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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 477 Documents
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Applying Structural Equation Modeling to Explore the Intention to Use Midi Kriing App Sangadji, Suwandi S.; Handriana, Tanti; Wisnujati, Nugrahini Susantinah; Karim, Sarbaini A.
Journal of Applied Data Sciences Vol 5, No 1: JANUARY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i1.157

Abstract

In the rapidly evolving digital landscape, the surge in e-commerce transactions underscores the need for innovative strategies to enhance user satisfaction, trust, and sustainable app usage. This research focuses on the Midi Kriing App, operated by PT Midi Utama Indonesia Tbk, a key player in the e-commerce industry. The study aims to bridge knowledge gaps by investigating factors influencing user intention, specifically e-service quality and e-trust, and their impact on user satisfaction. Employing a quantitative approach with an associative design, data was gathered from 190 Midi Kriing App users in Surabaya, Indonesia. Structural Equation Modeling (SEM), particularly Partial Least Squares (PLS) in SmartPLS, was utilized to explore relationships between variables. Research findings indicate that e-service quality and e-trust significantly impact user satisfaction, with a p-value of 0.00. Similarly, user satisfaction significantly influences the intention to use the Midi Kriing App, with a p-value of 0.00. Among these hypotheses, the statistical t-value of user satisfaction with the intention to use the Midi Kriing App, at 9.871, is higher than the relationship between e-service quality and e-trust with user satisfaction. Nevertheless, these hypothesis tests confirm statistically significant relationships, supporting the reliability and significance of each construct's measurement instruments. In conclusion, this research emphasizes the pivotal role of satisfaction in its relation to e-service quality, e-trust, and the intention to use the Midi Kriing App. Managerial implications stress the importance of enhancing these factors to drive app usage. Improving e-service quality can be achieved through active efforts such as enhancing responsiveness, reliability, and user-friendliness. Similarly, building e-trust involves securing user data and providing a positive user experience.
Spatial Estimation of Relative Risk for Dengue Fever in Aceh Province using Conditional Autoregressive Method Rahayu, Latifah; Sasmita, Novi Reandy; Adila, Wulan Farisa; Kesuma, Zurnila Marli; Kruba, Rumaisa
Journal of Applied Data Sciences Vol 4, No 4: DECEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i4.141

Abstract

Dengue Fever (DHF) is a dangerous infectious disease that can cause death in an infected person. DHF is a disease transmitted by the Aedes Aegypti mosquito. Dengue cases have been reported in 449 districts/cities spread across 34 provinces with deaths spread across 162 districts/cities in 31 provinces, one of which is in Aceh Province. However, there are districts and cities in Aceh Province with a large number of cases and population at risk, and there are also districts and cities with fewer cases and population at risk. As a result, the number of cases and population at risk of DHF varies. Therefore, it is important to do planning to see which districts and cities have a high chance of DHF. In this study, the type of data used is secondary data sourced from the Aceh Provincial Health Profile from 2016 to 2022. The approach used is the Bayesian Conditional Autoregressive (CAR) prior model Besag-York-Mollie (BYM). The results of this study showed that mortality in dengue cases in Aceh Province from 2016 to 2022 had the highest mortality values in 2016 and 2022. The results of estimating the relative risk of DHF cases using the Bayesian Conditional Autoregressive (CAR) approach of the Besag-York-Mollie (BYM) Model in Aceh Province fulfill all categories with their relative risk values. Some districts/cities have relative risk values. Some districts/cities have high relative risk values of DHF cases and low relative risk values of DHF cases. Sabang city had the highest relative risk value of 3.54 and Bener Meriah district had the lowest relative risk of 0.2.
Acceptance of Information Technology Security Among Universities: A Model Development Study Sulhi, Ahmad; Yahaya, Nor Adnan Bin; Subiyakto, Aang
Journal of Applied Data Sciences Vol 4, No 4: DECEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i4.142

Abstract

This study aims to investigate the acceptance model of information technology security among religious higher education institutions in Indonesia, especially focusing on lecturers or lecturers. This study adopts the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model with the addition of additional variables, namely security, privacy, and trust. As reflected in various studies of information systems (IS), many IS models are developed by adopting, combining, and adapting previous models. The researcher in this study developed his model based on input-process-output logic as well as processional and causal models of the information systems (IS) success model. The resulting model has a structure with ten variables and 43 indicators. The relationship between variables is explained through 19 influence links. In addition, in the implementation of the study, the authors break down the model into more detailed assessment instrument levels. Although this model development study may have limitations related to the assumptions used and the researcher's understanding, it has the potential to make a theoretical contribution in terms of the proposition of the new model. In addition, it is important to consider transparency in the development of proposed models and data collection instruments presented as practical points for further research in the context of religious higher education institutions in Indonesia.
A Mixed-Methods Data Approach Integrating Importance-Performance Analysis (IPA) and Kaiser-Meyer-Olkin (KMO) in Applied Talent Cultivation Zhang, Zhang; Sangsawang, Thosporn; Vipahasna, Kitipoom; Pigultong, Matee
Journal of Applied Data Sciences Vol 5, No 1: JANUARY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i1.170

Abstract

This study endeavors to establish an assessment framework for cultivating undergraduate applied talent, specifically emphasizing data science competencies, in alignment with the development of China's regional economy. A mixed-methods approach, integrating focus group interviews and questionnaire surveys conducted over three rounds of data collection, was employed. The collected data underwent rigorous reliability and validity analyses utilizing SPSS software. An Importance-Performance Analysis (IPA) was executed to construct a performance chart, evaluating the effectiveness of a 24-item framework designed to encompass key aspects of data science education. The initial internal consistency α coefficients for Questionnaire 2 and Questionnaire 3 were found to be .892 and .913, respectively, surpassing the 0.7 threshold, indicating a high level of reliability for all items related to data science competencies. The Kaiser-Meyer-Olkin (KMO) measurements approaching approximately 0.9 affirmed the efficiency of the questionnaire, specifically designed to gauge the relevance and effectiveness of data science-related indicators in the context of applied talent cultivation and regional economic development. Furthermore, the study underscores the significance of indicators such as teamwork, regional market research, and business opportunity identification within the domain of data science. It identifies gaps between key indicators and lower-performing indicators, proposing strategic improvement measures to enhance the alignment of applied talent cultivation objectives with the evolving needs of regional economic development, particularly in the data science landscape. The research findings not only contribute to a foundational understanding of data science competencies in applied talent cultivation but also lay the groundwork for innovative reforms in future talent cultivation models. By clarifying objectives and better aligning them with the dynamic demands of regional economic development, this study sets the stage for transformative advancements in the field of applied talent cultivation, particularly within the realm of data science.
Developing the Readiness and Success Model of Information System Implementation in the Indonesian Equestrian Industry Sopandi, Ajang; Yahaya, Nor Adnan; Subiyakto, Aang
Journal of Applied Data Sciences Vol 5, No 1: JANUARY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i1.145

Abstract

This study reports on the incorporation of technology readiness models in information system (IS) success models in the context of assessing readiness factors and the success of information system integration in the equestrian sports industry in Indonesia. As found in several information systems studies, many IS models are developed by adopting, combining, and adapting previous models. Researchers developed this model based on input-process-output logic and processional and causal models of information system success models. The developed model is structured by involving 12 variables and 62 indicators. The path of influence between variables is described by 30 links. In the research implementation stage, the model is also broken down into more detailed assessment instruments. Although these model development studies may have limitations on the assumptions used and the researchers' understanding, they can make theoretical contributions, particularly in terms of proposed new models. In addition, transparency in model development, proposed models, and data collection instruments may also be a practical consideration for advanced research in the context of readiness and successful implementation of information systems in the equestrian sports industry in Indonesia
Machine Learning Classifier Algorithms for Ransomware Lockbit Prediction El Emary, Ibrahiem M. M.; Yaghi, Khalil A.
Journal of Applied Data Sciences Vol 5, No 1: JANUARY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i1.161

Abstract

Advanced virus known as ransomware has been spreading quickly in recent years, resulting in considerable financial losses for a variety of victims, including businesses, hospitals, and people. Modern host-based detection techniques need to first infect the host in order to spot abnormalities and find the malware. When the system is infected, it can already be too late because some of the assets have been exfiltrated or encrypted by the malware. On the other hand, as most ransomware families attempt to connect to command-and-control servers before to executing their damaging payloads, network-based methods can be helpful in detecting ransomware attacks. Therefore, one of the most important methods for early identification can be a detailed examination of ransomware network activity. This study presents a thorough behavioral analysis of the ransomware LockBit. In early 2022, ransomware, particularly targeting data on endpoints in Indonesia, was enough to horrify the news online. LockBit ransomware is one of the ransomwares that is particularly worrisome in Indonesia, so study is required to combat the ransomware. Static and dynamic analyses are used to study the ransomware; the former involves deciphering the portable executable (PE) file, while the latter involves actually running the ransomware. These analyses will reveal the impurity and resolve of the LockBit ransomware. Examine the running operations, the resources utilized, the network activities the ransomware performed, and the effect it had on the impacted operating system to try to build a scenario for preventative measures. The real effects of the ransomware-as-a-service (Raas) attacks conducted by the LockBit ransomware are demonstrated in this research. In this work, we describe an attribute selection-based system for identifying and avoiding ransomware that uses a variety of machine learning techniques, such as neural network-based frameworks, to classify the malware's security grade. We used a range of machine learning approaches, such as Decision Tree-DT, Random Forest-RF, Naive Bayes-NB, and Logical Regression-LR based classifiers, on a selected set of attributes for ransomware detection. The results of the study demonstrate that the Random-Forest predictor outperformed different classifiers by achieving the best accuracy, precision, recall, and F1-Score.
Long short-term memory-based chatbot for vocational registration information services Langgeng, Yudo Sembodo Hastoro; Setiawan, Esther Irawati; Imron, Syaiful; Santoso, Joan
Journal of Applied Data Sciences Vol 4, No 4: DECEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i4.128

Abstract

The development of chatbots can communicate fluently like humans thanks to the Natural Language Processing (NLP) technology. Using this technology, chatbots can provide more accurate and natural responses, providing an almost the same experience as human interaction. Therefore, chatbot technology is in great demand by companies and government agencies as a cost-effective solution for information and administrative services that require little human effort and can operate 24/7. The registration information service at BLK Surabaya still uses an operator who serves prospective trainees and answers questions via social media or chat. However, these operators have limitations in terms of time and effort. The purpose of this study is to examine how to use chatbots to answer questions about registration information training at BLK Surabaya using the Long Short Term Memory (LSTM) algorithm with a dataset of questions collected in the form of Frequently Asked Questions (FAQ) in Indonesian. The dataset consists of 2,636 labeled samples of questions, which were divided into three sets: 60% for training (1,581 pieces), 20% for validation (527 samples), and 20% for testing (528 samples) to evaluate the model's performance. Several steps were taken in implementing this research, including changing the list of questions and answers into the JSON data format, preprocessing, creating LSTM modeling, data training, and data testing. The test results show that Chatbot can provide accurate solutions related to training registration questions with Precision of 88.4%, Accuracy of 87.6%, and Recall of 87.3%.
Data Envelopment Analysis of Scientific Research Performance for Higher Vocational Colleges Zhou, Lin; Boonsong, Sutthiporn; Siramaneerat, Issara; Sangsawang, Thosporn; Sawetmethikul, Pakornkiat
Journal of Applied Data Sciences Vol 5, No 1: JANUARY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i1.166

Abstract

This research aims to evaluate the scientific research performance of higher vocational colleges in Sichuan within the evolving landscape of data science. The study pursues two primary objectives: firstly, to assess the scientific research performance of these institutions using advanced methodologies such as Data Envelopment Analysis (DEA) and the Malmquist index models; secondly, to explore the intricate relationship between scientific research inputs and efficiency through the lens of Rough Set theory. The dataset comprises scientific research inputs and outputs from 30 higher vocational colleges, spanning the years 2019 to 2021. The findings underscore an overall positive trend in scientific research performance across the higher vocational colleges under examination. However, a nuanced analysis using DEA and Malmquist index models identified that only five institutions demonstrated robust performance during the specified period. Furthermore, the study delves into the influential factors affecting scientific research efficiency, revealing that internal expenditure on scientific research funds and the presence of senior and above professional teachers play pivotal roles. These insights are gleaned through the application of Rough Set theory, providing a unique perspective within the realm of data science. In conclusion, the research recommends strategic interventions to improve research management and resource allocation, emphasizing their role in enhancing efficiency and mitigating disparities among higher vocational colleges in Sichuan, particularly in the context of data science. The study adopts a holistic approach, employing an integrated model that combines DEA, Malmquist, and Rough Set theory for a comprehensive evaluation of research performance within the evolving landscape of data science.
Comparative Analysis of SVM and RF Algorithms for Tsunami Prediction: A Performance Evaluation Study Sukmana, Husni Teja; Durachman, Yusuf; Amri, Amri; Supardi, Supardi
Journal of Applied Data Sciences Vol 5, No 1: JANUARY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i1.159

Abstract

This study explores the use of machine learning algorithms, specifically SVM and RF, for predicting tsunamis, a crucial aspect of disaster management. The research utilized earthquake data from 2001 to 2023, evaluating these models based on accuracy, precision, recall, F1-score, and ROC AUC, emphasizing features like magnitude, depth, and alert levels. The SVM model demonstrated an accuracy of 65.61%, precision of 70.59%, recall of 19.67%, F1-score of 30.77%, and ROC AUC of 62.15%. In comparison, the RF model showed an accuracy of 61.15%, precision of 50.00%, higher recall of 36.07%, F1-score of 41.90%, and ROC AUC of 63.84%. These results highlight the distinct strengths of each model: SVM's precision makes it suitable for minimizing false positives, while RF's higher recall indicates its effectiveness in detecting actual tsunamis. The findings underscore the significance of selecting the appropriate model for tsunami prediction based on specific disaster management needs and the inherent trade-offs in model selection. The research concludes that SVM and RF models provide valuable yet distinct contributions to tsunami prediction. Their application should be customized to disaster management requirements, balancing accuracy and operational efficiency. This study contributes to disaster risk management and opens avenues for further research in enhancing the accuracy and reliability of machine learning in natural disaster prediction and response systems.
Analysis of Real Time Twitter Sentiments using Deep Learning Models Alsini, Raed
Journal of Applied Data Sciences Vol 4, No 4: DECEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i4.146

Abstract

Understanding attitudes regarding distinct topics and public opinions on the sentimental analysis of social media data is important. This research analyses the real-time twitter sentiments using deep learning. The major objective of the study is to create an efficient sentiment analysis algorithm to accurately ensure the sentiment polarity (positive, neutral or negative) of tweets. This study proposed a deep learning approach to capture the contextual information and complex patterns in social media data which leverages the power of neutral networks. To assess the performance of the algorithm the study relies on the evaluation of F1 score, accuracy, precision, and recall through rigorous evaluation metrics. The efficiency of the proposed approach is demonstrated by the numerical outcomes of the study. A novel contribution is provided with a specific emphasis on real-time Twitter sentiments by the study to enhance the sentiment analysis techniques for social media data. The significant implication from accurate and timely analysis of Twitter sentiments for several applications includes public opinion tracking, brand management, customer feedback analysis, and reputation monitoring. The potential to provide significant insights to researchers, organisations and business can be made from promptly addressing the sentiments expressed on real time data of twitter.

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