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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) Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) SmartComp The Indonesian Journal of Computer Science
Majid Rahardi
Universitas Amikom Yogyakarta
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Social Sciences Other

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Comparative Analysis of EfficientNet-B0 and ViT-B16 for Multiclass Classification of Green Coffee Beans Syaputra, Muh. Rezky; Rahardi, Majid
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11563

Abstract

Green coffee bean classification plays an important role in the coffee supply chain, as bean quality has a direct impact on the taste and final quality of the product. The USK-Coffee dataset, which consists of four bean object classes defect, longberry, peaberry, and premium, is photographed under varied lighting conditions and capture angles, thus challenging the accuracy of conventional visual models. Although lightweight CNN models have been used, not many studies have directly compared transformer-based architectures (ViT-B16) and modern efficient CNNs (EfficientNet-B0) for green coffee bean classification under real conditions. With transfer learning strategy, image augmentation (resize, flip, rotation, color jitter, random crop), and normalization, we evaluate the performance of both models on the dataset. ViT-B16 achieved 85% accuracy on the test data (F1-score 0.85), with a fast batch inference latency of 0.0074 seconds per batch. EfficientNet-B0 achieved 87% accuracy (F1-score 0.87), with a slower batch latency (0.0106 seconds per batch). However, EfficientNet-B0 is significantly faster for single image inference (real-time) (0.035 seconds) compared to ViT-B16 (0.426 seconds). This trade-off higher accuracy/faster single inference on EfficientNet-B0 vs. faster batch processing on ViT-B16 shows that both are feasible for edge computing-based classification systems.
L2IC and MobileViT-XXS for BISINDO Alphabet Recognition Artamma, Chanan; Rahardi, Majid
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11575

Abstract

This study proposes a Landmark-to-Image Conversion (L2IC) approach integrated with the MobileViT-XXS architecture for Indonesian Sign Language (BISINDO) alphabet recognition. The method converts 42 hand keypoints, extracted using MediaPipe Hands into normalized 224×224 grayscale images to capture spatial hand patterns more effectively. These L2IC representations are then used as input to the MobileViT-XXS model, trained for 30 epochs with a learning rate of 0.001. Experimental results show that the model achieves an accuracy and Macro F1-Score of 97.98%, outperforming baseline approaches using raw RGB images and MLP-based classification on numerical keypoints. While the model demonstrates strong performance in controlled offline experiments, further evaluation is required to assess its robustness under real-world dynamic BISINDO usage and deployment on resource-limited devices. These findings indicate that the L2IC representation effectively captures essential spatial information, contributing to high recognition accuracy in static BISINDO hand gesture classification.
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.
Analysis of Gradient Boosting Algorithms with Optuna Optimization and SHAP Interpretation for Phishing Website Detection Abu Bakar, Rahmat Fauzi; 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.v10i1.11857

Abstract

Phishing remains a persistent cybersecurity threat, evolving rapidly to bypass traditional blacklist-based detection systems. Machine Learning (ML) approaches offer a promising solution, yet finding the optimal balance between detection accuracy and model interpretability remains a challenge. This study aims to evaluate and optimize the performance of three state-of-the-art Gradient Boosting algorithms—XGBoost, LightGBM, and CatBoost—for phishing website detection. The research utilizes the UCI Phishing Websites dataset consisting of 11,055 instances. The novelty of this study lies in the implementation of the Optuna framework with the Tree-structured Parzen Estimator (TPE) for automated hyperparameter optimization and the application of SHAP (Shapley Additive Explanations) interaction values to interpret the "black-box" models. The experimental results demonstrate that the LightGBM model, optimized via Optuna, achieved the highest performance with an F1-Score of 0.9798, outperforming the baseline model (0.9713) by 0.87%. Furthermore, SHAP analysis identified 'SSLfinal_State' as the most critical determinant for distinguishing phishing sites. This study confirms that optimizing modern boosting algorithms significantly enhances phishing detection capabilities while providing necessary explainability for cybersecurity analysts.
Comparison of Naïve Bayes and Support Vector Machine for Sentiment Classification of Acne Skincare Reviews Arindika, Alti; 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.v10i1.11869

Abstract

The increasing popularity of skincare products for acne-prone skin had led to a surge in online consumer reviews, which are characterized by informal language, domain-specific terminology, and imbalanced sentiment distribution, posing challenges for sentiment classification tasks. This study aims not only to compare the performance but also to analyze the generalization behavior of two popular machine learning algorithms, Naïve Bayes and Support Vector Machine (SVM), for sentiment classification of skincare product reviews specifically targeting acne-prone skin. A comprehensive methodology was employed, including thorough text preprocessing, feature extraction using Term Frequency-Inverse Document Frequency (TF-IDF) with n-gram representation, and data balancing through Synthetic Minority Over-sampling Technique (SMOTE). The study utilized a dataset of 4,004 labeled reviews categorized into positive and negative sentiments. The models were evaluated using stratified 5-Fold cross-validation to ensure robust and fair assessment. Results indicate that Naïve Bayes slightly outperforms SVM on the testing set, achieving the highest accuracy of 91.14% compared to 90.64% for SVM. While SVM demonstrated higher performance during training, its testing performance suggested a tendency toward overfitting, whereas Naïve Bayes exhibited more stable generalization on unseen data. Further qualitative insight analysis revealed that product effectiveness and user experience are the primary drivers of consumer sentiment, while competitive analysis highlighted distinct brand perception patterns across skincare categories. These findings indicate that simpler probabilistic models such as Naïve Bayes can provide robust and reliable performance for sentiment analysis in specialized and imbalanced skincare review datasets.
Analysis of Gradient Boosted Trees Algorithm in Breast Cancer Classification Suryaputri, Cantika Okzen; 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.v10i1.11875

Abstract

Early and accurate classification of breast cancer is essential to support clinical diagnostic processes and improve patient outcomes. This study proposes a comprehensive machine learning pipeline based on Gradient Boosted Tree algorithms to classify breast tumors into benign and malignant categories. The proposed framework integrates several preprocessing stages, including outlier handling using the Local Outlier Factor (LOF), feature normalization with StandardScaler, class imbalance handling using SMOTE, and feature selection through ANOVA-based SelectKBest. Five ensemble learning models—XGBoost, LightGBM, CatBoost, HistGradientBoosting, and GradientBoosting—were trained and evaluated using accuracy, precision, recall, F1-score, and ROC-AUC metrics. The experimental results show that all models achieved strong and comparable classification performance. Among them, CatBoost obtained the highest ROC-AUC value of 0.9960, along with an accuracy of 0.9649, precision of 0.9750, recall of 0.9286, and F1-score of 0.9512. Statistical evaluation using the DeLong test indicated that the differences in ROC-AUC among the evaluated models were not statistically significant (p > 0.05), suggesting similar discriminative capabilities across models. To enhance model interpretability, SHAP (SHapley Additive exPlanations) was applied to the CatBoost model as a representative classifier. The results show that features related to nuclear size and shape, such as radius, area, perimeter, and concavity, contributed most significantly to malignant predictions. This study demonstrates that the integration of robust preprocessing techniques, Gradient Boosted Tree models, and explainable machine learning provides an accurate and interpretable approach for breast cancer classification. However, the evaluation was conducted on a single public dataset without external validation, and further studies using independent and real-world datasets are required before clinical deployment.
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.
Co-Authors Abdulloh, Ferian Fauzi Abiyyu, Ahmad Naufal Abu Bakar, Rahmat Fauzi Afrig Aminuddin Alfian Difa’ul Amien Ali Mustopa, Ali Amelia, Hemas Anggita Andrika, Muhammad Yusuf Anggita, Sharazita Dyah Arindika, Alti Aristawidya, Winda Ardelia Artamma, Chanan Asadulloh, Bima Pramudya Bernadhed, Bernadhed Devi Wulandari Dn, Ubaid Khoir Julio Ekayanda, Gilang Elik Hari Muktafin Fadzli, Alief Khairul Febianty, Dennita Noor Frobenius, Arvin Claudy Harianto, Fetrus Jari Ikmah Indriyatmoko, Toto Joang Ipmawati 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