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All Journal Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Infomedia MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Ilmu Komputer dan Bisnis Abdimas Galuh: Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Mitra Masyarakat (JPMM) SmartComp The Indonesian Journal of Computer Science
Majid Rahardi
Universitas Amikom Yogyakarta
Agriculture, Biological Sciences & Forestry Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Social Sciences Other

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Comparative Analysis of MobileNetV3 and EfficientNetv2B0 in BISINDO Hand Sign Recognition Using MediaPipe Landmarks Fadzli, Alief Khairul; 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.v10i1.11878

Abstract

Sign language is a vital communication medium for individuals with hearing and speech impairments. In Indonesia, more than 2.6 million people experience hearing disabilities, most of whom rely on Bahasa Isyarat Indonesia BISINDO for daily interaction. However, limited public understanding and the scarcity of professional interpreters continue to hinder inclusive communication. Recent advancements in computer vision and deep learning have enabled camera-based sign language recognition systems that are more affordable and practical compared to sensor-glove solutions. this study presents a comparative analysis between EfficientNetV2-B0 and MobileNetV3-Large in recognizing BISINDO hand sign alphabets using MediaPipe landmarks. The dataset was derived from BISINDO video recordings, from which hand landmarks were extracted using MediaPipe Hands and subsequently converted into two-dimensional skeletal images. In total, 10,309 skeletal images representing BISINDO alphabets A–Z were generated and used for model training and evaluation. Both models were trained under identical configurations using TensorFlow. The results show that MobileNetV3-Large achieved 89.67% test accuracy and an F1-score of 89.76%, while EfficientNetV2-B0 obtains 95.98% test accuracy and an F1-score of 95.93%. These findings highlight the trade-off between the higher classification accuracy of EfficientNetV2-B0 and the superior computational efficiency of MobileNetV3-Large. This research contributes to the development of lightweight, high-performance BISINDO recognition systems for assistive communication applications.
Comparison of Transfer learning Models MobileNetV3-Large and EfficientNet-B0 for Rice Leaf Disease Classification Abiyyu, Ahmad Naufal; 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.v10i1.12033

Abstract

Rice productivity strongly depends on early detection of leaf diseases, while manual identification is often delayed and subjective. This study investigates the use of lightweight CNN architectures MobileNetV3-Large and EfficientNet-B0 based on transfer learning to classify six rice leaf disease classes, namely bacterial leaf blight, brown spot, healthy, leaf blast, leaf scald, and narrow brown spot. The dataset is obtained from Kaggle and consists of 2,628 images with a balanced class distribution, stratified into training, validation, and test sets with a ratio of 80%:10%:10%. The images are resized to 224×224 pixels and data augmentation was applied to the training set. Pretrained ImageNet weights are first used as frozen feature extractors, followed by partial fine-tuning of the last 30% backbone layers, with custom classification layers trained using the Adam optimizer with an early stopping mechanism. Model performance is evaluated using accuracy, precision, recall, F1-score, and confusion matrices, while computational efficiency is assessed based on parameter count and inference speed measured in frames per second. The results show that under partial fine-tuning MobileNetV3-Large achieves 95.83% test accuracy and 95.80% macro F1-score with 3.12 million parameters, while EfficientNet-B0 obtains 93.18% accuracy and 93.02% macro F1-score with 4.21 million parameters. Both models achieve inference speeds above 50 frames per second, suggesting their potential suitability for deployment on resource-constrained devices. Bootstrap analysis suggests the performance gap is clear in the frozen stage but becomes less conclusive after partial fine-tuning. Overall, MobileNetV3-Large provides the best trade-off between accuracy and efficiency for rice leaf disease classification.
Analysis of SMOTE and Random Search on Machine Learning Algorithms for Stroke Disease Diagnosis Dn, Ubaid Khoir Julio; 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.v10i1.12046

Abstract

Stroke is a critical medical condition in which false negative predictions may lead to delayed treatment and increased mortality. Therefore, predictive models in the medical domain should prioritize sensitivity (recall) in addition to overall accuracy. This study analyzes the impact of the Synthetic Minority Over-sampling Technique (SMOTE) and Random Search hyperparameter optimization on five machine learning algorithms—Random Forest, XGBoost, Support Vector Machine (SVM), Logistic Regression, and CatBoost—for stroke disease diagnosis. Two experimental scenarios were conducted, namely models trained without SMOTE and models trained with SMOTE applied only to the training data to prevent data leakage. Model performance was evaluated using accuracy, precision, recall, and F1-score, with particular emphasis on recall due to its clinical relevance. In clinical practice, low recall may lead to false negative predictions, where high-risk stroke patients are not identified by the system, potentially resulting in delayed medical intervention. Therefore, recall is emphasized as the primary performance metric in this study. Experimental results demonstrate that SMOTE consistently improves recall across all models, while Random Search further enhances performance. CatBoost achieved the best performance with an accuracy of 96.61%, recall of 97%, and F1-score of 97%. Despite its superior performance, potential overfitting risks are critically discussed. These findings indicate that the proposed approach produces a clinically relevant decision-support model for stroke risk prediction.
Comparative Analysis of BERT and LSTM Models for Sentiment Classification of Mobile Game User Reviews Indriyatmoko, Toto; Rahardi, Majid; Utama, Hastari; Frobenius, Arvin Claudy
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.v10i1.12149

Abstract

Sentiment classification of user reviews for mobile games that rely on direct advertising (direct ads) is crucial for understanding player perceptions and improving user experience. This study aims to compare the performance of two deep learning architectures, Long Short-Term Memory (LSTM) and multilingual Bidirectional Encoder Representations from Transformers (BERT) in classifying sentiment in reviews into three categories, positive, negative, and neutral. The dataset used consists of reviews from games employing direct ads, which underwent rule-based labeling and text preprocessing. The LSTM model was built from scratch using a custom embedding layer, while the multilingual BERT model was fine-tuned using a transfer learning approach. Evaluation was conducted based on accuracy, precision, recall, and F1-score metrics. Experimental results show that multilingual BERT achieves superior validation loss compared to LSTM (0.37 vs. 0.44). BERT also outperforms LSTM significantly in terms of F1-score and its ability to understand multilingual linguistic context. However, LSTM demonstrates advantages in computational efficiency and training speed. These findings offer practical recommendations for developers in selecting an appropriate sentiment analysis model based on accuracy requirements and resource availability.
Comparison of LightGBM and CatBoost Algorithms for Diabetes Prediction Based on Clinical Data Latuconsina, Muhammad Sidik; 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.v10i1.12179

Abstract

Diabetes Mellitus presents a global health challenge necessitating accurate early detection to prevent fatal complications. However, clinical data often exhibit imbalanced class distributions, hindering standard prediction models from effectively detecting positive patients. This study aims to compare the performance of two modern Gradient Boosting algorithms, LightGBM and CatBoost, in predicting diabetes risk. Random Forest and Logistic Regression algorithms were included as baseline models to benchmark effectiveness. To address data imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied during the training data preprocessing stage. The dataset was sourced from the Kaggle public repository (Diabetes Prediction Dataset), comprising 100,000 patient medical records with clinical attributes such as age, body mass index (BMI), and HbA1c levels. Performance evaluation utilized Accuracy, Precision, Recall, F1-Score, and Area Under the Curve (AUC) metrics. Experimental results demonstrated a tight competition, where LightGBM achieved the highest Accuracy of 97.16%. However, CatBoost demonstrated superior sensitivity (Recall) of 69.71% and the highest F1-Score of 80.48%. This makes CatBoost the most reliable model in minimizing False Negatives compared to LightGBM and Random Forest, whereas Logistic Regression showed the lowest performance. Furthermore, interpretability analysis using SHAP (SHapley Additive exPlanations) revealed that HbA1c and blood glucose levels were the most dominant features in detection, validating the model's alignment with clinical diagnosis. This study concludes that the CatBoost algorithm combined with SMOTE offers a more sensitive, transparent, and efficient diabetes prediction for medical screening.
Co-Authors Abdulloh, Ferian Fauzi Abiyyu, Ahmad Naufal Abu Bakar, Rahmat Fauzi Afrig Aminuddin Alfian Difa’ul Amien Amelia, Hemas Anggita Andrika, Muhammad Yusuf Arindika, Alti Aristawidya, Winda Ardelia Artamma, Chanan Asadulloh, Bima Pramudya Bernadhed, Bernadhed Dn, Ubaid Khoir Julio Ekayanda, Gilang Elik Hari Muktafin Fadzli, Alief Khairul Febianty, Dennita Noor Frobenius, Arvin Claudy Harianto, Fetrus Jari Indriyatmoko, Toto Joang Ipmawati Kharisma, Rizqi Sukma Kusnawi Kusnawi Kusrini Kusrini Kusrini, Kusrini Laiskodat, Fransisko Andrade Latuconsina, Muhammad Sidik Latupono, Ali Samsul Lefi, Nadia Martha M. Ikhsan Malik Bagaskara Mawar, Heni Sapta Muktafin, Elik Hari Mulia Sulistiyono Musthofa, Alif Nafiatun Sholihah Nandini, Kety Nolejanduma, Dionisius Nusaca Redegnosis Nur'aini, Linnda Prawidya Nurhayati, Lutfiana Deka Pandiangan, Van Daarten Puspitaningrum, Niken Rayadhani, Windy Aldora Rida, Rachmasari Annisa Rizqi Sukma Kharisma Rohmad Fajarudin Salim, Edwin Ibrahim Saputro, Uyock Anggoro Suryaputri, Cantika Okzen Syaputra, Muh. Rezky Sylvester, Gunawan Toto Indriyatmoko Utama, Hastari Uyock Anggoro Saputro Vabilla, Sheila Anggun Yudha, Muhammad Agung Reza