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Comparative Study of XGBoost, Random Forest, and Logistic Regression Models for Predicting Customer Interest in Vehicle Insurance Airlangga, Gregorius
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 4 (2024): Article Research Volume 8 Issue 4, October 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.14194

In today’s competitive insurance market, accurately predicting customer interest in additional products, such as vehicle insurance, is crucial for optimizing marketing strategies and maximizing sales. This study presents a comparative analysis of three machine learning models such as XGBoost, RandomForest, and Logistic Regression to predict customer interest in vehicle insurance based on a dataset that includes demographic, vehicle, and policy-related features. The dataset was analyzed using five-fold cross-validation, and the performance of the models was evaluated using AUC-ROC, precision, recall, and F1-score. XGBoost demonstrated the highest recall (0.9525) and AUC-ROC (0.7854), making it the most effective model for identifying customers interested in vehicle insurance, though at the expense of lower precision (0.2585). RandomForest showed a more balanced trade-off between precision (0.3064) and recall (0.5341) but performed lower overall. Logistic Regression, while the most interpretable model, exhibited high variability in performance across different folds, with a lower average precision (0.2372). The findings of this research highlight that XGBoost is ideal for maximizing recall in high-volume campaigns, while RandomForest may be better suited for applications requiring fewer false positives. These results offer valuable insights into model selection based on business objectives and resource allocation.

Comparing BDD and TDD: Machine Learning Analysis of Software Quality with SHAP Interpretability Airlangga, Gregorius
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 4 (2024): Article Research Volume 8 Issue 4, October 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.14201

This study evaluates the impact of Behavior-Driven Development (BDD) and Test-Driven Development (TDD) on software quality using machine learning models, including Random Forest, XGBoost, and LightGBM. Key metrics such as bug detection, test coverage, and development time were analyzed using a dataset from multiple software projects. Polynomial feature expansion captured non-linear interactions, while SHapley Additive exPlanations (SHAP) enhanced interpretability. Results indicate that Random Forest achieved the best predictive accuracy, with an average RMSE of 7.64 and MAE of 6.39, outperforming XGBoost (average RMSE: 8.63, MAE: 7.37) and LightGBM (average RMSE: 6.89, MAE: 5.38). However, negative values across all models reveal challenges in generalization. SHAP analysis highlights the critical influence of higher-order interactions, particularly between test coverage and development time. These findings underscore the complexity of predicting software quality and suggest the need for additional features and advanced techniques to enhance model performance. This study provides a comprehensive, interpretable framework for assessing the comparative effectiveness of BDD and TDD in improving software quality.

Pelatihan Desain Grafis Untuk Meningkatkan Kreativitas Siswa SMAK 7 Penabur Jakarta Menggunakan Canva dan Photopea Eugenius Kau Suni; Stephen Aprius Sutresno; Henoch Juli Christanto; Julius Victor Manuel Bata; Denny Jean Cross Sihombing; Gregorius Airlangga; Pedro Manuel Lamberto Buu Sada
ABDI: Jurnal Pengabdian dan Pemberdayaan Masyarakat Vol 6 No 3 (2024): Abdi: Jurnal Pengabdian dan Pemberdayaan Masyarakat
Publisher : Labor Jurusan Sosiologi, Fakultas Ilmu Sosial, Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/abdi.v6i3.835

Kebiasaan pelajar yang lebih banyak menghabiskan waktunya untuk bermain gadget bisa berdampak pada menurunnya kreativitas. Salah satu upaya untuk meningkatkan kreativitas siswa-siswi di SMAK 7 Penabur Jakarta dengan menggelar kegiatan pelatihan desain grafis. Tim dosen dari program studi Sistem Informasi Universitas Katolik Indonesia Atma Jaya memberikan pelatihan kepada 27 orang pelajar dari kelas X, XI, dan XII untuk membuat karya desain grafis berupa poster dan flyer yang diselenggarakan selama 2 hari pada Kamis dan Jumat, 09-10 November 2023. Pelatihan tersebut menggunakan aplikasi desain grafis berbasis web yang dapat diakses secara online dan gratis yaitu Canva dan Photopea. Hasilnya menunjukkan bahwa terjadi peningkatan kreativitas siswa dimana nilai rata-rata hasil karya mereka berada pada skor 86,5 dengan kategori baik sekali, hal ini mengartikan bahwa hasil karya yang dibuat para peserta telah menerapkan tiga aspek penting seperti kreativitas, kombinasi elemen desain grafis, dan kelengkapan informasi 5W+1H. Para pelajar juga secara kreatif dapat menerapkan prinsip dasar desain grafis dan kombinasi elemen dasar desain grafis pada setiap hasil karya mereka.

Comparative Analysis of Deep Learning Architectures for Predicting Software Quality Metrics in Behavior-Driven and Test-Driven Development Approaches Airlangga, Gregorius
Jurnal Informatika Ekonomi Bisnis Vol. 6, No. 4 (December 2024)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v6i4.1045

The impact of software development methodologies on quality metrics is a crucial area of study in empirical software engineering. This research evaluates the performance of three deep learning architectures: Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM), in predicting key software quality indicators, including maintainability index, test coverage, and code complexity, for projects developed using Behavior-Driven Development (BDD) and Test-Driven Development (TDD) approaches. Using a static tabular dataset containing software quality metrics, the models are evaluated based on Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the R^2 coefficient. The MLP achieves the best performance, with the lowest RMSE (6.41) and MAE (6.34) and the highest R^2 value (−4.21), demonstrating its suitability for tabular data. The CNN performs moderately, while the LSTM underperforms due to its reliance on temporal dependencies absent from the dataset. These results emphasize the need for careful architectural alignment with dataset characteristics. The findings contribute to understanding the predictive power of deep learning models in software quality analysis and highlight the potential of MLP as a robust tool for such predictions. Future work can explore hybrid models and domain-specific feature engineering to enhance prediction accuracy.

Advancing Alzheimer’s Diagnosis: A Comparative Analysis of Deep Learning Architectures on Multidimensional Health Data Airlangga, Gregorius
Jurnal Informatika Ekonomi Bisnis Vol. 6, No. 4 (December 2024)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v6i4.1046

Alzheimer’s Disease (AD) is a leading cause of disability among the elderly, with its prevalence projected to triple by 2050. Early detection remains critical for effective disease management, yet traditional diagnostic methods are often time-intensive and subjective. This study investigates the effectiveness of three machine learning architectures: Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) in detecting Alzheimer’s Disease using a multidimensional dataset comprising demographic, lifestyle, medical, cognitive, and functional data from 2,149 patients. Each model was evaluated using 10-fold cross-validation, with performance metrics including accuracy, precision, recall, and F1-score. The CNN model demonstrated superior performance, achieving an average accuracy of 88.65%, surpassing both the MLP (84.41%) and LSTM (75.57%) models. These results highlight CNNs’ capability to effectively extract spatial patterns in health data, making them a promising tool for Alzheimer’s diagnosis. In contrast, LSTM underperformed due to the lack of temporal relationships in the dataset. This study underscores the importance of aligning model architecture with dataset characteristics and provides a foundation for integrating machine learning into clinical workflows. Future work will focus on hybrid architectures and real-world validation to enhance diagnostic accuracy and scalability.

Comparative Analysis of Deep Learning Architectures for Predicting Software Quality Metrics in Behavior-Driven and Test-Driven Development Approaches Airlangga, Gregorius
Jurnal Informatika Ekonomi Bisnis Vol. 6, No. 4 (December 2024)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v6i4.1045

The impact of software development methodologies on quality metrics is a crucial area of study in empirical software engineering. This research evaluates the performance of three deep learning architectures: Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM), in predicting key software quality indicators, including maintainability index, test coverage, and code complexity, for projects developed using Behavior-Driven Development (BDD) and Test-Driven Development (TDD) approaches. Using a static tabular dataset containing software quality metrics, the models are evaluated based on Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the R^2 coefficient. The MLP achieves the best performance, with the lowest RMSE (6.41) and MAE (6.34) and the highest R^2 value (−4.21), demonstrating its suitability for tabular data. The CNN performs moderately, while the LSTM underperforms due to its reliance on temporal dependencies absent from the dataset. These results emphasize the need for careful architectural alignment with dataset characteristics. The findings contribute to understanding the predictive power of deep learning models in software quality analysis and highlight the potential of MLP as a robust tool for such predictions. Future work can explore hybrid models and domain-specific feature engineering to enhance prediction accuracy.

Advancing Alzheimer’s Diagnosis: A Comparative Analysis of Deep Learning Architectures on Multidimensional Health Data Airlangga, Gregorius
Jurnal Informatika Ekonomi Bisnis Vol. 6, No. 4 (December 2024)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v6i4.1046

Alzheimer’s Disease (AD) is a leading cause of disability among the elderly, with its prevalence projected to triple by 2050. Early detection remains critical for effective disease management, yet traditional diagnostic methods are often time-intensive and subjective. This study investigates the effectiveness of three machine learning architectures: Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) in detecting Alzheimer’s Disease using a multidimensional dataset comprising demographic, lifestyle, medical, cognitive, and functional data from 2,149 patients. Each model was evaluated using 10-fold cross-validation, with performance metrics including accuracy, precision, recall, and F1-score. The CNN model demonstrated superior performance, achieving an average accuracy of 88.65%, surpassing both the MLP (84.41%) and LSTM (75.57%) models. These results highlight CNNs’ capability to effectively extract spatial patterns in health data, making them a promising tool for Alzheimer’s diagnosis. In contrast, LSTM underperformed due to the lack of temporal relationships in the dataset. This study underscores the importance of aligning model architecture with dataset characteristics and provides a foundation for integrating machine learning into clinical workflows. Future work will focus on hybrid architectures and real-world validation to enhance diagnostic accuracy and scalability.

EVALUATING MACHINE LEARNING MODELS FOR PREDICTING SLEEP DISORDERS IN A LIFESTYLE AND HEALTH DATA CONTEXT Airlangga, Gregorius
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 1 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i1.7870

Sleep disorders significantly impact public health, but their detection is often complicated by the multifaceted nature of causative factors. This study investigates the efficacy of various machine learning (ML) models in identifying sleep disorders based on comprehensive lifestyle and health data. We employed a dataset comprising 400 individual records with features including demographic information, sleep metrics, lifestyle factors, and health parameters. The dataset distinguished between individuals with no sleep disorder, insomnia, and sleep apnea. We evaluated a broad spectrum of ML models including logistic regression, decision trees, ensemble methods like RandomForest and GradientBoosting, support vector machines, and neural networks. The models' performances were assessed using accuracy, precision, recall, and F1 score metrics. Results indicated that ensemble methods, particularly RandomForest and XGBClassifier, outperformed other models in terms of accuracy, precision, and F1 scores, achieving values as high as 0.93. These methods proved effective in managing the complexity and variability of the dataset, thereby suggesting their robustness in clinical predictive analytics. The study's findings advocate for the use of advanced ensemble techniques in developing diagnostic tools for sleep disorders, highlighting their potential to enhance predictive accuracy and reliability in real-world healthcare settings. Further research is recommended to optimize these models and explore their integration into clinical practice.

EVALUATING HYBRID NEURAL NETWORK ARCHITECTURES FOR PREDICTING SLEEP DISORDERS FROM STRUCTURED DATA Airlangga, Gregorius
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 1 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i1.7873

The accurate diagnosis of sleep disorders is crucial for effective treatment and management, yet current methods often rely on subjective assessments and are not always reliable. This research examines the efficacy of various neural network architectures, including dense networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and innovative hybrid models, in predicting sleep disorders from structured health data. Our study focuses on comparing the performance of these models using metrics such as accuracy, precision, recall, and F1 score across a dataset comprising 400 individuals with detailed sleep and lifestyle data. Our findings demonstrate that while traditional models like dense networks and CNNs for structured data yield robust results, hybrid models, particularly the CNN-Transformer, significantly outperform others. This model effectively integrates convolutional layers with Transformerâ€™s attention mechanisms, excelling in handling complex data interactions and providing superior predictive accuracy with an F1 score and accuracy reaching as high as 0.91. Conversely, RNN models, designed to capture temporal data dependencies, showed less efficacy, underscoring the importance of model selection aligned with data characteristics. This suggests that for datasets not exhibiting strong temporal features, models leveraging spatial relationships or advanced attention mechanisms are more suitable. This study not only advances our understanding of neural network applications in medical diagnostics but also highlights the potential of hybrid models in enhancing diagnostic accuracy. These insights could lead to significant improvements in the early detection and treatment of sleep disorders, thereby enhancing patient outcomes and contributing to the broader field of medical informatics.

Pelatihan Content creator dan Video Profesional bagi Siswa SMA/SMK Sutresno, Stephen Aprius; Suni, Eugenius Kau; Bata, Julius Victor Manuel; Airlangga, Gregorius; Christanto, Henoch Juli; Sihombing, Denny Jean Cross; Piolo, Samuel
Yumary: Jurnal Pengabdian kepada Masyarakat Vol. 5 No. 2 (2024): Desember
Publisher : Penerbit Goodwood

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/yumary.v5i2.2882

Purpose: This training for social media content creators and professional video production techniques is conducted with the aim of providing motivation and insights into the tricks and techniques needed to become a content creator. The goal is for the participants, who are high school students, to utilize their gadgets and time more constructively rather than for consumptive purposes. Methodology: Organized by the Information Systems Department of Atma Jaya Catholic University of Indonesia, this training will be held at Campus 3 BSD Unika Atma Jaya on Saturday, November 25, 2023. Targeting 60 to 100 high school students from Banten, the activity will be evaluated using pre-tests and post-tests, calculated manually using the average formula. Results: The result of the training activity went smoothly and achieved the initial target of being attended by 90 active participants. The selection of speakers for the training was also appropriate, matching their expertise, and they were able to deliver the material clearly using various real-life examples. The evaluation results of the activity showed that the post-test scores increased by 43.7% compared to the pre-test scores. Therefore, it can be concluded that the participants understood what was conveyed by the speakers. Conclusions: The right resource persons and interactive learning methods contributed to the effectiveness of the training. The success of this activity shows a positive impact in increasing the creativity and productivity of the younger generation in utilizing gadgets constructively. Limitations: Due to time and budget constraints, the activity was not conducted with direct hands-on practice in the form of a workshop. Additionally, the target participants were limited to the surrounding area, specifically high school students in the Banten region. Contribution: This training gives high school students broader insights, enabling them to use their gadgets and time positively. It also equips them to become content creators by learning professional video production and social media management skills.

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