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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-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) Jurnal Sistem Komputer dan Informatika (JSON) 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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Optimizing SMS Spam Detection Using Machine Learning: A Comparative Analysis of Ensemble and Traditional Classifiers Airlangga, Gregorius
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 4 (2024): Articles Research October 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i4.4822

Abstract

With the rapid rise of mobile communication, Short Message Service (SMS) has become an essential platform for transmitting information. However, the growing volume of unsolicited and harmful spam messages presents significant challenges for both users and mobile network operators. This study explores the effectiveness of various machine learning models, including Random Forest, Gradient Boosting, AdaBoost, Support Vector Machine (SVM), Logistic Regression, and an Ensemble Voting Classifier, in detecting SMS spam. A dataset containing 5,572 SMS messages, labeled as either spam or ham (legitimate), was used to evaluate these models. Hyperparameter tuning was performed on each model to optimize accuracy, and the models were assessed using metrics such as precision, recall, F1-score, and accuracy. The results indicated that the SVM and Ensemble Voting Classifier achieved the highest performance, with accuracies of 0.9857 and 0.9848, respectively. Both models demonstrated superior recall for spam messages, making them highly effective for real-world spam detection systems. While Random Forest, Gradient Boosting, and AdaBoost also performed well, their slightly lower recall for spam suggests that they may misclassify some spam as legitimate messages. The study highlights the effectiveness of machine learning models in addressing the SMS spam problem, particularly when using ensemble methods. Future research should focus on addressing class imbalance and exploring deep learning approaches to further enhance model performance. These findings offer valuable insights for developing more accurate and scalable SMS spam detection systems.
A Comparative Analysis of Deep Learning Models for SMS Spam Detection: CNN-LSTM, CNN-GRU, and ResNet Approaches Airlangga, Gregorius
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 4 (2024): Articles Research October 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i4.4827

Abstract

Spam messages have become a growing challenge in mobile communication, threatening user security and data privacy. Traditional spam detection methods, including rule-based and machine learning techniques, are increasingly insufficient due to the evolving sophistication of spam tactics. This research evaluates the effectiveness of advanced deep learning models such as CNN-LSTM, CNN-GRU, and ResNet for SMS spam detection. The dataset used consists of diverse SMS messages labeled as either spam or legitimate (ham), ensuring broad coverage of real-world spam patterns. The study employs a robust ten-fold cross-validation approach to assess the generalization capabilities of the models, measuring performance based on accuracy, precision, recall, and F1 score. The results indicate that ResNet outperformed the other models, achieving an average accuracy of 99.08% and an F1 score of 0.9646, making it the most reliable model for spam detection. CNN-GRU demonstrated competitive performance with a balance between accuracy (98.97%) and computational efficiency, making it suitable for real-time applications. CNN-LSTM, while highly accurate (98.92%), showed a slightly lower recall compared to the other models, indicating a more cautious approach to detecting spam. These findings highlight the potential of hybrid deep learning models in addressing the complexities of SMS spam detection. Future research could focus on optimizing these models for deployment in resource-constrained environments, such as mobile devices, and further exploring the integration of residual connections for more effective spam filtering.
Comparative Analysis of Machine Learning Models for Real-Time Disaster Tweet Classification: Enhancing Emergency Response with Social Media Analytics Airlangga, Gregorius
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.3669

Abstract

In the realm of disaster management, the real-time analysis of social media data, particularly from Twitter, has become indispensable. This study investigates the efficacy of various machine learning models in classifying tweets pertaining to disaster scenarios, with the goal of bolstering emergency response systems. A dataset of tweets, categorized as related or unrelated to disasters, underwent a rigorous preprocessing regimen to facilitate the evaluation of five distinct machine learning models: Naïve Bayes, Random Forest, Logistic Regression, Support Vector Machines (SVM), and Long Short-Term Memory (LSTM) networks. The performance of these models was assessed based on accuracy, precision, recall, and F1 score. The results indicated that the SVM model excelled, achieving an accuracy of 89%, precision of 88%, recall of 89%, and an F1 score of 88%, making it the most robust for text classification tasks within the context of disaster-related data. The LSTM model also performed notably well, with an accuracy of 87%, precision of 86%, recall of 87%, and F1 score of 86%, underscoring the potential of deep learning models in processing sequential data. In comparison, Naïve Bayes, Random Forest, and Logistic Regression models demonstrated moderate performance, with accuracy and F1 scores in the range of 76-77% and 72-73%, respectively. These insights are crucial for the development of advanced social media monitoring tools that can significantly enhance the timeliness and precision of crisis response. The research not only highlights the necessity of selecting appropriate machine learning models for specific NLP tasks but also sets the stage for future investigations into the integration of hybrid analytical frameworks. This study establishes a foundation for leveraging machine learning to transform social media data into actionable intelligence, thereby contributing to more effective disaster management and community safety strategies.
Comparative Analysis of Machine Learning Algorithms for Multi-Class Tree Species Classification Using Airborne LiDAR Data Airlangga, Gregorius
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.3673

Abstract

Forests hold vital ecological significance, and the ability to accurately classify tree species is integral to conservation and management practices. This research investigates the application of machine learning techniques to airborne Light Detection and Ranging (LiDAR) data for the multi-class classification of tree species, specifically Alder, Aspen, Birch, Fir, Pine, Spruce, and Tilia. High-density LiDAR data from varied forest landscapes were subjected to a rigorous preprocessing and noise reduction protocol, followed by feature extraction to discern structural characteristics indicative of species identity. We assessed the performance of six machine learning models: Logistic Regression, Decision Tree, Random Forest, Support Vector Classifier (SVC), k-Nearest Neighbors (KNN), and Gradient Boosting. The analysis was based on metrics of accuracy, precision, recall, and F1 score. Logistic Regression and Random Forest models outperformed others, achieving accuracies of 0.81, precision of 0.80, recall of 0.81, and an F1 score of 0.80. In contrast, the KNN algorithm had the lowest accuracy of 0.60, precision and recall of 0.60, and an F1 score of 0.59. These results demonstrate the robustness of Logistic Regression and Random Forest for classifying complex LiDAR datasets. The study underscores the potential of these models to support ecological monitoring, enhance forest management, and aid in biodiversity conservation. Future research directions include the fusion of LiDAR data with other environmental variables, application of deep learning for improved feature extraction, and validation of the models across broader species and geographical ranges. This research marks a significant step towards leveraging advanced machine learning to interpret and utilize LiDAR data for environmental and ecological applications.
Comparative Evaluation of Machine Learning Models for UAV Network Performance Identification in Dynamic Environments Airlangga, Gregorius; Nugroho, Oskar Ika Adi; Sugianto, Lai Ferry
Buletin Ilmiah Sarjana Teknik Elektro Vol. 6 No. 4 (2024): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v6i4.12409

Abstract

The rapid integration of Unmanned Aerial Vehicles (UAVs) into critical applications such as disaster management, logistics, and communication networks has brought forth significant challenges in optimizing their performance under dynamic and unpredictable conditions. This study addresses these challenges by systematically evaluating the predictive capabilities of multiple machine learning models for UAV network performance identification. Models including RandomForest, GradientBoosting, Support Vector Classifier (SVC), Multi-Layer Perceptron (MLP), AdaBoost, ExtraTrees, LogisticRegression, and DecisionTree were analyzed using comprehensive metrics such as average accuracy, macro F1-score, macro precision, and macro recall. The results demonstrated the superiority of ensemble methods, with ExtraTrees achieving the highest performance across all metrics, including an accuracy of 0.9941. Other ensemble models, such as RandomForest and GradientBoosting, also showcased strong results, emphasizing their reliability in handling complex UAV datasets. In contrast, non-ensemble approaches such as LogisticRegression and MLP exhibited comparatively lower performance, suggesting their limitations in generalization under dynamic conditions. Preprocessing techniques, including SMOTE for addressing class imbalances, were applied to enhance model reliability. This research highlights the importance of ensemble learning techniques in achieving robust and balanced UAV performance predictions. The findings provide actionable insights into model selection and optimization strategies, bridging the gap between theoretical advancements and real-world UAV deployment. The proposed methodology and results have impact for advancing UAV technologies in critical, network performance-sensitive applications.
Deep Learning Approaches for Water Quality Prediction in Aquaponics Systems: A Comparative Study of Recurrent and Feedforward Architectures Airlangga, Gregorius; Nugroho, Oskar Ika Adi; Sugianto, Lai Ferry
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 1 (2025): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i1.12411

Abstract

Accurate prediction of water quality parameters is critical for the effective management and sustainability of aquaponics systems. This study evaluates the performance of four deep learning architectures: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Simple Recurrent Neural Network (SimpleRNN), and Dense Neural Network (DenseNN) for forecasting key water quality parameters, including temperature, turbidity, dissolved oxygen, pH, ammonia, and nitrate. A significant research gap is addressed by analyzing how these models perform on noisy and minimally preprocessed datasets, advancing prior studies that lack robust preprocessing techniques tailored for aquaponics systems. A ten-fold cross-validation framework was employed to rigorously assess the models, with Mean Squared Error (MSE) and Mean Absolute Error (MAE) as evaluation metrics. The results demonstrate that LSTM and GRU models outperform other architectures, achieving average validation losses of 0.0028 and 0.0028, respectively, and mean absolute errors of 0.0473 and 0.0478. These models effectively capture the temporal dependencies inherent in time-series data, making them highly suitable for the complex dynamics of aquaponics systems. Unlike previous studies, this research highlights the trade-offs between computational efficiency and predictive accuracy in these models. In contrast, the SimpleRNN model exhibited higher error rates due to its inability to model long-term dependencies, while the DenseNN model, lacking temporal processing mechanisms, showed the lowest performance with an average validation loss of 0.0075 and MAE of 0.0797. This study underscores the importance of selecting appropriate model architectures for time-series forecasting tasks and provides a foundation for deploying predictive systems to optimize aquaponics operations. Future work includes exploring hybrid models with attention mechanisms and real-time data integration for enhanced operational efficiency.
Pelatihan Pemrograman C++ Sebagai Bentuk Persiapan OSN-K SMA Tarakanita 2 Jakarta di Bidang Informatika Suni, Eugenius Kau; Sutresno, Stephen Aprius; Airlangga, Gregorius; Bata, Julius Victor Manuel; Sidhunata, Billy Macarius
Reswara: Jurnal Pengabdian Kepada Masyarakat Vol 6, No 1 (2025)
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/rjpkm.v6i1.5101

Abstract

Keterampilan pemrograman menjadi kebutuhan penting di era digital, terutama untuk mempersiapkan siswa menghadapi tantangan dalam kompetisi sains, seperti Olimpiade Sains Nasional tingkat Kota (OSN-K). Kegiatan pelatihan ini bertujuan untuk membimbing siswa SMA Tarakanita 2 Jakarta, khususnya 5 siswa dari kelas 10 dan 11 dalam mempersiapkan diri mengikuti OSN-K 2024 bidang informatika melalui pelatihan intensif pemrograman C++. Peserta siswa yang mengikuti pelatihan disini memiliki pengetahuan dasar pemrograman yang bervariasi, mulai dari siswa yang belum memahami dasar pemrograman, dan ada yang sudah mengenal dasar pemrograman di mata pelajaran kelas yang diberikan oleh guru. Metode yang digunakan mengombinasikan pembelajaran teori dan praktik dengan memanfaatkan platform pembelajaran interaktif, seperti TOKI, W3Schools, dan Kattis, serta alat pengembangan Visual Studio Code dan OnlineGDB. Pelatihan dilaksanakan secara hybrid (daring dan luring) selama 6 hari. Berdasarkan evaluasi yang dilakukan, terdapat peningkatan signifikan dalam skor ketepatan jawaban dan algoritma, serta kecepatan waktu pengerjaan soal oleh siswa. Rata-rata peningkatan skor mencapai 11,3 atau 27,8%, sementara waktu penyelesaian soal lebih cepat sebesar 17 detik atau 6,8%. Kegiatan pelatihan ini berhasil memperkuat kesiapan siswa dalam menghadapi OSN-K dan membekali mereka dengan keterampilan pemrograman yang lebih baik untuk masa depan
Neural Network Architectures for UAV Path Planning: A Comparative Study with A* Algorithm as Benchmark Airlangga, Gregorius; Bata, Julius; Nugroho, Oskar Ika Adi; Sugianto, Lai Ferry
International Journal of Robotics and Control Systems Vol 5, No 1 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i1.1757

Abstract

Autonomous path planning for Unmanned Aerial Vehicles (UAVs) plays a critical role in applications ranging from disaster response to urban logistics. Traditional algorithms, such as A*, are widely recognized for their reliability in generating collision-free and efficient trajectories but often struggle with scalability in complex and dynamic environments. This study evaluates the performance of several neural network architectures, including MLP-LSTM, CNN-GRU, CNN-LSTM, CNN BILSTM, and others, as potential alternatives to classical methods. A dataset of trajectories generated by the A* algorithm was used to train and benchmark the models, enabling direct performance comparison across key metrics such as path length, smoothness, clearance, collisions, and waypoint density. The results demonstrate that the MLP-LSTM model outperforms other neural architectures, producing paths that closely resemble A* trajectories with high smoothness and waypoint granularity. While some models, such as CNN-GRU and CNN-BILSTM, show promise in generating feasible paths, their performance is inconsistent across different UAV scenarios. Models like Residual CNN and Hybrid CNN-MHA failed to generate meaningful trajectories, highlighting the critical importance of architectural choices. This study underscores the potential of neural network models for UAV path planning.
Machine Learning for Tsunami Prediction: A Comparative Analysis of Ensemble and Deep Learning Models Airlangga, Gregorius
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 6, No 1 (2025): Edisi Januari
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v6i1.572

Abstract

Tsunamis, triggered by seismic activities, pose significant threats to coastal regions, necessitating accurate prediction models to mitigate their impact. This study explores the application of machine learning models, including ensemble methods (Random Forest, Gradient Boosting, XGBoost, LightGBM, and CatBoost) and deep learning (Neural Networks), for tsunami prediction based on seismic data. The dataset spans seismic events from 1995 to 2023, characterized by features such as magnitude, depth, and geographic location. A 10-fold cross-validation approach was employed to evaluate model performance using precision, recall, F1-score, accuracy, and ROC-AUC metrics. The results highlight that Gradient Boosting achieved the best balance between precision and recall, with an F1-score of 0.6544 and the highest ROC-AUC of 0.8606, demonstrating its strong discriminatory power. Random Forest excelled in precision (0.6920) and F1-score (0.6287), making it suitable for reducing false positives. Ensemble boosting models, such as CatBoost and LightGBM, offered consistent performance with low variability across folds. In contrast, Neural Networks underperformed, achieving an F1-score of 0.5497 and an ROC-AUC of 0.7936, indicating the need for further optimization. Despite promising results, challenges in recall scores underscore the need for enhanced detection of tsunami-triggering events. The findings establish ensemble methods, particularly Gradient Boosting and Random Forest, as robust tools for tsunami prediction, providing a foundation for early warning systems. Future work will focus on improving recall and exploring hybrid modeling techniques to optimize predictive accuracy and reliability.
Comparative Evaluation of CNN, LSTM, and GRU Architectures for Tsunami Prediction Using Seismic Data Airlangga, Gregorius
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 6, No 1 (2025): Edisi Januari
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v6i1.573

Abstract

Tsunamis are among the most catastrophic natural disasters, often triggered by seismic events such as earthquakes. Accurately predicting tsunami occurrences based on seismic parameters is critical for mitigating their devastating impacts. This study investigates the application of three advanced deep learning architectures such as Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), and Gated Recurrent Units (GRUs) for binary classification of tsunami events using seismic data. The dataset comprises earthquake records from 1995 to 2023, including features such as magnitude, depth, latitude, longitude, Modified Mercalli Intensity (MMI), and Community Internet Intensity (CDI). The models were evaluated using stratified 10-fold cross-validation and assessed across precision, recall, F1-score, accuracy, and ROC-AUC metrics. Results indicate that CNN outperformed the other architectures, achieving the highest accuracy (72.5%), precision (0.5987), and ROC-AUC (0.7838). GRU demonstrated moderate performance, balancing computational efficiency and predictive accuracy with an accuracy of 71.7% and ROC-AUC of 0.7709. LSTM, while theoretically adept at modeling temporal dependencies, showed the lowest performance due to challenges in capturing the dataset’s characteristics. The findings emphasize the importance of selecting architecture suited to the dataset’s features and task requirements. CNN’s superior performance highlights its effectiveness in spatial pattern extraction, while GRU offers a computationally efficient alternative. Future work will explore hybrid models and the integration of additional features to enhance prediction robustness. This study contributes to advancing tsunami prediction methodologies, supporting early warning systems for disaster preparedness.
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 KHAIRUL HUDA, KHAIRUL 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 Wicaksana, Hilman Singgih