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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
 88   89   90   91   92 
Comparative Sentiment Analysis of Indonesian Social Media Opinions on Fuel Subsidy Policy Using IndoBERT and NusaBERT Dini Ambarwati; Pungkas Subarkah; Septi Nurhayati
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.v10i3.12964

Abstract

Rising geopolitical tensions in the Middle East have triggered public concerns in Indonesia regarding fuel subsidy policies and fuel availability. This study aims to compare the performance of IndoBERT and NusaBERT in classifying Indonesian public sentiment on social media related to fuel subsidy policies. Data were collected from X (Twitter) and Instagram comments between October and November 2025 using keywords such as “BBM”, “Pertalite”, “fuel subsidy”, and “Middle East conflict”. After filtering duplicate, spam, and irrelevant content, a total of 1,500 opinion texts were manually annotated into positive, neutral, and negative sentiment classes and divided using an 80:20 train-test split configuration. The preprocessing stage included case folding, text cleansing, slang word normalization, emoji removal, duplicate filtering, and tokenization. Both Transformer models were fine-tuned using the AdamW optimizer with a learning rate of 2e-5, batch size of 16, and 3 training epochs. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. Experimental results show that NusaBERT achieved better performance than IndoBERT, obtaining an accuracy of 96.3% and weighted F1-score of 96.3%, while IndoBERT achieved an accuracy of 92.5%. Additional evaluation through confusion matrix and error analysis indicates that both models still face challenges in handling sarcasm, ambiguous expressions, and mixed-context sentences commonly found in informal Indonesian social media text. The findings suggest that NusaBERT is more effective for Indonesian social media sentiment classification due to its stronger adaptation to informal language patterns.
Comparative Analysis of LSTM Architectures for BPJS Drug Expenditure Forecasting Using Walk-Forward Validation Rizkinawati .; Ifnu Wisma Dwi Prasetya; Nur Mahmudah
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.v10i3.12969

Abstract

Drug demand planning in health facilities collaborating with BPJS Kesehatan is an important aspect in maintaining drug availability and improving service efficiency. However, drug expenditure data generally forms complex time series patterns that are fluctuating, nonlinear, and influenced by trend and seasonal components, making them difficult to model using conventional forecasting methods. Therefore, this study aims to compare the performance of Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Stacked LSTM models in forecasting BPJS patient drug expenditure by medication type at Basuki Rahmat Pharmacy using Walk-Forward Validation. The dataset used consists of monthly drug expenditure transaction data from January 2023 to May 2025 covering 41 types of drugs. The data preprocessing stages include data cleaning, transformation into time series format, Min-Max normalization, and windowing for input-output sequence generation. Time series characteristic analysis was conducted using the Augmented Dickey-Fuller (ADF) test, trend analysis, and seasonality analysis. The results showed that most drug data were stationary with p-values below 0.05, although several drugs still exhibited non-stationary patterns requiring additional transformation. Trend analysis indicated both increasing and decreasing consumption patterns, while seasonality analysis showed that all drug data exhibited seasonal patterns. The forecasting models were evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). Experimental results using Walk-Forward Validation showed that the Bidirectional LSTM model achieved the best forecasting performance with MAE of 51.99, RMSE of 72.06, and MAPE of 402.90, outperforming Single LSTM and Stacked LSTM models. These findings indicate that Bidirectional LSTM is more effective in capturing complex temporal dependencies in BPJS drug expenditure data and has potential to support decision-making in drug inventory management within healthcare facilities.
Hybrid CNN for Sleep Stage Classification Based on EEG Maria Angelina Cahyani Candrakasih; Bagus Adhi Kusuma; Pungkas Subarkah
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.v10i3.12999

Abstract

Sleep stage classification is essential for diagnosing sleep disorders such as insomnia and sleep apnea. However, manual scoring of polysomnography (PSG) is time consuming and subjective. Automatic systems based on single channel EEG are promising for home based monitoring, but they face challenges due to class imbalance and inter subject variability. This study proposes a hybrid model that combines 15 handcrafted features (statistical and spectral) with 128 dimensional features extracted by a one dimensional Convolutional Neural Network (1D CNN), followed by a stacking ensemble (Random Forest and Support Vector Machine as base learners, Logistic Regression as meta learner). Using 40 subjects from the Sleep EDF Expanded dataset, a strict subject independent split (80% train / 20% test) was applied to avoid data leakage. The dataset contained 107,258 epochs with extreme imbalance (Wake 67.6%, N1 2.95%). After SMOTE oversampling on the training set, the model achieved an accuracy of 67.5%, macro F1 score of 31.4%, and Cohen’s Kappa of 0.34. An ablation study showed that CNN features alone (72.2% accuracy) outperformed handcrafted features (70.4%) and hybrid features (67.5%). The confusion matrix revealed that minority stages (especially N1, N3, REM) were poorly recognized. These results highlight that cross subject generalization remains a major challenge in EEG based sleep staging, and proper subject independent validation is critical to avoid overoptimistic claims.
Implementation of Hybrid ResNet50 and XGBoost Model for Wheat Plant Disease Classification Aryanti Aryanti; Muhammad Aulia Dzikri; Ahmad Rifqi Nugraha; Khumairah Amira Sari; Dea Oktavia
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.v10i3.13004

Abstract

A hybrid artificial intelligence (AI) system was successfully developed in this study, combining the ResNet50 architecture as an image feature identifier and the XGBoost algorithm for final classification. This model was used to detect six disease variations using 5,505 wheat leaf photographs. To ensure model stability, rigorous testing was conducted using two methods: Stratified 5-Fold Cross-Validation on the entire data set and independent testing using 300 images (equally divided into 50 samples per class).The test results demonstrated very solid performance. The model recorded an average global accuracy of 94.66% (±0.21%) using the K-Fold method, and an accuracy of 94.67% and a Macro F1-Score of 0.9445 in the independent testing. Through confusion matrix mapping, the model successfully classified the Healthy, Black Rust, and Septoria categories perfectly (a score of 1.00). However, there was still a minor error in the case of eight samples being confused between Brown Rust and Yellow Rust due to the visual similarity of the orange-yellowish coloration early in the infection period. Furthermore, the Feature Importance assessment demonstrated that the XGBoost decision base is transparent (Explainable AI). This AI accurately focuses on clinical signs of plants such as chlorosis symptoms and spot texture, while ignoring background objects such as weeds and soil. This combination of methods creates a stable, efficient system with a response time of only 18.5 milliseconds per photo, and a biologically valid decision base.
Efficient Attention-Guided MobileNet V2 with Explainable AI for Multi-Class Skin Disease Classification on HAM10000 Devi Larasati; Ucta Pradema Sanjaya
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.v10i3.13010

Abstract

The increasing global incidence of skin cancer, particularly melanoma, coupled with a scarcity of dermatologists, necessitates the development of accurate and accessible AI-driven diagnostic tools. However, deep learning models often struggle with severe class imbalance in public dermoscopic datasets, leading to poor performance on minority lesion types. This research aims to enhance the diagnostic precision of a lightweight MobileNetV2 architecture for multi-class skin lesion classification by integrating the Convolutional Block Attention Module (CBAM) and employing Focal Loss. The methodology involves evaluating four model variants (Baseline, Baseline+CBAM, Baseline+Focal Loss, and Baseline+CBAM+Focal Loss) on the HAM10000 dataset, with performance measured by accuracy, precision, recall, and F1-score. The optimal model (M4) successfully achieved convergence without overfitting, demonstrating exceptional F1-scores for six of seven classes, including near-perfect classification for melanoma (0.96) and dermatofibroma (0.97). The primary limitation was the actinic keratosis class (F1-score 0.60) due to high morphological similarity with other lesions. In conclusion, the synergistic combination of CBAM and Focal Loss effectively mitigates class imbalance and enhances feature representation in a computationally efficient model, providing a robust and interpretable solution for skin cancer screening.
Comparative Analysis of CNN and YOLO for Aromatic Leaf Detection on Android-Based Deep Learning Applications Yuli Safrina; Muhammad Fikry; Mukhlis Abd Muthalib
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.v10i3.13015

Abstract

Commonly used aromatic leaves in Indonesian cuisine include bay leaves (Syzygium polyanthum), pandan leaves (Pandanus amaryllifolius), lime leaves (Citrus hystrix), curry leaves (Murraya koenigii), and turmeric leaves (Curcuma longa). Their similar shapes, colors, and textures often make manual identification difficult. Therefore, deep learning technology can be utilized to automatically identify and detect aromatic leaf types through digital images. This study aims to analyze the performance of a Convolutional Neural Network (CNN) using the EfficientNet-B0 architecture and the YOLOv11 model with the AdamW optimizer in detecting and classifying aromatic leaves. The system is implemented using a Python Flask framework for the web based backend and Flutter for the mobile application interface on Android devices. The dataset used in this study consists of 671 digital images obtained through direct image collection and supporting datasets. The dataset is categorized into five classes: bay leaf, pandan leaf, lime leaf, curry leaf, and turmeric leaf. Furthermore, the dataset is divided into training data (89%), validation data (7%), and testing data (4%). The results show that the YOLOv11 model outperforms the CNN (EfficientNet-B0) model. YOLOv11 achieved a precision of 73.36%, recall of 84.35%, mAP50 of 83.93%, and mAP50-95 of 71.38%. Meanwhile, EfficientNet-B0 achieved a best validation accuracy of 81.40% and a test accuracy of 62.07%. Based on experimental results, YOLOv11 demonstrates higher detection confidence and more consistent performance compared to EfficientNet-B0. In addition, YOLOv11 is more suitable for mobile deployment due to its real-time object detection capability with faster inference speed, while the system is supported by a Flask based backend and a Flutter mobile application interface.
Sentiment Classification of Health Education YouTube Comments Using IndoBERT Embeddings with Logistic Regression and Naïve Bayes Andre Septa Wijaya; Amiq Fahmi; Yuventius Tyas Catur Pramudi
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.v10i3.13016

Abstract

Class imbalance is a common issue in sentiment classification of social media data, particularly in mental health–related discussions where certain sentiment classes are underrepresented. This study focuses on sentiment classification of mental health–related YouTube comments by utilizing IndoBERT as a pre-trained language model to generate contextual text embeddings. Sentiment classification is subsequently performed using conventional machine learning algorithms, namely Logistic Regression and Naïve Bayes. The research framework includes data collection through the YouTube Data API, text preprocessing, semi-manual sentiment labeling into positive, neutral, and negative classes, and dataset partitioning using an 80:20 train–test split. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is applied exclusively to the training data to prevent data leakage. Feature representation is obtained from IndoBERT embeddings with a dimensionality of 768. Model performance is evaluated using accuracy, precision, recall, and F1-score. Experimental results show that Logistic Regression outperforms Naïve Bayes, achieving an accuracy of 78%, compared to 56% for Naïve Bayes. This indicates that Logistic Regression is more effective in handling dense contextual embeddings generated by transformer-based models. Overall, the findings demonstrate that combining contextual embeddings with data balancing techniques can improve sentiment classification performance in mental health–related social media analysis, particularly in low-resource language settings.
Hyperparameter Optimization of CNN Based Open Set Speaker Verification Using MFCC and Speaker Embedding for Voice Biometric Security Mirza Ardiana; Mat Syai’in; Alief Nur Aisyi Maulidhia; Aulia Rahma Annisa; Yudi Andika; Sholahuddin Muhammad Irsyad; Fauzan Izzul Haq
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.v10i3.13027

Abstract

The development of voice based biometric security systems has increased the demand for authentication methods capable of operating accurately and securely in open set speaker verification scenarios. In this scenario, the system is required not only to recognize registered users but also to reject unknown users who are not included in the system database. This study focuses on hyperparameter optimization in a Convolutional Neural Network Embedding based speaker verification system using Mel Frequency Cepstral Coefficient (MFCC) features and speaker embeddings. The optimization process was conducted through several experimental stages, including MFCC parameter tuning, CNN architecture tuning, embedding dimension tuning, and audio augmentation analysis. The dataset consisted of Indonesian speech recordings from 8 registered speakers and 1 unknown speaker, sampled at 16 kHz under controlled recording conditions. The dataset was divided into training, enrollment, and testing subsets to support open set speaker verification evaluation and reduce data leakage. System performance was evaluated using accuracy, validation loss, False Acceptance Rate (FAR), False Rejection Rate (FRR), best threshold, and inference time. The experimental results show that the best configuration was achieved using the MFCC-C parameters (N_MFCC = 40, N_FFT = 1024, HOP_LENGTH = 256, N_MELS = 40), the CNN-E architecture with three convolution blocks (32-64-128), an embedding dimension of 64, and lightweight augmentation consisting of noise injection, pitch shifting, and time stretching. This configuration achieved stable system performance with a test accuracy of 96.43% and a FAR of 8.7%, while maintaining lightweight computational complexity and real time inference capability. The results also indicate that excessive augmentation may increase embedding overlap between speakers, thereby reducing system security performance. However, the study was conducted on a limited scale dataset and has not yet evaluated robustness against spoofing attacks, replay attacks, or adversarial synthesized voice attacks. Overall, the study indicates that hyperparameter optimization influences the balance between accuracy, computational efficiency, and biometric security performance in lightweight CNN based voice biometric authentication systems under limited scale evaluation conditions.
Environmental Image Analysis for Smoke-Free Area Compliance Mapping Using YOLOv11 and Geographic Information Systems Lisa Mulia Al Ikhlas; Dahlan Abdullah; Nurdin Nurdin
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.v10i3.13043

Abstract

Smoke-Free Areas are implemented to protect public health; however, monitoring and evaluating compliance remain challenging due to the lack of automated and spatially integrated monitoring systems. This study aims to develop a mapping and classification system for Smoke-Free Area (KTR) compliance using the YOLOv11 object detection algorithm and Geographic Information System (GIS)-based spatial analysis on environmental images. Data collection was conducted in Banda Sakti District, Lhokseumawe, at nine observation locations consisting of places of worship, public open spaces, and workplaces. The dataset consisted of three object classes, namely smoking activity, cigarette, and ashtray, combined with spatial variables such as latitude and longitude, where latitude represents the north–south geographic position and longitude represents the east–west geographic position of each observation point. These spatial variables were integrated with the YOLOv11 detection results to enable the mapping and visualization of KTR violations within the GIS environment. The YOLOv11 model was evaluated using precision, recall, mAP50, and mAP50-95 metrics. Experimental results showed that the model achieved a precision value of 0.772, recall of 0.741, mAP50 of 0.770, and mAP50-95 of 0.602, indicating moderate object detection performance under various environmental conditions. Spatial analysis results revealed that out of 145 observation points, 112 points were categorized as major violations, 25 points as minor violations, and only 8 points as compliant areas. Therefore, the integration of YOLOv11 and GIS provides a digital-based approach for supporting Smoke-Free Area compliance monitoring and spatial analysis.
Improving YOLO12 Performance Using Efficient Channel Attention For Ship Object Detection Richard Christoper Subianto; Muhammad Naufal; Farrikh Alzami
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.v10i3.13067

Abstract

Ship object detection in aerial imagery remains a critical challenge due to complex marine backgrounds, varying object scales, and occlusion, which often lead to unstable model performance. This research proposes integrating the Efficient Channel Attention (ECA) module into the YOLO12-L architecture to enhance feature selectivity and prediction robustness. The model was trained for 500 epochs on the Ship Detection from Aerial Images dataset, comprising 621 images and 1,951 annotated ship instances, and performance was evaluated across five distinct random seeds to ensure statistical reliability. Quantitative results demonstrate that the proposed YOLO12-L + ECA model achieved a median Average Precision (mAP@50) of 71.32% and a Precision of 92.5%, outperforming the baseline YOLO12-L model. To evaluate statistical validity, a Paired Bootstrap Median Test with 100 resamples confirmed a statistically significant improvement in median performance (Δ = +1.01%, p = 0.02). Furthermore, the standard deviation of mAP@50 decreased from 1.1% in the baseline to 0.3% in the ECA model, representing a 72.7% reduction in performance variance. Computational efficiency analysis revealed that the ECA module introduced negligible overhead, adding merely 5 parameters (totaling 26,389,880) and keeping FLOPs constant at 89.4, while maintaining a high inference speed of 10.7 FPS (a marginal 2.5% reduction). These findings confirm that ECA effectively suppresses background noise, stabilizes detection outputs, and provides statistically significant improvements without compromising architectural efficiency. The proposed architecture offers a lightweight and reliable solution for automated maritime monitoring systems, particularly in challenging visual environments.

Page 90 of 101 | Total Record : 1006

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