bluuebunny/crossref_metadata_2025_split huggingface dataset:
From offline to online learning: various efforts to secure the learning process during covid-19 outbreaks
Authors: Faizah U , Ambarwati R , Rahayu DA 
Published: February 2021

Abstract
Coronavirus Disease (Covid) 19 outbreaks occurred in many countries led to the emergence of policy changes in the field of education for the implementation of learning at home in order to avoid the spread of the disease. Educational instructors must be responsive to adapt to changes in learning that was originally offline to be online to secure the learning process. This study aims to describe the various efforts of lecturers in carrying out online learning during the Covid-19 pandemic. The research was in March-April 2020 with 32 respondents (lecturers in the one of Universitas at Surabaya-Indonesia). Rapid survey with Google form by asking the facilities used in the implementation of online learning, the implementation of online learning, and the lecturers’ response to online learning. Data were analyzed using quantitative-descriptive manner. The results showed various lecturers’ efforts to secure the learning process during Pandemic Covid-19 Condition. They tried to use various platforms and applications to manage online learning, even in Learning Management Systems (LMS) and social media applications. The most widely used application was WhatsApp (84.4%) and in LMS was Google classroom (56.3%). In implementing online learning, lecturers had good design in teaching materials, preparation, implementation, evaluation of learning. In addition, 72% of lecturers responded positively to online implementation because they had benefited in increased digital literacy. The conclusion of this study were most lecturers responded positively by adapting to changes from offline to online learning; online learning has been well implemented.
Effect of Healthy Life Campaigns on Controlling Obesity Transmission: A Mathematical Study
Authors: Delavani  , Aldila Dipo , Handari Bevina D 
Published: February 2021

Abstract
In this study, we present a mathematical model that describes how obesity spread among the human population, considering human awareness levels to describe the difference in lifestyle of humans, in which the transition between this group depends on the media campaign from the authority about the importance of healthy lifestyles and persuasive capability of individuals who quit obesity. The model constructs as four-dimensional nonlinear ordinary differential equations. Possible equilibrium points are investigated regarding their existence and local stability criteria. Basic reproduction number (R
0) of the model obtained from the next-generation matrix approach. It has been shown that the obesity-free equilibrium is locally asymptotically stable if R
0 is less than one and unstable otherwise. A transcritical bifurcation when R
0 = 1 was investigated using the Castillo-Song bifurcation theorem. From the elasticity analysis, we find that the social contact rate is the most influential parameter in determining the magnitude of R
0, followed by a healthy life campaign from the government. A short discussion to understand the possible scenario in the field obtained numerically based on our analytical results conducted at last.
Improving Students Understanding on Fluid Dynamics through IBL-STEM Model with Formative Assessment
Authors: Parno  , Permana G A , Hidayat A , Ali M 
Published: February 2021

Abstract
Fluid dynamics is related to science and technology in everyday’s life, but students still have low understanding about it. This research aims to correct the understanding and describe students’ difficulties on the topic of fluid dynamics after Inquiry-Based Learning integrated STEM with Formative Assessment. This mixed methods with an embedded experimental design made use of research subject of 34 students (M=14 and F=20) of grade XI in a high school at Jombang, Indonesia, which were selected with purposive sampling. The research instrument used was the fluid dynamics understanding test instrument in the form of reasoned multiple-choice questions with a reliability of 0.774. The data analysis was done with Wilcoxon Signed-Rank test, N-gain, d-effect, and and the analysis of the description of students’ answer. The result of the study shows that the learning process was able to significantly improve students’ conceptual understanding with the N-gain value of 0.628 (medium category) and d-effect size of 3.645 (very large category). After the learning process, students still have great difficulty in learning two things, which are flow rate with constant magnitude and the relation between pressure, surface area of the space, and the flow speed. Other than that, students still feel some difficulty to analyze (1) the relation between speed of flow and radius of space, (2) the construction of the proof of constant debut, (3) aerodynamic lift of two wings of an aeroplane, and (4) the phenomena of someone standing near a moving train, and (5) the relation between the height of water reservoir and the flow rate.
Deep Learning Ability of Students from Superior and Non-Superior Classes at Microscopic Level of Protein
Authors: Erman E , Martini  , Rosdiana L , Wakhidah N 
Published: February 2021

Abstract
Deep learning is urgently required to achieve scientific literacy and to develop high order thinking skills. This study aimed to describe the deep learning ability of science education major students in a university in East Java at the microscopic level of protein. Twenty students from superior class and thirty students from non-superior class were involved voluntarily in the survey study. By using content analysis that is read carefully students’ tasks, examined students’ ability in identify, define, and explain biochemical aspects of protein case, and apply the knowledge to explain the case, and determined students’ learning status using rubrics, we found that students from both classes successful identified, defined, and explained aspects of protein cases macroscopically. However, microscopically, no students from the superior class explained protein aspects in the cases, while 11% of students from non-superior class explained them successfully. Seven percent of student from non-superior class used their protein knowledge to explain the protein cases they explored successfully. We also found that 7% of students from the non-superior class performed deep learning at microscopic level of protein, while no students from the superior class performed the same. However, majority students from both classes performed surface learning at microscopic level of protein. The results imply that both superior and non-superior students are difficult to learn protein microscopically. Learning strategy to help students attain meaningful learning of biochemistry are needed by the students.
Teachers’ Views about Nature of Science (NOS) Using Reconceptualised Family Resemblance Approach to Nature of Science (RFN) Questionnaire
Authors: Azninda H , Raharjo  , Sunarti T 
Published: February 2021

Abstract
This study aimed to identify teachers view about Nature of Science (NOS). One of the most recent framework that focused on educational adaptation of NOS is called Reconceptualised Family Resemblance Approach to Nature of Science (RFN). Based on RFN, NOS is categorized by its aims and values, scientific inquiry, methods, knowledge and social-institutional aspect of science. Teacher plays big role in learning process to teach NOS both in implicitly or explicitly. However, only few of research that have been revealed teacher’s view about NOS using RFN. A qualitative research method was considered in order to identify teacher’s responses based on categories of RFN. This study also used 70 items “RFN-Questionnaire” that was adapted from Kaya et al (2019). Total 15 science teachers and 10 non-science teachers such as social and language teachers was participated in written questionnaire and interview processes. The result show that non-science teachers have different view about NOS in each category compared to science-teacher based on interview. Though a quantitative data analyse, using Mann-Whitney based on score from RFN-Q, suggest that there is no significant differences between non-science teacher and science teacher’s overall score. This study concluded that teachers in both science and non-science majors have different view about NOS because of learning experience and processes they have received.
Students’ visual literacy: a study from plant anatomy learning
Authors: Susiyawati E , Treagust D F 
Published: February 2021

Abstract
Plant anatomy is a branch of plant biology that focuses on the internal structures and development of a plant. This topic relies heavily on visual representations such as photographs and drawings as teaching and learning tools. Therefore, visual literacy is a core skill for biology students to study plant anatomy effectively. However, do biology students have a sufficient level of visual literacy particularly in the case of plant anatomy? This current study was conducted to provide answers to this question. To collect relevant information, this study involved 79 university sophomores studying plant anatomy in Indonesia. Using a case study design, two data collection methods included a test and semi-structured interviews to investigate to what extent biology students are visually literate in interpreting plant anatomy-based photographs. The author-designed test, Plant Anatomy Diagnostic Test (PADI), consisted of two sections. Section 1 comprised five four-tier multiple choice items linked to photographs of a plant, whereas section 2 was a drawing task. The test was distributed to all student participants, while the interview sections involved a subgroup of 15 students. All collected data were analysed separately, then the results were compared and compiled to triangulate the findings. The results of this current study supported and triangulated the fact that the biology students had difficulties to interpret, understand, and generate visualizations of the presented plants’ photographs, indicating that their visual literacy in plant anatomy was insufficient. The primary reason for students’ insufficient visual literacy was lack of conceptual understanding that resulted in their inability to apply and integrate knowledge. The implications of the research findings are also discussed.
Mathematical reasoning structure of junior high school students in solving problems based on their working memory capacity
Authors: Palengka I , Juniati D , Abadi  
Published: February 2021

Abstract
Mathematical reasoning has a clear structure. In mathematical reasoning structure, working memory capacity is considered as something important. This study aimed at knowing the mathematical reasoning structure of junior high school students in solving problems reviewed based on working memory capacity. This research was a qualitative research. This study was carried out in state Junior High School in Makale. The selection of research subjects was done by providing tests to measure students’ working memory capacity. The test used was O-SPAN Test and the researcher chose 1 student with high category working memory capacity and 1 student with low category working memory capacity. Then the researcher gave a test to investigate the reasoning structure. Mathematical reasoning structure of research participant with low working memory capacity showed the stage of problematic situation, strategy choice, conclusion could be done. However, for the stage of strategy is implemented, subject implemented the strategy in one way and could not find a maximum result. Subject with low working memory capacity could provide conclusions but could not give verification arguments showing the strategy used was correct or reasonable and could solve the problem. Mathematical reasoning structure of research subject with high working memory capacity fulfilled the four mathematical reasoning structures namely problematic situation, strategy choice, implemented strategy, and conclusion.
Development of digital learning resources for realistic mathematics education in supporting virtual learning during covid-19
Authors: Fiangga S , Palupi E L W , Hidayat D , Prihartiwi N R , Siswono T Y E 
Published: February 2021

Abstract
The use of digital resource for learning has been increasing since the Industrial Revolution 4.0 was enlivened. This was further strengthened by the government’s policy to learn from home since the COVID pandemic 19. Various types of online learning can be effective but also sometimes do not succeed in achieving the expected learning goals. Drijver suggests that 3 main factors influence how digital learning resources can be effective in learning, namely how the design of digital content is, how the teacher’s role in digital content is, and the use of the educational context used in digital content. Realistic Mathematics Learning can be an approach that can accommodate all the factors. This study aims to provide appropriate digital learning resources for teachers and students during the COVID 19 pandemic. An adapted Plomp Design is used for developing digital content. This developmental research suggests that the characterization of RME supports the requirements for effective digital learning resources in a way that enables the student to foster their constructive learning activities. However, these rich didactical activities lack in providing an adequate presentation on the mathematics content.
Plankton Diversity at Rice Field-Pond in Glagah Village, Lamongan
Authors: Zahroh VRA , Sueb  
Published: February 2021

Abstract
This research aimed to describe plankton diversity at rice field-pond in Glagah Village, Lamongan that can be used for fish culture. Sampling was done by purposive sampling with 5 sampling points at each end of the pond and in the middle, the place of rice field-pond around of the school. Each sampling point is divided into 2 depth variations, surface (0 cm) and base (70 cm). The results showed in the first point found 1 type of phytoplankton and 1 type of zooplankton, the second point found 9 types of phytoplankton and 2 type of zooplankton, the third point found 10 types of phytoplankton and 3 types of zooplankton, in the fourth point found 2 types of zooplankton while in the fifth point found 1 type of phytoplankton and 1 type of zooplankton. From the observation concluded that phytoplankton diversity of H ‘2.27 (medium) and zooplankton of H’ 1.543 (medium). So it can be concluded that the diversity of phytoplankton in rice field-pond is greater than zooplankton. Physical and chemical characteristics at rice field-pond water affect the plankton diversity so that it must be maintained.
Software defect prediction using Binary Particle Swarm Optimization with Binary Cross Entropy as the fitness function
Authors: Malhotra R , Shakya A , Ranjan R , Banshi R 
Published: February 2021

Abstract
Software is a consequential asset because concrete software is needed in virtually every industry, in every business, and for every function. It becomes more paramount as time goes on – if something breaks within your application portfolio and expeditious, efficient, and efficacious fine-tune needs to transpire as anon as possible. Therefore, recognizing the faults in the early phase of the software development lifecycle is essential for both, diminishing the cost in terms of efforts and money Similarly, It is important to find out features which could be redundant or features which are highly correlated with each other, as it could largely affect the model’s learning process. This analysis refers to the use of crossover Artificial Neural Network (ANN) and Binary Particle Swarm Optimization (BPSO) with Binary Cross-Entropy (BCE) loss for the fitness function. The conclusion of proposed paper is to provide the significance and opportunity of using Binary Cross-Entropy (BCE) in Binary Particle Swarm Optimization (BPSO) for the feature reduction process in order to minimize the developer’s effort and costs for software development as well as for its maintenance.
Detection of Non-functional Bore wells Using Machine Learning Algorithms
Authors: Janani Priya T , Sabari Priya K P , Vincent Jennifer Priscilla , Uma K V , Pudumalar S 
Published: February 2021

Abstract
India is the largest user of ground water with approximately 27.5 million bore wells drawing over 230 cubic kilolitre per year. Bore wells are vertical drilled wells, bored into an underground water-bearing layer in the earth’s surface, to extract water for various purposes. Less rainfall, water scarcity, depletion of underground water has spearheaded the number of bore wells dug in a year. Excessive number of bore wells has led to squandering of groundwater at higher rates than the rate of water replenished and caused depletion of the groundwater levels. When the water gets dried, the motor is removed and the outer surface is not properly covered or sealed. The diameter is large enough for the child to fall inside. The inside of the bore well now non-functioning or left unused might have collapsed. According to a report, since 2009 more than 40 children fell into bore well, sadly seventy percentage of the rescue operations fail. The current paper incorporates the analysis of datasets of bore well acquired from Kaggle. A predictive model has been built by employing machine learning algorithms like Random forest, Extra-trees classifier, logistic regression ‘to predict the non-functional bore wells present which is to be reported and to be taken action in the immediate future’. The performance of the models have been evaluated by performance metrics, among which Random forest shows excellent performance in predicting non-functional killer bore wells.
Comparative analysis of Machine learning and Deep learning algorithms for Software Effort Estimation
Authors: Priya Varshini A G , Anitha Kumari K , Janani D , Soundariya S 
Published: February 2021

Abstract
Artificial Intelligence is a superset of Machine Learning and Deep learning, used to build intelligent systems that can solve problems. Software Effort Estimation is used to predict the number of hours of work required to complete the task. It is difficult and a challenging task to forecast Software Effort in the project during initial stages, due to several uncertainties. Software Effort Estimation helps in planning, scheduling, budgeting a project. Various experiments were proposed to predict effort alike expert judgment, analogy based estimations, regression estimations, classification approaches, deep learning algorithms. In this paper, comparison of deepnet, neuralnet, support vector machine and random forest algorithms were carried out and the results show that random forest outperforms other algorithms because of its robustness and capacity to handle large datasets. Evaluation metrics deliberated are Mean Absolute Error, Root Mean Squared Error, Mean Square Error and R-Squared.
Seizure Detection with Local Binary Pattern and CNN Classifier
Authors: Ramakrishnan S , Muthanantha Murugavel A S , Sathiyamurthi P , Ramprasath J 
Published: February 2021

Abstract
The present work proposes two novel approaches namely One Dimensional adaptive average Local Binary Pattern (1-D AaLBP) and One-Dimensional adaptive difference Local Binary Pattern (1-D AdLBP) for feature extraction from EEG signals and Convolutional Neural Network (CNN) for classification of EEG signals. Both the proposed feature extraction methods are computationally easy to implement. In the first step the histograms are formed from the extracted patterns, after that feature vectors of the histogram are given as input to the classifier. Two benchmark EEG datasets such as Bonn and CHB-MIT are employed for conducting experiments for comparing the performances of the proposed method with other existing research works. The performance measures such as sensitivity, specificity, classification accuracy and execution time are used for evaluating the proposed methods. It is learned from the experiments conducted that among various methods the proposed method provides improved performance in terms of sensitivity, specificity, classification accuracy and execution time.
Prediction of Solar Radiation Using Artificial Neural Network
Authors: Rahman Shahriar , Rahman Shazzadur , Bahalul Haque A K M 
Published: February 2021

Abstract
Most solar applications and systems can be reliably used to generate electricity and power in many homes and offices. Recently, there is an increase in many solar required systems that can be found not only in electricity generation but other applications such as solar distillation, water heating, heating of buildings, meteorology and producing solar conversion energy. Prediction of solar radiation is very significant in order to accomplish the previously mentioned objectives. In this paper, the main target is to present an algorithm that can be used to predict an hourly activity of solar radiation. Using a dataset that consists of temperature of air, time, humidity, wind speed, atmospheric pressure, direction of wind and solar radiation data, an Artificial Neural Network (ANN) model is constructed to effectively forecast solar radiation using the available weather forecast data. Two models are created to efficiently create a system capable of interpreting patterns through supervised learning data and predict the correct amount of radiation present in the atmosphere. The results of the two statistical indicators: Mean Absolute Error (MAE) and Mean Squared Error (MSE) are performed and compared with observed and predicted data. These two models were able to generate efficient predictions with sufficient performance accuracy.
Extended Adjacent Local Binary Pattern for Texture Based Image Retrieval
Authors: Ramakrishnan S , Nithya S , Meenachi L , Janani A P 
Published: February 2021

Abstract
In this paper, a novel local texture descriptor named Extended Adjacent Local Binary Pattern (EALBP) for images is proposed. In this method, each image is subdivided into local regions of size 4*4. For each local region, 13-bits of binary value are generated by comparing the adjacent neighborhood pixel in three phases. In the first phase, the inner 2x2 region from the local region is considered and each pixel is compared with the adjacent pixel which results in 4 bits. In the second phase, the outer region from the local region is considered and the diagonal pixels present in the 4 directions such as 45°, 135°, 225° and 315° are considered. The pixels values present in each direction are compared with the adjacent neighborhood pixels in the outer region and this comparison results in 4 bits. In the third phase, the pixels except the diagonal pixel values in the outer region are compared against each other and this result in 4 bit binary value. Finally, the mean of the local region is compared with the mean of the input image which results in one bit binary value. The 13-bit binary value of each local region is thus formed and the above steps are repeated for the entire image. The experimental results show that the proposed method could characterize the texture property of the image efficiently when compared with the existing methods on two databases.