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All Journal Jurnal Buana Informatika Sinkron : Jurnal dan Penelitian Teknik Informatika U Karst JURNAL MEDIA INFORMATIKA BUDIDARMA Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Informatika Universitas Pamulang Jiko (Jurnal Informatika dan komputer) J-SAKTI (Jurnal Sains Komputer dan Informatika) J-Dinamika: Jurnal Pengabdian Kepada Masyarakat Building of Informatics, Technology and Science Buletin Ilmiah Sarjana Teknik Elektro Jurnal Informatika Ekonomi Bisnis Abdi: Jurnal Pengabdian dan Pemberdayaan Masyarakat Jurnal Teknik Informatika C.I.T. Medicom JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Reswara: Jurnal Pengabdian Kepada Masyarakat Jurnal Teknologi Informatika dan Komputer Journal of Computer Networks, Architecture and High Performance Computing Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) Yumary: Jurnal Pengabdian kepada Masyarakat International Journal of Robotics and Control Systems KLIK: Kajian Ilmiah Informatika dan Komputer J-SAKTI (Jurnal Sains Komputer dan Informatika) Brilliance: Research of Artificial Intelligence Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Informatika Ekonomi Bisnis Jurnal Pengabdian Masyarakat Charitas Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Airlangga, Gregorius
Atma Jaya Catholic University Of Indonesia
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Public Health Social Sciences Other

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Advanced Forecasting of Maize Production using SARIMAX Models: An Analytical Approach Airlangga, Gregorius
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7268

Abstract

Agricultural production forecasting is crucial for food security and economic planning. This study conducts a detailed analysis of maize production forecasting using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model, emphasizing the applicability of time-series models in capturing complex agricultural dynamics. Following a comprehensive literature review, the SARIMA model was justified for its ability to integrate seasonal fluctuations inherent in agricultural time series. Optimal model parameters were meticulously determined through an iterative process, optimizing the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The best-performing SARIMA(1, 1, 2)x(2, 2, 2, 12) model achieved an AIC of 339914.85450182937 and a BIC of 339950.64499813004, indicating its strong fit to the historical data. This model was applied to a historical dataset of maize production, providing forecasts that align closely with actual production trends on a short-term basis. Notably, the model's short-term predictions for the subsequent year showed less than a 2% deviation from the actual figures, affirming its precision. However, long-term forecasts revealed greater variability, underscoring the challenge of accounting for unforeseen environmental and economic factors in agricultural production systems. This research substantiates the efficacy of SARIMA models in agricultural forecasting, delivering strategic insights for resource management. It also points towards the integration of SARIMA with other variables and advanced modeling techniques as a future avenue to enhance forecasting robustness, particularly for long-term projections. The findings serve as a valuable resource for policymakers and stakeholders in optimizing decision-making processes for agricultural production.
Integrating LightGBM and XGBoost for Software Defect Classification Problem Airlangga, Gregorius
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7267

Abstract

Software defect classification is a crucial process in quality assurance, pivotal for the development of reliable software systems. This paper presents an innovative approach that synergizes traditional software complexity metrics with advanced machine learning algorithms, namely Light Gradient Boosting Machine (LightGBM) and Extreme Gradient Boosting (XGBoost), to enhance the accuracy and efficiency of software defect classification. Leveraging a dataset characterized by McCabe's and Halstead's metrics, this study embarks on meticulous data preprocessing, feature engineering, and hyperparameter optimization to train and evaluate the proposed models. The LightGBM and XGBoost models are fine-tuned through the Optuna framework, aiming to maximize the ROC-AUC score as a measure of classification performance. The results indicate that both models perform robustly, with XGBoost demonstrating a slight superiority in predictive capability. The integration of machine learning with traditional complexity metrics not only enhances the defect classification process but also provides deeper insights into the factors influencing software quality. The findings suggest that such hybrid approaches can significantly contribute to the predictive analytics tools available to software engineers and quality assurance professionals. This research contributes to the field by offering a comprehensive methodological framework and empirical evidence for the effectiveness of combining machine learning algorithms with traditional software complexity metrics in software defect classification.
Comparison of Predictive Modeling Concrete Compressive Strength with Machine Learning Approaches Airlangga, Gregorius
UKaRsT Vol. 8 No. 1 (2024): APRIL
Publisher : Kadiri University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30737/ukarst.v8i1.5532

Abstract

Accurately predicting concrete compressive strength is fundamental for optimizing mix designs, ensuring structural integrity, and advancing sustainable construction practices. Increased demands for safer, more durable infrastructure necessitate effective predictive concrete compressive strength models. This research aims to compare the effectiveness of six machine learning models such as Linear Regression, Random Forest, Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Gradient Boosting, and XGBoost to predict concrete compressive strength. Used a dataset of 1030 instances with varying mixture compositions, conducted extensive exploratory data analysis, and applied feature engineering and data scaling to enhance model performance. Assessments were performed with a 5-fold cross-validation approach with the R-squared (R²) metric. In addition, the SHAP value is used to understand the influence of each feature on the compressive strength results. The results revealed that XGBoost significantly outperformed other models, achieving an average R² value of 0.9178 with a standard deviation of 0.0296. Notably, Random Forest and Gradient Boosting also demonstrated robust capabilities. Based on our experiment, these models effectively predicted compressive strengths close to actual measured values, confirming their practical applicability in civil engineering. SHAP values provided insights into the significant impact of age and cement quantity on model outputs. These results highlight the advanced ensemble methods' effectiveness in concrete compressive strength prediction and underscore the importance of feature engineering in enhancing model accuracy.
Forecasting Climate Change Impacts Using Machine Learning and Deep Learning: A Comparative Analysis Airlangga, Gregorius
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 8, No 1 (2024): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v8i1.784

Abstract

This study undertakes a comparative analysis of machine learning and deep learning models for forecasting the impacts of climate change, utilizing Cross-Validation Root Mean Squared Error (CV RMSE) to gauge performance. Analyzed models include Long Short-Term Memory (LSTM) networks (CV RMSE: 0.155), Linear Regression (CV RMSE: 5647815244.91), Random Forest (CV RMSE: 0.159), Gradient Boosting Machine (GBM) (CV RMSE: 0.164), Support Vector Regressor (SVR) (CV RMSE: 0.159), Decision Tree Regressor (CV RMSE: 0.199), and K-Nearest Neighbors (KNN) Regressor (CV RMSE: 0.166). The study rigorously processes climate change time series data to ensure accurate, generalizable results. LSTM networks demonstrated exceptional performance, indicating their strong capacity for modeling complex temporal sequences inherent in climate data, while Linear Regression lagged significantly behind, revealing limitations in addressing non-linear patterns of climate change. The promising results of Random Forest and SVR models suggest their applicability in environmental science forecasting tasks. Our findings offer valuable insights into the efficacy of various predictive models, aiding researchers and policymakers in leveraging advanced analytics for climate change mitigation strategies
Comparative Analysis of Machine Learning Models for Enhanced Chemical Detection in Sensor Array Data Airlangga, Gregorius
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 8, No 1 (2024): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v8i1.785

Abstract

The objective of this study was to compare the efficacy of various machine learning models for classifying chemical substances using sensor array data from a wind tunnel facility. Six widely recognized machine learning algorithms were assessed: Random Forest, Gradient Boosting, Logistic Regression, Support Vector Machine (SVM), Decision Tree, and K-Nearest Neighbors (KNN). The dataset, consisting of 288 sensor array features, was preprocessed and utilized to evaluate the models based on accuracy, precision, recall, and F1 score through a 5-fold cross-validation method. The results indicated that ensemble methods, particularly Random Forest and Gradient Boosting, outperformed other models, achieving an accuracy and F1 score of over 99%. KNN also demonstrated high efficacy with similar performance metrics. In contrast, Logistic Regression showed modest results in comparison. The study's outcomes suggest that ensemble machine learning models are highly suitable for chemical detection tasks, potentially contributing to advancements in environmental monitoring and public safety. The findings also highlight the importance of quality data preprocessing in achieving optimal model performance. Future research directions include exploring hybrid models, deep learning techniques, and assessing model robustness against environmental variabilities. This research underscores the transformative potential of machine learning in chemical detection and paves the way for developing more sophisticated and reliable detection systems.
Exoplanet Classification Through Machine Learning: A Comparative Analysis of Algorithms Using Kepler Data Airlangga, Gregorius
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 3 (2024): MALCOM July 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i3.1303

Abstract

This study delves into the classification of exoplanets using data from the Kepler Space Telescope, comparing a suite of machine learning (ML) models to ascertain their efficacy in distinguishing confirmed planets, candidates, and false positives. With a dataset meticulously preprocessed for quality, completeness, and relevance, we embarked on an analytical journey employing models like Decision Tree, Random Forest, Hist Gradient Boosting, CatBoost, AdaBoost, LightGBM, XGBoost, Extra Trees, Logistic Regression, and XGBoost RF. These models underwent rigorous evaluation across metrics such as Accuracy, Precision, Recall, and F1 Score, revealing an unprecedented level of performance. Our findings showcased a near-uniform perfection in model predictions, with scores touching the zenith of 1.0 across most metrics for the majority of models, indicating their flawless prediction capabilities. This remarkable performance, however, was nuanced by the Gaussian NB model's slightly less than perfect scores of 0.99, highlighting a minor deviation due to its probabilistic nature. While these results underscore the models' exceptional accuracy and reliability in classifying exoplanetary data, they also prompt a critical examination of potential overfitting, the dataset's complexity, and the models' generalizability to unseen data. 
Comparative Analysis of Machine Learning Models for Chronic Disease Indicator Classification Using U.S. Chronic Disease Indicators Dataset Airlangga, Gregorius
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 3 (2024): MALCOM July 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i3.1403

Abstract

The prevalence of chronic diseases poses significant challenges to public health systems worldwide. This study evaluates the performance of four machine learning models—Gradient Boosting Classifier, Support Vector Machine (SVM), Logistic Regression, and Random Forest—in classifying chronic disease indicators using the U.S. Chronic Disease Indicators (CDI) dataset. The models were assessed based on accuracy, precision, recall, F1 score, classification report, and confusion matrix to determine their effectiveness. The Gradient Boosting Classifier outperformed other models with an accuracy of 64.36%, precision of 63.72%, recall of 64.36%, and F1 score of 63.88%. While SVM and Random Forest demonstrated moderate performance, Logistic Regression served as a baseline for comparison. The study highlights the Gradient Boosting Classifier's superiority in handling the complexities of the CDI dataset, suggesting its potential for improving chronic disease prediction and management. Future research should focus on refining these models, addressing class imbalances, and incorporating domain knowledge to enhance interpretability and applicability in real-world scenarios.
Comparative Analysis of Neural Network Architectures for Predicting Chronic Disease Indicators Using CDC’s Chronic Disease Indicators Dataset Airlangga, Gregorius
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 3 (2024): MALCOM July 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i3.1406

Abstract

This research evaluates the performance of three machine learning models—Neural Network (NN), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) using Long Short-Term Memory (LSTM) units—in predicting chronic disease indicators using the CDC's Chronic Disease Indicators (CDI) dataset. The study employs a comprehensive preprocessing pipeline and 5-fold cross-validation to ensure robustness and generalizability of the results. The CNN model outperformed both the NN and RNN models across all key performance metrics, achieving an accuracy of 0.6303, precision of 0.6445, recall of 0.6303, and F1 score of 0.5950. The superior performance of the CNN is attributed to its ability to capture spatial hierarchies and interactions within the structured dataset. The findings underscore the importance of selecting appropriate machine learning architectures based on the data characteristics. This research provides valuable insights for public health officials and policymakers to enhance chronic disease monitoring, early detection, and intervention strategies. Future work will explore hybrid models and advanced techniques to further improve predictive performance. This study highlights the potential of CNNs in public health informatics and sets a foundation for further research in this domain
Synergistic Machine Learning: Enhancing Diabetes Prediction with Hybrid Deep Learning and Ensemble Models Airlangga, Gregorius
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 3 (2024): Edisi Juli
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i3.457

Abstract

Diabetes, a growing global health concern, necessitates improved predictive strategies for early and accurate detection. This study evaluates the efficacy of various machine learning and deep learning models in predicting the onset of diabetes, employing a comprehensive dataset that includes clinical and demographic variables. Traditional machine learning models such as Decision Trees, Random Forest, KNN, and XGBoost provided foundational insights, with ensemble methods showing superior performance. Furthermore, we explored the potential of deep learning by analyzing a Simple Dense Neural Network (DNN), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN). While these individual models yielded valuable findings, particularly in identifying true positive cases, they did not surpass the ensemble techniques in overall accuracy. The pinnacle of our research was the development of a Deep Learning Meta Learner that combined Random Forest and Gradient Boosting predictions, achieving near-perfect classification metrics, and underscoring the strength of model integration. Our findings advocate for a hybrid predictive approach that merges the nuanced feature detection of deep learning with the robust pattern recognition of ensemble models, providing an impactful direction for future diabetes prediction research. This study contributes to the advancement of medical informatics and aims to support healthcare professionals in delivering proactive and personalized patient care.
Enhancing Concrete Compressive Strength Prediction with Deep Learning: A Comparative Analysis of Model Architectures Airlangga, Gregorius
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 3 (2024): Edisi Juli
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i3.459

Abstract

The imperative to predict concrete compressive strength accurately is a crucial aspect of modern civil engineering, with significant implications for the safety and cost-effectiveness of construction projects. This research explores the application of deep learning techniques to enhance predictive accuracy in this domain. We conducted a comprehensive comparative analysis of five machine learning models: a Basic neural network model, a Dropout model, a Batch Normalization model, a Deep Dense Neural Network (Deep DNN), and a Convolutional Neural Network (CNN). Utilizing a dataset reflective of various concrete mixtures and their corresponding compressive strengths, each model underwent rigorous evaluation through a five-fold cross-validation scheme. Performance metrics, including Mean Squared Error (MSE) and R-Squared (R²), were computed to assess each model's predictive capabilities. The results indicated that models employing batch normalization and deeper architectures provided superior predictive performance, suggesting that these features are instrumental in understanding the complex relationships between the components of concrete mixtures. The Batch Normalization and Deep DNN models demonstrated remarkable accuracy and consistency, surpassing traditional and CNN models. This study not only enhances the current understanding of material property prediction through machine learning but also paves the way for the development of more efficient and robust predictive tools in civil engineering. The findings underscore the transformative potential of deep learning in material science, emphasizing its ability to deliver nuanced and precise predictions for critical engineering properties.
Co-Authors Afandi Nur Aziz Thohari, Afandi Nur Aziz Alamsyah - Amalia, Andika Elok Basuki, Widodo Widjaja Bata, Julius Victor Manuel Christanto, Henoch Juli Christiany Suwartono Denny Jean Cross Sihombing Denny Jean Cross Sihombing Eugenius Kau Suni Henoch Juli Christanto Julius Bata Julius Victor Manuel Bata Kristian, Yoel Lenson, Abraham K. S. Lim, Bobi Hartanto Pramudita Makin, See Jong Nugroho, Oskar Ika Adi Pedro Manuel Lamberto Buu Sada Philia, Gregoria Piolo, Samuel Pujo Hari Saputro Rahmananta, Radyan Ronald Sukwadi Setiawan, Yohanes Evan Sidhunata, Billy Macarius Stephen Aprius Sutresno, Stephen Aprius Sugianto, Lai Ferry Suni, Eugenius Kau