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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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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Detection of Ripeness in Oil Palm Fresh Fruit Bunches Using the YOLO12S Algorithm on Digital Images Nur'aini, Linnda Prawidya; Rahardi, Majid
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.10250

Abstract

Indonesia is the world's largest producer of palm oil, with a production volume reaching 46.82 million tons in 2022. This industry heavily relies on the quality of Fresh Fruit Bunches (FFB) harvests, which is determined by the accuracy of ripeness at the time of harvest. Unfortunately, ripeness assessment of FFB is still conducted manually and subjectively by field workers, posing risks to both efficiency and production accuracy. Although various studies have employed YOLOv5 and YOLOv8 for fruit ripeness detection, few have explored the potential of YOLO12s in classifying FFB ripeness in a comprehensive and efficient manner. In this study, we present the application of the YOLO12s algorithm to automatically classify the ripeness levels of oil palm FFB using digital images. The YOLO12s model was trained on 14,620 FFB images categorized into four ripeness levels: unripe, under-ripe, ripe, and overripe. Evaluation results showed a precision of 93.1%, recall of 95.9%, mAP@0.50 of 97.8%, and mAP@0.50–0.95 of 78.8%. The model was able to perform inference in approximately 4.7 milliseconds per image and demonstrated good generalization despite challenges related to varying lighting conditions. These results indicate that YOLO12s holds great potential to replace subjective manual methods with a more accurate, consistent, and efficient classification solution to support the harvesting process in the palm oil industry.
Comparative Study of Linear Regression, SVR, and XGBoost for Stock Price Prediction After a Stock Split Andrika, Muhammad Yusuf; Rahardi, Majid
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.10220

Abstract

This study aims to identify the most effective regression method for predicting the closing stock price of Bank Central Asia (BBCA) following the stock split event on October 12, 2021. Accurate post-split price predictions are crucial for helping investors comprehend new market dynamics, yet there is limited research evaluating the performance of regression models on BBCA’s stock after such corporate actions. Using data obtained through web scraping from the Indonesia Stock Exchange, this study tested three regression algorithms Linear Regression, Support Vector Regression, and XGBoost Regressor on post-split data. The selected input features were open_price, first_trade, high, low, and volume, while the target was close_price. The dataset was divided using an 80:20 train-test split and evaluated with RMSE, MAPE, and R-squared metrics. Results showed that Linear Regression achieved the best performance RMSE: 50.41, MAPE: 0.0048, R²: 0.9971, followed by XGBoost RMSE: 69.12, MAPE: 0.0058, R²: 0.9946, and SVR RMSE: 80.98, MAPE: 0.0069, R²: 0.9925. These findings indicate that BBCA’s post-split stock data exhibits a linear pattern, making Linear Regression the most suitable and efficient method. This suggests that simpler models can outperform more complex algorithms when applied to stable and structured financial datasets.
A Sentiment Analysis of Public Perception Toward Pets in Public Spaces Using Logistic Regression and Word Embedding Febianty, Dennita Noor; Rahardi, Majid
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.10245

Abstract

Addressing the complex social debate over pets in public areas, this study assesses public sentiment by analyzing a dataset of YouTube comments. We employed a machine learning pipeline beginning with data collection via the YouTube API, followed by rigorous text preprocessing and SMOTE-based class balancing for the training data. For classification, a Logistic Regression model was trained on contextual features generated by Word Embeddings (Word2Vec) and optimized through hyperparameter tuning. The final model proved highly effective, yielding a test accuracy of 92.74% with F1-scores of 0.84 for the negative class and 0.95 for the positive class. Ultimately, this research establishes an effective approach to measuring public opinion on social issues in Indonesia, providing actionable insights for public space administrators and policymakers.
Comparative Analysis of LightGBM and Random Forest for Daily Bitcoin Closing Price Prediction with Ensemble Approach Nolejanduma, Dionisius Nusaca Redegnosis; Rahardi, Majid
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10589

Abstract

This study performs a comparative analysis of the LightGBM and Random Forest algorithms in predicting daily Bitcoin closing prices, with an exploration of an Ensemble approach for potential improvements in accuracy. A quantitative research design is employed, utilizing historical Bitcoin (BTC-USD) data from September 2015 to July 2025, enriched with various technical indicators. Data preprocessing, model training, and evaluation were carried out using Python in Google Colaboratory, with the dataset split into 80% for training and 20% for testing. Model performance was evaluated using the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the R-squared (R²) statistic, with statistical significance tests to ensure robust comparisons. A simple Linear Regression model was also included as a baseline. The findings reveal that Random Forest outperformed LightGBM, achieving an MAE of 11,599.74, an RMSE of 19,262.31, and an R² of 0.431, compared to LightGBM’s MAE of 12,285.42, RMSE of 19,995.04, and R² of 0.386. Although the Ensemble model showed slight improvements over LightGBM, it did not surpass Random Forest. The relatively low R² values across all models reflect the inherent volatility and difficulty in predicting Bitcoin prices. The study concludes that Random Forest demonstrates superior robustness for Bitcoin forecasting. Importantly, this work provides a novel empirical contribution by being one of the first to directly benchmark RF, LightGBM, and their Ensemble for Bitcoin prediction, highlighting that a simple averaging Ensemble does not guarantee superior performance. This finding provides a foundation for developing more refined Ensemble strategies tailored to high-volatility assets.
Comparative Analysis Transfer Learning Models for Early Detection of Pneumonia using Chest X-ray Images Rida, Rachmasari Annisa; Rahardi, Majid
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10857

Abstract

Pneumonia is a serious respiratory disease that continues to be a major worldwide health issue, especially in nations that are struggling with limited medical resources. Early and accurate detection is essential to improve patient outcomes and reducing the rate of death. This study compares the performance of two deep learning architectures, DenseNet121 and ResNet50, using transfer learning for pneumonia detection from chest X-ray images. The dataset consists 5,856 images with two classes, NORMAL and PNEUMONIA, split into training 60%, validation 20%, and testing 20%. Pretrained ImageNet weights were used as fixed feature extractors, with a custom classification layers. Evaluation metrics included accuracy, precision, recall, F1-score, and confusion matrix. On the internal test set, DenseNet121 achieved 92% accuracy, with precision 0.79, recall 0.94, and F1-score 0.86 for NORMAL class, and precision 0.98, recall 0.91, and F1-score 0.94 for PNEUMONIA class. ResNet50 reached 81% accuracy, with precision 0.61, recall 0.80, and F1-score 0.70 for NORMAL class, and precision 0.92, recall 0.81, and F1-score 0.86 for PNEUMONIA class. External testing on an independent set of 200 images (100 images per class) yielded 98% accuracy for DenseNet121 and 85% for ResNet50. These results show that DenseNet121 provides better overall performance and lower false-negative risk for pneumonia cases, highlight the potential of DenseNet121 as a foundation for AI-assisted diagnostic tools in clinical practice.
Image-Based Classification of Indonesian Traditional Houses Using a Hybrid CNN-SVM Algorithm Ikhsan, M.; Rahardi, Majid
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10864

Abstract

The diversity of Indonesian traditional houses represents a cultural heritage that must be preserved. However, the lack of interest among younger generations and the difficulty in recognizing the distinctive architectural characteristics of traditional houses present challenges to preservation efforts. This study aims to develop an image classification model for Indonesian traditional houses using a hybrid CNN-SVM approach to improve recognition accuracy. The dataset consists of 3,919 images from five classes of traditional houses, namely gadang, joglo, panjang, tongkonan, and honai, with an 80% training split, 10% validation, and 10% testing. The data were processed through resizing, augmentation, and normalization before being trained using a CNN architecture with five convolutional layers as a feature extractor and an SVM serving as a multi-class classifier. The experimental results show that the hybrid CNN-SVM model achieved an accuracy of 96.68%, with consistently high precision, recall, and F1-score across all classes. These findings demonstrate that integrating CNN as a feature extractor and SVM as the final classifier can enhance the model’s generalization capability in distinguishing images of Indonesian traditional houses.
Sentiment Analysis of Economic Policy Comments on YouTube Using Ensemble Machine Learning Nandini, Kety; Rahardi, Majid
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10897

Abstract

Public sentiment analysis of economic policies is increasingly important in the digital age, as social media platforms have become the main arena for public discussion. This study analyzes YouTube comments related to Tom Lembong's economic policies to address the lack of policy sentiment analysis tools in Indonesian. A dataset containing 1,029 comments was collected and systematically processed using normalization, stop word removal, and stemming techniques tailored to Indonesian. To overcome data scarcity and class imbalance, advanced data augmentation methods—synonym replacement, random insertion, and random deletion—were applied, expanding the dataset to 2,169 samples. Feature extraction used TF-IDF vectorization (unigram, bigram, trigram) and CountVectorizer, followed by an 80:20 split into training and testing sets. Several machine learning algorithms, including Support Vector Machine (SVM), Logistic Regression, Random Forest, Gradient Boosting, and Naïve Bayes, were evaluated with hyperparameter tuning through grid search. The results showed that SVM with TF-IDF bigrams achieved the best performance (accuracy: 96.08%, F1-score: 96.03%). Class-level evaluation showed high performance for negative sentiment (F1-score: 0.97) and positive sentiment (F1-score: 0.97), while neutral sentiment was more challenging (F1-score: 0.90) due to ambiguity, sarcasm, and fewer samples. The ensemble model, which combines several optimized SVM variants with soft voting, achieved robust and stable performance (accuracy and F1-score: 95.16%). These findings confirm the effectiveness of the ensemble-based approach for Indonesian sentiment analysis, while providing valuable insights into public perceptions of economic policy in the digital space.
Impact of SMOTE and ADASYN on Class Imbalance in Metabolic Syndrome Classification Using Random Forest Algorithm Nurhayati, Lutfiana Deka; Rahardi, Majid
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10657

Abstract

Metabolic Syndrome is a collection of medical conditions that can increase the risk of stroke, cardiovascular disease, and type 2 diabetes. Early detection of this condition requires a machine learning model capable of accurate classification to support timely treatment. However, class imbalance in data often hampers the performance of classification algorithms, particularly in recognizing minority classes, namely individuals diagnosed with Metabolic Syndrome. This study aims to analyze the effect of applying the SMOTE and ADASYN data balancing techniques in classifying Metabolic Syndrome using the Random Forest algorithm. These algorithms were chosen for their ability to produce accurate predictions, although their performance can decline when faced with imbalanced class distributions. The results showed that the model without data balancing techniques achieved 86% accuracy with a minority class recall of 75%. The application of SMOTE increased accuracy to 91% and recall to 93%, while ADASYN achieved 92% accuracy and a minority class recall of 95%. These findings indicate that the ADASYN technique combined with the Random Forest algorithm provides significant performance improvements in the classification of Metabolic Syndrome on imbalanced data.
Comparative Analysis of Random Forest and XGBoost Models for Cervical Cancer Risk Prediction using SHAP-based Explainable AI Yudha, Muhammad Agung Reza; 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.v9i6.10357

Abstract

Cervical cancer remains one of the leading causes of cancer-related deaths among women, particularly in developing countries such as Indonesia. This study aims to develop an accurate and interpretable predictive model for cervical cancer risk using Random Forest (RF) and Extreme Gradient Boosting (XGBoost) algorithms. The dataset used is the Cervical Cancer Risk Factors from the UCI Repository, consisting of 858 patient records and 36 clinical and demographic features. The preprocessing stages include missing value imputation, class balancing using Synthetic Minority Oversampling Technique (SMOTE), and hyperparameter optimization through Randomized Search CV. Experimental results show that both models achieved high performance, with accuracy exceeding 96% and AUC above 0.95, while the XGBoost (Tuned + SMOTE) model slightly outperformed RF in detecting positive cases. The interpretability analysis using SHapley Additive exPlanations (SHAP) identified clinical features such as Schiller Test, Hinselmann Test, and Cytology Result as the most influential factors in the classification process, consistent with established clinical evidence. Therefore, the integration of XGBoost, SMOTE, and SHAP provides a predictive framework that is not only highly accurate but also clinically explainable, supporting the development of decision-support systems for early cervical cancer detection.
Machine Learning Based Prediction of Osteoporosis Risk Using the Gradient Boosting Algorithm and Lifestyle Data Salim, Edwin Ibrahim; 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.v9i6.10483

Abstract

Osteoporosis is a degenerative disease characterized by decreased bone mass and an increased risk of fractures, particularly among the elderly population. Early detection is essential; however, standard diagnostic methods such as Dual-Energy X-ray Absorptiometry (DEXA) remain limited in terms of availability and cost. This study aims to develop a machine learning-based risk prediction model for osteoporosis by utilizing lifestyle data with the Gradient Boosting algorithm. The secondary dataset was obtained from the Kaggle platform, consisting of 1,958 samples covering lifestyle and clinical attributes such as age, gender, physical activity, smoking habits, calcium intake, vitamin D consumption, and family history. Preprocessing involved normalization and categorical feature encoding, along with a balance check of class distribution, which indicated that the dataset was relatively balanced. The data were then divided using stratified sampling with an 80% training set and 20% testing set. Model performance was evaluated using accuracy, precision, recall, F1-score, and the area under the ROC curve (AUC). The results showed that the Gradient Boosting algorithm achieved an accuracy of 91%, precision of 90.8%, recall of 90.2%, F1-score of 90.5%, and an AUC of 0.92, outperforming baseline methods such as Logistic Regression and Random Forest. These findings demonstrate that Gradient Boosting is effective as a decision-support tool for early osteoporosis screening based on lifestyle data and has the potential to be integrated into clinical decision-making systems to enhance early detection in healthcare services. Nevertheless, since this study relied on a secondary dataset from Kaggle, the results require further validation using real clinical data from Indonesia to ensure representativeness for the local population.
Co-Authors Abdulloh, Ferian Fauzi Afrig Aminuddin Alfian Difa’ul Amien Amelia, Hemas Anggita Andrika, Muhammad Yusuf Aristawidya, Winda Ardelia Artamma, Chanan Asadulloh, Bima Pramudya Bernadhed, Bernadhed Ekayanda, Gilang Elik Hari Muktafin Febianty, Dennita Noor Harianto, Fetrus Jari Indriyatmoko, Toto Joang Ipmawati Kusnawi Kusnawi Kusrini Kusrini Laiskodat, Fransisko Andrade Latupono, Ali Samsul Lefi, Nadia Martha M. Ikhsan Malik Bagaskara Mawar, Heni Sapta Mulia Sulistiyono Musthofa, Alif Nafiatun Sholihah Nandini, Kety Nolejanduma, Dionisius Nusaca Redegnosis Nur'aini, Linnda Prawidya Nurhayati, Lutfiana Deka Pandiangan, Van Daarten Rayadhani, Windy Aldora Rida, Rachmasari Annisa Rizqi Sukma Kharisma Rohmad Fajarudin Salim, Edwin Ibrahim Syaputra, Muh. Rezky Sylvester, Gunawan Toto Indriyatmoko Uyock Anggoro Saputro Vabilla, Sheila Anggun Yudha, Muhammad Agung Reza