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Husni Teja Sukmana
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INDONESIA
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 38 Documents
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Search results for , issue "Vol 5, No 2: MAY 2024" : 38 Documents clear
Exploring Visitor Sentiments: A Study of Nusantara Temple Reviews on TripAdvisor Using Machine Learning Hariyono, Hariyono; Wibawa, Aji Prasetya; Noviani, Erina Fika; Lauretta, Giovanny Cyntia; Citra, Hana Rachma; Utama, Agung Bella Putra; Dwiyanto, Felix Andika
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.v5i2.208

Abstract

This study examines the mood of tourist evaluations for the Nusantara Temples, such as Borobudur, Prambanan, Ijo, Plaosan, and Mendut Temples, on TripAdvisor using Stochastic Gradient Descent (SGD), Logistic Regression (LR), and Support Vector Machine (SVM) classification techniques. The study examines the viewpoints and encounters of tourists from different nations on Indonesia's cultural legacy through English-language evaluations. The evaluation findings show that LR achieves the highest performance in sentiment classification, with an accuracy rate of 91.66%. The research offers valuable insights but has limits in portraying local visitors and relies heavily on the English language. Future studies might focus on doing sentiment analysis on more historical tourism sites in Indonesia, integrating multilingual data, and experimenting with novel categorization methods. This study significantly enhances our understanding of how technology and social media impact tourists' impressions of cultural heritage in the digital age via strengthening analytical methodologies and investigating alternative destinations.
Applied Regression Modelling to Propose Recommendations for the Development of Banking Services: A Case Study of Commercial Banks in Vietnam Linh, Phan Thi
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.v5i2.200

Abstract

The trend of global economic integration has created conditions for the strong development of our country's economy during the integration process. More and more foreign banks have been participating in our country's financial market, creating increasingly fierce competitive pressure in the commercial banking system. Despite many efforts, domestic banks still face difficulties in surviving and developing. Facing so many competitive challenges and the entry of foreign banks requires banks to have a clear strategy to maintain and increase market share. Many banks have made great strides in service quality, improving management levels, and applying applications such as Internet banking, ebank, Fintech, etc. Providing customers, the best quality of service has become more critical than ever if banks want to survive and develop in the current period. Therefore, the article aims to discover the essential factors contributing to developing commercial banking services in Vietnam. In addition, policy implications for banking service development are proposed. Based on the goal, the author surveyed 500 customers using the banking services, applied the multiple linear regression method, and processed data using SPSS 20.0. The key findings showed seven factors affecting banking service development with a significance of 0.05. The contributions of this study have focused on analyzing and identifying factors and the level of influence of factors on banking service development. Based on the research results, the author proposed some recommendations to help commercial bank leaders develop banking services in the coming time. The research novelty discusses the proposed seven policy recommendations, which include (1) tangibles, (2) responsiveness, (3) competence, (4) empathy, (5) reliability, (6) management capacity, and (7) technological capacity. Finally, the results are also scientific evidence and are very important for managers and policymakers for Vietnamese commercial banks to apply to contribute to developing the banking services to the higher demand of customers.
Enhancing Federated Learning Performance through Adaptive Client Optimization with Hyperparameter Tuning Putra, Made Adi Paramartha; Utama, I Komang Ram Pramartha; Utami, Nengah Widya; Putra, I Gede Juliana Eka
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.v5i2.251

Abstract

The effectiveness of Industrial Internet of Things (IIoT) systems requires a robust fault detection mechanism, a task effectively accomplished by leveraging Artificial Intelligence (AI). However, the current centralized learning approach proves inadequate. In response to this limitation, Federated Learning (FL) enables decentralized training, ensuring the protection of individual data. The traditional FL settings are not sufficient to provide an effective learning process, which needs to be refined. This paper introduces an Adaptive Distributed Client Training (ADCT) mechanism designed to optimize performance for each FL participant, thereby establishing an efficient and resilient system. The proposed ADCT utilizes two parameters, namely the accuracy threshold and grid search step, to find the optimal hyperparameter for each client in a specific number of federation rounds. The evaluation results, conducted using the MNIST and FMNIST datasets in non-IID settings, indicate that the proposed ADCT enhances the F1-score by up to 37.13% compared to state-of-the-art methods.
The Mediating Role of Perceived Value in the Relationship Between Brand Image and Repurchase Intention: A Case Study of the Chinese Tea Market Luo, Rui; Sriboonlue, Umawasee; Onputtha, Suraporn
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.v5i2.227

Abstract

This study is dedicated to exploring how brand image influences repurchase intention through perceived value in the Chinese tea market. Five randomly selected tea brands among the top twenty tea brands in Sichuan Province were chosen as the study sample. Corporate image, product image and user image were used to measure brand image; meanwhile, functional value, emotional value, social value and price value were used to assess perceived value; and repurchase intention was directly measured through questionnaires. Six hundred valid questionnaires from consumers of these five brands were collected through the Questionnaire Star platform and analyzed by structural equation modeling using SMARTPLS 4.0 software. The results show that brand image has a significant positive effect on perceived value and repurchase intention, and perceived value plays a significant mediating role between brand image and repurchase intention. These findings not only enrich the theoretical framework, but also provide practical strategic recommendations for brand management in the Chinese tea market, emphasizing the need to pay attention to the impact of brand image on consumer repurchase intention through perceived value in the process of brand image construction and management, so as to enhance consumer loyalty and promote sustained purchase behavior.
Predicting Financial Failure in Algerian Public Insurance Companies Using the Kida Model El Bachir, Morkane Mohamed; Mili, Khaled; Bengana, Ismail; Benaouali, Imane
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.v5i2.212

Abstract

This study evaluates the effectiveness of financial ratios in predicting the financial health of Algerian public insurance companies using the Kida model, a robust tool for identifying potential financial failure and bankruptcy risks. The primary objective is to assess the predictive power of the Kida model for early detection of financial failure. The research employs a case study approach, analyzing financial statements from three major public insurance companies in Algeria (SAA, CAAR, CAAT) over the period from 2015 to 2021. Key contributions include a comprehensive analysis of financial ratios such as profitability, solvency, liquidity, and management efficiency, and their integration into the Kida model. The methodology involves a detailed examination of financial data, application of the Kida model, and interpretation of the financial failure index. Our findings reveal that the Kida model accurately predicts financial failure risks, with all values of the financial failure index being negative, indicating potential vulnerability. The study underscores the importance of early detection systems and proactive financial management to ensure stability and sustainability in the insurance sector. The results have significant implications for policymakers and stakeholders, emphasizing the need for tailored financial failure prediction models for the Algerian insurance industry. Future research could expand on this work by incorporating real-world data and exploring other predictive models to enhance accuracy and reliability.
Applied Regression Modelling to Recommend Sustainable Tourism Development Policies: A Case Study of Danang City in Vietnam Tien, Nguyen Duc
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.v5i2.188

Abstract

Sustainable tourism development is one of the top focuses not only in Vietnam but also around the world. Especially when Covid-19 began to break out in early 2020, causing most tourism activities to stop and causing many communities that considered tourism to be completely independent from other industries to have time to look back. Thus, the article aims to identify the main factors contributing to sustainable tourism development in Danang City, Vietnam. Accordingly, some contents need to clarify factors affecting sustainable tourism development, measuring the characteristics and the role of each factor impacting sustainable tourism development. Finally, policy implications for sustainable tourism development in the future are proposed. Based on the goal, the author surveyed 600 tourists and used the regression modeling method from data processed using SPSS 20.0. In addition, the results showed six factors affecting sustainable tourism development with a significance of 0.01. This situation requires more practical recommendations from state management agencies and tourism enterprises. The contributions of this study suggest sustainable tourism development policies to balance economic development, social stability, and environmental protection. The article evaluates sustainability, considering the cohesion and balance of sustainable development aspects, such as economic, social, and environmental, thereby going deeper and identifying groups of activities. The main drivers to achieve sustainable tourism development include economics, society, environment, tourism resources, tourism products and services, and infrastructure. The research novelty discusses proposed six recommendations, which include (1) infrastructure development, (2) developing technical facilities for the tourism industry, (3) developing human resource training, (4) level of organization and management of the tourism industry, (5) quality of tourism services, and (6) community participation for sustainable tourism development in Danang City, Vietnam.
Machine Learning Techniques for Diabetes Prediction: A Comparative Analysis Abdelhafez, Hoda A.; Amer, Abeer A.
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.v5i2.219

Abstract

Diabetes mellitus, characterized by chronic hyperglycemia, presents significant challenges due to its associated complications and increasing morbidity rates. This study examines a range of machine learning algorithms such as Naïve Bayes, Decision Tree, Logistic Regression, Random Forest, Neural Network, Support Vector Machine, LogitBoost, and Voting classifier to develop accurate predictive models for diabetes. The data used in this research is drawn from a comprehensive dataset available on mendeley.com, sourced from the laboratory of Medical City Hospital in Iraq. The focus of the study is on feature selection and evaluation metrics to effectively gauge model performance. Eight classification techniques are employed and compared, including Decision Trees (DT), Random Forests (RF), and LogitBoost. The study's findings highlight DT and RF as the top-performing algorithms, demonstrating comparable predictive abilities, with LogitBoost also showing promising results. Conversely, Support Vector Machine (SVM) shows reduced performance due to its sensitivity to outliers. These insights enable healthcare practitioners to adopt appropriate machine learning methods to improve diabetes prediction, thus enabling timely interventions and enhancing patient outcomes.
The Design of IoT-based Business Process for SME Digital Transformation: a Case of Unofficial Car Service Workshop Widjaja, Albertus Hendrawan; Gunawan, Gunawan
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.v5i2.247

Abstract

The Internet of Things (IoT) offers innovation processes that transform industry, finance, healthcare, agriculture, hospitality, and other sectors through process automation. Integrating IoT into business processes will transform an organization for better time, cost efficiency, and customer satisfaction. While the advantages of adopting IoT in the business process are widespread, the clear guidelines for implementing IoT for SMEs are limited. SMEs often do not recognize the potential of digital transformation and do not receive the necessary assistance to undertake critical development activities. This paper addresses this issue by focusing on IoT solutions for a car service workshop as an SME. This study aims to analyze the current business processes and design an IoT-based business process model for a car service workshop. The system development life cycle was adopted partially to design IoT-based business processes. The proposed business process model is designed with Business Process Modeling Notation (BPMN) to minimize time, effort, and cost inefficiencies. The concept and design of IoT systems were validated by the manager of five car service workshops. They perceived the transformation of car service using IoT as innovative, potentially increasing their business competitiveness. The respondents suggested that the implementation was executed gradually because of human resource readiness and investment costs. The design of IoT-based business process could become the guideline for car service workshops to transform the business into Industry 4.0.
Cognition-Based Document Matching Within the Chatbot Modeling Framework Jatmika, Sunu; Patmanthara, Syaad; Wibawa, Aji Prasetya; Kurniawan, Fachrul
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.v5i2.209

Abstract

The aim of the study is to examine cognitive methods for document matching in a chatbot modeling framework by utilizing Euclidean Distance, Cosine Similarity, and BERT methodologies. Five primary indications are used to carry out evaluation in testing: document matching accuracy, document matching execution time, document search efficiency, consistency of document matching results, and the quality of the document representation in the matrix. Document matching accuracy is evaluated by precision; document matching execution time is measured from the beginning to the end of the document matching process; document search efficiency is measured through evaluation of execution time and matching accuracy; the consistency of document matching results is assessed by comparing method results when tested against the same or similar queries and the quality of document representation is assessed based on the method's ability to represent documents in a matrix or vector. The test findings offer a comprehensive understanding of how well the three approaches operate and exhibit their capacity to address the unique requirements of chatbot users. These results may contribute to the advancement of language technology applications, making it possible for chatbots to deliver pertinent information more rapidly and precisely. There are 1,755 labeled question samples in the dataset, which were split up into two sets: 60% for training (1,053 pieces), and 40% for testing (702 samples) to evaluate the model's performance. The test results show the accuracy of the three methods based on five measured evaluation indications, namely Euclidean Distance 0,45%, Cosine similarity 0,59%, and BERT 0,91%.  By comprehending the benefits and drawbacks of each approach, this research strengthens contributions to the growth of chatbot systems to better serve user demands and opens the door for the creation of more complex human-machine interaction solutions.
Forms and Field Trials of a Digital Evaluation Tool: Integrating F-S Model, WP Method, and Balinese Local Wisdom for Effective E-Learning Ariawan, I Putu Wisna; Sugandini, Wayan; Ardana, I Made; Sugiharni, Gusti Ayu Dessy; Gama, Adie Wahyudi Oktavia; Divayana, Dewa Gede Hendra
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.v5i2.201

Abstract

This study purposed to show the tool display and the results of field trials on the digital evaluation tool. This tool is an evaluation tool in digital format which was from a combination of the concept of the educational evaluation model “F-S (Formative-Summative)”, the decision support system method “WP (Weighted Product)”, and Balinese local wisdom “TP (Tri Pramana)”. The importance of combining these concepts and methods is it makes it easier to obtain accurate calculation results following the needs of evaluation tools to determine the dominant aspects determining the effectiveness of e-learning. This research approach was development research. The development model was Borg and Gall, which focused on the field trial and field trial revision stages. The reason for focusing on those two stages was that we wanted to know how effective the evaluation tool was in getting the dominant aspects determining the effectiveness of e-learning. The research location was at several health colleges in Bali. Field trials data collection was using a measuring instrument in the form of a questionnaire. The respondents who were involved in conducting field trials were 54 people. Data analysis on the results of field trials was comparing the results of field trials with the standard effectiveness of five’s scale. The results of this study show that the appearance of the digital evaluation tool and the percentage of its effectiveness through field trials was 81.73%, so the tool was categorized as good. The impact of this research on informatics observers/informatics experts is that they will know an innovative evaluation tool used to determine the dominant aspect determining the effectiveness of e-learning based on decision support system methods and Balinese local wisdom.

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