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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Teknika PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal Ilmu Komputer dan Agri-Informatika Jurnal Informatika Proceeding International Conference on Information Technology and Business Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) International conference on Information Technology and Business (ICITB) Jurnal SIMADA (Sistem Informasi dan Manajemen Basis Data) Proceeding of the Electrical Engineering Computer Science and Informatics International Journal of Artificial Intelligence Research INTEGER: Journal of Information Technology Faktor Exacta JMM (Jurnal Masyarakat Mandiri) Jurnal Teknoinfo JUTIM (Jurnal Teknik Informatika Musirawas) Jurnal Informasi dan Komputer Jurnal Teknik Elektro dan Komputasi (ELKOM) Jurnal Infortech Jurnal Generic Journal of Applied Data Sciences Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Bioscientist : Jurnal Ilmiah Biologi Jurnal Pengabdian kepada Masyarakat Journal of Research in Social Science and Humanities International Journal of Advanced Science and Computer Applications TECHSI - Jurnal Teknik Informatika Jurnal INFOTEL Journal of Digital Literacy and Volunteering Journal of Digital Literacy and Volunteering
M Said Hasibuan
Institute Informatika Dan Bisnis Darmajaya
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Journal : Journal of Applied Data Sciences

 1 
Information Security Measurement using INDEX KAMI at Metro City Savitri, Ratna; Firmansyah, Firmansyah; Dworo, Dworo; Hasibuan, Muhammad Said
Journal of Applied Data Sciences Vol 5, No 1: JANUARY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i1.152

Abstract

Information security is a crucial issue that affects the overall business process, therefore it must be protected and secured. This research was conducted to assess the information security risks at Metro City Communication and Information Office in a structured manner towards information assets in identifying efforts to reduce risks as part of the information security management program. The research method begins with defining the scope, collecting data and supporting documents, evaluating the Information Security Index (KAMI), determining scores in 7 security areas, where strengths/maturity and weaknesses/deficiencies will be identified in each security area. Finally, after obtaining the evaluation results, recommendations will be made. The Information Security Index (KAMI) is a computer-based tool in excel format that can assess and evaluate the completeness and maturity level of information security implementation based on the SNI ISO/IEC 27001 criteria that describe the readiness of the information security framework. The data obtained by the researcher is based on interview results, examination of the availability of Information Security Management System (SMKI) documents, and evidence of SMKI implementation records/archives. The dashboard evaluation results for electronic system category score 17, which is in the high category, governance score is 69, risk management score is 29, framework score is 33, information asset management score is 69, technology score is 81 and supplement score is 0%. Based on verification of the results of the KAMI Index version 4.2 assessment file, a score of 275 was obtained, indicating that information security
Scalable Machine Learning Approaches for Real-Time Anomaly and Outlier Detection in Streaming Environments Dewi, Deshinta Arrova; Singh, Harprith Kaur Rajinder; Periasamy, Jeyarani; Kurniawan, Tri Basuki; Henderi, Henderi; Hasibuan, M. Said
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.444

Abstract

The prevalence of streaming data across various sectors poses significant challenges for real-time anomaly detection due to its volume, velocity, and variability. Traditional data processing methods often need to be improved for such dynamic environments, necessitating robust, scalable, and efficient real-time analysis systems. This study compares two advanced machine learning approaches—LSTM autoencoders and Matrix Profile algorithms—to identify the most effective method for anomaly detection in streaming environments using the NYC taxi dataset. Existing literature on anomaly detection in streaming data highlights various methodologies, including statistical tests, window-based techniques, and machine learning models. Traditional methods like the Generalized ESD test have been adapted for streaming data but often require a full historical dataset to function effectively. In contrast, machine learning approaches, particularly those using LSTM networks, are noted for their ability to learn complex patterns and dependencies, offering promising results in real-time applications. In a comparative analysis, LSTM autoencoders significantly outperformed other methods, achieving an F1-score of 0.22 for anomaly detection, notably higher than other techniques. This model demonstrated superior capability in capturing temporal dependencies and complex data patterns, making it highly effective for the dynamic and varied data in the NYC taxi dataset. The LSTM autoencoder's advanced pattern recognition and anomaly detection capabilities confirm its suitability for complex, high-velocity streaming data environments. Future research should explore the integration of LSTM autoencoders with other machine-learning techniques to enhance further the accuracy, scalability, and efficiency of anomaly detection systems. This study advances our understanding of scalable machine-learning approaches and underscores the critical importance of selecting appropriate models based on the specific characteristics and challenges of the data involved.
Performance Evaluation of Fuzzy Logic System for Dendrobium Identification Based on Leaf Morphology Putra, Arie Setya; Syarif, Admi; Mahfut, Mahfut; Sulistiyanti, Sri Ratna; Hasibuan, Muhammad Said
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.224

Abstract

Dendrobium is the second-largest family of flowering plants in the world. There are several classes of Dendrobium, which usually identify by its, including leaves and flowers. Due to the similarity of its characteristics, identifying orchid types is complicated and usually can only be done by an expert. Moreover, those characteristics are typically non-deterministic; examining the orchid species is very challenging. This research aims to develop a novel fuzzy-based system to identify the species of orchid based on unprecise existing leaf characteristics. We used the main characteristics of Dendrobium leaves, including shape, length, width, and tips of the leaves. Based on the information from the expert, we develop the membership for each class of Dendrobium. By adopting this knowledge, we develop the system by using compatible programming with this case, and Borland Delphi as complex application development. The experiment is done by using 200 real datasets from the Liwa Botanical Gardens, West Lampung Regency, Lampung Province, Indonesia. The results are compared with those given by a Dendrobium expert. A confusion matrix is a valuable evaluation tool for measuring the performance of classification models. From the above results, we can determine the confusion matrix and calculate the TP (True Positive), TN (True Negative), FP (False Positive), and FN (False Negative). The confusion matrix given from the experiments is shown in Table 6. This indicates that the system can provide the same results as experts recommended. It is shown that the system can identify orchid types with an accuracy value of 94,6 %.  Thus, this system will be beneficial for automatically determining the orchid genus.
A Proposed Model for Detecting Learning Styles Based on the Felder-Silverman Model Using KNN and LR with Electroencephalography (EEG) Hasibuan, Muhammad Said; Isnanto, R Rizal; Dewi, Deshinta Arrova; Kurniawan, Tri Basuki; Yeh, Ming-Lang; Wijaya, Adi
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.659

Abstract

The identification of learning styles plays a crucial role in enhancing personalized education and optimizing learning outcomes. This research proposes a model for detecting learning styles based on the Felder-Silverman model using two machine learning algorithms: K-Nearest Neighbors (KNN) and Linear Regression (LR). Electroencephalography (EEG) data, known for its ability to capture cognitive and neural activity, serves as the primary dataset for this study. The proposed model was tested on a dataset comprising EEG signals collected during various learning tasks. Feature extraction and preprocessing techniques were employed to ensure high-quality input for the learning algorithms. The experimental results revealed that the LR-based model achieved an accuracy of 96.4%, significantly outperforming the KNN-based model, which obtained an accuracy of 89.9%. These findings highlight the potential of EEG-based models for accurately identifying learning styles, offering valuable insights for educators and researchers aiming to implement adaptive learning systems. This study demonstrates the feasibility and effectiveness of combining EEG data with machine learning techniques for learning style detection, paving the way for more personalized and efficient educational approaches. Future research will explore the integration of additional physiological data and advanced machine learning methods to further improve model accuracy and applicability.
Integrating Convolutional Neural Networks into Mobile Health: A Study on Lung Disease Detection Hasibuan, Muhammad Said; Isnanto, R Rizal; Dewi, Deshinta Arrova; Triloka, Joko; Aziz, RZ Abdul; Kurniawan, Tri Basuki; Maizary, Ary; Wibaselppa, Anggawidia
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.660

Abstract

This study presents the development and evaluation of a Convolutional Neural Network (CNN) model for lung disease detection from chest X-ray images, complemented by a mobile application for real-time diagnosis. The CNN model was trained on a diverse dataset comprising images labeled as "NORMAL" and "PNEUMONIA," achieving an overall accuracy of 96%. Compared to traditional machine learning methods such as Support Vector Machine (SVM) and Random Forest, which typically achieve accuracies ranging from 85% to 92%, the proposed CNN model demonstrates superior performance in classifying lung conditions. The model achieved high precision (0.98) and recall (0.96) for pneumonia detection, as well as precision (0.89) and recall (0.95) for normal cases, ensuring both sensitivity and specificity in diagnostic performance. These results indicate that the model minimizes false positives and false negatives, which is crucial for reducing misdiagnoses and improving patient outcomes in clinical settings. To enhance accessibility, an Android-based application was developed, allowing users to upload chest X-ray images and receive instant diagnostic results. The application successfully integrated the trained CNN model, offering a user-friendly interface suitable for healthcare professionals and patients alike. User testing demonstrated reliable performance, facilitating timely and accurate lung disease detection, particularly in areas with limited access to radiologists. These findings highlight the potential of CNNs in medical imaging and the critical role of mobile technology in expanding healthcare accessibility. This innovative approach not only improves diagnostic accuracy but also enables real-time disease detection, ultimately supporting clinical decision-making. Future research will focus on expanding the dataset, incorporating additional lung conditions, and optimizing the model for enhanced robustness in diverse clinical scenarios.
Incorporate Transformer-Based Models for Anomaly Detection Dewi, Deshinta Arrova; Singh, Harprith Kaur Rajinder; Periasamy, Jeyarani; Kurniawan, Tri Basuki; Henderi, Henderi; Hasibuan, M. Said; Nathan, Yogeswaran
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.762

Abstract

This paper explores the effectiveness of Transformer-based models, specifically the Time-Series Transformer (TST) and Temporal Fusion Transformer (TFT), for anomaly detection in streaming data. We review related work on anomaly detection models, highlighting traditional methods' limitations in speed, accuracy, and scalability. While LSTM Autoencoders are known for their ability to capture temporal patterns, they suffer from high memory consumption and slower inference times. Though efficient in terms of memory usage, the Matrix Profile provides lower performance in detecting anomalies. To address these challenges, we propose using Transformer-based models, which leverage the self-attention mechanism to capture long-range dependencies in data, process sequences in parallel, and achieve superior performance in both accuracy and efficiency. Our experiments show that TFT outperforms the other models with an F1-score of 0.92 and a Precision-Recall AUC of 0.71, demonstrating significant improvements in anomaly detection. The TST model also shows competitive performance with an F1-score of 0.88 and Precision-Recall AUC of 0.68, offering a more efficient alternative to LSTMs. The results underscore that Transformer models, particularly TST and TFT, provide a robust solution for anomaly detection in real-time applications, offering improved performance, faster inference times, and lower memory usage than traditional models. In conclusion, Transformer-based models stand out as the most effective and scalable solution for large-scale, real-time anomaly detection in streaming time-series data, paving the way for their broader application across various industries. Future work will further focus on optimizing these models and exploring hybrid approaches to enhance detection capabilities and real-time performance.
Detecting Gender-Based Violence Discourse Using Deep Learning: A CNN-LSTM Hybrid Model Approach Kurniawan, Tri Basuki; Dewi, Deshinta Arrova; Henderi, Henderi; Hasibuan, M. Said; Zakaria, Mohd Zaki; Ismail, Abdul Azim Bin
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.761

Abstract

Gender-Based Violence (GBV) is a critical social issue impacting millions worldwide. Social media discussions offer valuable insights into public awareness, sentiment, and advocacy, yet manually analyzing such vast textual data is highly challenging. Traditional text classification methods often struggle with contextual understanding and multi-class categorization, making it difficult to accurately identify discussions on Sexual Violence, Physical Violence, and other topics. To address this, the present study proposes a hybrid deep learning approach combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. CNN is utilized for extracting key linguistic features, while LSTM enhances the classification process by maintaining sequential dependencies. This hybrid CNN+LSTM model is evaluated against standalone CNN and LSTM models to assess its performance in classifying GBV-related tweets. The dataset was sourced from Kaggle, containing real-world Twitter discussions on GBV. Experimental results demonstrate that the hybrid model surpasses both CNN and LSTM models, achieving an accuracy of 89.6%, precision of 88.4%, recall of 89.1%, and F1-score of 88.7%. Confusion matrix and ROC curve analyses further confirm the hybrid model’s superior performance, correctly identifying Sexual Violence (82%), Physical Violence (15%), and Other (3%) cases with reduced misclassification rates. These results suggest that combining CNN’s feature extraction with LSTM’s contextual learning provides a more balanced and effective classification model for GBV-related text. This work supports the development of AI-based tools for social media monitoring, policy-making, and advocacy, helping stakeholders better understand and respond to GBV discussions. Future research could explore transformer-based models like BERT and real-time classification applications to further improve performance.
An Artificial Neural Network-Based Geo-Spatial Model for Real-Time Flood Risk Prediction Using Multi-Source High-Resolution Data Aziz, RZ Abdul; Nurpambudi, Ramadhan; Herwanto, Riko; Hasibuan, Muhammad Said
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.913

Abstract

Flood prediction presents a pressing challenge in disaster management, especially in regions vulnerable to extreme weather events. In response, this study offers a novel approach to flood risk prediction by developing a deep learning-based Geo-Spatial Artificial Neural Network (ANN). The model actively integrates high-resolution satellite imagery, meteorological data, and topographic indicators, such as rainfall, elevation, and land use to capture complex spatial and environmental relationships that influence flood risk. This study conducted data preprocessing using Principal Component Analysis (PCA) and normalization to ensure consistency across datasets. It built the ANN with multiple hidden layers and trained it using the backpropagation algorithm on historical flood data. Furthermore, it designed the ANN model with multiple hidden layers and trained it using the backpropagation algorithm. The model achieved a notable 92% prediction accuracy, significantly outperforming traditional flood prediction methods, which typically yield 75–85% accuracy. Conventional metrics were Mean Squared Error (1.41) and R-squared (0.94). It confirmed the model’s superior ability to predict high-risk flood zones. The model also effectively captured non-linear patterns that conventional statistical or deterministic methods often failed to detect. The results showed that the model generalizes well and adapts effectively, making it suitable for real-time and data-driven flood forecasting. By integrating artificial intelligence with geo-spatial analytics, this study offers a scalable, accurate, and efficient tool for early warning systems and risk management. It recommends that future research should focus on incorporating additional data sources and refining model training techniques to further enhance scalability and performance.
Co-Authors - Nurfiana A Adven Tonny A Feriyanto Abdi Darmawan Abror , Muhamad Achmad Aldi Sakoni Adam Japal Adi Wijaya Admi Syarif Afdal Wahyu Prayuda Agus, Isnandar Ali Nasution Andry Ferianto anggalia wibasuri Anuar Sanusi Anuar Sanusi Arbi Gunawan ARDIANSYAH ARDIANSYAH Ari Rohmawati Arie Setya Putra Arman Suryadi Karim, Arman Suryadi Aziz, RZ. Abdul Bagus Yuda Pratama Baruna Wisnu Wardana Baskoro Baskoro Dani Apriansyah Danil, Sapni Delli Maria Denny Prastiawan Destiawan Destiawan Dewi, Deshinta Arrova Dian Saputra Dika Tondo W Diki Andita Kusuma Doni Andrianto Dworo, Dworo Effendi, M. Junius Eko Budi Wicaksono Eko Zulkaryanto Elis Malana Fauziah Zahra Ramadhani Febriana, Annisa Arsya Fely Dany Prasetya Fernando, Rhino Firmansyah Firmansyah Firmansyah Firmansyah Firmansyah Fitria Fransiska, Devi Guntur Tiara Wahyu Hidayah handoyo widi nugroho Hardiansyah, Deni Henderi . Hendri Purnomo Herawadi S, Novi Hermansyah, Idi Herwanto, Riko Ismail, Abdul Azim Bin Isnaini Bastari Iwan Tri Bowo Khristina Henny R Kumala, Dian Agustin Arta Kurnia Muludi Kurniawan Kurniawan, Tri Basuki Laila, Siti Nur M. Arif Prayoga M. Arif Rifai M. Royan Fauzi Mahfut Maizary, Ary Marzuki Marzuki Melda Agarina Melda Agharina Muhammad Fahmi Hafidz Mukhas Munif Ahsani Munaa Munaa Munaa, Munaa Nathan Nurdadyansyah Nathan, Yogeswaran Netty Sefriyanti Nosiel Nosiel Novi Herawadi Sudibyo Novi Herawadi Sudibyo, Novi Herawadi Novita Sari Nurdiyanto, Heri Nurpambudi, Ramadhan Nuryana, Sapta Adi Onno W Purbo Periasamy, Jeyarani Pratama, Bagus Yuda Pratama, Tomy Adi Prayoga, M. Arif Prayuda, Afdal Wahyu Prilian Ayu Winarni Purbo, Onno W R Rizal Isnanto R, Khristina Henny Rahmadi, Lendy Rahmalia Syahputri Rahmalia Syahputri Ramadhani, Fauziah Zahra Rangga Firdaus Ratih Pratiwi Ratna Nurhaya Renita Dwi Astuti Ridho Kurniawan RIDHO KURNIAWAN, RIDHO Rizky Yulizar Rahman Romadhoni, Nuzul Rahmat Rosandi, Triowali Ruki Rizal Sakoni, Achmad Aldi Sapni Danil Savitri, Ratna Selfiyana, Reva Setiyono . Sigit Andriyanto Singagerda, Faurani Santi Singh, Harprith Kaur Rajinder Siti Khodijah Situmorang, Klaudia SB SRI RAHAYU Sri Ratna Sulistiyanti Suci Mutiara Sutedi Sutedi Suwandi Tetra Praja Utama Triloka, Joko Wahyu Bintono Wasilah Wibaselppa, Anggawidia Winda Rika Lestari Y Verawati Y. Suhendro Yeh, Ming-Lang Yogi Maulana Yoni Hisbullah Yudha, Efrian Prama Yusuf, Suhendro Zainal A. Hasibuan Zakaria, Mohd Zaki