ETHNOMATHEMATICS IN PERSPECTIVE OF SUNDANESE CULTURE

This study is an exploratory research aims to find and know about a phenomenon by exploration. Therefore, the approach used in this study is ethnographic approach, an empirical and theoretical approach to get description and deep analysis about a culture based on field study. From the sustainable interviews and confirmation about field research with some community leaders in Cipatujah district, Tasikmalaya regency and in Santolo Pameungpeuk beach, Garut regency; it is found that Ethnomathematics is still widely used by Sundanese people especially in rural areas: the use of measurement units, mathematical modeling, and the use of clock symbols. The results of this study can be useful for Sundanese people and the government of West Java in education, cultural services, and tourism. Keywords: Ethnomathematics, Unit Calculation, Modeling, Symbolic Time DOI: http://dx.doi.org/10.22342/jme.8.1.3877.1-1

PRINCIPAL COMPONENT ANALYSIS-VECTOR AUTOREGRESSIVE INTEGRATED (PCA-VARI) MODEL USING DATA MINING APPROACH TO CLIMATE DATA IN THE WEST JAVA REGION

Over a long time, atmospheric changes have been caused by natural phenomena. This study uses the Principal Component Analysis (PCA) model combined with Vector Autoregressive Integrated (VARI) called the PCA-VARI model through the data mining approach. PCA reduces ten variables of climate data into two principal components during ten years (2001-2020) of climate data from NASA Prediction Of Worldwide Energy Resources. VARI is a non-stationary multivariate time series to model two or more variables that influence each other using a differencing process. The Knowledge Discovery in Database (KDD) method was conducted for empirical analysis. Pre-processing is an analysis of raw climate data. The data mining process determines the proportion of each component of PCA and is selected as variables in the VARI process. The postprocessing is by visualizing and interpreting the PCA-VARI model. Variables of solar radiation and precipitation are strongly correlated with each measurement location data. A forecast of the interaction of variables between locations is shown in the results of Impulse Response Function (IRF) visualization, where the climate of the West Java region, especially the Lembang and Bogor areas, has strong response climate locations, which influence each other

THE IMPLEMENTATION OF FINITE-STATES CONTINUOUS TIME MARKOV CHAIN ON DAILY CASES OF COVID-19 IN BANDUNG

Markov chain is a stochastic process to describe a phenomenon in the future based on a previous state. In practice, Markov chains are distinguished by time into two, namely discrete-time Markov chain and continuous-time Markov Chain. This research will discuss the continuous-time Markov chain with finite-state. COVID-19 phenomena can describe and predict using the continuous-time Markov chain. Authors use the data daily cases of COVID-19 in Greater Bandung including Bandung City, Bandung District, West Bandung District, Cimahi City and Sumedang District. Used data came from simulated data of daily cases of COVID-19 in Greater Bandung from August, 2020 until November 14, 2021 that recorded through the website COVID-19 of West Java. In terms of described and predicted the COVID-19 phenomenon in Greater Bandung for long-term probability, authors use stationary distribution and limit distribution. COVID-19 phenomenon is described into two states: state 0 (lower than average of data) and state 1 (higher than average of data). The result of continuous-time Markov chain with finite-state shows that the probability of the daily cases of COVID-19 for five locations in Greater Bandung is state 0 have a larger probability than state 1. It means that COVID-19 in Greater Bandung over the long-term will decrease

Feature Extraction Analysis for Hidden Markov Models in Sundanese Speech Recognition

Sundanese language is one of the popular languages in Indonesia. Thus, research in Sundanese language becomes essential to be made. It is the reason this study was being made. The vital parts to get the high accuracy of recognition are feature extraction and classifier. The important goal of this study was to analyze the first one. Three types of feature extraction tested were Linear Predictive Coding (LPC), Mel Frequency Cepstral Coefficients (MFCC), and Human Factor Cepstral Coefficients (HFCC). The results of the three feature extraction became the input of the classifier. The study applied Hidden Markov Models as its classifier. However, before the classification was done, we need to do the quantization. In this study, it was based on clustering. Each result was compared against the number of clusters and hidden states used. The dataset came from four people who spoke digits from zero to nine as much as 60 times to do this experiments. Finally, it showed that all feature extraction produced the same performance for the corpus used

Literature review on the information system for digitization of royal history and Waqf

There has been a significant increase in the study of the history and culture of historical artifacts, whether they take the form of cultural heritage or Waqf. A literature review of web-based information systems was conducted for digitizing historical preservation and Waqf. Papers were sourced from various databases, including Publish or Perish, which produced 1043 journals, 370 articles, and 673 items from reputable sources, Google Scholar, and Crossref, respectively. The focus of the literature review was the information system for digitizing history and Waqf and integrating ontology databases. This literature review study aims to trace the evolution of study objects related to history and endowments. The results showed that most studies emphasized the user-understanding aspect of digitization, while the technical aspect was focused on using cutting-edge technology, such as 3D and virtual reality

Comparison of Spatial Weight Matrices in Spatial Autoregressive Model: Case Study of Intangible Cultural Heritage in Indonesia

Intangible Cultural Heritage (ICH) can effectively contribute to Sustainable Development Goals (SDGs) in all economic, social, and environmental dimensions, along with peace and security. Studying ICH in Indonesia cannot be separated from the spatial aspect of how an area's attributes are related to other areas located close to each other. Spatial regression modeling needs to be done by considering the selection of spatial weight matrix. Using the wrong spatial weight matrix will increase the standard error in parameter estimation. Therefore, this study aims to determine: the best spatial weight matrix to accommodate the spatial autocorrelation in analyzing the description of the spread of ICH in Indonesia; and the variables that are thought to influence the number of ICH determination in Indonesia. The spatial regression modeling used in this study is the Spatial Autoregressive (SAR) model and the spatial weight matrices compared in this study are queen contiguity and inverse distance. The best model is the SAR model used the queen contiguity spatial weight matrix because it has minimum values of AIC, BIC, RMSE and MAPE which are 310.397, 319.555, 18.857 and 57.169 respectively. Simultaneously, involved in performing arts, wearing traditional dress, knowing Indonesian folklore and the spatial lag contribute significantly to number of ICH determination in Indonesia. Partially, only knowing Indonesian folklore have a significant effect on number of ICH determination in Indonesia at significance level α=5%. Each additional 1% of population that knowing Indonesian folklore in an area increases number of ICH determination in that area by 0.6719 units .

Development of the GSTARIMA(1,1,1) model order for climate data forecasting

The space-time model combines spatial and temporal elements. One example is the Generalized Space-Time Autoregressive (GSTAR) Model, which improves the Space-Time Autoregressive (STAR) model. The GSTAR model assumes that each location has heterogeneity characteristics, and that the data is stationary. In this research, the moving average component is calculated by involving the relationship between variable values at a certain time and residual values at a previous time, and it is assumed that the data is not stationary, so the model used is the Generalized Space-Time Autoregressive Integrated Moving Average (GSTARIMA) Model. The model order for GSTARIMA is determined through the Space-Time Autocorrelation Function (STACF) and Space-Time Partial Autocorrelation Function (STPACF) to ensure accurate forecasting. Previous research only discussed the GSTARIMA(1,1,1) model, so in this research, the GSTARIMA(3,1,1) model will be addressed as a form of development of the GSTARIMA(1,1,1) model and applied to climate data. The climate data used in this research is sourced from NASA POWER and consists of rainfall variables with large data sizes, requiring the use of the data analytics lifecycle method to analyse Big Data. The lifecycle includes six phases: discovery, data preparation, model planning, model building, communicating results, and operationalization. Based on the data processing results with Python software, the GSTARIMA(3,1,1) model has a MAPE value of 9% for out-sample data and 11% for in-sample data. In contrast, the GSTARIMA(1,1,1) model has a MAPE value of 11% for out-sample data and 12% for in-sample data. So the GSTARIMA(3,1,1) model provides more accurate forecasting results. Therefore, selecting the correct model order is crucial for accurate forecasting

A Computational Study on the Effects of Molecular Structures of Di-n-butyldithiophosphate and of its Derivatives on the Stability of Their Complex Compounds with Rare-Earth Elements

The stability of complex compounds  formed from the ligand di-n-butyldithiophosphate (DBDTP) and its derivatives, with ions of rare-earth elements (REEs), such as gadolinium ion (Gd3+), is an important factor in the separation and purification processes of the elements using solvent extraction method. The complex stability is dependent, one of which, on the partial charge of the donor atom (S atom in this case) in the molecule of DBDTP or its derivatives. The more negative the partial charge of the donor atom, the more stable is the complex compound formed. The purpose of this study is to explore the effect of electron donating, and of electron withdrawing groups, as well as the effect of the structure of the butyl group in the molecules of  DBDTP and or its derivatives on the partial charge of the donor atom. The method used was the semi empirical quantum mechanical calculations, i.e. the Austin Model 1 (AM1). The results of the study showed that the electron withdrawing group of -CN had resulted in the most positive charge on the donor atom, if it is on the second carbon atom of the butyl group in the DBDTP and or its derivatives. Conversely, in the same carbon atom position, the donating electron group of -CH=CH2 had generated the most negative partial charge on the donor atom. Furthermore, the results of this study also revealed that the sec-butyl isomer produced the most negative partial charge on the donor atom, among other isomers

ETHNOMATHEMATICS IN PERSPECTIVE OF SUNDANESE CULTURE

This study is an exploratory research aims to find and know about a phenomenon by exploration. Therefore, the approach used in this study is ethnographic approach, an empirical and theoretical approach to get description and deep analysis about a culture based on field study. From the sustainable interviews and confirmation about field research with some community leaders in Cipatujah district, Tasikmalaya regency and in Santolo Pameungpeuk beach, Garut regency; it is found that Ethnomathematics is still widely used by Sundanese people especially in rural areas: the use of measurement units, mathematical modeling, and the use of clock symbols. The results of this study can be useful for Sundanese people and the government of West Java in education, cultural services, and tourism. Keywords: Ethnomathematics, Unit Calculation, Modeling, Symbolic Time DOI: http://dx.doi.org/10.22342/jme.8.1.3877.1-1

Spasial data mining menggunakan model SAR-Kriging (Spatial Autoregressive-Kriging) untuk pemetaan mutu pendidikan di Indonesia

Survey Base of National Education year 2003 (SDPN 2003) is a data realization of education mapping in Indonesia; it gave a large education database with 3,89 GB (4,178,499.369 bytes) and 2,395 files involved 203,590 records and 569 indicators, also it has a measured variables, high dimension with a hundreds heterogeneous attribute and distributed spatial data geographically . Data mining can be used to extract knowledge from a large database. Data mining develop to be spatial data mining to extract knowledge from spatial relation to get some of explicit pattern which is not found in database. Because of Indonesia has a wide location distribution: provinces, cities, and districts which have many social culture, so data mining can be applied in mapping of quality education in Indonesia. The aims of this research are to study and to apply spatial data mining for classification the quality of education at various rates in Indonesia using: Expansion Spatial AutoRegressive-Kriging (SAR-Kriging) which is combine expansion SAR model and Kriging method. The development of SAR-Kriging model theoretically based on the weaknesses of SAR model, it is only applicable for prediction at sample locations. In the other side Kriging method can be used to predict observations at unsample locations. The expansion SAR model is an extension of model SAR to give information the influence of total independent variables to dependent variable through spatial heterogeneity using locations coordinate. The combination of expansion SAR model and Kriging method is studied to get the causal model which can be used to predict at unsample locations. Using SAR-Kriging model we can predict the quality of education at unsample locations in Indonesia at province or district level. Methodology in this research is a data mining processes and knowledge discovery in database (KDD) using three stages. The first stage is preprocessing data involve database preparation of Survey Base of National Education year 2003 (SDPN 2003). SDPN 2003 is a realization of education mapping in Indonesia. In SDPN 2003 we have a complex and large data, especially for education at elementary, junior and senior schools. We have a large school database 203,590 records and 569 indicators. It can be divided for elementary school (SD) 158,590 records and 122 indicators, junior high school (SMP) 28,949 records and 138 indicators, for senior high school (SMA) 10,810 records and 142 indicators, and SMK 5,156 records and 167 indicators. Furthermore, in the first steps the data is cleaned and transformed, variable is selected by factor analysis and Structural Equation Model (SEM), and the last is combining spatial and non spatial data. The second stage is data mining, using Moran index, SAR, Expansion SAR and SAR-Kriging models to get description and prediction of quality education. The last stage is post processing through interpretation, evaluation and visualization to get knowledge. The result of data processing of SDPN 2003 using SAR-Kriging model shows that the prediction of quality education of elementary school (SD), junior high xxiii school (SMP) and senior high school (SMA) at twelve districts at West Java Province has Mean Absolute Percentage Error (MAPE) 8.39; 23.63 and 40.45. It shows that the SAR-Kriging model has MAPE less than 10% for elementary school, so it fit forprediction of education quality at SD. The processing data at provinces in Java Island gives the MAPE of SD, SMP and SMA: 8.15; 8.10 and 29.36. The result shows that the SAR-Kriging model can be used to predict the quality of education for SD and SMP at unsample provinces in Java Island. Using SAR-Kriging we also predict the quality of education at province Aceh which gives MAPE for SD, SMP and SMA. It gives the MAPE 8.44; 14.51 and 42.25. It shows that the SAR-Kriging model can be used to predict quality of education SD at Aceh province. For the extension province as West Sulawesi, we get the MAPE of SAR-Kriging model for SD, SMP and SMA are 1.02; 2.19 and 81.76. So, at West Sulawesi the SAR-Kriging model also fit to the data SD and SMP. The processing data for 13 provinces in Indonesia for SD, SMP and SMA give SAR-Kriging MAPE 8.07; 7.48 and 29.21. We can conclude that the SARKriging model is a good model for prediction of quality of education for SD and SMP. All of the results show that SAR-Kriging model at elementary and junior high schools is a good model for prediction a quality of education at unsample provinces in Indonesia. To get processing data easier we built the application software of Spatial Data Mining using SAR, Expansion SAR and SAR-Kriging models. Using this software we can apply the model to get description and prediction of quality education at various locations, districts or provinces in Indonesia. The result of prediction of quality education using SAR-Kriging can be used as a recommendation for management of education in Indonesia