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Journal of Applied Data Sciences
Published by Bright Publisher
ISSN : -     EISSN : 27236471     DOI : doi.org/10.47738/jads
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management
One of the current hot topics in science is data: how can datasets be used in scientific and scholarly research in a more reliable, citable and accountable way? Data is of paramount importance to scientific progress, yet most research data remains private. Enhancing the transparency of the processes applied to collect, treat and analyze data will help to render scientific research results reproducible and thus more accountable. The datasets itself should also be accessible to other researchers, so that research publications, dataset descriptions, and the actual datasets can be linked. The journal Data provides a forum to publish methodical papers on processes applied to data collection, treatment and analysis, as well as for data descriptors publishing descriptions of a linked dataset.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 518 Documents
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An Improved Prediction of Transparent Conductor Formation Energy using PyCaret: An Open-Source Machine Learning Library Olanipekun, Ayorinde Tayo; Mashao, Daniel
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.v5i4.202

Abstract

Designing innovative materials is necessary to solve vital energy, health, environmental, social, and economic challenges. Transparent conductors are compounds that combine low absorption visible range and good electrical conductivity, which are essential properties for conductors. Technological devices such as photovoltaic cells, transistors, photovoltaic cells and sensors majorly rely on combining the two properties due to their relevancy in an optoelectronic application. Meanwhile, fewer compounds exhibit both outstanding conductivity and transparency suitable for their application in transparent conducting materials. Kaggle hosted an open big-data competition organized by novel material discovery (NOMAD) to address the importance of finding new material with the ideal functionality. The competition was organized to identify the best machine learning (ML) to predict formation enthalpy (indicating stability) for 3000  (AlxGaylnz)2NO3Ncompounds datasets; where x, y, and z can vary from the constraints x+y+z=1. Here we present a prediction using an open-source machine learning library in Python called PyCaret to summarise top-ranked ML algorithms. The gradient boosting regressor (GBR) model performed best with MAE 0.0281, MSE 0.0018 and R2 0.84. The research shows that Machine learning can significantly accelerate the discovery and optimization of materials while reducing cost of computation and required time. Low code tools like PyCaret were used to enhance the machine learning applications in materials science, paving way for more efficient materials discovery processes.
Classification of Starling Images Using a Bayesian Network Hananto, April Lia; Rahman, Aviv Yuniar; Paryono, Tukino; Priyatna, Bayu; Hananto, Agustia; Huda, Baenil
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.v6i1.423

Abstract

The classification of starling species is vital for biodiversity conservation, especially as some species are endangered. This research investigates the effectiveness of the Bayesian Network (BayesNet) for classifying starling species and compares its performance with Artificial Neural Networks (ANN) and Naive Bayes models. The dataset comprises 300 images of five starling species—Bali, Rio, Moon, Kebo, and Uret—captured under controlled conditions. Feature extraction focused on color, texture, and shape, while data augmentation through slight image rotations was applied to enhance model generalization. The BayesNet model achieved an accuracy of 96.29% using a 90:10 training-to-testing split, outperforming ANN (90.74%) and Naive Bayes variants. Precision, recall, F1-score, and AUC-ROC values further validated the robustness of the BayesNet model, with precision at 0.90, recall at 0.91, F1-score at 0.92, and AUC-ROC at 0.95. These results demonstrate the superior performance of multi-feature Bayesian Networks in starling classification compared to other machine learning models. The novelty of this study lies in its application of a probabilistic approach using Bayesian Networks, which enhances interpretability and performance, especially in scenarios with limited data. Future work may explore additional feature sets and advanced machine learning models to further improve classification accuracy and robustness.
Enhancing Field-Controlled DC Motors with Artificial Intelligence-Infused Fuzzy Logic Controller Natsheh, Essam
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.v6i1.508

Abstract

Servomotors play a pivotal role in a wide array of everyday and industrial applications. Field-controlled DC motors particularly stand out for positioning tasks owing to their advantageous speed-torque characteristics. An optical encoder, integrated with the rotor, provides feedback to a PID controller, which in turn generates corrective signals for precise motor positioning. To enhance response speed and minimize hunting, the PID controller incorporates fuzzy logic programming. This paper introduces a novel optimization design approach utilizing a Performance-Oriented Rule-Based Controller (PDFCS) in conjunction with various PID fuzzy controller design methods to attain specific performance goals. Given the criticality of constructing membership functions in fuzzy controllers, a self-optimized membership functions algorithm is proposed. Accuracy analysis demonstrates that the proposed design method achieves a 2.9-second reduction in rise time, a 2.0-second decrease in settling time, and a 1.9% reduction in overshoot compared to conventional design methods. Furthermore, robustness analysis reveals a 4.0-second improvement in rise time, a 1.7-second enhancement in settling time, and a 0.79% decrease in overshoot. These findings underscore the superior accuracy and robustness of employing the proposed performance model alongside various PID fuzzy controller design methods, compared to relying solely on conventional design approaches.
Convolutional Neural Network for Battery System Monitoring and SOC Estimation for Ev Applications to Achieve Sustainability S, Priya; P, Vinoth Kumar; V, Sridevi; Batumalay, M; D, Gunapriya
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.v5i4.237

Abstract

The necessity to develop an alternative energy source to handle the looming energy crisis has arisen due to the recent rise in energy consumption. This is most likely to happen with grid-synchronized electric vehicles (EVs), since vehicle-to-grid (V2G) technology is one of the smart grid's technological advancements that permits energy exchange between EVs and the grid. The merging of EVs with the grid influences the whole electricity system and is susceptible to imbalances in supply and demand, frequency, and voltage. The proposed work focuses on effective and smart control of the Single Ended Primary Inductance Converter (SEPIC) converter with efficient control techniques employed for battery management systems for electric vehicle charging. PI oversees controlling the converter. The battery's calculated state of charge (SOC) is used to make a paradigm-shifting sequence for the converter with workable optimization strategies to lower imbalance issues when EVs are connected to the grid. This leads to the achievement of sustainable development goals (SDGs). Purpose. When the SEPIC converter is connected to a photovoltaic source, it needs to be analyzed in terms of how it switches operations. The source also needs to be used efficiently when it is connected to a battery. Monitoring and SOC estimation of the battery need to be efficiently performed with a quicker response for EV applications. Methods. Convolutional neural networks (CNN) were used to solve the issue; these networks considerably enhance response times and boost system reliability overall. Results. The system operates on the principle that when the battery level is less than 60%, the battery is charged through buck operation, and it is discharged through the boost mode when the SOC exceeds 60%. When linked to the grid, the PI controller regulates both power and practical value. The proposed system demonstrates how battery management-based CNN and SEPIC can switch at high speeds. The system's research directions were established for the results' later application to experimental samples for energy efficiency and process innovation.
Enhancing Online Batik Shopping Experience through Live Streaming Commerce and the LYFY Application Wiradinata, Trianggoro; Wibowo, Wilbert Bryan; Oktian, Yustus Eko; Maryati, Indra; Soekamto, Yosua Setyawan
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.v6i1.504

Abstract

Online batik shopping often results in buyer dissatisfaction due to discrepancies between product descriptions and the actual items received. Static images and text on e-marketplace platforms are insufficient to convey the intricate details of batik designs, leading to mismatches in customer expectations. To mitigate this issue, Live Streaming Commerce (LSC) features, such as those on Shopee Live, allow sellers to showcase products in real-time, providing more accurate representations. However, sellers face challenges in managing overwhelming volume of comments during live streams, making it difficult to prioritize important queries. LYFY, a comment management app developed to streamline these interactions, aims to address this problem by improving the quality of interaction between live streamers and prospective buyers through filtering important comments. This study examines the determinants affecting the adoption of LYFY by online batik vendors. The research integrates the Task-Technology Fit (TTF), Technology Acceptance Model (TAM), and Expectation-Confirmation Model (ECM) frameworks to evaluate LYFY's performance in fulfilling user requirements. Data were collected from 243 respondents with LSC experience, and the research model underwent evaluation through Partial Least Squares Structural Equation Modeling (PLS-SEM). The measurement model exhibited high reliability and validity, with values surpassing the suggested thresholds, thereby providing solid support for subsequent analysis. Key factors such as TTF, confirmation, perceived usefulness, ease of use, and satisfaction were examined to determine their impact on user adoption. The analysis revealed that TTF has the strongest influence on confirmation, perceived usefulness, satisfaction, and individual performance. Additionally, perceived ease of use and confirmation substantially influence continuance intentions and satisfaction. These results suggest that enhancing LYFY's task-technology fit and simplifying its user interface are crucial for improving user satisfaction and adoption. By addressing these areas, LYFY can better support live stream sellers, reduce product expectation discrepancies, and improve overall customer experience, particularly in the online batik market.
Artificial Intelligence Techniques for Early Autism Detection in Toddlers: A Comparative Analysis Shambour, Qusai; Qandeel, Nazem; Alrabanah, Yousef; Abumariam, Anan; Shambour, Mohd Khaled
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.v5i4.353

Abstract

Individuals with autism spectrum disorder are often characterized by complications in social interaction and communication, which can be attributed to disruptions in brain development affecting their perception and interaction with others. While ASD is not a treatable condition, early detection and diagnosis can significantly mitigate its effects. Recent advancements in artificial intelligence have enabled the development of novel diagnostic tools, which can detect ASD at an earlier stage than traditional methods. This study attempts to enhance and automate the diagnostic process by employing a variety of machine learning techniques to identify the most critical characteristics of ASD. To this end, we employed six state-of-the-art machine learning classification models, including Support Vector Machine, Random Forest, k-Nearest Neighbors, Logistic Regression, Decision Tree, and Naive Bayes classifiers, to analyze and predict ASD in toddlers using a non-clinical ASD dataset. Our evaluation focused on a range of performance metrics, including accuracy, precision, recall, and F1-score, to assess the efficacy of each model. Notably, the Logistic Regression model demonstrated the highest accuracy in diagnosing ASD in toddlers, achieving a perfect score of 100% across all metrics.
Developing and Evaluating a Rhythm Reading Practice Kit: A Study on Learning Outcomes and Music Learners Satisfaction in Music Education Using Quantitative Analysis Chantanasut, Thaworada
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.v5i4.471

Abstract

This study presents the development and evaluation of a rhythm reading practice kit aimed at improving rhythm comprehension and overall student satisfaction in music education. The kit was specifically designed for students in the Western Music and Vocal Education Studies Program at Rajamangala University of Technology Thanyaburi and Nawaminthrachinuthit School, Horwang, Nonthaburi. It includes progressive exercises across multiple time signatures, enabling both guided classroom uses and self-directed practice. A quantitative research approach was adopted, utilizing pre-test and post-test assessments to measure academic improvement in rhythm reading skills, coupled with a satisfaction survey to gauge student perceptions. Findings revealed a statistically significant increase in students' post-test scores, with university students achieving a mean improvement of 18.35 points and high school students 15 points. The paired samples t-test results indicated strong significance at the .05 level, underscoring the kit's positive impact on rhythm reading proficiency. Furthermore, the student satisfaction survey highlighted high levels of approval, particularly in areas such as instructional clarity, content alignment, and ease of use for independent learning. These results suggest that the rhythm reading practice kit not only enhances students' rhythm skills but also supports their engagement and enjoyment of the learning process. The study concludes with recommendations for future research, suggesting potential digital integrations to increase accessibility and considering adaptations to address a broader range of musical skills beyond rhythm. The findings contribute to the field of music education by providing evidence of the kit's effectiveness and promoting its use as a tool to foster comprehensive rhythm education in diverse educational settings.
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.
Environment Sentiment Analysis of Bali Coffee Shop Visitors Using Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformer 2 (GPT2) Model Yuniari, Ni Putu Widya; Iswari, Ni Made Satvika; Kumara, I Made Surya
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.v5i4.302

Abstract

Bali is one of the provinces with the most abundant natural and cultural wealth in Indonesia. One commodity that supports it is coffee. Bali Coffee is not only a gastronomic identity, but also a cultural identity which makes it have added value to be developed into various business lines. One business derivative that is quite promising is a coffee shop. However, these favorable conditions also need to be maintained to ensure good quality reaches consumers. One thing that can do is analyze reviews from customers. One of the most popular methods is Sentiment Analysis. This technique allows business to analyze customer reviews on social media. It can be a feedback to maintaining and improving quality and good relationships with customers. This research aims to create a machine learning model to analyze customer reviews at several coffee shops in Bali which are divided into three labels, namely: positive, negative and neutral. The methods used are: scraping, cleaning, stopword removal, embedding, undersampling, and modeling. The algorithms used are Bidirectional Encoder Representation from Transformer (BERT) and Generative Pre-trained Transformers (GPT). The performance metrics used in this research are precision, recall, accuracy and loss. This research succeeded in creating a sentiment analysis model for coffee shop customers in Bali. The BERT model obtained an accuracy value of 78% without undersampling with a loss in the 10th iteration of 0.27. Meanwhile, the BERT model with undersampling obtained an accuracy value of 32.85% with a loss in the 10th iteration of 0.16. The GPT2 model without undersampling gets an accuracy of 78% with a loss in the 10th iteration of 0.25. Meanwhile, the GPT model with undersampling obtained an accuracy value of 32.85% with a loss in the 10th iteration of 0.15.
An Adaptive Cuckoo Search Algorithm with Deep Learning for Addressing Cyber Security Problem Jeyaboopathiraja, J.; Mariajohn, Princess; Maidin, Siti Sarah; Sun, Jing
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.v5i4.366

Abstract

IoT (Internet of Things) offers continued services to organizations by connecting systems, application and services using the medium of internet. They also leave themselves open to threats including virus attacks and software thefts where the risks of losing crucial information are high. These threats harm both the business’ finances and reputation. This work offers a combined Deep Learning strategy using Artificial Neural Networks that can assist in detecting illegal software and malware tainted files. The proposed cyber security architecture uses data mining techniques to forecast cyber-attacks and prepare Internet of Things for suitable countermeasures. This framework uses two phases namely detections and predictions. This paper proposes Adaptive Cuckoo Search Optimization-based Algorithms for cloud network routes. Adaptive Cuckoo Search Algorithm are a bio-inspired protocol based on cuckoo birds’ characteristics. Artificial Neural Networks classify assaults on cloud environments. The major goal of this work is to separate malicious servers from legitimate servers that are impacted by Denial of Service and Distributed Denial of Service assaults and thus safeguard server data and ensuring they are sent to legitimate servers. The outcome from this research proposed scheme shows better performances for protecting systems from cyber-attacks in terms of values for accuracy, Precision, Recall and F1-Measure when compared to existing algorithms.

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