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Implementation of online learning during the COVID-19 pandemic in primary schools

AIP Conference Proceedings 2023年第1期2594卷

作者： A. Anugrahana E. Liswantiani D. Sudigyo T. Suparyanto B. Pardamean Elementary School Teacher Education Study Program (PGSD) Faculty of Teacher Training and Education Sanata Dharma University Yogyakarta Indonesia 55281 Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

The COVID-19 pandemic requires face-to-face learning to shift to online or distance learning. Therefore, the elementary school, which is the most basic level of education affected by this phenomenon, where this research is carried out. This study aimed to analyze the process of implementing online learning, the supporting and inhibiting factors for teachers, students, and parents in implementing online learning during the COVID-19 pandemic. In addition, good practices for online learning were also analyzed in this study to understand these phenomena in elementary schools. The research method utilized descriptive qualitative research and was performed at a private primary school in Yogyakarta. The subjects consisted of teachers, students, and parents that were involved in the online learning process. Online questionnaires and interviews were used to collect data. The results of this study show the enormous impact of the COVID-19 pandemic on the learning process. Learning that is usually carried out face-to-face (conventional) has now been converted into online learning in elementary school.

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Bioinformatics Pathway Analysis Pipeline for NGS Transcriptome Profile Data on Nasopharyngeal Carcinoma

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IOP Conference Series: Earth and Environmental science 2021年第1期794卷

作者： Digdo Sudigyo Gisti Rahmawati Dicka W. Setiasari Risky H. Poluan Tjeng Wawan Cenggoro Arif Budiarto Alam Ahmad Hidayat Sri R. Indrasari Afiahayati Sofia M. Haryana Bens Pardamean Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Study Program of Biotechnology Gadjah Mada University Yogyakarta Indonesia 55281 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Faculty of Medicine Public Health and Nursing Gadjah Mada University Yogyakarta Indonesia 55281 Department of Computer Science and Electronics Faculty of Mathematics and Natural Science Gadjah Mada University Yogyakarta Indonesia 55281 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

Next-Generation Sequencing (NGS)-based genomics data have a huge potential to be used in transcriptomic profiling of Nasopharyngeal Carcinoma (NPC) to study the biosynthesis mechanism behind it. The high dimensionality of NGS data is the main challenge in performing the data analysis to extract useful information. In this workflow pipeline, memory-efficient Linux-based software such as HISAT2 and HTSeq are utilized to process the raw NGS data. Furthermore, Differential Expression Gene (DEG) list can be obtained by performing advanced analysis to the aligned Ribonucleic Acid (RNA) sequence using the edgeR protocol. This DEG list is one of the main inputs of biological pathway analysis that can be done in DAVID and PANTHER web-based software. Both tools generate a different pathway result related to inflammation.

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A Model of Visual Intelligent System for Genus Identification of Fish in the Siluriformes Order

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IOP Conference Series: Earth and Environmental science 2021年第1期794卷

作者： Taufik Budhi Pramono R Ardharyan Islamy Saprudin Joni Johanda Putra Teddy Suparyanto Kartika Purwandari Tjeng Wawan Cenggoro Bens Pardamean Aquaculture Study Program Faculty of Fisheries and Marine Science Jenderal Soedirman University Purwokerto Central Java Indonesia 53123 Aquatic Resources Management Brawijaya University Malang East Java Indonesia Graduate Program Aquatic Resources Faculty of Fisheries and Marine Science Jenderal Soedirman University Purwokerto Central Java Indonesia 53123 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

Ambiguity in the fish naming is present in several fish species database, especially for fish in Siluformes order. To fix the ambiguity, a visual intelligent system is needed to automate the fish naming correction in the database. In this study, we developed a deep-learning-based model as the core of the intelligent system. The proposed model achieved 89% accuracy for the classification of three genera in Siluformes order: Mystus, Hemibagrus, and Glyptothorax.

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computer vision in image detection case study of tree damage type using convolutional neural network (CNN) algorithm

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AIP Conference Proceedings 2024年第1期2970卷

作者： Z. Nopriyanto R. Andrian R. Safe’i Department of Computer Science Faculty of Mathematics and Natural Science Lampung University Jl. Sumantri Brojonegoro No. 1 Bandar Lampung Lampung 35141 Indonesia Forestry Master Study Program Faculty of Agriculture Lampung University Jl. Sumantri Brojonegoro No. 1 Bandar Lampung Lampung 35141 Indonesia

So far, the identification of 16 types of tree damage is still following the guidelines stated in the Forest Health Monitoring method. The types of tree damage can be recognized by human vision, as well as by computers. computer vision allows computers to identify things that humans can remember. In this case, the study can be realized with computer vision to make work easier. This study aimed to identify 16 types of damage in Forest Health Monitoring using image data or photos with computer vision. The stages of this research are image acquisition (image acquisition), image processing (image preprocessing), and feature extraction (feature extraction). The results showed that computer vision could identify images in JPG/JPEG (Joint Photographic Experts) format, assisted by the Convolutional Neural Network (CNN) algorithm. The percentage value of success using the CNN algorithm reaches 99.06%, with an error detection of 0.94%. However, several classes were incorrectly identified, indicating that the dataset needs improvement. Thus, it can be concluded that identifying 16 types of damage using image data or photos with computer vision has been successfully carried out.

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Industrial revolution 4.0 on big data adoption factors on social media users and popularity brands

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Journal of Physics: Conference Series 2021年第1期1839卷

作者： W Sardjono G Rama Putra E Selviyanti A Cholidin G Salim Information Systems Management Department BINUS Graduate Program – Master of Information Systems Management Bina Nusantara University Jakarta Indonesia 11480 Computer Science Study Program Faculty of Mathematics & Natural Sciences Pakuan University Bogor Indonesia Health Department Politeknik Negeri Jember Jember Jawa Timur Indonesia 68124 Law Studi program Faculty of Law University of Muhammadiyah Jakarta Indonesia Post Graduate Program Faculty of Economic Science University of Trisakti Jakarta Indonesia

The purpose of writing this paper is to look at how factors of big data adoption affect the users of social media and brand popularity. The method used in the writing of this paper is by using path analysis and also by doing literature study to find reference material. The results of the analysis indicate that there is influence of big data adoption factors that affect the popularity of the brand. With this research, we get big data model that influence the brand popularity.

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Application of factor analysis method to support the users acceptance model of ERP systems implementation

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Journal of Physics: Conference Series 2021年第1期1836卷

作者： W Sardjono J Sudirwan W Priatna G R Putra Information Systems Management Department BINUS Graduate Program -Master of Information Systems Management Bina Nusantara University Jakarta Indonesia – 11480 School of Information Systems Department BINUS Undergraduate Program Bina Nusantara University Jakarta Indonesia 11480 Informatics Engineering Engineering Bhayangkara University Jakarta Raya Indonesia Computer Science Study Program Faculty of Mathematics & Natural Sciences Pakuan University Bogor Indonesia

An e-Commerce company has been using an Enterprise Resource Planning (ERP) system for several years, but is still constrained in its implementation, this is reflected in the number of issue/change request tickets submitted by users who have not been able to reach ideal conditions. To overcome this problem, we need an appropriate reference model, the ERP system user acceptance model in companies. The study conducted factors that influence the level of acceptance of SAP ERP users in the XYZ group to then determine a model that is able to explain practically the level of ERP system user acceptance, which in turn can produce concrete proposals to increase ERP system user acceptance. Research is quantitative descriptive using factor analysis and regression analysis methods. The research method uses a questionnaire with a total of 107 valid respondents coming from 8 company names under the XYZ group in 6 Southeast Asian countries. The conclusion of the study is that there are 24 indicators and 4 factors that influence user acceptance of the SAP ERP system. These four factors are the low contribution of the ERP system, the quality of ERP systems and data, the development of ERP systems, and also the low quality of ERP system supporters. It is hoped that by optimizing the value of the 4 factors, the XYZ group can increase user acceptance of the SAP ERP system so that the system can provide benefits expected by the company to be competitive advantage.

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Assessment of the implementation of customer relationship management systems to support the competitive advantage of the company

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IOP Conference Series: Earth and Environmental science 2021年第1期747卷

作者： W Sardjono M N F Izrullah E Selviyanti M Mukhlis G R Putra Information Systems Management Department BINUS Graduate Program – Master of Information Systems Management Bina Nusantara University Jakarta Indonesia 11480 Health Department Politeknik Negeri Jember Jember Jawa Timur Indonesia 68124 Faculty of Teacher Training and Education State Islamic Institute of Jember Indonesia Indonesia Computer Science Study Program Faculty of Mathematics & Natural Sciences Pakuan University Bogor Indonesia

The purpose of this study is to look for factors, indicators, and build a model of readiness for effective and efficient implementation in order to answer the problems that arise at PT XYZ related to the readiness of Customer Relationship Management (CRM) implementation. The research method used is the method of collecting data by observing, interviewing, and distributing questionnaires to respondents using the CRM Value Chain theory as a conceptual framework and the research method used in this research is factor analysis to process data obtained from questionnaires. In this study managed to get new factors along with the constituent indicators of these factors as well as an ideal readiness model. It can be concluded that factors related to the readiness of implementing Customer Relationship Management among others are Customer Service Information, Customer Relations Value, Lack of Service and Communication.

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Perception analysis of the Indonesian society on twitter social media on the increase in BPJS kesehatan contribution in the Covid 19 pandemic era

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Journal of Physics: Conference Series 2021年第1期1722卷

作者： F C Permana Z M Wicaksono C Kurniawan A S Abdullah B N Ruchjana Study Program of Multimedia Education Universitas Pendidikan Indonesia Jl. Dr. Setiabudi No.229 Bandung 40154 Indonesia. Student of Master of Management Program Universitas Padjadjaran Jl. Raya Bandung Sumedang km 21 Jatinangor Sumedang 45363 Indonesia. Department of Computer Science Universitas Padjadjaran Jl. Raya Bandung Sumedang km 21 Jatinangor Sumedang 45363 Indonesia. Department of Mathematics Universitas Padjadjaran Jl. Raya Bandung Sumedang km 21 Jatinangor Sumedang 45363 Indonesia.

In the conditions of the Covid-19 Pandemic, the president of Indonesia, Ir Joko Widodo, on May 13, 2020, announced an increase in BPJS Kesehatan contribution. This announcement made everyone in the community boisterous, including Indonesians on social media twitter. Currently, Indonesia is ranked fifth in the world using social media twitter, so data mining on Twitter is a good opportunity to see the public's response to a developing issue. This paper will discuss perceptions analysis of the Indonesian people on social media regarding the issue of increasing the contribution of BPJS Kesehatan. The issue of increasing the contribution of BPJS Kesehatan has been a topic of conversation on Twitter for a long time. After 30 days of data crawling, 145,359 tweets were obtained. This amount of data proves that the Indonesian people are very active in issuing opinions regarding the issue of increasing contribution BPJS Kesehatan. Various kinds of differences of opinion in each conversation are classified into three types of opinion using the Naive Bayes method. The three types of opinions are classified into positive opinions, negative opinions, and neutral opinions. The opinion classification results obtained were 73% containing negative opinions, 18% positive opinions, and 9% neutral opinions. This can serve as an early warning for the government to see the public's response in every policy taken. So that each policy can be evaluated for the better.

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Identification of types of tree damage based on forest health monitoring (FHM) in the management of TAHURA WAR utilization blocks

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AIP Conference Proceedings 2024年第1期2970卷

作者： Rahmat Safe’i Rico Andrian Tri Maryono Sepia Tapasya Tri Rizky Putra Master of Forestry Study Program Forestry Department Faculty of Agriculture University of Lampung Jl. Prof. Dr. Ir. Soemantri Brodjonegoro No. 1 Bandar Lampung Lampung 35145 Indonesia Dept. Computer Science Faculty of Mathematics and Natural Science University of Lampung Jl. Prof. Dr. Ir. Soemantri Brodjonegoro No. 1 Bandar Lampung Lampung 35145 Indonesia Plant Protection Study Program Faculty of Agriculture University of Lampung Jl. Prof. Dr. Ir. Soemantri Brodjonegoro No. 1 Bandar Lampung Lampung 35145 Indonesia Departement of Forestry Faculty of Agriculture University of Lampung Jl. Prof. Dr. Ir. Soemantri Brodjonegoro No. 1 Bandar Lampung Lampung 35145 Indonesia

Regarding management, TAHURA WAR is divided into blocks, one of which is the utilization block. In this block, the community can carry out utilization and use of forest areas. One of the causes of forest use by the community in this block is damage to trees due to high human activities or other concomitant causes. This study aimed to identify the damage to trees based on FHM in the TAHURA WAR utilization block. This research was conducted on five FHM cluster plots of utilization blocks that have been built. Identification is made by measuring and analyzing trees’ location, type, and severity based on the FHM method. The results showed 14 varieties of tree damage from 345 trees. The most common types of damage found were open wounds (code 03) with 162 trees and cancer (code 01) with 78 trees with an average severity of 20-29%. The two types of tree damage were often found at the location of the lower bole (code 3) and lower and upper bole (code 4), with the status of the level of tree damage in the moderate category (6.7 – 9.9). Thus, in the TAHURA WAR utilization block, two dominant types of tree damage were identified. This condition resulted in a considerable decrease in the quantity and quality of wood in the TAHURA WAR utilization block.

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Mitigating errors in mobile-based dietary assessments: Effects of a data modification process on the validity of an image-assisted food and nutrition app

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Nutrition 2023年 116卷 112212-112212页

作者： Ho, Dang Khanh Ngan Chiu, Wan-Chun Kao, Jing-Wen Tseng, Hsiang-Tung Yao, Chih-Yuan Su, Hsiu-Yueh Wei, Pin-Hui Le, Nguyen Quoc Khanh Nguyen, Hung Trong Chang, Jung-Su School of Nutrition and Health Sciences College of Nutrition Taipei Medical University Taipei Taiwan Department of Nutrition Wan Fang Hospital Taipei Medical University Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Dietetics Taipei Medical University Hospital Taipei Taiwan Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei Taiwan Research Center for Artificial Intelligence in Medicine Taipei Medical University Taipei Taiwan Translational Imaging Research Center Taipei Medical University Hospital Taipei Taiwan Department of Adult Nutrition Counselling National Institute of Nutrition Hanoi Viet Nam Department of Clinical Nutrition and Dietetics National Hospital of Endocrinology Hanoi Viet Nam Graduate Institute of Metabolism and Obesity Sciences College of Nutrition Taipei Medical University Taipei Taiwan Nutrition Research Center Taipei Medical University Hospital Taipei Taiwan Chinese Taipei Society for the Study of Obesity (CTSSO) Taipei Taiwan TMU Research Center for Digestive Medicine Taipei Medical University Taipei Taiwan

Objective: Mobile nutrition applications (apps) provide a simple way for individuals to record their diet, but the validity and inherent errors need to be carefully evaluated. The aim of this study was to assess the validity and clarify the sources of measurement errors of image-assisted mobile nutrition apps. Methods: This was a cross-sectional study with 98 students recruited from School of Nutrition and Health sciences, Taipei Medical University. A 3-d nutrient intake record by Formosa Food and Nutrient Recording App (FoodApp) was compared with a 24-h dietary recall (24-HDR). A two-stage data modification process, manual data cleaning, and reanalyzing of prepackaged foods were employed to address inherent errors. Nutrient intake levels obtained by the two methods were compared with the recommended daily intake (DRI), Taiwan. Paired t test, Spearman's correlation coefficients, and Bland–Altman plots were used to assess agreement between the FoodApp and 24-HDR. Results: Manual data cleaning identified 166 food coding errors (12%;stage 1), and 426 food codes with missing micronutrients (32%) were reanalyzed (stage 2). Positive linear trends were observed for total energy and micronutrient intake (all P_trend < 0.05) after the two stages of data modification, but not for dietary fat, carbohydrates, or vitamin D. There were no statistical differences in mean energy and macronutrient intake between the FoodApp and 24-HDR, and this agreement was confirmed by Bland–Altman plots. Spearman's correlation analyses showed strong to moderate correlations (r = 0.834 ∼ 0.386) between the two methods. Participants’ nutrient intake tended to be lower than the DRI, but no differences in proportions of adequacy/inadequacy for DRI values were observed between the two methods. Conclusions: Mitigating errors significantly improved the accuracy of the Formosa FoodApp, indicating its validity and reliability as a self-reporting mobile-based dietary assessment tool. Dietitians a

关键词： Food records Mobile application Mobile health Smartphone application

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