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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Dinamik Jurnal Sains dan Teknologi Semantik Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Prosiding SNATIF Journal of ICT Research and Applications Scientific Journal of Informatics JAIS (Journal of Applied Intelligent System) Proceeding SENDI_U Jurnal Ilmiah Dinamika Rekayasa (DINAREK) Proceeding of the Electrical Engineering Computer Science and Informatics JADECS (Journal of Art, Design, Art Education and Culture Studies) Jurnal Teknologi dan Sistem Komputer SISFOTENIKA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Indonesian Journal of Information System Jurnal Eksplora Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURIKOM (Jurnal Riset Komputer) Indonesian Journal of Electrical Engineering and Computer Science Abdimasku : Jurnal Pengabdian Masyarakat BERNAS: Jurnal Pengabdian Kepada Masyarakat Jurnal Teknik Informatika (JUTIF) Jurnal Program Kemitraan dan Pengabdian Kepada Masyarakat Journal of Computing Theories and Applications Jurnal Informatika: Jurnal Pengembangan IT Journal of Fuzzy Systems and Control (JFSC) Journal of Information System and Application Development Journal of Multiscale Materials Informatics Journal of Future Artificial Intelligence and Technologies
De Rosal Ignatius Moses Setiadi
Fakultas Ilmu Komputer, Universitas Dian Nuswantoro
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Neuroscience Nursing Public Health Social Sciences Veterinary Other

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Comprehensive Exploration of Machine and Deep Learning Classification Methods for Aspect-Based Sentiment Analysis with Latent Dirichlet Allocation Topic Modeling Setiadi, De Rosal Ignatius Moses; Marutho, Dhendra; Setiyanto, Noor Ageng
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 1 (2024): June 2024
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.2024-3

Abstract

This research explores the effectiveness of machine learning (ML) and deep learning (DL) classification methods in Aspect-Based Sentiment Analysis (ABSA) on product reviews, incorporating Latent Dirichlet Allocation (LDA) for topic modeling. Using the Amazon reviews dataset, this research tests models such as Naive Bayes (NB), Support Vector Machines (SVM), Random Forest (RF), Long Short-Term Memory (LSTM), and Gated Recurrent Units(GRU). Important aspects such as the product's quality, practicality, and reliability are discussed. The results show that the RF and DL models provide competitive performance, with the RF achieving an accuracy of up to 94.50% and an F1 score of 95.45% for the reliability aspect. The study's conclusions emphasize the importance of selecting an appropriate model based on specifications and data requirements for ABSA, as well as recognizing the need to strike a balance between accuracy and computational efficiency.
Analyzing InceptionV3 and InceptionResNetV2 with Data Augmentation for Rice Leaf Disease Classification Firnando, Fadel Muhamad; Setiadi, De Rosal Ignatius Moses; Muslikh, Ahmad Rofiqul; Iriananda, Syahroni Wahyu
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 1 (2024): June 2024
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.2024-4

Abstract

This research aims to evaluate and compare the performance of several deep learning architectures, especially InceptionV3 and InceptionResNetV2, with other models, such as EfficientNetB3, ResNet50, and VGG19, in classifying rice leaf diseases. In addition, this research also evaluates the impact of using data augmentation on model performance. Three different datasets were used in this experiment, varying the number of images and class distribution. The results show that InceptionV3 and InceptionResNetV2 consistently perform excellently and accurately on most datasets. Data augmentation has varying effects, providing slight advantages on datasets with lower variation. The findings from this research are that the InceptionV3 model is the best model for classifying rice diseases based on leaf images. The InceptionV3 model produces accuracies of 99.53, 58.94, and 90.00 for datasets 1, 2, and 3, respectively. It is also necessary to be wise in carrying out data augmentation by considering the dataset's characteristics to ensure the resulting model can generalize well.
Integrating SMOTE-Tomek and Fusion Learning with XGBoost Meta-Learner for Robust Diabetes Recognition Setiadi, De Rosal Ignatius Moses; Nugroho, Kristiawan; Muslikh, Ahmad Rofiqul; Iriananda, Syahroni Wahyu; Ojugo, Arnold Adimabua
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 1 (2024): June 2024
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.2024-11

Abstract

This research aims to develop a robust diabetes classification method by integrating the Synthetic Minority Over-sampling Technique (SMOTE)-Tomek technique for data balancing and using a machine learning ensemble led by eXtreme Gradient Boosting (XGB) as a meta-learner. We propose an ensemble model that combines deep learning techniques such as Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Units (BiGRU) with XGB classifier as the base learner. The data used included the Pima Indians Diabetes and Iraqi Society Diabetes datasets, which were processed by missing value handling, duplication, normalization, and the application of SMOTE-Tomek to resolve data imbalances. XGB, as a meta-learner, successfully improves the model's predictive ability by reducing bias and variance, resulting in more accurate and robust classification. The proposed ensemble model achieves perfect accuracy, precision, recall, specificity, and F1 score of 100% on all tested datasets. This method shows that combining ensemble learning techniques with a rigorous preprocessing approach can significantly improve diabetes classification performance.
Analyzing Quantum Feature Engineering and Balancing Strategies Effect on Liver Disease Classification Safriandono, Achmad Nuruddin; Setiadi, De Rosal Ignatius Moses; Dahlan, Akhmad; Rahmanti, Farah Zakiyah; Wibisono, Iwan Setiawan; Ojugo, Arnold Adimabua
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 1 (2024): June 2024
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.2024-12

Abstract

This research aims to improve the accuracy of liver disease classification using Quantum Feature Engineering (QFE) and the Synthetic Minority Over-sampling Tech-nique and Tomek Links (SMOTE-Tomek) data balancing technique. Four machine learning models were compared in this research, namely eXtreme Gradient Boosting (XGB), Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR) on the Indian Liver Patient Dataset (ILPD) dataset. QFE is applied to capture correlations and complex patterns in the data, while SMOTE-Tomek is used to address data imbalances. The results showed that QFE significantly improved LR performance in terms of recall and specificity up to 99%, which is very important in medical diagnosis. The combination of QFE and SMOTE-Tomek gives the best results for the XGB method with an accuracy of 81%, recall of 90%, and f1-score of 83%. This study concludes that the use of QFE and data balancing techniques can improve liver disease classification performance in general.
Phishing Website Detection Using Bidirectional Gated Recurrent Unit Model and Feature Selection Setiadi, De Rosal Ignatius Moses; Widiono, Suyud; Safriandono, Achmad Nuruddin; Budi, Setyo
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 2 (2024): September 2024
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.2024-15

Abstract

Phishing attacks continue to be a significant threat to internet users, necessitating the development of advanced detection systems. This study explores the efficacy of a Bidirectional Gated Recurrent Unit (BiGRU) model combined with feature selection techniques for detecting phishing websites. The dataset used for this research is sourced from the UCI Machine Learning Repository, specifically the Phishing Websites dataset. This approach involves cleaning and preprocessing the data, then normalizing features and employing feature selection to identify the most relevant attributes for classification. The BiGRU model, known for its ability to capture temporal dependencies in data, is then applied. To ensure robust evaluation, we utilized cross-validation, dividing the data into five folds. The experimental results are highly promising, demonstrating a Mean Accuracy, Mean Precision, Mean Recall, Mean F1 Score, and Mean AUC of 1.0. These results indicate the model's exceptional performance distinguishing between phishing and legitimate websites. This study highlights the potential of combining BiGRU models with feature selection and cross-validation to create highly accurate phishing detection systems, providing a reliable solution to enhance cybersecurity measures.
Exploring Deep Q-Network for Autonomous Driving Simulation Across Different Driving Modes Setiawan, Marcell Adi; Setiadi, De Rosal Ignatius Moses; Astuti, Erna Zuni; Sutojo, T.; Setiyanto, Noor Ageng
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 3 (2024): December 2024
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.3048-3719-31

Abstract

The rapid growth in vehicle ownership has led to increased traffic congestion, making the need for autonomous driving solutions more urgent. Autonomous Vehicles (AVs) offer a promising solution to improve road safety and reduce traffic accidents by adapting to various driving conditions without human intervention. This research focuses on implementing Deep Q-Network (DQN) to enhance AV performance in different driving modes: safe, normal, and aggressive. DQN was selected for its ability to handle complex, dynamic environments through experience replay, asynchronous training, and epsilon-greedy exploration. We designed a simulation environment using the Highway-env platform and evaluated the DQN model under varying traffic densities. The performance of the AV was assessed based on two key metrics: success rate and total reward. Our findings show that the DQN model achieved a success rate of 90.75%, 94.625%, and 95.875% in safe, normal, and aggressive modes, respectively. Although the success rate increased with traffic intensity, the total reward remained lower in aggressive driving scenarios, indicating room for optimization in decision-making processes under highly dynamic conditions. This study demonstrates that DQN can adapt effectively to different driving needs, but further optimization is needed to enhance performance in more challenging environments. Future work will focus on improving the DQN algorithm to maximize both success rate and reward in high-traffic scenarios and testing the model in more diverse and complex environments.
Hypertension Detection via Tree-Based Stack Ensemble with SMOTE-Tomek Data Balance and XGBoost Meta-Learner Odiakaose, Christopher Chukwufunaya; Aghware, Fidelis Obukohwo; Okpor, Margaret Dumebi; Eboka, Andrew Okonji; Binitie, Amaka Patience; Ojugo, Arnold Adimabua; Setiadi, De Rosal Ignatius Moses; Ibor, Ayei Egu; Ako, Rita Erhovwo; Geteloma, Victor Ochuko; Ugbotu, Eferhire Valentine; Aghaunor, Tabitha Chukwudi
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 3 (2024): December 2024
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.3048-3719-43

Abstract

High blood pressure (or hypertension) is a causative disorder to a plethora of other ailments – as it succinctly masks other ailments, making them difficult to diagnose and manage with a targeted treatment plan effectively. While some patients living with elevated high blood pressure can effectively manage their condition via adjusted lifestyle and monitoring with follow-up treatments, Others in self-denial leads to unreported instances, mishandled cases, and in now rampant cases – result in death. Even with the usage of machine learning schemes in medicine, two (2) significant issues abound, namely: (a) utilization of dataset in the construction of the model, which often yields non-perfect scores, and (b) the exploration of complex deep learning models have yielded improved accuracy, which often requires large dataset. To curb these issues, our study explores the tree-based stacking ensemble with Decision tree, Adaptive Boosting, and Random Forest (base learners) while we explore the XGBoost as a meta-learner. With the Kaggle dataset as retrieved, our stacking ensemble yields a prediction accuracy of 1.00 and an F1-score of 1.00 that effectively correctly classified all instances of the test dataset.
Stacked Learning Anomaly Detection Scheme with Data Augmentation for Spatiotemporal Traffic Flow Binitie, Amaka Patience; Odiakaose , Christopher Chukwufunaya; Okpor, Margaret Dumebi; Ejeh, Patrick Ogholuwarami; Eboka, Andrew Okonji; Ojugo, Arnold Adimabua; Setiadi, De Rosal Ignatius Moses; Ako, Rita Erhovwo; Aghaunor, Tabitha Chukwudi; Geteloma, Victor Ochuko; Afotanwo, Anderson
Journal of Fuzzy Systems and Control Vol. 2 No. 3 (2024): Vol. 2, No. 3, 2024
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v2i3.267

Abstract

The digital revolution births transformation in many facets of today’s society. Its adoption in transportation to curb traffic congestion in major cities globally advances smart-city initiatives. Challenges of population growth, lack of datasets, and aging infrastructure have necessitated the need for traffic analytics. Studies have estimated an associated global annual loss of $583 billion to traffic congestion for 2023. This, caused fuel wastage, loss of time, and increased costs across congested areas. With the cost of building more road networks, cities must advance new ways to improve traffic flow via anomaly detection as an early warning in the flow pattern. Our study posits stacked learning with extreme gradient boost as a meta-learner to help address imbalanced datasets, yield faster model construction, and ensure improved performance via enhanced anomalous data detection.
Comparison of EfficientNetB1 Model Effectiveness in Identifying Fish Diseases in South Asian Fish Diseases and Salmon Fish Diseases Afridiansyah, Rahmanda; Setiadi, De Rosal Ignatius Moses
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8677

Abstract

The purpose of this study is to evaluate the effectiveness of the EfficientNetB1 model in identifying fish diseases across two distinct datasets: South Asian Fish Diseases and Salmon Fish Diseases. The South Asian Fish Diseases dataset includes seven categories: red bacterial disease, aeromoniasis, gill bacterial disease, fungal saprolegniasis, parasitic disease, and white tail viral disease. The Salmon dataset is divided into two parts: FreshFish and InfectedFish. Using the EfficientNetB1 algorithm, each dataset was separately trained and tested to predict species and disease. Results showed an accuracy of 98.14% for the South Asian Fish Diseases dataset and 99.18% for the Salmon Diseases dataset. These findings support the argument that the model possesses sufficient capability to detect diseases affecting various fish species. This suggests that the model could be a valuable tool in the aquaculture industry for disease management and detection strategies.
Model Pembelajaran Mesin untuk Deteksi Penipuan Kartu Kredit yang Dioptimalkan Menggunakan SMOTE-Tomek dan Rekayasa Fitur Wibowo, Mochammad Abdurrochman Ari; Setiadi, De Rosal Ignatius Moses
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8732

Abstract

In today's digital economy, credit cards are essential and both credit card usage and theft have increased significantly in recent years. Credit card fraud can be categorized using machine learning models using data from suspicious transaction history. However, credit data is often imbalanced. Therefore, machine learning models are biased towards the majority class resulting in poor performance on publicly accessible Kaggle credit card classification datasets. We balance the class distribution in the dataset using a hybrid synthetic minority oversampling strategy to address this difficulty. The findings show that the random forest machine learning model combined with oversampling techniques combined with feature engineering and cross-validation yields optimal results of more than 99% for all assessment measures. It performs better compared to three other models, namely decision tree, gradient boosting, and XGBoost. It can be concluded that the use of feature engineering, cross-validation, and oversampling are useful approaches to handle imbalanced credit card data and ultimately help in preventing credit card transaction fraud.
Co-Authors Abdul Syukur Abdussalam Abdussalam Abdussalam Abdussalam Abdussalam Abugor Okpako, Ejaita Aceng Sambas Achmad Nuruddin Safriandono Achmad Nuruddin Safriandono Adhitya Nugraha Adigwe, Wilfred Adimabua Ojugo, Arnold Afotanwo, Anderson Afridiansyah, Rahmanda Aghaunor, Tabitha Chukwudi Aghware, Fidelis Obukohwo Agustina, Feri Ahmad Rofiqul Muslikh Ahmad Salafuddin Ajib Susanto Akbar Aji Nugroho Akbar, Ismail Akhmad Dahlan Ako, Rita Erhovwo Alvin Faiz Kurniawan Amir Musthofa Anak Agung Gede Sugianthara Andik Setyono Antonio Ciputra Antonius Erick Handoyo Aprilah, Thania Arnold Adimabua Ojugo Arya Kusuma Ayu Pertiwi Bimo Haryo Setyoko Binitie, Amaka Patience Budi Widjajanto Budi, Setyo Cahaya Jatmoko Chaerul Umam Chaerul Umam Chris Chukwufunaya Odiakaose Christian, Henry Christy Atika Sari Chukwudi Aghaunor, Tabitha Cinantya Paramita Ciputra, Antonio Daniel Nomolas Wicaksono Danu Hartanto Daurat Sinaga Desi Purwanti Kusumaningrum Desi Purwanti Kusumaningrum Devi Purnamasari Dhendra Marutho Dian Kristiawan Nugroho Dumebi Okpor, Margaret Dwi Puji Prabowo Dwi, Bernadetta Sri Endah Eboka, Andrew Okonji Edy Winarno Eferhire Valentine Ugbotu Egia Rosi Subhiyakto Ejeh, Patrick Ogholuwarami Eko Hari Rachmanto Eko Hari Rachmawanto Eko Septyasari Elkaf Rahmawan Pramudya Ella Budi Wijayanti Eluemnor Anazia, Kizito Enadona Oweimeito, Amanda Erhovwo Ako, Rita Erlin Dolphina Erna Zuni Astuti Etika Kartikadarma Etika Kartikadarma Fachrul Mustofa Farah Zakiyah Rahmanti Farooq, Omar Ferda Ernawan Fidelis Obukohwo Aghware Firnando, Fadel Muhamad Fita Sheila Gomiasti Fittria Shofrotun Ni'mah Florentina Esti Nilawati Florentina Esti Nilawati Frances Uche Emordi Gan, Hong-Seng Geteloma, Victor Ochuko Ghosal, Sudipta Kr Giovani Ardiansyah Hanny Haryanto Harish Trio Adityawan Harun Al Azies Henry Christian Herowati, Wise Heru Agus Santoso Hong-Seng Gan Hussain Md Mehedul Islam Hussain Md Mehedul Islam Ibnu Gemaputra Ramadhan Ibnu Utomo Wahyu Mulyono Ibnu Utomo Wahyu Mulyono Ibnu Utomo Wahyu Mulyono Ibnu Utomo Wahyu Mulyono Ibor, Ayei Egu Ihya Ulumuddin, Dimas Irawan Imanuel Harkespan Imanuel Harkespan Indra Gamayanto Irnanda, Muhammad Diva Islam, Hussain Md Mehedul Isworo Nugroho Iwan Setiawan Wibisono Jutono Gondohanindijo Kusuma, Edi Jaya L. Budi Handoko Lalang Erawan M. Dalvin Marno Putra Macellino Setyaji Sunarjo Mamet Adil Araaf Margaret Dumebi Okpor Maureen Ifeanyi Akazue Md Kamruzzaman Sarker Md Kamruzzaman Sarker Md Kamruzzaman Sarker Minh T. Nguyen Mohamad Afendee Mohamed Mohammad Rizal, Mohammad Muchamad Akbar Nurul Adzan Muh Galuh Surya Putra Kusuma Muhamad Akrom Muhamad Akrom Muhamada, Keny Mulyono, Ibnu Utomo Wahyu Musfiqur Rahman Sazal Muslikh, Ahmad Rofiqul Nantalira Niar Wijaya Nartriani, Yulian Dwi Nizar Rafi Pratama Noor Ageng Setiyanto Noor Ageng Setiyanto, Noor Ageng Nova Rijati Ochuko Geteloma, Victor Octara Pribadi Odiakaose , Christopher Chukwufunaya Odiakaose, Christopher Chukwufunaya Ojugo, Arnold Adimabua Okpor, Margaret Dumebi Omar Farooq Omar Farroq Omoruwou, Felix Patrick Ogholuwarami Ejeh Pradana, Akbar Ganang Prajanto Wahyu Adi Pratama, Ananta Surya Purnamasari, Devi Pushan Kumar Dutta Rahadian Kristiyanto Rachman Ramadhan, Pramudia Reuben Akporube Abere Ricardus Anggi Pramunendar Rita Erhovwo Ako Robet Robet Rume Elizabeth Yoro Ruri Suko Basuki Sahu, Aditya Kumar Sandy Nugroho Santoso, Siane Sarker, Md Kamruzzaman Sasono Wibowo Satrio Bagus Imanulloh Setiawan, Marcell Adi Sinaga, Daurat Sinaga, Daurat Sinaga, Daurat Sinaga, Daurat Stefanus Santosa Sudibyo, Usman Sukamto, Titien S Suyud Widiono Suyud Widiono Suyud Widiono Syahputra, Zulfikar Adi Syahroni Wahyu Iriananda Syahroni Wahyu Iriananda, Syahroni Wahyu T Sutojo T. Sutojo T. Sutojo Tabitha Chukwudi Aghaunor Tan Samuel Permana Tan Samuel Permana Titien S sukamto Trisnapradika, Gustina Alfa Ugbotu, Eferhire Valentine Umam, Taufiqul Valentine Ugbotu, Eferhire Victor Ochuko Geteloma Warto Wellia Shinta Sari Wellia Shinta Sari Wibowo, Mochammad Abdurrochman Ari Wise Herowati Yusianto Rindra Zuama, Leygian Reyhan