Identifikasi Tema Perbincangan Masyarakat Tentang Vaksinasi Covid-19 di Media Sosial

Informasi mengenai vaksin covid-19 dan program vaksinasi pemerintah merupakan isu yang mendapat perhatian besar masyarakat dan menjadi perbincangan utama di media sosial, termasuk twitter. Beragam tema dan sudut pandang telah disampaikan oleh masyarakat, dan penelitian ini berupaya mengidentifikasi opini apa saja yang berkembang.  Pengetahuan ini dapat menjadi masukan bagi pemerintah dan pemangku kepentingan lain untuk secara bersama membantu proses pemulihan dampak pandemi.  Identifikasi opini masyarakat mengenai vaksin covid-19 dilakukan menggunakan metode text clustering terhadap tweets hasil crawling data di Twitter dalam kurun waktu 1 s.d 7 Agustus 2021. Hasil analisis menunjukkan terdapat enam tema besar yang menjadi isu perbincangan yaitu: (1) Kepercayaan terhadap efek vaksin, (2) keikutsertaan dalam vaksinasi untuk mencegah terpapar covid, (3) vaksinasi sebagai upaya herd immunity, (4) keampuhan vaksin melawan virus, (5) jenis-jenis vaksin  (6) riset medis tentang

PENGENALAN ALGORITMA GENETIK UNTUK PEMILIHAN PEUBAH PENJELAS DALAM MODEL REGRESI MENGGUNAKAN SAS/IML

Genetic algorithm has been a popular alternative in the various fields of optimization problem.  This paper describes some basic ideas of this algorithm and its application for selecting significant variables in the regression analysis.  Simple SAS/IML commands are presented in order to emphasize how the algorithm works.  It is also available to do some modification in some parts of those commands

Image Classification Modelling of Beef and Pork Using Convolutional Neural Network

The high price of beef makes some people manipulate sales in markets or other shopping venues, such as mixing beef and pork. The difference between pork and beef is actually from the color and texture of the meat. However, many people do not understand these differences yet. One of the solutions is to create a technology that can recognize and differentiate pork and beef. That is what underlies this research to build a system that can classify the two types of meat. Since traditional machine learning such as Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) uses manual feature extraction in pattern recognition, we use Convolutional Neural Network (CNN) that can extract the feature automatically through the convolution layer. CNN is one of the deep learning methods and the development of artificial intelligence science that can be applied to classify images. There is no research on using CNN for pork and beef classification. Several regularization techniques, including dropout, L2, and max-norm with several values in them are applied to the model and compared to get the best classification results and can predict new data accurately. The best accuracy of 97.56% and the lowest loss of 0.111 were obtained from the CNN model by applying the dropout technique using p=0.7 supported by hyperparameters such as two convolution layers, 128 neurons in the fully connected layer, ReLU activation function, and two fully connected layers. The results of this study are expected to be the basis for making beef and pork recognition applications

PENGGUNAAN ALGORITMA SIMULATED ANNEALING UNTUK MENYELESAIKAN TEKA-TEKI BINARY DAN SUDOKU ( Solving Binary and Sudoku Puzzles with a Simulated Annealing Algorithm )

Binary and Sudoku puzzles could be seen as optimization problems by using a score  of  rules  violation  as  the  objective  function  which  is  minimized. The simulated annealing algorithm is a good alternative to solve the puzzles.  This paper  describes the  approach  which  implements  the  algorithm  and  presents the  SAS/IML  program  of  it.    Empirical  trials  show  that  the  approach  works well to find the solution of the puzzles in a satisfying run time.  Keywords : meta-heuristic, simulated annealin

KLASIFIKASI RANCANGAN FAKTORIAL PECAHAN JENUH TIGA TARAF DALAM 27 RUN

Tulisan ini memberikan klasifikasi terhadap gugus rancangan jenuh tiga faktor OA(27, 313) yang berguna dalam penentuan rancangan terbaik untuk diterapkan.  Kriteria A3 dan A­4 tidak dapat digunakan karena memiliki nilai yang sama untuk seluruh array.  Dengan mengasumsikan hanya ada tiga faktor yang aktif, kriteria projection aberration menggunakan vektor A3(3) mengkelaskan 68 OA yang non-isomorphic ke dalam 54 kelas.  Dua array terbaik menurut kriteria ini ditampilkan sebagai rujukan untuk digunakan.Kata kunci: rancangan jenuh, orthogonal array, klasifikasi, projection aberratio

ALTERNATIVE DETERMINANT VARIABLES IN URBAN/RURAL VILLAGE CLASSIFICATION IN INDONESIA

Classification of “kelurahan” and rural area into urban/rural class basically meant to form a layer (stratum) were used in the survey sampling techniques. With the status of urban and rural areas, the sample can represent the entire population correctly. Proper selection of variables could distinguish village into urban and rural class. The purpose of this study was to provide an alternative selection of the most influential variables to determine the classification of villages in Indonesia with a mix method of bootstrap and binary logistic regression. The data used in this case is data Potensi Desa (PODES) 2011 which conducted by Badan Pusat Statistik. The methods used in this study are binary logistic regression and bootstrap. Logistic regression is one method of non-parametric regression where the response variable is categorical data. This method can also be used for data classification. Bootstrap, is known as one of the data simulation method, intended to simplify the inferential statistical analysis but produces a more robust analysis. From previous studies showed that the variable density of population, the number of farm households, and the presence of the primary facility is the most influential variables in the classification of villages in Indonesia. From the previous studies also can be concluded that the bootstrap approach give small mistake of goodness in variance covariance matrix. The more bootstrap replication is used, the more robust the resulting analysis. The results showed that the presence of variable existence of Junior High School and hotels can be removed from the model without effecting goodness of fit of the model. The addition of new variables, existence of the internet cafe and bank is able to produce more powerful model for classification of the village. Keywords: bootstrap, binary logistic regression, urban/rural village classificatio

MULTIVARIATE ADAPTIVE REGRESSION SPLINE (MARS) FOR MODELLING OF CHILD LABOR IN JAKARTA

A Child is defined as male or female under the age of 18 years unless under the law of maturity has been reached earlier. Based on data from the ILO, there were four million child labors in Indonesia in 2002. Jakarta as the capital city of Indonesia has a high economic growth in 2010 by reaching 6.51 percent (LKPJ 2010). Behind that growth, Jakarta has a problem of high number of child workers as well, which is about 93 571 children in 2010. Some researchers often use regression analysis to determine the description of the factors that contribute to a response variable. Regression analysis has several assumptions that must be met, while research in the social subjects often violates those assumptions. To overcome this limitation required nonparametric method that is not tied to the assumption. One method is non-parametric regression Multivariate Adaptive Regression Spline (MARS). MARS method is an approach for nonparametric regression model that can accommodate multicollinearity in the model. This study uses secondary data drawn from SUSENAS in 2013 in DKI Jakarta. Response variable used is the status of work in children aged 10-17 years, while the predictor variables are fifteen variables that represent the characteristics of children and household. Based on the results of processing with MARS, obtained models are affected by Status of Child’s Education, Child’s Education, Child Live with Parent, Education of Head of Household, and etc. Key words: child labor, MARS, SUSENAS, Jakart

BUILDING A MODEL TO PREDICT SCHOOL ACCREDITATION RANK USING BOOSTED CLASSIFICATION TREE

Education has a key role to make a better life. The Education for All (EFA) is a global movement led by UNESCO, aiming to provide good basic education for all children, youths and adults. Indonesian government has committed to improve the education quality as stated in law on national education system (Law No. 20/2003). School accreditation rank which is issued by National Accreditation Board for School/Madrasah (BAN S/M) is depiction of education quality provided by school. However the number of accredited school has not met the target yet so that the government faces difficulty in the planning of budget and actions. The prediction of school classification based on accreditation rank to the un-accredited schools, therefore, has important role as reference to improve quality of education. In recent years the introduction of aggregation methods led to many new techniques within the field of prediction and classification. Boosting is one of the widely used ensemble for classification with a goal of improving the accuracy of classifier. The objective of this study is to predict school accreditation rank using boosted classification tree compared to single tree utilizing the education database. It is showed that the accuracy of prediction is improved by use of boosting method. Comparisons between the methods are based on misclassification rates as well as criteria that take ordinality into account, like mean absolute error, mean square error and Kendall’s association measures. Key words: boosting, classification tree, school accreditation ran

An Empirical Study of Macroeconomic to Portfolio Performance : Sub-sector Building Construction with Crude Petroleum and Natural Gas

The Indonesian capital markets have show a remarkable recovery after financial crisis at the 1990. Although there was down at 2008, but consistency growth has succeed to record an outstanding performance at 2016. This paper aims to observate influence variable of macroeconomic in Indonesia as inflation, exchange rate, interest rate and oil price to return of building contruction with crude petroleum and natural gas sub-sector, and benchmark period 2014 to 2016. The methodology in used is multiple linear regression model, the independent and dependent variables is issued macroeconomic and return of both sub-sector and benchmark. The primary results from this research is increase of exchange range has negative effect or decrease to stock return on the sub-sector building construction ( = 5%), sub-sector crude petroleum and natural gas ( = 10%), and benchmark ( = 5%). Increase of crude oil price has negative effect or decrease to stock return on the sub-sector building construction ( = 5%) and benchmark ( = 10%). But different with variables inflation and interest rate, the both variables not significantly. Keywords: Building construction, Crude petroleum and natural gas, Benchmark, Macroeconomi

An Empirical Study of Macroeconomic to Portfolio Performance : Sub-sector Building Construction with Crude Petroleum and Natural Gas

The Indonesian capital markets have show a remarkable recovery after financial crisis at the 1990. Although there was down at 2008, but consistency growth has succeed to record an outstanding performance at 2016. This paper aims to observate influence variable of macroeconomic in Indonesia as inflation, exchange rate, interest rate and oil price to return of building contruction with crude petroleum and natural gas sub-sector, and benchmark period 2014 to 2016. The methodology in used is multiple linear regression model, the independent and dependent variables is issued macroeconomic and return of both sub-sector and benchmark. The primary results from this research is increase of exchange range has negative effect or decrease to stock return on the sub-sector building construction ( = 5%), sub-sector crude petroleum and natural gas ( = 10%), and benchmark ( = 5%). Increase of crude oil price has negative effect or decrease to stock return on the sub-sector building construction ( = 5%) and benchmark ( = 10%). But different with variables inflation and interest rate, the both variables not significantly. Keywords: Building construction, Crude petroleum and natural gas, Benchmark, Macroeconomi