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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 52 Documents
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Search results for , issue "Vol 5, No 3: SEPTEMBER 2024" : 52 Documents clear
Applied Random Forest Algorithm for News and Article Features on The Stock Price Movement: An Empirical Study of The Banking Sector in Vietnam Nhat, Nguyen Minh
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.v5i3.338

Abstract

In 2023, in the context of the world economic and political situation continuing to experience many difficulties and challenges, the global stock market has suffered many unfavorable impacts. In that general context, Vietnam's stock market faces many problems, challenges, and strong fluctuations due to unexpected changes in the world's macro economy and geopolitics. Therefore, the study's goal is to investigate the impact of news articles on the stock price movement of commercial banks in Vietnam. Using a dataset of 94,784 news articles from January 2023 to April 2024 and applying the Random Forest algorithm, the author analyzes the significance of various news features. The study identifies that the proportion of news sources with positive evaluations and the proportion of news sources mentioning commercial banks are the most influential features of the stock price movement. The findings reveal that positive news boosts investor confidence, increasing stock prices, while high media attention significantly influences trading activity. Other notable features include the number of news sources and the total sentiment score of articles, which also play crucial roles. This research provides valuable insights for investors and analysts to understand the effect of news articles on stock prices, enhancing their decision-making process in the banking sector. Finally, the research results are scientific proof that helps the Vietnamese stock market to have more positive and robust changes, continue to be an attractive destination for domestic and foreign investment capital flows, and a channel for medium and long-term capital important term for the economy, making an increasingly more outstanding contribution to the country's socio-economic development in the new era.
Assessing the Performance and Competitive Strategies of Bamboo Weaving MSMEs in Bali Using Quantitative Associative Causal Design Kartika, I Made; Sumada, I Made; Suwandana, I Made Adi; Adnyana, Yudistira; Sedana, I Dewa Gede Putra; Meryawan, I Wayan
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.v5i3.320

Abstract

This study explores the factors influencing the competitiveness and performance of bamboo weaving Micro, Small, and Medium Enterprises (MSMEs) in Bali, using a quantitative associative causal design. Bamboo weaving, an integral part of Balinese culture, faces significant challenges post-COVID-19. This research aims to analyze key factors such as business capabilities, product quality, infrastructure, technology, resources, government policies, and external support, and their impact on the competitiveness and performance of these MSMEs. Data were collected from 100 bamboo weaving MSMEs in Buleleng and Bangli using structured surveys. The collected data were analyzed using Partial Least Squares (PLS) structural equation modeling to validate the proposed hypotheses. The study reveals that product quality (path coefficient = 0.275, t-statistic = 3.048, p-value = 0.003), infrastructure (path coefficient = 0.187, t-statistic = 2.176, p-value = 0.032), technology (path coefficient = 0.239, t-statistic = 3.231, p-value = 0.002), resources (path coefficient = 0.179, t-statistic = 2.048, p-value = 0.043), and external support (path coefficient = 0.185, t-statistic = 2.387, p-value = 0.019) significantly influence the competitiveness of bamboo weaving MSMEs. In contrast, business capabilities (path coefficient = 0.169, t-statistic = 1.856, p-value = 0.066) and government policies (path coefficient = - 0.031, t-statistic = 0.283, p-value = 0.778) were found to be insignificant in this context. The findings indicate that improvements in product quality, better infrastructure, adoption of modern technologies, effective resource management, and robust external support systems are critical in enhancing competitiveness. However, the expected positive impact of competitiveness on MSME performance (path coefficient = 0.182, tstatistic = 1.269, p-value = 0.207) was not statistically significant. This suggests that factors beyond competitiveness, such as market conditions and internal business processes, play a more substantial role in determining performance outcomes. This study provides practical recommendations for MSMEs to enhance competitiveness, emphasizing the need for improved product quality, infrastructure, technology adoption, resource management, and leveraging external support. Additionally, the research highlights the necessity for stronger, more effective government policies tailored to the unique challenges of bamboo weaving MSMEs. These insights are valuable for MSME owners, policymakers, and stakeholders aiming to support the bamboo weaving industry in Bali, ensuring its sustainability and growth in the post-pandemic era.
Performance Fuzzy Decision Model for Evaluating Employees’ Work-from-Home Performance Immanuel, Christiawan; Utama, Ditdit Nugeraha
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.v5i3.269

Abstract

This study aims to identify key workplace environmental parameters and develop a Decision Support Model (DSM) to evaluate the performance of work-from-home (WFH). The methods utilized include Tsukamoto Fuzzy Logic and conventional techniques. Key parameters incorporated into the DSM-WFHP model include room temperature, internet speed, number of children, virtual office setup, and physical activity (sport). The research culminates in the DSM-WFHP model, which provides accurate assessments of WFH employee performance. Findings indicate that variations in these parameters significantly impact performance, with specific quantitative results demonstrating that optimal room temperature, high internet speed, fewer children present, an effective virtual office setup, and regular physical activity correlate with higher performance scores. Thus, this research concludes that the DSM-WFHP model effectively offers precise performance evaluation guidance for remote employees, making a valuable contribution to remote work management. With regards to the novelty of this study, this is the first time that the synergetic effect of multiple environmental factors has been incorporated into a comprehensive DSM.
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.
Novel Battery Management with Fuzzy Tuned Low Voltage Chopper and Machine Learning Controlled Drive for Electric Vehicle Battery Management: A Pathway Towards SDG P, Vinoth Kumar; S, Priya; D, Gunapriya; Batumalay, M
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.v5i3.236

Abstract

Electric vehicles have a significant impact on the SDGs, specifically climate action, affordable and clean energy, and responsible consumption and production patterns. The present work focuses on a battery management system to effectively utilize the power from the battery to drive the brushless DC motor (BLDC) by tuning the low-voltage buck boost converter as a chopper circuit with fuzzy. The photovoltaic system acts as an additional source to charge the battery when the battery is not connected to the load, and at running conditions, fuzzy logic control enhances efficiency and provides smooth, adaptive control under varying load conditions. Also, the machine learning technique is used for drive control and automation operations. The energy in the BLDC is regulated by managing the voltage and current in a photovoltaic-powered low-voltage chopper by tuning the proportional integral derivative (PID) controller for an ideal balance between reliability and a quicker reaction. The K- Nearest Neighbour (KNN) machine learning algorithm, due to its simplicity and effectiveness in classification, ensures the enhanced reliability and efficiency of the BLDC motor system with commutation and speed control. When fuzzy and the KNN machine learning algorithm are used, the development of systems for control and automation is expedited. The work also shows the results of a study that compared the interoperability of proportionate machine learning and fuzzy controlling algorithms developed with MATLAB. In order to do a critical analysis of the data, the results are compared with the graphs. The integration of the Internet of Things (IoT) and cloud technology with the use of KNN for BLDC motor control can enhance system proficiency with monitoring and display of the observed voltage, current values of the motor, sensorless control, fault diagnosis, and predictive maintenance. The work is also connected with the SDG and impacts due to the efficient operation of electric vehicles.
An Effective Investigation of Genetic Disorder Disease Using Deep Learning Methodology Vidhya, B.; Shivakumar, B. L.; Maidin, Siti Sarah; Sun, Jing
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.v5i3.370

Abstract

This study evaluates the performance of four neural network models—Artificial Neural Network (ANN), ANN optimized with Artificial Bee Colony (ANN-ABC), Multilayer Feedforward Neural Network (MLFNN), and Forest Deep Neural Network (FDNN)—across different iteration levels to assess their effectiveness in predictive tasks. The evaluation metrics include accuracy, precision, Area Under the Curve (AUC) values, and error rates. Results indicate that FDNN consistently outperforms the other models, achieving the highest accuracy of 99%, precision of 98%, and AUC of 99 after 250 iterations, while maintaining the lowest error rate of 2.8%. MLFNN also shows strong performance, particularly at higher iterations, with notable improvements in accuracy and precision, but does not surpass FDNN. ANN-ABC offers some improvements over the standard ANN, yet falls short compared to FDNN and MLFNN. The standard ANN model, though improving with iterations, ranks lowest in all metrics. These findings highlight FDNN's robustness and reliability, making it the most effective model for high-precision predictive tasks, while MLFNN remains a strong alternative. The study underscores the importance of model selection based on performance metrics to achieve optimal predictive accuracy and reliability. 
Development and Research of an Autonomous Device for Sending a Distress Signal Based on a Low-Orbit Satellite Communication System Ondyrbayev, Nurbolat; Zhumagali, Sabyrzhan; Chezhimbayeva, Katipa; Zhumanov, Yelaman; Nurzhauov, Nursultan
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.v5i3.289

Abstract

Due to the importance of providing reliable communication for sending distress signals, research on the development of an autonomous device via low-orbit satellites is becoming particularly relevant, offering innovative solutions capable of providing fast and reliable communication in extreme situations. The purpose of this study was to investigate a device capable of operating autonomously in emergency situations and providing fast transmission of a signal about the need for help. The comparative method, statistical method, and analysis were used in the framework of research. The results of the study showed the significant potential of Long-Range Wide Area Networks (LoRaWAN) technology in the field of wireless communication. It provides high stability and noise immunity of data transmission, which makes it an attractive choice for various applications. Due to its high scalability, LoRaWAN is capable of servicing tens and hundreds of thousands of devices, making it an ideal solution for large-scale projects. LoRaWAN can achieve data transmission rates between 0.3 kbps to 50 kbps, with power consumption as low as 1.2 µA in sleep mode and 28 mA in transmit mode, and communication ranges up to 15 km in rural environments. Because of its low power consumption, it is ideal for use in battery-powered devices such as smart and distress sensors. In addition, it was found that the use of EBYTE E32 modules in LoRaWAN devices ensures reliable and efficient data transfer. The study confirms the potential of LoRaWAN technology for developing efficient and reliable wireless communication systems for various Internet of Things applications, ensuring reliable data transmission under various conditions. The results obtained are of great practical importance for the creation and further improvement of autonomous devices for the rapid sending of distress signals, contributing to increased safety and responsiveness to emergency situations.
Mitigating Healthcare Information Overload: a Trust-aware Multi-Criteria Collaborative Filtering Model Shambour, Qusai Y; Abualhaj, Mosleh M; Abu-Shareha, Ahmad; Hussein, Abdelrahman H; Kharma, Qasem M
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.v5i3.297

Abstract

The rapid growth of online health information resources has made it difficult for users, as well as providers of healthcare, to cope with large volumes of information that are becoming increasingly complex. Hence, there is an urgent demand for developing new advanced recommendation techniques in the healthcare domain to enhance decision-making processes. However, most current health recommendation systems, which recommend personalized healthcare services and items such as diagnoses, medications, and doctors based on users' health conditions and needs, are hindered by the data sparsity issue that compromises the reliability of their recommendations. In this paper, we intend to address this issue by proposing a Trust-aware Multi-Criteria Collaborative Filtering model for recommendation services in the healthcare domain. This model leverages multi-criteria ratings and integrates user-item trust relationships to improve the precision and coverage of recommendations, thus facilitating more informed healthcare choices that align closely with their individual needs. Our empirical analysis on two healthcare multicriteria rating datasets, including those with sparse data, shows the proposed model's superior performance over existing baseline methods. On the RateMDs dataset, our model improved the average MAE by 24% and RMSE by 19% compared to baseline methods. For the WebMD dataset, it enhanced the average MAE by 6% and RMSE by 2%. In sparse data scenarios, the model boosted the average MAE by 18% and Coverage by 6% compared to baseline approaches.
A Grid-search Method Approach for Hyperparameter Evaluation and Optimization on Teachable Machine Accuracy: A Case Study of Sample Size Variation Malahina, Edwin Ariesto Umbu; Iriane, Gregorius Rinduh; Belutowe, Yohanes Suban; Katemba, Petrus; Asmara, Jimi
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.v5i3.290

Abstract

This study aims to evaluate the effectiveness of the grid-search method in hyperparameter optimization on Teachable Machine (TM) using a varying number of image samples. The hyperparameters studied include epoch (e), batch size (b), and learning rate (l). A structured grid-search method approach will be applied to test 216 hyperparameter combinations across 6 categories of sample size per class, namely 10, 25, 50, 100, 250, and 500. The results showed that the optimal combination findings were obtained based on variations in the number of samples as follows: 10 samples using e:100, b:256, l:0.001 get an accuracy range of ≥ 90%; for 25 samples using e:500, b:16, l:0.001 get an accuracy range ≥ 97%; for 50 samples using e:100, b:512, l:0.001 get an accuracy range ≥ 88%; for 100 samples using e:500, b:32, l:0.001 get an accuracy range ≥ 88%; for 250 samples using e:50, b:16, l:0.001 get an accuracy range ≥ 92%, and finally 500 samples using e:500, b:256, l:0.001 get an accuracy range ≥ 96% and on average are able to achieve 100% accuracy from the detection test results of the best value performed for each sample variation of the image object. This research provides significant contributions or benefits in finding the optimal hyperparameter configuration, minimizing overfitting, and shortening the search time for TM accuracy in image classification, particularly in human face recognition. The findings support the development of more efficient and accurate TMs and provide practical guidance for finding better hyperparameter optimization using the grid-search method approach. The results of this study have implications for improving the effectiveness and accuracy of TM models and their development in mobile web applications
Optimizing Emergency Logistics Identification: Utilizing A Deep Learning Model in the Big Data Era Sumathi, V.; Shivakumar, B. L.; Maidin, Siti Sarah; Ge, Wu
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.v5i3.369

Abstract

This study investigates the dynamics of commodity flow across different facilities and settings, evaluating the performance of Simulation and Feed Forward Neural Network (FFNN) methods in optimizing these flows. Analyzing data from various configurations, the research reveals significant variations in commodity distribution patterns. At Facility_1 from the K1 disposer market, the flow of Commodity_1 increased from 770 units to 830 units, while Commodity_2 decreased from 192 units to 166 units. At Facility_2, Commodity_1's flow decreased from 851 units to 793 units, and Commodity_2's flow slightly increased from 139 units to 148 units. Similar trends are observed at facilities from the K2 disposer market, reflecting the complex impact of different settings on commodity flow. The comparative analysis of Simulation and FFNN methods demonstrates their relative strengths. In Setting I, the Simulation method achieved an objective value of 1,800,574.36 Rs with a computation time of 46.78 seconds, while the FFNN method yielded a slightly lower objective value of 1,800,352.24 Rs in 42.01 seconds. In Setting II, the Simulation method provided an objective value of 1,801,025.36 Rs with a computation time of 103.86 seconds, whereas FFNN achieved a comparable objective value of 1,800,847.27 Rs in 63.05 seconds. In Setting III, Simulation resulted in an objective value of 1,801,527.36 Rs with a computation time of 61.12 seconds, while FFNN produced a higher objective value of 1,806,997.32 Rs in 50.03 seconds. The results highlight the trade-offs between solution quality and computational efficiency. The Simulation method consistently delivers higher objective values but requires more time, making it suitable for applications where result accuracy is crucial. Conversely, the FFNN method offers faster computation with competitive or superior objective values, making it advantageous for scenarios where time constraints are significant. This study underscores the importance of selecting appropriate computational methods based on specific operational needs, optimizing both the efficiency and effectiveness of commodity flow management.

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