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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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Multimodal Sensor Evaluation for Fish Pond Water Quality Monitoring Zein Rifal; Syafruddin Syarif; Imran Taufik; Mashur Razak; Supriadi Sahibu; Respaty Namruddin
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12656

Abstract

Freshwater aquaculture requires continuous water quality monitoring because rapid changes in temperature, pH, dissolved oxygen, turbidity, total dissolved solids, and water level can affect fish health and pond productivity. This study evaluates a multimodal sensor system for real-time fish pond water quality monitoring and dashboard-based actuator control. The system integrates six sensors with Arduino Mega for signal acquisition, ESP32 for Wi-Fi communication, Firebase for cloud data storage and command exchange, and a Flutter dashboard for visualization and manual control. Field testing was conducted in two tilapia ponds with different initial conditions. Sensor performance was evaluated by comparing five measurable parameters with reference instruments using percentage error, accuracy, mean absolute error, and root mean square error, while turbidity was assessed through functional contrast testing and short-term stability because a turbidity reference instrument was unavailable. The average accuracy of the five validated parameters was 87.37% in pond 1 and 95.58% in pond 2. Temperature and water level showed the highest accuracy, above 98% in both ponds. Dissolved oxygen and total dissolved solids showed larger deviations, especially in pond 1, indicating sensitivity to field conditions and calibration stability. Actuator commands for the aerator and circulation pumps responded within 1-2 seconds under stable network conditions. The results show that the system is useful as a preliminary field-validated monitoring and semi-automatic control platform, but further work is required for long-term drift testing, turbidity validation using a commercial meter, and automatic control evaluation.
Attention-Enhanced Multivariate Forecasting for Intelligent Microservice Autoscaling Nur Saifuddin; Mula Agung Barata; Ifnu Wisma Dwi Prastya
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12662

Abstract

Proactive autoscaling in cloud-native microservices requires anticipatory decisions because reactive controllers often lag under abrupt workload shifts. This study aims to improve autoscaling decision quality through a two-stage machine learning pipeline. The research adopts an experimental design using production-grade microservice traces, with strict time-respecting train-validation-test splits and training-only fitting for preprocessing and oracle-threshold estimation to prevent leakage. In the first stage, multivariate forecasting models predict future CPU and memory utilization from engineered temporal features. In the second stage, the predicted signals are combined with observed features to classify three autoscaling actions: scale down, hold, and scale up. Benchmarking shows recurrent neural models are strong baselines, while an attention-enhanced encoder-decoder performs best. The best Bahdanau-attention model with residual connection reduces test CPU RMSE from 0.030977 to 0.028924 and memory RMSE from 0.010322 to 0.005452 relative to the strongest BiLSTM baseline. For decision learning, the optimized Extreme Gradient Boosting model using prediction-augmented features achieves an accuracy of 0.950602 and an F1 score of 0.951026. Supporting downstream validation also yields lower SLO violation rates than horizontal and vertical baselines while maintaining zero downtime in the evaluated scenarios. These findings indicate that improving forecasting quality and explicitly transferring predictive signals to the decision stage strengthens proactive autoscaling performance.
Development of an Intelligent Waste Identification System Based on the YOLOv11 Algorithm Sheryl Nicole Gunawan; Usman Sudibyo
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12672

Abstract

Effective waste management is a critical challenge in urban environments, necessitating the development of automated systems for efficient waste classification. This study aims to develop an intelligent system for detecting and classifying waste into organic and inorganic categories using the You Only Look Once (YOLO)v11 object detection algorithm. The proposed system identifies the presence and location of waste objects through bounding boxes and classifies them based on their visual characteristics to facilitate real-time detection. Experimental results demonstrate the model's effectiveness, achieving an overall accuracy of 0.717. Performance analysis indicates consistent reliability, with "Anorganik" waste achieving an accuracy of 0.708 and "Organik" waste reaching an accuracy of 0.725. Notably, despite the moderate accuracy, the model demonstrates significant robustness when deployed in real-world conditions; it is highly capable of identifying multiple objects within a single frame and maintains consistent detection performance even when objects are overlapping or clustered, confirming its viability for practical, real-time sorting applications. Despite these promising results, the study identifies several influencing factors, including object clustering, visual material similarity, lighting conditions, and camera-to-object distance. The current prototype faces limitations due to dataset size, particularly in detecting uncommon waste objects. Future development efforts will focus on expanding the training dataset to include a wider variety of waste items and enhancing robustness for detecting small or occluded objects in real-world scenarios.
Comparing Decision Tree and Optimized LightGBM for Attrition Prediction Dhea Maharani; Farrikh Alzami; MY. Teguh Sulistyono; Aris Nurhindarto; Dewi Agustini Santoso; Muslih Muslih; Henry Bastian
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12678

Abstract

Employee turnover poses a considerable challenge for organizations, impacting productivity and raising recruitment expenses. This research seeks to evaluate the effectiveness of Decision Tree and Light Gradient Boosting Machine (LightGBM) models in forecasting employee attrition. The study utilizes a quantitative experimental design, leveraging a secondary dataset sourced from Mendeley. Before model development, data preprocessing was performed, and model evaluation was carried out using metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. Each algorithm was assessed under three different configurations baseline, regularization, and hyperparameter tuning through GridSearchCV. The experimental findings indicate that the Decision Tree model is prone to overfitting and has limited capabilities in detecting attrition classes, even though optimization raises the ROC-AUC score to 0.80. In comparison, LightGBM demonstrates more reliable and consistent performance. The Tuned LightGBM model achieved the highest performance on the test dataset, with an Accuracy of 0.81, a Precision of 0.82, a Recall of 0.71, F1-Score of 0.76, and an ROC-AUC of 0.85. An analysis of feature importance reveals that job satisfaction, work-life balance, emotional commitment, work experience, and allowances are the key factors influencing attrition prediction. These results indicate that LightGBM not only performs exceptionally well, but it is also able to offer insights into the critical factors that are important for data-driven retention strategies.
Forecasting the Demand for Freshmen Alma Mater Jackets and Sports T-Shirts by Size Using a Hybrid Bayesian–Machine Learning Approach Miranti Verdiana; Eko Dwi Nugroho; Leslie Anggraini; Radhinka Bagaskara
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12679

Abstract

This study addresses an operational procurement problem in university admissions, where alma mater jackets and sports T-shirts for incoming students must be ordered several months before complete size information becomes available. In the case of ITERA admissions, procurement decisions are typically made in March or April, whereas actual student size data are only gradually collected during the re-registration period from April to July. To support earlier and more reliable procurement planning, this study formulates the problem as a size-demand forecasting task covering six categories: S, M, L, XL, XXL, and XXXL. Historical data from 2015 to 2025 were analyzed, with reliable size records concentrated in the 2019–2025 period. The main novelty of this study lies in formulating freshman uniform procurement as a staged forecasting problem that follows the actual admissions workflow. Specifically, the study proposes a hybrid framework that combines: (i) a time-weighted Bayesian Dirichlet–Multinomial model for early-stage aggregate forecasting when current-year size data are not yet available, and (ii) a CatBoostClassifier-based multiclass machine learning model for prediction updates when student attributes become available. Model performance was evaluated using an expanding-window rolling/forward chaining scheme with a one-year forecasting horizon. In addition to conventional historical baselines, the study also included Simple Exponential Smoothing (SES) as a time-series benchmark. Performance was assessed using cross-entropy for size-distribution accuracy, MAE/size and WAPE for quantity prediction, and stockout/overstock for operational impact. The results show that the previous-year proportion remains a strong baseline, while the Bayesian model provides competitive performance and yields posterior uncertainty estimates that are useful for determining safety-oriented order quantities. The statistical analysis further confirms that gender is the most influential predictor of size, while study program, admission track, and province provide complementary but weaker signals. The findings indicate that the proposed framework can support more adaptive and evidence-based procurement planning, reduce the risk of size shortages and excess inventory, and provide a transferable forecasting workflow that may be adapted to other institutions after local recalibration.
Spatiotemporal Analysis of Peatland Fire Hotspots and Fire Intensity in Riau Province Using MODIS–VIIRS Multisensor Satellite Data Najwa Ratu Afi; Ramadhan Rakhmat Sani; Ricardus Anggi Pramunendar; Nurul Anisa Sri Winarsih; Ika Novita Dewi
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12686

Abstract

Peatland fires in Riau Province frequently occur during the dry season and contribute significantly to regional haze, environmental degradation and carbon emissions. Effective monitoring of these fires remains challenging due to their widespread distribution and varying intensity across peatland areas. This research aims to analyze the spatiotemporal characteristics of peatland fire hotspots in Riau Province using multisensor satellite observations from the NASA Fire Information for Resource Management System (FIRMS). The dataset integrates Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) data from the Suomi-NPP, NOAA-20 and NOAA-21 satellites. After applying filtering criteria of confidence ≥70% and Fire Radiative Power (FRP) ≥5 megawatts (MW), a total of 7,297 significant hotspots were identified during the July–October 2025 dry season. The results show that fire activity peaked in July with a maximum daily FRP of 25,611 MW and a monthly total of 65,120 MW, followed by a decline in September and a slight increase in October. The FRP distribution was highly right-skewed, with an average value of13.2 MW, while the most intense hotspots reached 189.4 MW. Estimated carbon dioxide (CO₂) emissions reached approximately 122,472 tons, indicating substantial environmental impacts. Spatial clustering and persistence analysis revealed several high-risk peatland zones with repeated fire occurrences. These findings demonstrate the importance of multisensor satellite monitoring for improving early fire detection, emission assessment and disaster mitigation strategies in peatland regions.
Explainable Deep Learning for Diabetic Retinopathy Detection: A Quantitatively Validated Framework Tinashe Ngwazi; Belinda Ndlovu; Kudakwashe Maguraushe
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12687

Abstract

Diabetic retinopathy (DR) is a leading cause of preventable blindness, where early and accurate detection is critical for effective intervention. While deep learning models have demonstrated strong performance in DR classification, their limited interpretability and inconsistent evaluation practices hinder clinical trust and deployment. This study proposes an explainable deep learning framework for DR detection based on MobileNetV2, complemented by Integrated Gradients for feature attribution. A curated dataset of 4,464 retinal images was constructed from publicly available sources through systematic preprocessing, including quality filtering, deduplication, and class balancing across five DR stages. To ensure robust evaluation, a multi-level validation strategy was employed, incorporating stratified train–validation–test splits and k-fold cross-validation. The proposed framework achieved 87.0% accuracy and an F1-score of 0.868, outperforming baseline models including EfficientNet-B0, DenseNet121, and VGG16. Beyond predictive performance, explainability was quantitatively evaluated using deletion and insertion metrics, demonstrating that Integrated Gradients provides more faithful feature attribution compared to Grad-CAM and LIME. Error analysis further reveals that misclassifications are concentrated between adjacent DR stages, reflecting the inherent difficulty of fine-grained disease progression modelling. The findings highlight that combining rigorous validation with quantitative explainability evaluation can improve the reliability and transparency of deep learning models for medical imaging. While results are promising, the framework is validated on publicly available datasets and requires further external clinical validation before real-world deployment.
Comparative Analysis of CNN, ResNet50, and Vision Transformer Architectures for Brain Tumor Classification from MRI Images Matthieu Kayembe; Franklin Mwamba; Pierre Kafunda; Fiston Oshasha; John Poma
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12699

Abstract

The classification of brain tumors from Magnetic Resonance Imaging (MRI) is a crucial task in computer-aided medical diagnosis. Recent advances in deep learning have significantly improved performance in this domain. In this work, a comparative analysis of three architectures is conducted: a Convolutional Neural Network (CNN) trained from scratch, a transfer learning-based model using ResNet50, and a Vision Transformer (ViT). The models are evaluated on a multi-class dataset containing four categories: glioma, meningioma, pituitary tumor, and no tumor. Experimental results show that the CNN achieves limited performance with moderate generalization capability. The ResNet50 model reaches high accuracy during training but suffers from severe overfitting, leading to a significant drop in performance on the test set. In contrast, the Vision Transformer achieves the best overall performance, with a test accuracy of 0.76 and a good balance between precision and recall. These results highlight the effectiveness of Transformer-based architectures for complex medical image classification tasks.
Reducing Cognitive Load in Micro-School Systems through Frugal Interaction Design Muhamad Akda Fathul Barri; Ayu Permata Sari; Suprih Widodo
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12705

Abstract

The rapid digitalization of education marginalizes peri-urban micro-schools, forcing them to adopt complex digital administration tools to maintain institutional legitimacy. However, conventional management systems impose excessive cognitive load on non-technical school stakeholders, leading to low adoption and persistent digital enclaves. This study aims to reduce the extraneous cognitive load of these stakeholders by designing and evaluating a serverless school administration portal based on frugal computing principles. A Targeted Participatory Action Research approach was employed with four core decision-makers at an Islamic micro-school, integrating a web interface directly with habitual messaging applications. Usability was evaluated using task completion metrics and heuristic assessments. The intervention resulted in a complete independent success rate for low-complexity tasks. Furthermore, administrative task completion times decreased dramatically from an average of forty-five seconds to fifteen seconds, with users praising the seamless integration into their daily communication routines. These findings suggest that frugal interaction design and cognitive offloading can effectively bridge the digital divide, providing a sustainable and highly usable administrative model for resource-constrained educational institutions.
Exploring Basic Programming Education in Primary Schools as a Foundation for Introducing Artificial Intelligence, Augmented Reality and Virtual Reality Technologies Beauty Mugoniwa; Mampilo Phahlane; Charles Mbohwa
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12712

Abstract

As emerging technologies such as artificial intelligence (AI), augmented reality (AR), virtual reality (VR) become increasingly integrated into educational context, primary school children need to be equipped with basic programming principles so that they can then be engaged in more advanced applications. The study guided by Pedagogical Content Knowledge (PCK) and a conceptual framework, conducted an SLR to investigate the basic programming courses designed for primary school children. The aim of the study is to evaluate how these basic principles support cognitive development and technological readiness in relation to AI/AR/VR education and its applications. Article search was conducted in Scopus, ERIC, IEEE Xplore, ACM digital library for peer reviewed articles and Google Scholar for more supporting literature published between 2018 to 2025. The combination of SPAR-4-SLR and PRISMA methods was employed to ensure rigor in article selection, screening and compilation of findings. In addition, thematic synthesis was used to analyse the findings. The study analysed the gap between programming and demands of 4IR technologies and applications. We uncovered that although there is limited literature on programming in primary schools in South Africa (S.A), the few studies that we came across are coming up with many ways of introducing programming to students, plugged and unplugged. These are being administered as game-based, teacher instruction-based, collaboration and /or individual teaching methods. And this is gradually adding to the computational thinking and basic programming skills need by students to be able to tackle the development of AI/AR/VR tools and applications.

Page 84 of 101 | Total Record : 1006

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