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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
 97   98   99   100   101 
Adaptive AI Tutors in African Classrooms: A Systematic Literature Review on Personalised Learning Beauty Mugoniwa
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.v10i4.13588

Abstract

Artificial intelligence (AI) tutors are gaining traction in African education to facilitate personalised learning, feedback, evaluation, and educational access. However, the evidence available is dispersed in terms of country, level of education, technology and context of implementation. This systematic literature review aimed to aggregate evidence regarding types, educational impacts, implementation conditions and sustainability of adaptive AI tutors and personalised learning systems in African education in the period 2019- March 2026. The review was designed to combine the Scientific Procedures and Rationales for Systematic Literature Reviews with the PRISMA 2020 reporting guidelines. A total of 312 records were found in the databases searched, 54 of which were duplicates, 258 were screened, and 26 studies were included in the final analysis. The accompanying research studies included secondary, primary, basic, vocational, teacher, engineering, and higher education settings, and focused on the use of intelligent tutoring systems, adaptive learning platforms, mobile AI tutors, educational robotics, generative AI, predictive analytics, and AI-supported assessment. Overall, the evidence suggested gains for learner engagement, personalised pacing, formative feedback, academic outcomes, and teaching efficiency, but the extent and ability to generalise findings across studies were highly variable. The need for greater connectivity, a reliable electricity supply, devices, teacher preparation, funding, data privacy concerns, algorithmic bias, and cultural and linguistic localisation were recurrent barriers to implementation. The review shows that the interaction of adaptive technological functions, active participation of the learner, the facilitation of the teacher and the enabling institutional conditions pave the way for the emergence of educational results based on Programmed Logic for Automatic Teaching Operations and Constructivist Learning Theory. This study brings an Africa-centred Science and Technology framework depicting the mobile compatibility, contextual localisation, ethical regulation and human-in-the-loop model as the most likely features of adaptive AI tutors that will support equitable learning.
Implementation of the ML-KEM Protocol for Securing Parameter Exchange in FedAvg-Based Federated Learning Architecture Against Quantum Computing Threats Ariq Arsalan; Eko Hari Rachmawanto; Christy Atika Sari
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.v10i4.13589

Abstract

Federated Learning (FL) enables collaborative machine learning by aggregating local models from decentralized clients without sharing raw data. However, the parameter exchange process in standard FL architectures is vulnerable to emerging quantum computing threats. Specifically, Shor's algorithm, executable on large-scale quantum computers, can break conventional asymmetric cryptography such as RSA and ECDH in polynomial time, thereby threatening the security of FL systems that rely on these traditional public-key infrastructures for parameter exchange. This study addresses this vulnerability by implementing the recently standardized post-quantum cryptographic protocol, ML-KEM (Module-Lattice-Based Key-Encapsulation Mechanism), within a FedAvg-based FL architecture. The integration is designed to secure the parameter exchange pipeline without compromising the neural network's performance. Experimental results on a simulated environment utilizing the MNIST dataset demonstrate that the ML-KEM integration preserves 100% of the global model accuracy. Furthermore, the cryptographic latency overhead introduced by encapsulation and decapsulation remains highly efficient, proving its potential as a robust security layer. While current evaluations focus on small-scale deployments, the proposed architecture establishes a foundational post-quantum security framework for future privacy-preserving distributed learning systems.
Weighted Ensemble of GRU, LSTM and XGBoost for Multi-Horizon Temperature Forecasting at a Tropical Highland Station Kalimi Kalimi; Ahmad Musyafa; Taswanda Taryo; Marzuki Sinambela; Tonny Wahyu Aji
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.v10i4.13593

Abstract

This research evaluates multi-horizon forecasting of daily mean temperature (TAVG) and maximum temperature (TMAX) at the Malang/Karangploso Climatology Station WMO 96943 using daily meteorological observations from 2014–2023. The predictors include humidity, rainfall, atmospheric pressure, wind variables, weather conditions, and sunshine duration. Forecasts are generated for H+1, H+3, H+7, and H+14 using a chronological train, validation, and test split. The study compares GRU, LSTM, Hybrid Gated LSTM-GRU, XGBoost, and Weighted Ensemble models against Persistence and Climatology baselines. To prevent data leakage, preprocessing includes missing-date handling, placeholder correction, training-set-based imputation, lag and rolling feature construction, and input normalization. Optuna is used to tune recurrent models, while ensemble weights are optimized on the validation set. Model performance is assessed using MAE, RMSE, and R², with the lowest test RMSE as the main selection criterion. Results show that the Weighted Ensemble achieves the best aggregate performance, with mean MAE of 0.796, mean RMSE of 1.007, and mean R² of 0.472. GRU is the strongest individual model, with mean RMSE of 1.022. However, the best model varies by target and horizon. Weighted Ensemble leads in five of eight scenarios for TAVG H+1, TAVG H+3, TAVG H+14, TMAX H+1 and TMAX H+14, Hybrid Gated performs best for TAVG H+7, and GRU is superior for TMAX H+3 and H+7.  
Brain Tumor Classification in MRI Images Using Convolutional Neural Networks with Explainable Artificial Intelligence Muhammad Abdul Ghofur; Nirma Ceisa Santi; Hastie Audytra
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.v10i4.13594

Abstract

Brain tumors are a condition requiring rapid and accurate diagnosis. This study aims to enhance the transparency of Convolutional Neural Network (CNN)-based brain tumor classification models by implementing Explainable Artificial Intelligence (XAI) techniques, specifically Eigen-CAM and LIME. The models were developed using a transfer learning approach on ResNet50 and EfficientNetB0 architectures, utilizing a dataset of 3,000 MRI images categorized into glioma, meningioma, and pituitary tumor classes. Test results indicate that ResNet50 achieved the best performance, with accuracy, precision, recall, and F1-score values of 94%, while EfficientNetB0 achieved 93%. The application of 5-fold cross-validation improved the models' generalization capabilities and reduced the risk of overfitting. Visualizations using Eigen-CAM and LIME demonstrate that the models focus on relevant tumor regions, thereby increasing the transparency and reliability of MRI-based classification.
Support Vector Machine and Multilayer Perceptron Optimization for Banana Leaf Disease Classification Using Hue Saturation Value and Gray Level Co-occurrence Matrix Haris Pujianto; Christy Atika Sari; Musab Iqtait
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.v10i4.13603

Abstract

Banana leaf disease poses a significant threat to the quality and productivity of banana plants. Conventional disease identification relies heavily on expert knowledge and is time-consuming, highlighting the need for an automated and efficient solution. This study presents a classification system for banana leaf diseases by comparing the performance of Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel and Multilayer Perceptron (MLP). The dataset employed was the Banana Leaf Spot Diseases (BananaLSD) dataset, comprising four disease categories: healthy, cordana, pestalotiopsis, and sigatoka. Preprocessing steps included image resizing, data cleaning, feature extraction using Hue Saturation Value (HSV) color features and Gray Level Co-occurrence Matrix (GLCM) texture features, and data normalization via StandardScaler. Experimental results demonstrate that SVM achieved an accuracy of 94.34%, outperforming MLP which reached 93.40%. These findings confirm that the integration of HSV and GLCM features with SVM constitutes an effective approach for automated banana leaf disease classification, offering a promising foundation for intelligent plant health monitoring systems.
Four-Class Brain Tumor Classification Using ConvNeXt with Grad-CAM-Based Explainable Ni Kadek Jegeg Anastasya Dwipayanti; I Gusti Ngurah Lanang Wijayakusuma
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.v10i4.13607

Abstract

Brain tumor is one of the most critical diseases with high mortality rates requiring early and accurate diagnosis. This study proposes a deep learning-based brain tumor classification system using the ConvNeXt-Base architecture to classify four categories: glioma, meningioma, pituitary tumor, and no tumor. To further advance this field, this study addresses two open challenges in prior literature, the need for larger and more diverse datasets to ensure model robustness, and the demand for model transparency (resolving the black-box problem) to facilitate clinical adoption.. To address these, this study combines MRI images from three public sources, Figshare (Cheng et al., 2016), Br35H (Hamada, 2020), and Mendeley (Hira et al., 2025), totaling 11,474 unique images after perceptual hash-based deduplication. The model was trained using progressive unfreezing with AdamW optimizer, CosineAnnealingLR scheduler, and class-weighted cross-entropy loss. Gradient-weighted Class Activation Mapping (Grad-CAM) is integrated as an Explainable AI (XAI) approach to visualize the regions influencing model decisions. The proposed ConvNeXt-Base model achieved an accuracy of 99.25%, macro precision of 99.23%, macro recall of 99.22%, macro F1-score of 99.22%, and macro AUC-ROC of 0.9995 on the test set. Grad-CAM visualizations confirm that the model focuses on clinically relevant tumor regions, particularly the sella turcica for pituitary tumors and distinct mass boundaries for meningioma, thereby improving interpretability and clinical trustworthiness of the AI system.
Comparative Analysis of LSTM, BiLSTM, Stacked LSTM, and Attention-LSTM for Multi-Resolution Rainfall Forecasting in Bali Muhammad Nur Rizqi; Cahya Sugiarto; Wisnu Syahid
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.v10i4.13615

Abstract

Accurate rainfall forecasting remains challenging in tropical islands such as Bali, Indonesia, where localized convective rainfall limits the reliability of physics-based models. While deep learning architectures based on Long Short-Term Memory (LSTM) are increasingly used for rainfall forecasting, most prior studies compare LSTM variants at a single temporal resolution without a classical baseline, leaving it untested whether their reported advantage holds across resolutions or is an artifact of the resolution chosen. This study evaluates four recurrent architectures — LSTM, BiLSTM, Stacked LSTM, and Attention-LSTM — for univariate rainfall forecasting at daily, decadal, and monthly resolutions, using 26 years (2000–2025) of observations from a BMKG station in Bali, benchmarked against three classical baselines (Persistence, Climatology, and ARIMA) and tuned via random search. Results show that inter-architecture differences within a single resolution are small and inconsistent, with no architecture winning across all resolutions: Stacked LSTM is marginally best at daily (RMSE 15.26 mm/day) and decadal (73.53 mm/decade) scales, while BiLSTM leads at the monthly scale (154.64 mm/month). More critically, the deep learning models' advantage over classical baselines is strongly resolution-dependent: substantial at daily and decadal resolutions, but nearly indistinguishable from Persistence and ARIMA at the monthly resolution, where only 312 training sequences are available. These findings indicate that model selection for rainfall forecasting should be conditioned on temporal resolution and data availability rather than treated as a fixed architectural choice, and that simpler LSTM models offer a more computationally efficient default than their more complex variants for tropical, data-limited settings.
Variational Mode Decomposition and Deep Learning for Geomagnetic K-Index Prediction Asmadi Djasman; Ahmad Musyafa; Kahfi Heryandi Suradiradja
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.v10i4.13616

Abstract

Geomagnetic K-index characterizes local disturbance intensity on a quasi-logarithmic scale and serves as a critical input for space weather early warning systems. Predicting this index is challenging due to its non-stationary, chaotic nature and severely imbalanced class distribution. This study develops and compares two univariate hybrid deep learning models, VMD-CNN 1D and VMD-LSTM, for K-index prediction using data from BMKG Tuntungan Observatory from January 2020 to June 2025 (16,064 samples, 3-hour resolution). The preprocessing pipeline applies Variational Mode Decomposition (VMD) with six intrinsic mode functions, followed by an eight-timestep sliding window. An ablation study confirms that VMD substantially contributes to predictive performance, reducing RMSE by 55.4% and 52.4% for CNN 1D and LSTM respectively compared to their non-VMD counterparts with comparable parameter capacity. Both models were evaluated using RMSE, MAE, and R², with statistical significance confirmed via Wilcoxon signed-rank tests. VMD-CNN 1D achieved superior overall performance (RMSE 0.3608, R² 0.8846) compared to VMD-LSTM (RMSE 0.3889, R² 0.8660), and both substantially outperformed a Persistence baseline (RMSE 0.9643). However, under storm conditions (K ≥ 5, p = 0.024), VMD-LSTM outperformed VMD-CNN 1D (R² 0.6632 versus 0.5430) and achieved higher storm-detection recall (0.891 versus 0.812). These findings indicate that architecture choice should reflect the target operational context, with VMD-CNN 1D suited for routine monitoring and VMD-LSTM for storm-period forecasting.
Comparison of VGG16 and ResNet50 Performance in Rice Leaf Disease Classification Valda Laura Uswary; Amriana Amriana
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.v10i4.13628

Abstract

Paddy (Oryza sativa L.) is a strategic staple food commodity in Indonesia, yet its production is frequently disrupted by various plant diseases that cause significant yield losses each year. Conventional visual disease identification is inefficient and prone to error, necessitating the adoption of more reliable, automated diagnostic technology. This study compares the performance of two pre-trained convolutional neural network architectures, VGG16 and ResNet50, in classifying rice leaf images into four categories: Brown Spot, Leaf Blast, Healthy Rice Leaf, and Rice Hispa. Both models were fine-tuned using transfer learning under an identical experimental configuration, including the same data split, optimizer, learning-rate schedule, and augmentation pipeline, to ensure a controlled architectural comparison. Model evaluation was conducted using accuracy, precision, recall, F1-score, the Matthews Correlation Coefficient (MCC), confusion matrix analysis, training convergence behaviour, inference-time computational efficiency, and Grad-CAM interpretability visualization. Experimental results show that ResNet50 achieved a test accuracy of 99% and an MCC of 0.9874, outperforming VGG16, which achieved a test accuracy of 95% and an MCC of 0.9306. Confusion matrix analysis revealed that ResNet50's errors were concentrated almost exclusively within the visually similar brown spot–leaf blast class pair, whereas VGG16 exhibited additional confusion between the healthy rice leaf and rice hispa classes. ResNet50 also demonstrated faster and more stable training convergence, more spatially coherent Grad-CAM activation patterns, and substantially higher throughput under batched inference (356.27 vs. 207.63 images/second at batch size 32), while VGG16 retained a marginal latency advantage under single-image inference. These findings indicate that, under the configuration examined in this study, ResNet50 is the more suitable architecture for rice leaf disease classification, offering an advantageous combination of accuracy, interpretability, and computational efficiency for potential deployment in agricultural monitoring systems.
Sequential Multi-Factor Authentication for Attendance Using LBPH and Geolocation Fitrada Kurnialdi Assrofi Ulla; Aris Tri Jaka Harjanta; Bambang Agus Herlambang
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.v10i4.13629

Abstract

Conventional attendance systems remain vulnerable to proxy attendance, location manipulation, and inefficient administrative processes, while many existing intelligent attendance solutions rely on computationally intensive deep learning models that are less suitable for lightweight web-based implementations. This study addresses these limitations by proposing a web-based smart attendance system integrating GPS-based geofencing and Local Binary Pattern Histogram (LBPH) within a Sequential Multi-Factor Authentication (MFA) framework. The proposed framework also incorporates GPS spoofing detection based on abnormal geolocation properties and IP geolocation consistency to improve resistance against location manipulation. The authentication mechanism performs geofencing validation before facial verification to improve security and avoid unnecessary biometric processing. The system was developed using the Laravel framework integrated with Python-based facial recognition and evaluated through black-box functional testing. Experimental evaluation involving 120 attendance scenarios achieved an overall accuracy of 96.67% while effectively detecting GPS spoofing and presentation attacks with low false acceptance and false rejection rates. The implementation results demonstrated that all major functional modules operated according to the predefined requirements, while the confidence threshold of 40 provided reliable facial verification under moderate environmental variations. These findings indicate that the proposed framework provides a practical, secure, and lightweight solution for web-based attendance management and offers an effective alternative to computationally intensive deep learning-based attendance systems.

Page 99 of 101 | Total Record : 1006

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