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All Journal Jurnal Teknologi JOIN (Jurnal Online Informatika) Science and Technology Indonesia JOIV : International Journal on Informatics Visualization Emerging Science Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Indonesian Physical Review Jurnal Varian Journal of Robotics and Control (JRC) Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences International Journal for Applied Information Management Volksgeist: Jurnal Ilmu Hukum dan Konstitusi Maritime Park: Journal Of Maritime Technology and Socienty Bulletin of Applied Mathematics and Mathematics Education Jurnal Ilmiah Ilmu Terapan Universitas Jambi Jurnal Pemberdayaan Masyarakat Eastasouth Journal of Effective Community Services Journal of Educational Technology and Learning Creativity International Journal of Advances in Artificial Intelligence and Machine Learning Journal of Digital Market and Digital Currency Journal of Current Research in Blockchain International Journal Research on Metaverse
Dewi, Deshinta Arrova
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Aerospace Engineering Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering 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 Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Journal : journal of applied data sciences

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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.
Deep Learning Incorporated with Augmented Reality Application for Watch Try-On Andri, Andri; Kurniawan, Tri Basuki; Dewi, Deshinta Arrova; Alqudah, Mashal Kasem; Alqudah, Musab Kasim; Zakaria, Mohd Zaki; Hisham, Putri Aisha Athira binti
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.529

Abstract

In evaluating the dynamic landscape of online shopping, the integration of Augmented Reality (AR) technologies has emerged as a transformative force, redefining the way consumers engage with products in virtual environments. This research project investigates the intersection of deep learning and AR in the context of online shopping, with a particular focus on a Watch Try-On application. The experimentation involves the use of SSD MobileNet's models for real-time object detection aimed at enhancing the user experience during online watch shopping. Training both SSD MobileNet's V1 and V2 models through 50,000 iterations, the results reveal intriguing insights into their performance. SSD MobileNet's V1 demonstrated superior results, boasting a mean average precision (mAP) of 0.9725 and a significant reduction in total loss from 0.774 to 0.5405. However, the longer training time of 7 hours and 42 minutes prompted the selection of SSD MobileNet's V2 for real-time applications due to its faster inference capabilities. Extending beyond traditional online shopping experiences, the research explores the potential of AR technologies to revolutionize product visualization and interaction. The choice of the Vuforia model target for the Watch Try-On application showcases the synergy between deep learning and AR, allowing users to virtually try on watches and visualize them in their real-world environment. The application successfully detects users' hands with high accuracy, creating an immersive and visually enriching experience. In conclusion, this project contributes to the ongoing discourse on the fusion of deep learning and AR for online shopping. The exploration of SSD MobileNet's models, coupled with the integration of AR technologies, underscores the potential to elevate the online shopping experience by providing users with dynamic, interactive, and personalized ways to engage with products.
Convolutional Neural Network Based Deep Learning Model for Accurate Classification of Durian Types Diana, Diana; Kurniawan, Tri Basuki; Dewi, Deshinta Arrova; Alqudah, Mashal Kasem; Alqudah, Musab Kasim; Zakari, Mohd Zaki; Fuad, Eyna Fahera Binti Eddie
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.480

Abstract

Durian recognition is significant among fans of the durian community since many people tend to get confused, especially if they are not familiar with durian species, which can lead them to be involved in durian fraud. The development of this prototype can detect and classify durian fruits into three categories, including Musang King, Black Thorn, and D24, which can significantly benefit consumers. The prototype in this research involves training using a dataset of durian images, specifically in Musang King, Black Thorn, and D24 varieties. Preprocessing techniques such as resizing and scaling data are applied to enhance the quality and consistency of the dataset. The models chosen to develop this prototype include VGG-16 and Xception, and each model is compared according to its accuracy percentage. The accuracy outcomes of VGG-16 and Xception models are 56.64% and 92%, respectively. The models used a total of 1,372 images of durian with three classifications. Based on the findings, further enhancement of the CNN models for durian classification can be done by implementing different architectures, techniques, and methods. Moreover, future models can consider real-time image capture and processing capabilities to enhance the practicality of the system for durian consumers. The prototype developed in this study demonstrates the feasibility of using deep learning techniques for accurate and efficient durian classification, paving the way for future advancements in automated fruit grading and quality control systems in the durian industry.
Leveraging Data Analytics for Student Grade Prediction: A Comparative Study of Data Features Misinem, Misinem; Kurniawan, Tri Basuki; Dewi, Deshinta Arrova; Zakaria, Mohd Zaki; Nazmi, Che Mohd Alif
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.v5i4.442

Abstract

In educational settings, a persistent challenge lies in accurately identifying and supporting students at risk of underperformance or grade retention. Traditional approaches often fall short by applying generalized interventions that fail to address specific academic needs, leading to ineffective outcomes and increased grade repetition. This study advocates for integrating machine learning algorithms into educational assessment practices to address these limitations. By leveraging historical and current performance data, machine learning models can help identify students needing additional support early in their academic journey, allowing for precise and timely interventions. This research examines the effectiveness of three machine learning algorithms: Naive Bayes, Deep Learning, and Decision Trees. Naive Bayes, known for its simplicity and efficiency, is well-suited for initial data screening. Deep Learning excels at uncovering complex patterns in large datasets, making it ideal for nuanced predictions. Decision Trees, with their interpretable and actionable outputs, provide clear decision paths, making them particularly advantageous for educational applications. Among the models tested, the Decision Tree algorithm demonstrated the highest performance, achieving an accuracy rate of 86.68%. This high precision underscores its suitability for educational contexts where decisions need to be based on reliable, interpretable data. The results strongly support the broader application of Decision Tree analysis in educational practices. By implementing this model, educational administrators can better identify at-risk students, tailor interventions to meet individual needs, and ultimately improve student success rates. This study suggests that Decision Trees could become a vital tool in data-driven strategies to enhance student retention and optimize academic outcomes.
Recommender System for Book Review based on Clustering Algorithms Udariansyah, Devi; Kurniawan, Tri Basuki; Dewi, Deshinta Arrova; Zakaria, Mohd Zaki; Hanan, Nur Syuhana binti Abd
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.492

Abstract

Book reviews show the expression of the reviewers that are to be evaluated and describe the book. Today, the amount of the book is growing rapidly, and it offers people a lot of choices. The recommender system on book reviews is mostly mentioned, and we will recommend a book based on the keyword selected. This study highlights two primary objectives. The first objective is to identify the keywords of the book review, and the last objective is to design and develop a book review analysis visualization using the result of the k-means clustering algorithm. The methodology of this research consists of ten phases, which start with the preliminary study, knowledge acquisition and analysis phase, data collection phase, data pre-processing phase, and modeling phase. The research then continues with the design and implementation, dashboard development, testing and evaluation, and finally, the documentation phase. The data from this study is scraped from Amazon.com and focuses on three genres: Fiction and Fantasy, Mystery and Thriller, and Romance. All the data will be clean before it can be applied to k-means clustering. The result of clustering will define the keywords for every genre and will compare with the keywords for each book that was collected from Amazon.com.
Efficient Fruit Grading and Selection System Leveraging Computer Vision and Machine Learning Dewi, Deshinta Arrova; Kurniawan, Tri Basuki; Thinakaran, Rajermani; Batumalay, Malathy; Habib, Shabana; Islam, Muhammad
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.v5i4.443

Abstract

Automated fruit grading is crucial to overcoming the time and accuracy challenges posed by manual methods, which are often limited by subjective human judgment. This study introduces an intelligent grading system leveraging computer vision and AI to improve speed and consistency in assessing fruit quality. Using high-resolution imaging and advanced feature extraction, including grayscale processing, binarization, and enhancement, the system achieves non-destructive, efficient sorting for fruits like apples, bananas, and oranges. Grayscale processing reduces image complexity while preserving essential details, binarization isolates the fruit from its background, and enhancement highlights critical features. Notably, the Edge Pixel method proved most effective, achieving 79.20% accuracy in grading, while the Grayscale Pixel method reached 93.94% accuracy for fruit types. Edge Pixel also achieved 80.32% in differentiating grading types, showcasing its ability to capture essential shapes and edges. Fruits are classified into four grades: Grade_01 (highest quality), Grade_02 (minor imperfections), Grade_03 (notable defects but consumable), and Grade_04 (unfit for consumption). A specialized dataset supports model training, ensuring practical real-world application. The study concludes that this automated system offers significant improvements over traditional grading, providing a scalable, objective, and reliable solution for the agricultural sector, ultimately enhancing productivity and quality assurance.
Utilizing Sentiment Analysis for Reflect and Improve Education in Indonesia Henderi, Henderi; Asro, Asro; Sulaiman, Agus; Kurniawan, Tri Basuki; Dewi, Deshinta Arrova; AlQudah, Mashal
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.527

Abstract

This study explores the potential of sentiment analysis in providing valuable insights into education in Indonesia based on comments from the YouTube platform. Utilizing the Naive Bayes Classifier method, this research analyzed 13,386 processed comments out of 17,920 original comments. The results show that 53.8% of comments were negative, while 28.5% were positive, and 17.7% were neutral, reflecting diverse perspectives on existing educational issues. The Accuracy of this model reached up to 72.51% with testing on various sample sizes (10%-30%), indicating the model's effectiveness in identifying sentiments. Although the model tends to classify comments as unfavorable, this opens opportunities for introspection and improvement within the educational system. Further analysis with a word cloud revealed dominant keywords, indicating areas that require more attention in public discussions about education. By leveraging this sentiment analysis, the study offers practical and valuable guidance for policymakers to reflect on and enhance educational strategies and policies in Indonesia. This research measures public reactions and aims to foster more constructive and inclusive discussions about the sustainable development of education in Indonesia.
Machine Learning Techniques for Distinguishing Android Malware Variants Irwansyah, Irwansyah; Kurniawan, Tri Basuki; Dewi, Deshinta Arrova; Zakaria, Mohd Zaki; Azmi, Nurhafifi Binti
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.493

Abstract

The advancement of portable devices has been quickly and dramatically reshaping the usage trend and consumer preferences of electronic devices. Android, the most common mobile operating system, has a privilege-separated protection system with a complex access control mechanism. Android apps require permission to get access to confidential personal data and device resources. However, studies have shown that various malicious applications can acquire permission and target systems and applications by misleading users. In this study, we suggest a machine-learning approach to classifying Android malware variants by mining requested permissions, real permissions, suspicious calls, and API calls that were obtained and used in Android malware applications. Selected features were selected using a feature selection called KBest. Feature selection techniques are used to minimize the scale of the features and increase the performance. Two types of Naïve Bayes classifiers, called Multinomial distribution and multivariate Bernoulli distribution, are used and compared in malware family classification for text classification. Both naïve Bayes types are evaluated using a confusion matrix based on 4022 Android malware applications belonging to 10 families. Experimental findings show that the Multinomial distribution offers a reliable performance from three tests experiment with an average accuracy of 95%.
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.
Co-Authors - Kurniawan, - Achsan, Harry Tursulistyono Yani Adi Suryaputra Paramita Adi Wijaya Afriyani, Sintia Agustina, Dea Ahmad Sanmorino Alde Alanda, Alde Ali Amran Almohab, Hadi Alqudah, Mashal Kasem Alqudah, Musab Kasim Andri Andri Andriani, Putu Eka Anita Desiani Armoogum, Sheeba Armoogum, Vinaye Aryananda, Rangga Laksana Asro Asro Azali, Lalu M. Panji Aziz, RZ. Abdul Azmi, Nurhafifi Binti Bappoo, Soodeshna Batumalay, Malathy Bin Abdul Hadi, Abdul Razak Budi Prasetyo Bujang, Nurul Shaira Binti Chandra, Anurag CSA Teddy Lesmana Devi Udariansyah Diana Diana Dita Amelia, Dita Efrizoni, Lusiana Elyakim Nova Supriyedi Patty, Elyakim Nova Supriyedi Endro Setyo Cahyono, Endro Setyo Eva Yulia Puspaningrum Fadly Fadly Fara Disa Durry Fatoni, Fatoni Fikri, Ruki Rizal Nul Firosha, Ardian Fitriyani, Amelia Sofa Fuad, Eyna Fahera Binti Eddie Habib, Shabana Hanan, Nur Syuhana binti Abd Hasibuan, M.S. Hasibuan, Muhammad Siad Henderi . Hendra Kurniawan Heng, Chang Ding Hidayani, Nieta Hisham, Putri Aisha Athira binti Humairah, Sayyidah I Gede Susrama Mas Diyasa Irianto, Suhendro Y. Irwansyah Irwansyah Ismail, Abdul Azim Bin Isnawijaya, Isnawijaya Jayawarsa, A.A. Ketut Junfithranaa, Anggy Pradifta Kezhilen, Motean Khasanah, Eka Uswatun Kijsomporn, Jureerat Kurniawan, Tri Basuki Lexianingrum, Siti Rahayu Pratami Lies Sulistiani Lin, Leong Chi M Said Hasibuan M. Anjar Pamungkas M. Fariz Fadillah Mardianto Maizary, Ary Malik Cahyadin Mantena, Jeevana Sujitha MARIA BINTANG Mashal Alqudah Melanie, Nicolas Misinem, Misinem Mohd Salikon, Mohd Zaki Motean, Kezhilen Muhammad Islam, Muhammad Muhammad Nasir Muhayeddin, Abdul Muniif Mohd Murnawan, Murnawan Nathan, Yogeswaran Nazmi, Che Mohd Alif Nella Sumika Putri Okengwu, Ugochi Oktavia, Fania Onn, Choo Wou Panguluri, Padmavathi Periasamy, Jeyarani Pratiwi, Ananda Pratiwi, Firda Aulia Praveen, S Phani Putra, Muhammad Daffa Arviano Putrie, Andi Vania Ghalliyah R Rizal Isnanto Rahmadani, Olivia Rendra Gustriansyah Rizky, Wahyu Rizqi, Zakka Ugih Rufi'i Saelan, Saelan Samihardjo, Rosalim Saringat, Zainuri Setiawan, Ariyono Shinta Puspasari Singh, Harprith Kaur Rajinder Sirisha, Uddagiri Slamet Riyadi Sri Astuti Iriyani Sri Karnila Sri Lestari Sugiyarto Surono, Sugiyarto Sulaiman Helmi Sulaiman, Agus Sunda Ariana, Sunda Taqwa, Dwi Muhammad Tarigan, Masmur Thinakaran, Rajermani Triloka, Joko Trinawarman, Dedi Valentina, Amara Wahyu Caesarendra Wahyu Dwi Lestari Wahyuningdiah Trisari Harsanti Putri Wei, Aik Sam Wibaselppa, Anggawidia Widyangga, Pressylia Aluisina Putri Widyaningsih , Upik Wijayanti, Dian Eka Yahya Darmawan Yeh, Ming-Lang Yorman Zakari, Mohd Zaki Zakaria, Mohd Zaki