process, with systematic exploration of hidden neuron configurations and training epochs to identify the optimal architecture. Model performance is evaluated using multiple classification metrics, including accuracy, precision, recall, F1-score, confusion matrix analysis, and receiver operating characteristic and precision–recall curves. Results indicate that the optimal Multilayer Perceptron configuration, consisting of 80 hidden neurons and 200 training epochs, achieves an accuracy of 96.62%, surpassing the deep neural network benchmark accuracy of 95.69%. The proposed model demonstrates strong class-level performance, clear separation between water quality categories, stable convergence behavior, and reduced computational overhead compared to deeper architectures. These findings highlight that increasing model depth does not necessarily improve predictive performance for heterogeneous aquaculture datasets. In conclusion, this study provides empirical evidence that a well-optimized shallow neural network can outperform deeper models in aquaculture water quality assessment. The results emphasize the importance of model parsimony and systematic hyperparameter optimization, offering a practical and efficient solution for real-time aquaculture water quality monitoring applications.
A Systematic Review of Post-Quantum Cryptography for Healthcare Data Protection: Performance, Readiness, and Deployment Challenges Ngwenya, Taboka; Ndlovu, Belinda
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11836

Abstract

The traditional cryptographic methods used to protect healthcare data, especially for the long-term storage of medical imaging records, are becoming increasingly threatened by the quick development of quantum computing. The purpose of this study is to assess the challenges, efficacy, and preparedness of integrating Post-Quantum Cryptography (PQC) into healthcare information systems. Twenty peer-reviewed studies published between 2020 and 2025 were analysed following the Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) protocol. The review was conducted using a systematic research design that included qualitative thematic synthesis, predetermined eligibility criteria, and database searching. According to the results, lattice-based PQC schemes, specifically, CRYSTALS-Kyber for encryption and CRYSTALS-Dilithium for authentication, show great promise because of their effectiveness, resilience, and suitability for decentralized architectures like blockchain and Internet-of-Medical-Things environments. Nonetheless, the review points out a notable deficiency of empirical assessment in actual healthcare settings, particularly with regard to cloud-based platforms and Picture Archiving and Communication Systems utilized in medical imaging processes. Scalability limitations, intricate key-management specifications, system interoperability restrictions, and the requirement for conformity with regulatory and compliance frameworks are some of the major issues noted. The results indicate that lattice-based PQC schemes have great promise, deployment readiness remains largely at the conceptual and experimental stage, particularly for cloud-based PACS environments. Real-world implementation validation in a healthcare setting has not been achieved.
SemetonBug: Next-Generation Machine Learning-Powered Code Analyzer for Precision Bug Detection and Dynamic Error Localization Erniwati, Surni; Imran, Bahtiar; Muahidin, Zumratul; Zaeniah, Zaeniah; Juhartini, Juhartini
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11837

Abstract

Bug detection in Python programming is a crucial challenge in software development. This research proposes SemetonBug, a machine learning-based system for automatically detecting bugs in Python code. The system utilizes a Random Forest Classifier as the main model, with features extracted from the syntactic structure of the code using an Abstract Syntax Tree (AST). The dataset consists of 200 Python files, divided into 100 files with bugs and 100 files without bugs. The model is optimized using Grid Search Cross Validation, with the best combination of n_estimators = 300, max_depth = 20, min_samples_split = 5, and min_samples_leaf = 2. Evaluation results show that the model achieves 85% accuracy, 0.84 precision, 0.87 recall, and 0.86 F1-score. The detected bugs are stored in an Excel file for further analysis. By leveraging machine learning, SemetonBug enhances efficiency and accuracy in bug identification compared to traditional rule-based methods. These findings highlight the potential of machine learning models in improving software quality and reducing coding errors automatically.
Performance Evaluation of Web Applications Using JMeter Load Testing for Server Capacity and Response Efficiency Annisa, Aulia; Ardiana, Mirza; Rahayu, Putri Nur; Brian, Thomas; Jami’in, Mohammad Abu
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11840

Abstract

The reliability of web-based conference systems is crucial for ensuring smooth services during periods of high activity. This research evaluates the performance of the ICOMTA PPNS journal website by conducting load testing using Apache JMeter with scenarios ranging from 50 to 5000 virtual users, each executed for one hour. The evaluation focuses on response time, error rate, throughput, and bandwidth usage. The results indicate that the website performs reliably with up to 200 concurrent users, demonstrating stable response times and no recorded errors. However, once the load surpasses 300 users, response times increase sharply exceeding 60 seconds and errors begin to appear, suggesting that the server has reached its performance limit. Under the heaviest load of 5000 users, throughput continues to rise, but overall service quality declines significantly. These findings highlight the need for server enhancements or migration to cloud-based infrastructure to ensure stable performance during peak usage.

Page 74 of 81 | Total Record : 805

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