bluuebunny/crossref_metadata_2025_split huggingface dataset:
The implementation of tpack framework in mathematics mobile edugame design: basic multiplication and division concept
Authors: Rahayu Y , Ahmad H A , Alamsyah I M 
Published: January 2021

Abstract
Mobile edugames were popular and has grown rapidly due to their ease of access, affordability, and interactivity. There have been numerous arithmetic related mobile edugames developed and available online. However, there are not so many mathematics mobile edugames existed which emphasize the basic concepts in accordance with Indonesia available curriculum and optimalize specific learning methods that suitable for targeted learner have been developed, particularly in basic division and multiplication materials. Through this study, the design of mathematics mobile edugame about basic division and multiplication with repetitive addition and subtraction concept will be discussed using TPACK framework that generates design recommendation and its application in mathematics mobile edugame with discovery-based learning model as an outcome. It uses specific TPACK plot from PCK to TPACK. The result shows in optimization used of technology in mathematics mobile edugame design user cognitive psychology development, gameplay and applied learning method are the most important aspect that then generates an adventure-puzzle mathematics mobile edugame which utilizes storytelling as final result.
The development of inner product spaces and its generalization: a survey
Authors: Sylviani S , Garminia H 
Published: January 2021

Abstract
An inner product space is a vector space with an additional structure called the inner product. This additional structure associates each vector pair in space with a scalar quantity known as the product. This paper will discuss a survey related to the development of the inner product space and its generalization. These generalization include semi-inner product space, sesquillinear space, indefinite inner product space, and bilinear space.
On application of differential transformation method to solve host-vector-predator system
Authors: Suryaningrat W , Anggriani N , Supriatna A K 
Published: January 2021

Abstract
The mathematical model has been used to understand many phenomena and natural interactions. Since including many variables and parameters, the complex models are not easy to find analytical solutions. The Differential Transformation Method (DTM) is an alternative numerical method used to solve a system of differential equations. In this paper, we develop the DTM for the Host-Vector-Predator model. The models consist of systems of differential equations describing the plant disease spread taking into account the biological agent as a predator for the vector. To illustrate the capability of the methods, some simulations with different parameters have been constructed. Numerical comparison showed good agreement with Runge-Kutta Method. These results show that the technique introduced here is accurate and easy to apply.
The effect of social inequality on the growth of COVID-19 death case
Authors: Fahira S T , Cahyono E C , Farida Haryanto S A N , Anggriani N 
Published: January 2021

Abstract
World Health Organization (WHO) declared COVID-19 has become a pandemic on March 11 2020, and counted as a dangerous disease including in Indonesia since it is causing immune drop after an infection that could emerge other diseases. Therefore, many people, both poor or wealthy, are worried. We formulate a modified Susceptible Infected Recovered compartmental model (SIR), where the Infected compartment could be dead because of the disease. Assuming that the poor have less access to excellent health facilities, this population is more likely to have more diseases that can recur after being infected with COVID-19. From pandemic history, life expectancy and death rates are disproportionate between the wealthiest and most deprived populations. It is based on previous pandemic cases that the life expectancy and death rates between the wealthy and poor communities are excessive. We show the effect of social inequality on the growth of the COVID-19 death case using the SIR model for the COVID-19 outbreak considering that the reinfection of COVID-19 could happen in some cases knowing that immune could be waning between people that got recovered from the virus. By numerical calculations and illustrating it in the graph, the results show that cases with high social inequality tend to have higher death rates and cases with low social inequality levels tend to have lower death rates. Thus, social inequality could affect the death rate caused by COVID-19 cases.
A mathematical model to study the effect of travel between two regions on the COVID-19 infections
Authors: Pratama M A A , Supriatna A K , Anggriani N 
Published: January 2021

Abstract
COVID-19 has attracted a lot of researchers’ attention since it has emerged in Wuhan, China in December 2019. Numerous model predictions on the COVID-19 epidemic have been created in case of Wuhan and the other regions. In this paper, a new COVID-19 epidemic model between two regions is proposed. The model differentiates asymptomatic infectious compartment and symptomatic infectious compartment. It is assumed that the symptomatic population cannot infect the susceptible population due to direct isolation, but the asymptomatic population can. The symptomatic population is also assumed to be unable to travel between regions. We analyse the stability of the model using Lyapunov Function. The Basic Reproduction Number for the model is presented. The numerical simulation and sensitivity analysis are explored to determine the significant parameter of the model.
Mitigation and models for determining premiums for natural disaster insurance due to excessive rainfall
Authors: Kalfin  , Sukono  , Supian S , Mamat M 
Published: January 2021

Abstract
Natural disasters due to excessive rainfall often cause huge losses in the form of impacts including casualties and economic losses. To minimize casualties, disaster mitigation is needed, while financing for disasters (emergency response) and financing after disasters (rehabilitation and reconstruction) can be carried out with insurance. This paper aims to determine mitigation efforts and insurance premiums for natural disasters. The method as a mitigation effort is studied based on several methods that are suitable for the conditions of the community in areas that are often affected by disasters. Meanwhile, regarding loss insurance, the premium is calculated based on the number of cases, the rate set by the insurance company and the amount insured (loss). Based on the results of natural disaster mitigation studies, it is necessary to adjust it to the type of disaster. In the case of landslides, housing buildings must be standardized, while in cases of flooding, river normalization is required. Mitigation in general, namely the installation of disaster sirens and disaster response training. Regarding loss insurance, it is found that the optimal premium for the government and insurance companies is obtained.
Comparison of SARIMA and SVM model for rainfall forecasting in Bogor city, Indonesia
Authors: Abdullah A S , Ruchjana B N , Jaya I G N M , Soemartini  
Published: January 2021

Abstract
Heavy rainfall and flash floods are still significant for several cities in Indonesia, especially Bogor City. Rainfall prediction is essential to develop early warning systems for early detection of massive and flash floods. The accurate forecasting method is needed to produce accurate predictions of rainfall. Several methods have been developed to get a precise rainfall forecast, such as the classical approach, such as seasonal autoregressive integrated moving average (SARIMA), and machine learning approach, for example, gradient boosting and support vector machine (SVM). In this study, we introduce for forecasting rainfall at Bogor city, Indonesia. The SVM provides an accurate result with a small MAPE.
Determinants of business performance of village-owned enterprises (V-OE) in Jembrana regency of Bali using SEM
Authors: Gandhiadi G K 
Published: January 2021

Abstract
One of the ideas offered is the establishment of economic institutions such as Village-Owned Enterprises (V-OE) which are supported by the role of the local government and are based on the wishes of the village community. The role of the government is needed in enhancing the culture of entrepreneurship in the managers and management of V-OE in addition to strengthening the capacity of social capital. SEM as a basis for multivariate analysis involving latent variables will be used to determine the factors affecting the business performance of V-OE. The survey through structured interviews was carried out on active V-OE involving 90 respondents from managers in Jembrana Regency of Bali. The results, are : (1) social capital and the role of government directly have a positive effect but not significant, only entrepreneurial orientation has a positive and significant effect on business performance; and (2) social capital and the role of the government in a total positive and significant effect, and require full mediation through entrepreneurial orientation to the business performance in Jembrana Regency. The results of this study are expected to be able to contribute to stakeholders in formulating rural economic development strategies through V-OE in Jembrana Regency of Bali.
Evaluation of the North American multi-model ensemble for monthly precipitation forecast
Authors: Faidah D Y , Kuswanto H , Suhartono  
Published: January 2021

Abstract
The North American multi-model ensemble (NMME) is a multi-model seasonal forecasting system consisting of a collection of models generated from several climate modelling centers. This research examined the monthly precipitation in North Maluku generated by five NMME models. The purpose of this research is to assess the performance of monthly precipitation prediction by using RMSE and Rank Histogram analysis. The NMME models are verified against observed precipitation. The analysis shows that they are biased and underdispersive. Among the five NMME models, the Center for Ocean-Land-Atmosphere Studies (COLA) exhibits the best predictive skill. The performances of the Canadian Meteorological Centre (CMC) are relatively worse than that of the other models. The COLA model shows relatively high skill when used to forecast May-November monthly precipitation. Meanwhile, the National Oceanic and Atmospheric Administration (NOAA)’s Geophysical Fluid Dynamics Laboratory (GFDL) model shows high skill in December-April periods. The ensemble forecast is calibrated with the BMA approach in order to obtain reliable forecasts.
Implementation of centrality measures in graph represented information spreads with hashtag #bersatulawancorona in Twitter
Authors: Carnia E , Fermadona B , Napitupulu H , Anggriani N , Supriatna A K 
Published: January 2021

Abstract
Twitter is a social network that is very popular among the people that allows the users to send and read text-based messages up to 140 characters, known as tweets. Since it was first launched until now, this social network has become one of the ten most visited sites on the internet. Because there are so many people use this social network, Twitter has become a source of information that can disseminate information in a short time. Dissemination of information on Twitter is very preferred by the accounts of Twitter users. The most important account in the dissemination of information on Twitter can be searched by calculating the centrality measurement. In this paper, four centrality measures are used to find the most important accounts, that are degree centrality, closeness centrality, betweenness centrality, and eigenvector centrality. Before calculating the centrality of the Twitter accounts, data must be collected first, wherein this paper the data to be used is tweeted data from Twitter using hashtag #BersatuLawanCorona obtained using Python with the help of Twitter API. In calculating this centrality value, we can get the most influential account in distributing information related to hashtag #BersatuLawanCorona. Based on the data, obtained one account that became the most influential account because it obtained the highest value for each centrality measurement, which is SapawargaSby account.
Covid-19 classification using X-Ray imaging with ensemble learning
Authors: Siswantining T , Parlindungan R 
Published: January 2021

Abstract
Coronavirus (Covid-19) first appeared in Wuhan, December 2019, and continues to spread rapidly to other countries. one of the countries infected with the Covid-19 virus is Indonesia. In Indonesia, the spread of this virus is very fast. Therefore, we need a detection system to detect people who are infected with this virus or not. Rapid detection of Covid-19 can contribute to control the spread of this disease. Chest x-ray images are one of the first imaging techniques to play an important role in the diagnosis of Covid-19. This research data uses chest x-ray images dataset in the Covid-19 cases. The data used in this study were 170 images data with 130 data for training data and 40 for testing data. In this study, the Neural Network, Support Vector Machine (SVM), and Convolutional Neural Network (CNN) methods were used, then applied to Stacking which is one of the methods of Ensemble Learning. The results of this study indicate that the best accuracy is obtained from the Stacking model with an accuracy of 95%.
Graphical exploration of robustness of support vector regression for bearing vibration acceleration
Authors: Darwis Sutawanir , Hajarisman Nusar , Suliadi  , Widodo Achmad 
Published: January 2021

AbstractBearing defect is a major factor in failure of rotating machinery. As a defect is detected the machinery is shut down as soon as possible to avoid catastrophic damage. Therefore, it is important to monitor the condition of bearings and to predict its remaining useful life. Six run to failure datasets are provided by pronostia laboratory to build prognostics models and to estimate the remaining useful life of eleven remaining bearings acceleration vibration signals have been gathered during the experiment. Vertical and horizontal acceleration was measured by two accelerometer mounted on the bearing housing. Support vector regression was proposed as a tool to develop the bearing remaining useful life. It is known that the method is affected by outliers because there are possibilities that outliers were selected as support vectors. Some robust support vector regression have been proposed. However, empirical study of robustness of support vector regression for bearing acceleration vibration has not yet been studied. This paper proposes a graphical exploration to study the robustness of support vector regression of bearing acceleration vibration. The results shows that the graphical approach can be used as a tool for preliminary study of robustness of support vector regression for bearing vibration acceleration.
Comparison of IDW and GP models with application to spatiotemporal interpolation of rainfall in Bali Province, Indonesia
Authors: Jaya I G N M , Ruchjana B N , Abdullah A S , Andriyana Y 
Published: January 2021

Abstract
Precipitation is a critical weather component in our daily life. Spatiotemporal variation in rainfall is very influential in agriculture, health, tourism, and many more. Low or large precipitation volume or intensity can be a problem. However, precipitation is not easy to control, so minimize the negative impact of rainfall is an essential thing that can be done. Precipitation volume or intensity in an area is generally reported based on meteorology stations at several location points, which are then used to describe the value of precipitation in the broader region. However, the number of weather observation stations is often minimal. So using this average of little station information can produce incorrect rainfall information. Various approaches have been developed to be able to provide accurate information based on several observation points. These approaches include Inverse Distance Weighted (IDW) and Gaussian Process (GP). This study aims to compare the accuracy of the IDW and GP methods in conducting rainfall interventions in Bali’s Indonesian province. Bali is one of the world’s tourist destinations where rainfall is very influential on tourist visits. We found IDW provides a good prediction for close location, and GP is much better for a distant place.
Causal impact of PSBB and new normal in West Java
Authors: Pontoh R S , Zahroh S , Sunengsih N 
Published: January 2021

Abstract
Indonesia is one of the countries affected by COVID-19. This disease has spread to all provinces of Indonesia, including West Java. West Java is one of the provinces in Indonesia with the most cases of COVID-19 and becomes one of the epicentres, therefore, the regional government has issued a policy to manage the transmission chain. The guidelines implemented by the government, including social distancing and the Large-Scale Social Restrictions (PSBB). In addition, the implementation of PSBB is expected to disrupt the transmission chain because it is more strictly with more sanctions. After the spread is expected to decrease, the government, then, introduced a New Normal system, in which activities will gradually return to normal, but with strict health protocols. Furthermore, this study will find out whether PSBB and New Normal have a significant impact on daily new cases in West Java. The causal impact analysis will be conducted in this study to showed that the implementation of the PSBB and New Normal system has a significant impact on the increase of daily new cases in West Java. The results obtained indicate that there are significant changes after the PSBB policy changed to the new normal. If the number of cases continues to increase based on the results of this study, it can be implemented, among others, that the West Java Provincial Government can enforce the PSBB in this region.
Covid-19 daily forecasting during ramadhan in countries with high muslim population
Authors: Darmawan G , Rosadi D , Ruchjana B N 
Published: January 2021

Abstract
For Muslims, the month of Ramadhan is a month of full ritual and non-ritual worship, such as Tarawih, Eid al-Fitr, and going home to hometown. These activities are very risky if carried out during the outbreak of Covid-19, as the spread of Covid-19 occurs within a short distance from one person to another. This study aims to look at the Covid-19 movement in Ramadhan for countries with the largest Muslim populations, such as Indonesia, Malaysia, Saudi Arabia, Egypt, Kuwait, Pakistan, Afghanistan, Oman, Qatar, Morocco, Bangladesh and India. Model the movement of Covid-19 using the Singular Spectrum Analysis Model. The method used is visual scanning and modelling using the Singular Spectrum Analysis Model. Daily forecasting is done twice as much as the first forecast for the weekly MAPE (Mean Absolute Percentage Error) from May 8th, 2020 to May 14th, while the second forecast (Data is not yet known) for the weekly MAPE from May 15th, 2020 to Eid. Based on the results of the graphical exploration, in general, the Muslim-majority countries rose in mining cases in Covid-19 during the month of Ramadhan. The states that declined in the case of Covid-19 were Oman, Qatar and Morocco. The Singular Spectrum Analysis Model with the Long Memory Time Series approach is more accurate than the Alexandrov version of the Auto grouping Model. The overall results of the MAPE.
The comparison between recurrent neural network and grey model to predict Indonesia tuberculosis morbidity rate
Authors: Harlianto D. , Mardiyati S. , Lestari D. 
Published: January 2021

Abstract
Many insurance companies offer health insurance products that cover the risk of tuberculosis (TB). The risk of disease is generally stated as the morbidity rate that is the ratio of the number of residents suffers from an illness to the total population. In 2018, the World Health Organization (WHO) reported that Indonesia was at rank two for the case numbers of TB, so Indonesia has a high risk of this disease. The recurrent neural network (RNN) and grey model are two models that can be employed to predict TB morbidity rates. In this research, the accuracy of these two models was compared. The results of this research may give the insurance company an assist to choose an appropriate mathematical model to provide a competitive and profitable premium. TB morbidity rate in a certain year had been predicted based on the past several year’s morbidity rates as model input. The size of past years data used as model input was made varied to observe how information availability influences the model accuracy measured by mean squared error (MSE) and mean absolute percentage error (MAPE). The results show that the grey model has better accuracy when the small data used as input. On the other hand, the accuracy of RNN is not affected significantly by the setting of the data input size used in this research.
Application of partitioning around medoids cluster for analysis of stunting in 100 priority regencies in Indonesia
Authors: Ramdani M A , Abdullah S 
Published: January 2021

Abstract
In 2018, Indonesian government set 100 districts as the priority to reduce stunting. In this study, we hypothesize that the 100 determined districts should not be treated on an equal policy, due to some underlying factors that might affect the stunting in those districts. Thus, it is necessary to identify and analyze the grouping of 100 priority districts for stunting interventions in 2018 based on the National Team’s indicators for the Acceleration of Poverty Reduction to see the severity of stunting. It is hoped that this clustering could be a reference for the government in determining priority regency groups to reduce stunting rates. Data on 100 districts, represented by eight numerical measurements and six categorical measurements were analyzed using Partitioning Around Medoids (PAM) method. Data similarity was measured using Gower distance, which can handle the clustering of mixed data types. We identified five priority district groups which provide meaningful insights. One of the groups has the worst stunting severity condition among other groups, for each indicator; implying the high priority to follow-up by the government. The majority of districts in Papua and East Nusa Tenggara Provinces are districts with poor stunting severity. We also found that poverty, proportion of the population with defecation in latrines, access to clean water and proper sanitation, number of integrated healthcare centers (posyandu) in a village, and number of doctors in each district are important factors that explain the stunting severity.
Audio fingerprint for automatic Balinese rindik music identification using gaussian mixture model
Authors: Widarsa A A R S , Darmawan I D M B A 
Published: January 2021

Abstract
Rindik is a traditional gamelan instrument that has been known for a long time in which is made from bamboo. Rindik music can be identified by just hearing a little bit snippets from the music that can only be done by professional rindik players. This research assumes doing some training in the system can make the system recognize the music by just using some rindik music snippets. This research focuses on comparing some features extraction method for automatic rindik music identification by using some rindik music snippet to find out which of these features perform the best to represent rindik music. This research will use an audio fingerprint method for song identification. MFCC (Mel Frequency Cepstral Coefficient) and Spectral Subband Centroid will be used and compared as the music’s feature, and Gaussian Mixture Model will be used to model the music’s fingerprint. The result of this research show both methods give excellent results. Both features only need 10-second duration data to get over 90% overall accuracies. Both Feature gives excellent performance with GMM for automatic rindik song identification task. However, Spectral subband centroid show better result with the highest accuracies is 99.3%.