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Researchers' information needs in the bibliographic database: A literature review

information Services and Use 2014年第3-4期34卷 241-248页

作者： De Andrade, Morgana Carneiro Baptista, Ana Alice Doctoral Program in Technology and Information Systems University of Minho Guimarães Portugal Algoritmi Research Center Information Systems Department University of Minho Guimarães Portugal

This article presents a literature review whose aim was to identify the reported information needs of researchers when they consult bibliographic databases. Initially, 192 articles were retrieved using Scopus, Web of Science and Google Scholar databases. After applying the criteria for exclusion, the number of articles was reduced to 16, which is already an indicator of the small number of studies on this specific topic. The results show that it is hard to identify the information needs of researchers. They also show that the researchers have been requiring information with a higher degree of granularity. We conclude that although the available studies provide important information about the researchers' information needs and hints on how to address them, there is a need for more in-depth studies. The results of these deeper studies may be useful to serve as an indication for the creation of new procedures and tools, including those based on new metadata elements drawn to improve search results on Linked Open Data tools. © 2014-IOS Press and the authors.

关键词： Database systems

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Classification of Land Cover from Sentinel-2 Imagery Using Supervised Classification Technique (Preliminary Study) 3

Classification of Land Cover from Sentinel-2 Imagery Using S...

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3rd International Conference on information Management and technology, ICIMTech 2018

作者： Miranda, Eka Mutiara, Achmad Benny Ernastuti Wibowo, Wahvu Catur Information Systems Department Doctoral Program in Information Technology Gunadarma University School of Information Systems Bina Nusantara University Jakarta Indonesia Faculty of Computer Science and Information Technology Gunadarma University Jakarta Indonesia Computer Science Faculty Universitas Jakarta Indonesia

ISBN: (纸本)9781538658215

This paper intended to classify land cover of high-resolution satellite image using supervised classification method. The object of this research was the land cover image of the central Java area in Indonesia which chosen as an area observed with the cloud cover consideration. The Satellite image was obtained from Sentinel-2 through https://***. Supervised Classification and ArcMap 10.5 was used to classified image object. This research classified the land cover into four classifications class namely Water, Forest, Urban and Bare Land due to show the main classes in the Land Cover Classification of RSNI-l National Standardization Body of Indonesia, RSNI-l was used as reference for classification process. The confusion matrix used to calculate classification accuracy value. Subsequently, confusion matrix results compared to the truth information. The truth information was derived from the actual value of RASTERVALUE obtained from Google Earth. This research shown supervised classification maximum-likelihood classification has been classified land cover into four class (forest, water body, urban and bare land) with overall accuracy = 1 and kappa value = 0.4896. This result showed this research got moderate kappa accuracy value but high overall accuracy value. High accuracy value reached due to fully supervised experiment during classification process. © 2018 IEEE.

关键词： Standardization

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Examining Gen-Z Intention to Participate in Deposit Refund systems: A Convergent Mixed-Method Study in Indonesia 7

Examining Gen-Z Intention to Participate in Deposit Refund S...

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7th International Conference on Vocational Education and Electrical Engineering, ICVEE 2024

作者： Gunawan, Muhamad Rachmat Samopa, Febriliyan Mukhlason, Ahmad Institut Teknologi Sepuluh Nopember Doctoral Program in Technology Management Interdiciplinary School of Management and Technology Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Information Systems Surabaya Indonesia

ISBN: (纸本)9798331505103

The rising environmental challenges in waste management have prompted an urgent need for effective recycling initiatives. This study investigates the intention of Generation Z (Gen-Z) in Indonesia to participate in a Deposit Refund System (DRS), a system aimed at promoting recycling behaviors. Using a convergent mixed-method approach, quantitative data from 228 respondents were analyzed via Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess relationships between intention, distrust, environmental concern, and behavioral intention. Qualitative data analysis, employing vector embedding and k-means clustering, revealed key patterns and themes in Gen-Z's attitudes. Integration of qualitative and quantitative results through PLS-SEM provided a comprehensive understanding of Gen-Z motivations towards environmental sustainability. The results show that environmental concern positively impacts behavioral intention, while distrust was found to be statistically insignificant. This study offers practical and academic insights for policymakers to design more effective DRS initiatives. Future research could explore the long-term behavioral shifts resulting from DRS participation and the role of digital platforms in enhancing engagement. © 2024 IEEE.

关键词： Deposit Refund System Distrust Environmental Concern Waste Management

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Navigating Consumer Skepticism: Conceptual Model of Distrust Detection and Recognition technology Adoption Among Online Shop Owners in Indonesia 1

Navigating Consumer Skepticism: Conceptual Model of Distrust...

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1st IEEE International Symposium on Consumer technology, ISCT 2024

作者： Gunawan, Muhamad Rachmat Samopa, Febriliyan Mukhlason, Ahmad Doctoral Program in Technology Management School of Management and Technology Institut Teknologi Sepuluh Nopember Surabaya Surabaya Indonesia Institut Teknologi Sepuluh Nopember Surabaya Department of Information Systems Surabaya Indonesia

ISBN: (纸本)9798350365191

Background: Consumer skepticism affects trust between online shoppers and shop owners in digital commerce, necessitating effective distrust detection mechanisms. This study presents a conceptual model aimed at promoting the adoption of distrust detection and recognition technology among online shop owners, focusing on technologies powered by Large Language Models (LLMs). The model builds on Taylor's Decomposed Theory of Planned Behavior (DTPB) and incorporates factors relevant to online shop owners' perceptions of consumer distrust. Method: A quantitative research approach was used to survey 100 online shop owners to validate the model empirically. Results: The results indicate a significant effect of Perceived Usefulness (PU) and Social Influence (SI) on intention to adopt the technology. For small businesses such as online shops, PU (an internal factor) and SI (an external factor) can predict owners' intention to adopt distrust detection technology. Implications: This study highlights a simplified method for measuring small business owners' intention to adopt advanced information technologies and suggests business opportunities for e-commerce platforms to strategically position online shop owners. © 2024 IEEE.

关键词： Marketplaces

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Purchasing Intention of Tour Package Brands in Access to Digital technology and User Generated Content on Bali Travel Tour 1

Purchasing Intention of Tour Package Brands in Access to Dig...

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1st IEEE International Symposium on Consumer technology, ISCT 2024

作者： Raka Novandra Asta, Ngr. Putu Suryani, Erma Hari Ginardi, Raden Venantius Interdisciplinary School of Management Technology Institut Teknologi Sepuluh Nopember Surabaya Surabaya Indonesia Politeknik NegeriBali Doctoral Program in Technology Management Bali Indonesia Institut Teknologi Sepuluh Nopember Surabaya Department of Information Systems Surabaya Indonesia

ISBN: (纸本)9798350365191

Digital technology is currently a vital medium to influence consumers with diverse behaviours and a high level of sensitivity when purchasing goods and services. The purpose of this study is to analyse the impact of exposure to digital technology and user-generated content on the brand purchase intention of travel packages in the Bali tourism sector. As digital technology changes the way we communicate or convey information to attract consumers, one of the methods used to attract consumers is user-generated content (UGC) to create a more intimate engagement between products, services, and consumers. A total of 200 tourists who bought tour packages were used as respondents using accidental sampling. The results of the study explain that digital technology and user integration center have a significant influence on brand purchase intention, and brand exposure and brand familiarity have a considerable positive influence on brand purchase intention. A different influence is found: brand knowledge and attitude have a negative impact on brand purchase intention. The results of this study can enrich knowledge about the currently developing topic of e-marketing aimed at academics. From a practical perspective, this research can provide marketers with insight into the effectiveness of content as an advertising platform in the travel and tourism industry. © 2024 IEEE.

关键词： Leisure industry

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Integrating System Dynamics and Random Forest Algorithm for Sales Forecasting 1

Integrating System Dynamics and Random Forest Algorithm for ...

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1st IEEE International Symposium on Consumer technology, ISCT 2024

作者： Magdalena Rafu Mamulak, Natalia Suryani, Erma Dwi Trijoyo Purnomo, Jerry Institut Teknologi Sepuluh Nopember Interdisciplinary School of Management and Technology Doctoral Program in Technology Management Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Information Systems Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Statistics Surabaya Indonesia

ISBN: (纸本)9798350365191

This study proposes an approach to improve sales forecasting accuracy by integrating System Dynamics (SD) modeling with the Random Forest (RF) algorithm. The combination of these two methods offers a promising way to make sales predictions more reliable, especially in uncertain market conditions. Using SD principles within the RF algorithm helps us simulate expected sales growth, gain insights into market dynamics, and make our forecasting model more understandable to stakeholders. This study using synthetic sales data shows that the integrated approach is offered as an open idea and customizable, particularly in scenarios with dynamic growth patterns and feedback effects. © 2024 IEEE.

关键词： machine learning marketing random forest sales forecasting simulation system dynamics

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7th International Conference of Computer and Informatics Engineering, IC2IE 2024

作者： Wandy, Wandy Adi, Kusworo Ayu, Media Anugerah Diponegoro University Doctoral Program of Information Systems School of Postgraduate Studies Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Physics Semarang Indonesia Sampoerna University Faculty of Engineering and Technology Department of Computer Science South Jakarta Indonesia

ISBN: (纸本)9798331505530

Full-marathon and Half-marathon distances are categorized as road running. Full-marathon running is becoming increasingly popular, and Half-marathon is increasing worldwide in both sexes and all age groups. Some aspects might relate to Full-marathon and Half-marathon running performance during training and races. technology also plays an essential role in supporting runners and running races. technology like artificial intelligence (AI) now supports the running athlete, not only predicting performance and results. It can also be used later to help the coach generate training programs for the athlete. This research aimed to find many aspects of marathons and performance and analyze them to see if artificial intelligence could later support them. It used secondary data and a systematic literature review proposed by Kitchenham. Out of the 58 articles, 21 of them (36.21%) received a score of 1 from Q1. Additionally, 19 articles (32.76%) received a score of 1 from both Q2 and Q3. Among the 58 articles, 9 (15.52%) received a total score of 3, with all three Q1, Q2, and Q3 scores being 1. This indicates that artificial intelligence will likely support the content of these nine articles. Several factors were also discovered to be connected to marathons and athletic performance. These findings suggested that additional investigation into marathons and performance, later backed by artificial intelligence, remained pertinent and essential. © 2024 IEEE.

关键词： artificial intelligence aspects marathon performance

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Air Particulate Matters Auto-Rule-Based Labeling to Support Long-Distance Run Environment Data Classification 13

Air Particulate Matters Auto-Rule-Based Labeling to Support ...

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13th IEEE Symposium on Computer Applications and Industrial Electronics, ISCAIE 2023

ISBN: (纸本)9798350347319

Sports Science is an interdisciplinary and multidisciplinary science that strives to increase athletic performance and endurance. Sport Science recognizes and prevents injuries. Sensors and statistics formalize Sports Science. Runners need coaches and teams to support them before, during, and after the run race. Coaches generate running training plans to boost performance. Running race performances may be impacted by air pollution exposure while training, so coaches should consider limiting air pollution exposure when training. One of the external factors is Particulate Matter (PM2.5 and PM10). Sensors connecting to the Internet can record external factors and produce csv data. The foundation of supervised machine learning is the labeling process. Labeling a set of data is one of the laborious and time-consuming phases in every machine-learning application because it requires verifying the accuracy of the labels and making any necessary revisions. This research aimed to find a solution to automatically label numerous air particulate matter raw data using a rule based on parameters to reduce manual work, human errors and faster processes. This labeled data will later be used for supervised machine learning classification to support the coach in generating training programs for the runners in a Sports information System. Based on Indonesia Air Quality index rule-based approach, labeled text data in csv has been generated and tested with PM2.5 and PM10 parameters in three scenarios with a 100% success rate. It was possible to automate the labeling process, and it explained how automation results in fast and accurate results. © 2023 IEEE.

关键词： Classification (of information)

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Rice Variety Identification Using Edge Feature Extraction and Texture-Based Segmentation

Rice Variety Identification Using Edge Feature Extraction an...

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2023 International Seminar on Application for technology of information and Communication, iSemantic 2023

作者： Setiawan, Aji Adi, Kusworo Widodo, Catur Edi Diponegoro University Doctoral Program of Information Systems School of Postgraduate Studies Semarang Indonesia Faculty of Engineering Darma Persada University Department of Information Technology Jakarta Indonesia Diponegoro University School of Postgraduate Studies Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Physics Semarang Indonesia

ISBN: (纸本)9798350339215

This research proposes a methodology for identifying the optimal feature combination using Support Vector Machine (SVM) based on edge and texture features. Canny, Sobel, and Prewitt for edge detection and GLCM, Gabor, and LBP for texture analysis are popular feature extraction techniques widely employed in various cases. The approach combines edge and texture features with SVM to classify rice types into five categories: Ipsala, Jasmine, Karacadag, Arborio, and Basmati. Experimental results demonstrate that SVM using Canny, Sobel, and Prewitt combined Gabor texture features outperforms the comparative methods. A comparative analysis based on accuracy scores using confusion matrices is conducted. Combining all feature edge methods, Canny, Sobel, and Prewitt with Gabor on SVM classification is the best model for rice object classification, achieving an accuracy of 96%. © 2023 IEEE.

关键词： Support vector machines

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Classification of Land Cover from Sentinel-2 Imagery Using Supervised Classification Technique (Preliminary Study)

Classification of Land Cover from Sentinel-2 Imagery Using S...

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International Conference on information Management and technology (ICIMTech)

作者： Eka Miranda Achmad Benny Mutiara Ernastuti Wahvu Catur Wibowo Information Systems Department Doctoral Program in Information Technology Gunadarma University Jakarta Indonesia Faculty of Computer Science and Information Technology Gunadarma University Jakarta Indonesia Computer Science Faculty Universitas Jakarta Indonesia

This paper intended to classify land cover of high-resolution satellite image using supervised classification method. The object of this research was the land cover image of the central java area in Indonesia which chosen as an area observed with the cloud cover consideration. The Satellite image was obtained from Sentinel-2 through https://***. Supervised Classification and ArcMap 10.5 was used to classified image object. This research classified the land cover into four classifications class namely Water, Forest, Urban and Bare Land due to show the main classes in the Land Cover Classification of RSNI-l National Standardization Body of Indonesia, RSNI-l was used as reference for classification process. The confusion matrix used to calculate classification accuracy value. Subsequently, confusion matrix results compared to the truth information. The truth information was derived from the actual value of RASTERVALUE obtained from Google Earth. This research shown supervised classification maximum-likelihood classification has been classified land cover into four class (forest, water body, urban and bare land) with overall accuracy = 1 and kappa value = 0.4896. This result showed this research got moderate kappa accuracy value but high overall accuracy value. High accuracy value reached due to fully supervised experiment during classification process.

关键词： Satellites Forestry Training Java Probability Standardization Vegetation mapping

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