PENGGUNAAN SOCIOGRAM UNTUK MENGIDENTIFIKASI POLA JARINGAN SOSIAL PEMBELAJARAN MANDIRI MAHASISWA (Identification of Social Network of Student’s Independent Learning using Sociogram)

This paper presents a useful tool to help universities to increasing the level of their graduate outcome by using the information about social network among students. Such a quantitative tool is a sociogram which depicts how students interact with others. The graph can be easily generated when the pattern of the connectivity among individuals is known. We apply sociogram to portray the network of a class of students in Department of Statistics – Bogor Agricultural University which represent the way they interact when they want to discuss the academic related problems. We found some interesting results are practically valuable for the one who is responsible to the study result of the students. Some results are not new, but this approach could provide more informative features than conventional tables or such things.Keywords : sociogram, social network analysi

PEMODELAN DATA PANEL SPASIAL DENGAN DIMENSI RUANG DAN WAKTU (Spatial Panel Data Modeling with Space and Time Dimensions)

The modeling of spatial panel data is a method of analysis that include the dimension of space and time. In this analysis, the set of data that is required is a combination of cross sections and time series data, that is, either the data observed in each observation location periodically from time to time. On modeling of panel data, there are three approaches, namely pooled least square model, fixed and random effects model. While on modeling of spatial panel data there are several approaches which is a combination of these three approaches in modeling panel data with spatial autoregression model (SAR) and spatial error model (SEM). This research aims to apply a spatial panel data model analysis to include the dimension of space and time in a model. The data that used in this research is GDP, local revenues, a total population and total regional expenditures of ten districts in Jambi province during the years 2000-2008. The results from spatial panel data analysis obtained that model regression of spatial panel data corresponding to the data is panel data models with fixed effect model and spatial error model. From the results of such analysis can also be seen an increase in R2 compared with panel data analysis.Keywords : the modeling of panel data, the modeling of spatial panel data, SAR, SE

PREFERENSI MAHASISWA IPB TERHADAP MATA KULIAH METODE STATISTIKA MENGGUNAKAN ANALISIS KONJOIN

Statistical method is one of the interdepth courses in Bogor Agricultural University (BAU) therefore, it is necessary to conduct an  evaluation in order to know the student's preference towards Statistics Methods course. Conjoint analysis is an analysis that can be used to determine the preference of students on teaching methods of Statistical Methods course. The combination of teaching methods are made using fractional factorial in which the level of  factor determined  was based on preliminary survey. Sampling techniques that  has been used was multistage sampling of students who had took the Statistical Methods course in 2009/2010. Based on conjoint analysis, the module, the number of students, and the time period of lectures are the top three  choices. The students tend to prefer materials that are appropriate with their major, modules that are well structured, a communicative lecturer, students as a teacher in review session, the number of student which is less than 50 students per class, and the time period of lecture is between 7-12 am.   Keywords :  statistical methods, preferences, conjoint analysis

APLIKASI MODEL ARIMA GARCH DALAM PERAMALAN DATA NILAI TUKAR RUPIAH TERHADAP DOLAR TAHUN 2017-2022

The Indonesian rupiah (IDR) exchange rate is used to gauge Indonesia's economic stability. Maintaining the IDR exchange rate's stability is critical since it has a direct impact on Indonesia's national monetary situation, particularly during the Covid-19 pandemic. Forecasting the rupiah exchange rate is important to do and is one way to assess government policy. The data series to be used here are IDR exchange rate from the Yahoo Finance. It consists of 271 data taken from August 2017 to October 2022. This study aims to use the Autoregressive Integrated Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) modeling method using the R-studio software and predict the IDR exchange rate. The ARIMA method describes the data based on a certain time series. ARCH-Lagrange Multiplier (ARCH-LM) was applied on the residuals of the best ARIMA model to test whetoer the data is heteroscedasticity. The testing result shows that the residual of the IDR exchange rate is heteroscedasticity. Therefore, the GARCH model can be used to handle it. The results of this study are obtained for the ARIMA(2,1,3) GARCH(3,6) model as the best and describe the actual data pattern with a mean absolute percentage error (MAPE) forecasting value is 1,99%

ANALISIS KONJOIN: METODE FULL PROFILE DAN CBC UNTUK MENELAAH PERSEPSI MAHASISWA TERHADAP PILIHAN PEKERJAAN

Tulisan ini membahas perbandingan analisis konjoin metode full profile dan metode CBC.  Metode full profile merupakan metode yang klasik dan cukup mudah diterapkan terutama dalam pembuatan disain pengumpulan dan analisis data, tetapi cukup merepotkan dalam tahap pengumpulan data.  Sedangkan Metode CBC, walaupun agak sulit dalam disain  pengumpulan dan analisis datanya tetapi pada saat pengumpulan datanya relatif lebih mudah dan dipandang lebih alamiah. Penerapan kedua metode ini dalam menelaah faktor yang paling dipertimbangkan oleh mahasiswa dalam memilih pekerjaan memberikan hasil yang relatif sama.  Faktor utama yang berpengaruh terhadap pilihan pekerjaan mahasiswa adalah besarnya gaji pertama dan kesesuaian bidang pekerjaan dengan latar belakang pendidikannya

Sentiment Analysis on Covid-19 Vaccination in Indonesia Using Support Vector Machine and Random Forest

World Health Organization (WHO) stated Covid-19 as a global pandemic in March, 2020. This pandemic has influenced people’s life in many sectors such as the economy, health, tourism, and many more. One way to end this pandemic is to make herd immunity obtained through the vaccination program. This program still raises pros and cons at the beginning of its implementation in Indonesia. Many people doubt the safety and side effects of the vaccine. There are also pros and cons to vaccination programs in social media such as Twitter. This platform generates a huge amount of text data containing people's perceptions about vaccines. This research aims to predict sentiment using supervised learning such as support vector machine (SVM) and random forest and capture sentiment about vaccines in Indonesia in the first two weeks of the program. The result shows SVM was a better model than random forest based on the precision and F1-score metrics. The SVM approach produces a precision value of 0.50, a recall of 0.64, and an F1-score of 0.52. In the study, it was also found that tweets with neutral sentiment dominated the twitter user sentiment in the study period. Tweets with negative sentiment decreased after the first week of the COVID-19 vaccination program

Estimating the Variance of Estimator of the Latent Factor Linear Mixed Model Using Supplemented Expectation-Maximization Algorithm

This paper deals with symmetrical data that can be modelled based on Gaussian distribution, such as linear mixed models for longitudinal data. The latent factor linear mixed model (LFLMM) is a method generally used for analysing changes in high-dimensional longitudinal data. It is usual that the model estimates are based on the expectation-maximization (EM) algorithm, but unfortunately, the algorithm does not produce the standard errors of the regression coefficients, which then hampers testing procedures. To fill in the gap, the Supplemented EM (SEM) algorithm for the case of fixed variables is proposed in this paper. The computational aspects of the SEM algorithm have been investigated by means of simulation. We also calculate the variance matrix of beta using the second moment as a benchmark to compare with the asymptotic variance matrix of beta of SEM. Both the second moment and SEM produce symmetrical results, the variance estimates of beta are getting smaller when number of subjects in the simulation increases. In addition, the practical usefulness of this work was illustrated using real data on political attitudes and behaviour in Flanders-Belgium