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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.

Predicting Diabetes with Machine Learning: Evaluating Tree-Based and Ensemble Models with Custom Metrics and Statistical Validation Airlangga, Gregorius
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6419

This study investigates the predictive performance of machine learning models in diagnosing diabetes using the Pima Indians Diabetes Dataset. Seven models, including Logistic Regression, Random Forest, Gradient Boosting, XGBoost, LightGBM, Stacking Classifier, and Voting Classifier, were evaluated. A 10-fold cross-validation strategy was employed to ensure robust and reliable performance assessment. The evaluation incorporated standard metrics such as accuracy, precision, recall, F1 score, and ROC AUC, as well as a custom metric designed to prioritize recall while maintaining precision, addressing the clinical importance of minimizing false negatives. LightGBM and Random Forest emerged as the top-performing individual models, achieving competitive scores across metrics. Ensemble methods, particularly the Stacking Classifier, demonstrated robustness by leveraging the complementary strengths of base models. Statistical validation using the Friedman test confirmed significant differences in model rankings, with a test statistic of 22.77 and a p-value of 0.00088. However, pairwise comparisons using the Wilcoxon signed-rank test revealed that the differences between top models, such as LightGBM and Random Forest, were not statistically significant. These results emphasize the effectiveness of tree-based and ensemble models in addressing clinical diagnostic challenges. The study highlights the importance of using a custom metric to align model evaluation with clinical priorities. Future work should explore hybrid modeling approaches and larger datasets to further enhance predictive accuracy and generalizability in real-world healthcare applications.

A Comparative Analysis of Diabetes Prediction through Deep Learning Architectures Airlangga, Gregorius
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6446

Diabetes prediction plays a vital role in healthcare, enabling early diagnosis and timely interventions to mitigate the risks associated with the disease. This study investigates the application of advanced machine learning architectures to predict diabetes using the Pima Indians Diabetes Dataset, a widely used benchmark for medical diagnostics. Five models: Deep Neural Network (DNN), Convolutional Neural Network (CNN) with Attention, LSTM with Residual Connections, Bidirectional LSTM (BiLSTM) with Attention, and GRU with Dense Layers were developed and evaluated on multiple performance metrics, including accuracy, precision, recall, F1 score, and ROC AUC. A stratified five-fold cross-validation strategy was employed to ensure robustness, while SHAP analysis was conducted to enhance interpretability. Among the models, the GRU with Dense Layers achieved superior performance, recording the highest accuracy (76.17%), F1 score (69.85%), and ROC AUC (83.52%). SHAP analysis revealed Glucose as the most influential feature, with significant interactions identified between Glucose and Pregnancies, aligning with established medical insights. Statistical analysis confirmed the reliability of the results, with all metrics demonstrating statistically significant improvements over a baseline of random chance (p < 0.05). These findings underscore the efficacy of GRU-based models in capturing complex patterns in medical data while maintaining computational efficiency. Future work will explore hybrid architectures and larger datasets to enhance generalizability and real-world applicability, contributing to more effective decision-making in healthcare.

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