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All Journal Jurnal Teknologi JUITA : Jurnal Informatika JOIN (Jurnal Online Informatika) Science and Technology Indonesia JOIV : International Journal on Informatics Visualization Emerging Science Journal Jurnal Varian Journal of Robotics and Control (JRC) Journal of Applied Data Sciences International Journal for Applied Information Management Maritime Park: Journal Of Maritime Technology and Socienty Bulletin of Applied Mathematics and Mathematics Education Jurnal Pemberdayaan Masyarakat 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 Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Other

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IoT Platform for Monitoring Systems Water pH in the Freshwater Fish Cultivation Process Kurnia, Deni; Riyadi, Slamet; Dewi, Deshinta Arrova; Suprianto, Adolf Asih; Rahmadani, Olivia
Jurnal Teknologi Vol 16, No 1 (2024): Jurnal Teknologi
Publisher : Fakultas Teknik Universitas Muhammadiyah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24853/jurtek.16.1.11-16

Abstract

This study describes the implementation of the Internet of Things using the MQTT protocol and mosquitto as a broker combined with a 2x16 LCD for a pH monitoring system in a freshwater fish farming system at BPBIAT Purwakarta, West Java, Indonesia. To obtain accurate data, the calibration process is carried out in several stages, including using buffer 6.8 and distilled water. The calibration results on the 6.8 buffered liquid sample mean that the data obtained are appropriate, while the aquades obtained a value of 7.73, this is due to the storage factor of the distilled water so that it can change the pH value. The final result of the research shows that the data that appears online on the Node-Red dashboard is the same as that which appears locally on the 2x16 LCD. This means the MQTT protocol is working fine. The data displayed on the Node-Red UI is sent periodically every 30 minutes. The consideration is that, during the experiments, the pH value of the water did not change significantly beyond the range of 6.5-8.5. This data illustrates that the pH quality of the water for freshwater fish farming at BPBIAT is ideal for use. 
Efficient Web Mining on MyAnimeList: A Concurrency-Driven Approach Using the Go Programming Language Putra, Muhammad Daffa Arviano; Dewi, Deshinta Arrova; Putri, Wahyuningdiah Trisari Harsanti; Achsan, Harry Tursulistyono Yani
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.352

Abstract

Anime is a globally popular form of entertainment, with the industry experiencing rapid growth in recent years. Despite the wealth of anime data available on MyAnimeList, the largest community-driven platform for anime enthusiasts, existing publicly available datasets are often outdated and incomplete. This presents a challenge for data science research, as the increasing volume of anime information requires more efficient data extraction methods. This research aims to address this challenge by developing a concurrent web mining program using the Go programming language. Leveraging Go's concurrency capabilities, our program efficiently extracted anime data from MyAnimeList, iterating through anime pages from ID 1 to 52,991. To overcome potential issues like rate limits and server timeouts, we implemented a two-phase execution strategy. As a result, the program successfully gathered 23,105 anime records within 8.5 hours. The extracted data has been transformed into a comprehensive dataset and made publicly available in CSV format. This research demonstrates the effectiveness of concurrent web mining for large-scale data extraction and offers a valuable resource for future data-driven research in the anime industry.
Unveiling Criminal Activity: a Social Media Mining Approach to Crime Prediction Armoogum, Sheeba; Dewi, Deshinta Arrova; Armoogum, Vinaye; Melanie, Nicolas; Kurniawan, Tri Basuki
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.350

Abstract

Social media platforms have become breeding grounds for abusive comments, necessitating the use of machine learning to detect harmful content. This study aims to predict abusive comments within a Mauritian context, focusing specifically on comments written in Mauritian Kreol, a language with limited natural language processing tools. The objective was to build and evaluate four machine learning models—Decision Tree, Random Forest, Naïve Bayes, and Support Vector Machine (SVM)—to accurately classify comments as abusive or non-abusive. The models were trained and tested using k-fold cross-validation, and the Decision Tree model outperformed others with 100% precision and recall, while Random Forest followed with 99% accuracy. Naïve Bayes and SVM, although achieving 100% precision, had lower recall rates of 35% and 16%, respectively, due to imbalanced data in the training set. Pre-processing steps, including stop-word removal and a custom Kreol spell checker, were key in enhancing model performance. The study provides a novel contribution by applying machine learning in a Mauritian context, demonstrating the potential of AI in detecting abusive language in underrepresented languages. Despite limitations such as the absence of a Kreol lemmatization tool and incomplete coverage of Kreol spelling variations, the models show promise for wider application in social media crime detection. Future research could explore expanding this approach to other languages and domains of social media crimes.
Analyzing Factors that Influence Student Performance in Academic Hidayani, Nieta; Dewi, Deshinta Arrova; Kurniawan, Tri Basuki
Journal of Applied Data Sciences Vol 5, No 2: MAY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i2.221

Abstract

Student performance analysis is a complex and popular study area in educational data mining. Multiple factors affect performance in nonlinear ways, making this topic more appealing to academics. The broad availability of educational datasets adds to this interest, particularly in online learning. Although previous studies have focused on analyzing and predicting students' performance based on their classroom activities, this study did not take into account student's outside conditions, such as sleep hours, extracurricular activities, and a sample of question papers that they had practiced.  These three variables are included among others in our study. In this paper, we describe an analysis of 10,000 student records, with each record containing information on numerous predictors and a performance index. The dataset intends to shed light on the relationship between predictor variables and the performance indicator. To create the correlation variable heatmap, we use both univariate and bivariate studies to produce a linear equation. Following that, we perform data preprocessing and modeling to facilitate predictive analysis. Finally, we showed the outcomes of actual and expected student performance using the model we constructed. The findings demonstrate that our prediction model was 98% accurate, with a mean absolute error of 1.62. 
Sustainable Educational Data Mining Studies: Identifying Key Factors and Techniques for Predicting Student Academic Performance Murnawan, Murnawan; Lestari, Sri; Samihardjo, Rosalim; Dewi, Deshinta Arrova
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.347

Abstract

This research paper presents a systematic literature review of sustainable educational data mining (EDM) studies published between 2017 and 2022 with the objective of identifying the primary factors that affect student academic performance. The purpose of this study is to provide a comprehensive analysis of sustainable EDM research and identify the most important factors that influence student performance while highlighting commonly used data mining techniques in the EDM field. The results suggest that student demographics, previous grades and class performance, social factors, and online learning activities are the most common and widely used factors for predicting student performance in educational institutions. Furthermore, Decision Trees, Naive Bayes, and Random Forests are the most frequently used categories of data mining algorithms in the studies included in the dataset. The methodology used in this study is a systematic literature review, which is a widely used technique for literature review that provides a reliable and unbiased process for reviewing data from diverse sources. The findings of this study provide valuable insights into the factors influencing student performance in educational institutions and can be used by researchers to inform future research and identify relevant factors to consider when predicting student performance.
Clustering the Unlabeled Data Using a Modified Cat Swarm Optimization Dewi, Deshinta Arrova; Kurniawan, Tri Basuki; Zakaria, Mohd Zaki; Armoogum, Sheeba
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.349

Abstract

This paper presents a modified version of the Cat Swarm Optimization (CSO) algorithm aimed at addressing the limitations of traditional clustering methods in handling complex, high-dimensional datasets. The primary objective of this research is to improve clustering accuracy and stability by eliminating the mixture ratio (MR), setting the counts of dimensions to change (CDC) to 100%, and incorporating a new search equation in the tracing mode of the CSO algorithm. To evaluate the performance of the modified algorithm, five classic datasets from the UCI Machine Learning Repository—namely Iris, Cancer, Glass, Wine, and Contraceptive Method Choice (CMC)—were used. The proposed algorithm was compared against K-Means and the original CSO. Performance metrics such as intra-cluster distance, standard deviation, and F- measure were used to assess the quality of clustering. The results demonstrated that the modified CSO consistently outperformed the competing algorithms. For example, on the Iris dataset, the modified CSO achieved a best intra-cluster distance of 96.78 and an F-measure of 0.786, compared to 97.12 and 0.781 for K-Means. Similarly, for the Wine dataset, the modified CSO reached a best intra-cluster distance of 16399, surpassing K-Means which recorded 16768. In conclusion, the modifications introduced to the CSO algorithm significantly enhance its clustering performance across diverse datasets, producing tighter and more accurate clusters with improved stability. These findings suggest that the modified CSO is a robust and effective tool for data clustering tasks, particularly in high-dimensional spaces. Future work will focus on dynamic parameter tuning and testing the scalability of the algorithm on larger and more complex datasets.
Breast Cancer Prediction Using Metrics-Based Classification Armoogum, Sheeba; Dewi, Deshinta Arrova; Kezhilen, Motean; Trinawarman, Dedi
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.351

Abstract

Breast cancer remains the most prevalent form of cancer among women, with rising mortality rates worldwide. Early detection and accurate classification are crucial for improving patient outcomes, but manual detection methods are often time-consuming, complex, and prone to inaccuracies. This study aims to develop a machine learning (ML)-based desktop application to automate the detection and classification of breast cancer, thereby improving the efficiency and accuracy of diagnosis. Various ML algorithms, including Random Forest, Decision Tree, Support Vector Machine, Logistic Regression, Gaussian Naive Bayes, and K-nearest Neighbors, were employed to build classification models. The Wisconsin Diagnostic Breast Cancer (WDBC) dataset was used, and pre-processing techniques such as data cleaning, over-sampling, and feature selection were applied to optimize model performance. Experimental results demonstrate that the Random Forest classifier outperformed the other models, achieving an accuracy of 95.54%, precision of 96.72%, recall (sensitivity) of 95.16%, specificity of 96%, and an F1-score of 95.93%. These results highlight the potential of ML techniques in enhancing breast cancer diagnosis by offering a more reliable and efficient classification process. Future work could focus on improving feature selection techniques and applying the model to more diverse datasets for broader applicability.
Water Quality Prediction using Random Forest Algorithm and Optimization Dewi, Deshinta Arrova; Wei, Aik Sam; Lin, Leong Chi; Heng, Chang Ding
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.348

Abstract

In the field of environmental conservation, the integration of Artificial Intelligence (AI) into pollution control strategies offers a transformative approach with significant potential. This paper presents a study on the application of AI techniques, specifically Random Forest algorithms, to predict and manage water quality in river systems. The objective of this research was to evaluate the performance of Random Forest models in comparison to Artificial Neural Networks (ANNs) for predicting the Water Quality Index (WQI). The study's findings revealed that the Random Forest model achieved a Mean Absolute Error (MAE) of 7.87 and a Root Mean Squared Error (RMSE) of 27.99, significantly outperforming the ANN model, which had a MAE of 121.40 and an RMSE of 215.04. These results demonstrate the superior accuracy and reliability of the Random Forest algorithm in capturing complex environmental data patterns. The novelty of this research lies in its comprehensive comparison of AI models for environmental monitoring, providing a data-driven approach to improving water quality management. This contribution is particularly relevant in the context of achieving Sustainable Development Goal (SDG) 6, which focuses on ensuring clean water and sanitation. By advancing traditional environmental planning methods with AI, this study highlights the potential of these technologies to make a substantial impact on environmental protection efforts.
Gum Disease Identification Using Fuzzy Expert System Nasir, Muhammad; Kurniawan, Tri Basuki; Dewi, Deshinta Arrova; Zakaria, Mohd Zaki; Bujang, Nurul Shaira Binti
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.346

Abstract

Gum disease, including Gingivitis and Periodontitis, is among the most common dental conditions, primarily caused by dental plaque, a bacterial biofilm. These conditions are strongly linked to various systemic illnesses, including cancer, atherosclerosis, hypertension, stroke, and respiratory and cardiovascular conditions like aspiration pneumonia, as well as adverse pregnancy outcomes. Gum inflammation is typically characterized by symptoms such as increased redness, swelling (edema), and a loss of surface texture (stippling; gum fiber attachment). These symptoms are site-specific, meaning that an individual can have both healthy and diseased areas within their mouth. In this research, we developed a fuzzy expert system using MATLAB to identify gum diseases. The system was tested on various cases and produced an output value of 0.133, which successfully identified Gingivitis. This value was derived using a fuzzy logic system that processes input data through predefined rules within the Fuzzy Expert System (FES). The system utilizes several input variables such as the frequency of gum bleeding, the extent of plaque accumulation, the depth of gum recession, and the degree of tooth mobility. The key contribution of this study lies in the integration of fuzzy logic to handle the inherent uncertainties in clinical diagnosis, providing a more nuanced assessment compared to traditional methods. The novelty of this research is the application of a fuzzy expert system in dental diagnostics, offering a promising tool for improving the accuracy and efficiency of gum disease identification in clinical settings. This system has the potential to assist dentists in making more informed decisions, ultimately leading to better patient outcomes.
Fake vs Real Image Detection Using Deep Learning Algorithm Fatoni, Fatoni; Kurniawan, Tri Basuki; Dewi, Deshinta Arrova; Zakaria, Mohd Zaki; Muhayeddin, Abdul Muniif Mohd
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.v6i1.490

Abstract

The purpose of this research project is to address the growing issues presented by modified visual information by developing a deep learning model for identifying between real and fake images. To enhance accuracy, this project evaluates the effectiveness of deep learning algorithms such as Residual Neural Network (ResNet), Visual Geometry Group 16 (VGG16), and Convolutional Neural Network (CNN) together with Error Level Analysis (ELA) as preprocessing the dataset. The CASIA dataset contains 7,492 real images and 5,124 fake images. The images included are from a wide range of random subjects, including buildings, fruits, animals, and more, providing a comprehensive dataset for model training and validation. This research examined models' effectiveness through experiments, measuring their training and validation accuracies. It comes out with the best accuracy of each model, which is for Convolutional Neural Network (CNN), 94% for training accuracy, and validation accuracy of 92%. For VGG16, with both training and validation accuracy reaching 94%. Lastly, Residual Neural Network (ResNet) demonstrated optimal performance with 95% training accuracy and 93% validation accuracy. This project also constructs a system prototype for practical applications, offering an interface for real-world testing. When integrating into the system prototype, only Residual Neural Network (ResNet) shows consistency and effectiveness when predicting both fake and real images, and this led to the decision to choose ResNet for integration into the system. Furthermore, the project identified several areas for improvement. Firstly, expanding the model comparison for discovering more successful algorithms. Next, improving the dataset preprocessing phase by incorporating filtering or denoising techniques. Lastly, refining the system prototype for greater appeal and user-friendliness has the potential to attract a larger audience.
Co-Authors - Kurniawan, - Achsan, Harry Tursulistyono Yani Adi Wijaya Afriyani, Sintia Alde Alanda, Alde Ali Amran Alqudah, Mashal Kasem Alqudah, Musab Kasim Andri Andri Andriani, Putu Eka Anita Desiani Aris Thobirin, Aris Armoogum, Sheeba Armoogum, Vinaye Aryananda, Rangga Laksana Asro, Asro Aziz, RZ. Abdul Azmi, Nurhafifi Binti Bappoo, Soodeshna Batumalay, Malathy Bin Abdul Hadi, Abdul Razak Bujang, Nurul Shaira Binti Chandra, Anurag Diana Diana Dita Amelia, Dita Elyakim Nova Supriyedi Patty, Elyakim Nova Supriyedi Endro Setyo Cahyono, Endro Setyo Eva Yulia Puspaningrum Fachry Abda El Rahman Fadly Fadly Fara Disa Durry Fatoni, Fatoni Fikri, Ruki Rizal Nul Firosha, Ardian 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 Irianto, Suhendro Y. Irwansyah Irwansyah Ismail, Abdul Azim Bin Isnawijaya, Isnawijaya Jayawarsa, A.A. Ketut Kezhilen, Motean Kijsomporn, Jureerat Kurniawan, Tri Basuki Larasati, Anggit Lexianingrum, Siti Rahayu Pratami Lin, Leong Chi M Said Hasibuan M. Fariz Fadillah Mardianto Maizary, Ary Mantena, Jeevana Sujitha MARIA BINTANG Mas Diyasa, I Gede Susrama 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 Onn, Choo Wou Pamungkas, Anjar Periasamy, Jeyarani Pratiwi, Ananda Pratiwi, Firda Aulia Praveen, S Phani Putra, Muhammad Daffa Arviano Putrie, Andi Vania Ghalliyah R Rizal Isnanto Rahmadani, Olivia Samihardjo, Rosalim Saringat, Zainuri Setiawan, Ariyono Singh, Harprith Kaur Rajinder Sirisha, Uddagiri Slamet Riyadi Sri Karnila Sri Lestari Sugiyarto Surono, Sugiyarto Sulaiman, Agus Taqwa, Dwi Muhammad Thinakaran, Rajermani Triloka, Joko Trinawarman, Dedi Udariansyah, Devi Wahyu Caesarendra Wahyu Dwi Lestari Wahyuningdiah Trisari Harsanti Putri Wei, Aik Sam Wibaselppa, Anggawidia Widyangga, Pressylia Aluisina Putri Widyaningsih , Upik Wijayanti, Dian Eka Yeh, Ming-Lang Yorman Yuli Andriani Zakari, Mohd Zaki Zakaria, Mohd Zaki