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Husni Teja Sukmana
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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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Design of Green City with Lower Carbon based on Vegetation in Banjarbaru using Sentinel-2 Nirwana, Hanifah Dwi; Saidy, Akhmad Rizalli; Hatta, Gusti Muhammad; Nugroho, Agung
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.v5i2.218

Abstract

In addressing the pervasive issue of Urban Heat Islands (UHI) and the related carbon sequestration challenges in urban settings, this study utilizes Sentinel-2 imagery to propose a vegetative blueprint for the design green city with lower carbon in Banjarbaru. This research intricately links the role of increased vegetation cover in mitigating UHI effects and enhancing carbon absorption in urban environments. By employing a combination of Geographic Information Systems (GIS), field data, and real-time data via Wireless Sensor Networks (WSN), the study highlights the significant cooling and environmental benefits of strategically increasing green spaces in urban areas. Moreover, the study identifies specific zones within Banjarbaru that are optimal for the strategic placement of vegetation to maximize thermal comfort and carbon storage. This focus on localized green infrastructure development not only provides a pathway to more sustainable urban living conditions but also serves as a model for other cities facing similar ecological and climatic challenges. The integrated approach adopted here emphasizes continuous monitoring and dynamic adjustments in urban planning, ensuring long-term sustainability and resilience against the ongoing threats posed by climate change and urban expansion.
Elements Influencing the Caliber of Electronic Logistics Services in Vietnam Tan, Trinh Le; Tran, Tung 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.v5i3.277

Abstract

This study investigated the factors influencing the quality of electronic logistics (E-logistics) services in Da Nang, Vietnam, aiming to assist service providers in improving their operations. The purpose of the study was to identify key determinants of E-logistics service quality to provide actionable insights for service enhancement. The research utilized both qualitative and quantitative methods, starting with qualitative research involving interviews with 20 managers to refine the scale and survey questions tailored to the Vietnamese context. Subsequently, quantitative analysis was conducted using data from 196 respondents, evaluated through linear regression models. The findings revealed six key factors significantly impacting E-logistics service quality in Da Nang: customer perception, technology and security, legal infrastructure, intellectual property and consumer protection, electronic payment systems, and human resources. Together, these factors explained 80% of the variance in E-logistics service quality. Among these, human resources (β=0.464, p0.001) and customer perception (β=0.226, p0.001) were found to be the most influential, followed by technology and security (β=0.143, p0.05), electronic payment systems (β=0.125, p0.05), legal infrastructure (β=- 0.016, p0.05), and intellectual property and consumer protection (β=0.046, p0.05). The reliability of the measurement scales was high, with Cronbach's alphas ranging from 0.832 to 0.857. The novelty of this research lies in its comprehensive analysis of E-logistics service quality within the context of an emerging market, providing valuable insights for both academic and practical applications. The results underscore the importance of businesses adapting their strategies in response to digital transformation to enhance service quality and meet evolving customer expectations. Future research should focus on longitudinal studies to assess the impact of these factors over time and explore additional variables that may influence E-logistics service quality in different regional contexts. 
Coverless Text Information Hiding Based on Built-in Features of Arabic Scripts Rashid, Sabaa Hamid; Nasrawi, Dhamyaa 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.v5i2.243

Abstract

Text steganography is crucial in information security due to the limited redundancy in text. The Arabic language features offer a new method for data concealment. In this paper, the researchers propose a new coverless text information hidingmethod based on built-in features of Arabic scripts. The first word of each row in the dataset is tested based on eight features to get one byte containing 1 or 0. That is a result of the presence or absence of the following features: mahmoze, diacritics, isolated, two sharp edges, vowels, dotted, looping, and high frequency.Then, each byte is converted to a decimal number (ASCII code) to implement a dynamic mappingprotocol with the most frequent letter.In the hiding process, each character in the secret message is converted to ASCII code and successfully matched in the dataset. Thus, after matching, the candidate text is sent to the receiver. In contrast, the pre-agreed dynamic mappingprotocol was implemented in a receiver to extract secret messages. Three Arabic datasets are used in this paper (SANAD (Single-Label Arabic News Articles Dataset) includes 45500 articles, Arabic Poem Comprehensive Dataset (APCD) contains 1,831,770 poetic verses in total, Arabic Poetry Dataset contains more than 58000 poems). The suggested approach withstands existing detecting methods because of no modification or generation. Moreover, there is an enhancement in hiding capacity, which can conceal a (character /word). Hence, all the messages are embedded successfully using dynamic mapping.
Policy Optimization Recommendation Algorithm Based on Mapping Network for Behavior Enhancement Shan, Linlin; Jiang, Guisong; Li, Shuang; Zhao, Shuai; Luo, Kunjie; Zhang, Long; Li, Yi
Journal of Applied Data Sciences Vol 3, No 3: SEPTEMBER 2022
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v3i3.67

Abstract

The algorithm of policy optimization with learning behavior enhancement based on mapping network technology was proposed, aiming to address the issues of lack and sparsity of learning behavior data and weak generalization ability of the model in AI education. Based on the basic recommendation algorithm and the framework of rein- forcement learning, and model introduces the correlation mapping network to realize the transformation of strong and weak correlation, so as to optimize the input agent policy to improve the performance model of course recommendation. Experiment on MOOC da- tasets show that the proposed algorithm model has a stable improvement compared with the baseline models, and can effectively improve the accuracy of course recommendation.
A Multilingual Corpus for Panic and Worry in Code-Mixed Tweets by VADER Sentiment Analysis Abdul Rashid, Razailin; Ab Hamid, Siti Hafizah; Fahmi, Faisal
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.v5i3.259

Abstract

The phenomenon of code-mixing in online discourse, on platforms such as X, offers an interesting setting to detect preliminary markers of anxiety within diverse linguistic expressions. The usage of more than one language within a single text or tweet necessitates the creation of a multilingual corpus to identify initial indicators of anxiety in code-mixed texts or tweets, contributing to a comprehensive understanding of mental health in the digital age. Existing research on code-mixed textual context primarily centres on code-mixed language of English with Spanish or Hindi, leaving a gap in our comprehension of other code-mixed languages, in particular; English with Malay or Indonesian language. Thus, our study focuses on anxiety-related linguistic expressions in Malay and Indonesian languages, such as ‘bimbang’, ‘bingung’, ‘panik’, ‘gelisah’, ‘cemas’, ‘takut’, ‘kacau’, ‘gemetar’, ‘gugup’, ‘teror’ and occasionally the usage of slangs such as ‘neves’, ‘gabra’, and ‘cape bgt’. In this paper, we introduce CORPUS4PANWO, an annotated sentiment-driven multilingual corpus for panic and worry detection in tweets. To experiment the corpus, we applied a corpus-based sentiment analysis utilizing VADER on diverse events, achieving accuracy of between 76.6% - 88.0% when used on tweets in negative circumstances. The corpus is a valuable resource for Southeast Asian linguistics, enabling exploration of emotional expression in diverse contexts.
Modeling Ramadan Hilal Classification with Image Processing Technology Using YOLO Algorithm Anggraini, Nenny; Zulkifli, Zulkifli; Hakiem, Nashrul
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.v5i3.311

Abstract

This research aims to create a model for classifying hilal using the YOLO algorithm. The determination of the beginning of the month of Ramadan is an important aspect of the Islamic calendar that has an impact on the implementation of fasting. With technological advances, especially in image processing, there is potential to overcome the limitations of conventional methods currently used in hilal detection for determining the beginning of Ramadan. This research uses the prototyping method in its implementation. The dataset in this research comes from videos on the BMKG Youtube channel and images from various sources such as NASA Planetary Data System and Google Images. YOLOv5 and YOLOv8 algorithms are used to develop the object detection model. The novelty of this research is the use of the YOLO algorithm with video datasets to detect hilal to determine the beginning of the month of Ramadan and Shawwal. The best-performing model, YOLOv5m with 100 epochs and a batch size of 30, achieved a precision of 0.838 and a mAP of 0.5-0.95 of 0.735. The results indicate that YOLOv5m is more effective in hilal detection, providing a novel approach to determine the beginning of Ramadan and Shawwal with greater accuracy and consistency. This integration of advanced object detection technology with religious practice offers a significant improvement over traditional method.
Performance Improvement of Covid-19 Cough Detection Based on Deep Learning with Segmentation Methods Suyanto, Suyanto; Zanjabila, Zanjabila; Atmaja, Bagus Tris; Asmoro, Wiratno Argo
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.v5i2.205

Abstract

COVID-19 is an emergency problem that is being widely discussed in the world, one of which is the deep learning-based COVID-19 detection method which has been developed based on images of the patient's chest or cough. In this research, we propose a way to improve the performance of deep learning-based COVID-19 cough detection by using a segmentation method to produce several audio files containing one cough signal from one audio file containing several cough sound signals. In addition, we enabled two automatic cough segmentation methods, namely a Hysteresis Comparator based on the power spectrum and an RMS threshold based on the RMS energy value. The results obtained show that using the segmentation method for cough sounds can improve the model's performance in detecting COVID-19 coughs by 4% to 8%. The segmentation process can also remove noise between cough sound signals and provide a standard input model in the form of one cough signal. In addition, the segmentation results show information related to the characteristics of COVID-19 cough. The evaluation results show that the hysteresis comparator method has better results with an unweighted accuracy (UA) value of 83.19% compared to the RMS threshold method with a UA value of 79.06%.
The Antecedents Affecting the Job Performance of Private Enterprises Li, Xiang; Tresirichod, Teetut
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.v5i3.265

Abstract

This study explores the antecedents that affect employee job performance (JP) in private enterprises: organizational culture (OC), work-life balance (WLB), and job satisfaction (JS), and reveals the relationship between these variables.The study used quantitative analysis methods and partial least squares structural equation (PLS-SEM) method to conduct descriptive statistics and analysis on questionnaire survey data of 553 private enterprises above designated size in the food industry in Sichuan Province, China, verifying the theoretical framework and hypothesis relationship.The research results found a significant positive correlation between organizational culture and job satisfaction . Job satisfaction positively affects job performance and plays a mediating role between organizational culture and job performance.In addition, Work-Life Balance significantly increased the strength of the relationship between Organizational Culture and Job Satisfaction. These findings not only enrich the application of social exchange theory and resource security theory in theory, but also provide valuable insights for enterprises to formulate human resource policies and management practices, emphasizing the importance of shaping a positive organizational culture and supporting work- life balance in improving employee job performance. However, this study was only conducted in the food industry of private enterprises in Sichuan Province, and an online questionnaire survey was used, which may affect the universality of the research results and the bias of measurement data. Future research should consider a wider range of regions and industries, and adopt longitudinal designs to explore more variables in order to obtain a more comprehensive understanding.
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.
Information Technology Readiness and Acceptance Model for Social Media Adoption in Blended Learning: A Case Study in Higher Education Institutions in West Java, Indonesia Yusuf, Fahmi; Rahman, Titik Khawa Abdul; Subiyakto, Aang
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.v5i2.195

Abstract

Technological developments, including the internet, and learning opportunities are increasing. This also encourages the development of learning strategies and models. The blended learning model is applied in almost all universities in Indonesia and the world. With so many universities in Indonesia, implementing blended learning is a challenging thing because it requires a lot of technological preparation and human resources. This research aims to identify factors, develop a model, and evaluate the model to see the readiness and acceptance of technology for adopting social media in blended learning among private higher education institutes students in Indonesia. The population of this research is students from private higher education institutes in West Java, Indonesia, who are directly involved in using blended learning and social media. This quantitative research used a research instrument with five-Likert’s scale. The research population was 663,307, with a sample of 384 students spread across West Java. The contribution of this research is to make a significant contribution to the theoretical framework by expanding and refining existing concepts, providing a more comprehensive understanding of the readiness and acceptance factors for the adoption of social media in blended learning so that it has the potential to provide information to learning planners at private higher education institutes in West Java, Indonesia to help make the right decisions and optimize blended learning planning using social media technology. These findings statistically explain that 19 of 31 the hypotheses are the accepted ones. Moreover, nine of 12 variables influenced the readiness and acceptance of social media technology in blended learning based on the student perception among the private higher education institutions. They were the technological literacy factor, perceived validity, perceived trust, and technology readiness factors, namely optimism and Innovativeness, and technology acceptance factors, namely perceived effectiveness, perceived easy to use, intention to use and usage behaviour.

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