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All Journal FORUM STATISTIKA DAN KOMPUTASI JUITA : Jurnal Informatika Jurnal Matematika Sains dan Teknologi Indonesian Journal of Statistics and Its Applications Jurnal Statistika dan Aplikasinya Xplore: Journal of Statistics

Yenni Angraini

Department Of Statistics, IPB University, Indonesia

Author-ID : 2524304

Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Mathematics Social Sciences Other

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PREFERENSI MAHASISWA IPB TERHADAP MATA KULIAH METODE STATISTIKA MENGGUNAKAN ANALISIS KONJOIN Eka Dewi Pertiwi; Utami Dyah Syafitri; Yenni Angraini
FORUM STATISTIKA DAN KOMPUTASI Vol. 16 No. 1 (2011)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (374.201 KB)

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.

ANALISIS KONJOIN: METODE FULL PROFILE DAN CBC UNTUK MENELAAH PERSEPSI MAHASISWA TERHADAP PILIHAN PEKERJAAN Hari Wijayanto; Yenni Angraini; Riana Riskinandini
FORUM STATISTIKA DAN KOMPUTASI Vol. 12 No. 1 (2007)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

Show Abstract | Download Original | Original Source | Check in Google Scholar

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.

PEMODELAN DATA PANEL SPASIAL DENGAN DIMENSI RUANG DAN WAKTU (Spatial Panel Data Modeling with Space and Time Dimensions) Tendi Ferdian Diputra; Kusman Sadik; Yenni Angraini
FORUM STATISTIKA DAN KOMPUTASI Vol. 17 No. 1 (2012)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (523.173 KB)

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, SEM

PENGGUNAAN SOCIOGRAM UNTUK MENGIDENTIFIKASI POLA JARINGAN SOSIAL PEMBELAJARAN MANDIRI MAHASISWA (Identification of Social Network of Student’s Independent Learning using Sociogram) Yenni Angraini; Bagus Sartono; Dian Kusumaningrum
FORUM STATISTIKA DAN KOMPUTASI Vol. 17 No. 1 (2012)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (648.761 KB)

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 analysis

Sentiment Analysis on Covid-19 Vaccination in Indonesia Using Support Vector Machine and Random Forest I Made Sumertajaya; Yenni Angraini; Jamaluddin Rabbani Harahap; Anwar Fitrianto
JUITA : Jurnal Informatika JUITA Vol. 10 No. 1, May 2022
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

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.

Comparison of The SARIMA Model and Intervention in Forecasting The Number of Domestic Passengers at Soekarno-Hatta International Airport Anistia Iswari; Yenni Angraini; Mohammad Masjkur
Indonesian Journal of Statistics and Applications Vol 6 No 1 (2022)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v6i1p132-146

The Covid-19 pandemic has had a massive effect on the air transportation sector. Soekarno-Hatta International Airport (Soetta) skilled a lower variety of passengers because of the Covid-19 pandemic, even though Soetta Airport persisted to perform normally. Forecasting the number of passengers needs to be done by the airport to decide the proper policy. Therefore, the airport wishes to estimate the range of passengers to determine the right coverage and prepare the facilities provided if there may be a boom withinside the range of passengers throughout the Covid-19 pandemic. Forecasting the number of domestic passengers at Soetta Airport on this examination makes use of the SARIMA model and intervention. This examination compares the SARIMA model and the intervention in forecasting the number of domestic passengers at Soetta Airport. The effects confirmed that the best SARIMA model became ARIMA ARIMA(0,1,0)(1,0,0)12 with MAPE and RMSE of 55,18% and 588887.4, respectively. The best intervention model became ARIMA0,1,1) (1,0,0)12 b = 0, s = 5, r = 1 with MAPE of 35,25% and RMSE of 238563,4. The MAPE and RMSE values acquired suggest that the intervention model is better than the SARIMA model in forecasting the number of domestic passengers at Soetta Airport throughout the Covid-19 pandemic.

Analisis Unggahan Media Sosial pada Instagram Rachel Vennya Menggunakan Metode Importance Performance Else Virdiani; Aam Alamudi; Yenni Angraini
Xplore: Journal of Statistics Vol. 11 No. 1 (2022)
Publisher : Department of Statistics, IPB

Instagram is one of the social media applications that can publish photos or videos for its users. Rachel Vennya is a well-known Instagram user who has more than five million followers. This research was conducted to see the expected posts by Rachel Vennya's followers on Instagram. Through the importance-performance analysis (IPA) it will be known the types of posts that are interesting and need to be increased in publication. This study's two IPA approaches, namely expected performance analysis (EPA) and importance-performance matrix analysis (IPMA). The results of each analysis are then mapped into a Cartesian diagram so that it is known that several posts increase follower loyalty and posts that need to be increased or decreased. After comparing the two Cartesian diagrams, it is known that there is no difference in the placement of variables between the two analyzes. Posts that deserve to be maintained include Motivation, Cooking, Family, and posts considered excessive in the publication are Business and Endorsements. Furthermore, customer satisfaction index (CSI) analysis was carried out to see follower satisfaction. The CSI value obtained is 72.69, which indicates the follower satisfaction index belongs to the satisfied criteria.

Penerapan Metode Generalized Auto-Regressive Conditional Heteroscedasticity untuk Peramalan Harga Minyak Mentah Dunia Putri Zainal; Yenni Angraini; Akbar Rizki
Xplore: Journal of Statistics Vol. 12 No. 1 (2023): Vol. 12 No. 1 (2023)
Publisher : Department of Statistics, IPB

Crude oil is one of the commodities that are needed in various fields. World crude oil prices that continue to fluctuate, of course, have a big influence on the country's economy. Crude oil price data collected is time series or the collection process is carried out from time to time with monthly periods. Therefore, we need a system that can forecast future world crude oil prices which are expected to be taken into consideration by the government for decision making. One method that can be used to predict world crude oil prices is ARIMA (Auto-Regressive Integrated Moving Average) and GARCH (Generalized Auto-Regressive Conditional Heteroskedasticity) model. After modeling, it is proven that the world crude oil price data for the period January 2002 to June 2022 has a heteroscedasticity effect that cannot be overcome if only using the ARIMA model. The results of data processing show that the ARIMA (0,1,2) followed by the ARCH (2) is the best model with a MAPE value of 5,32%. The accuracy values obtained are classifield as very good for forecasting world crude oil prices.

PERAMALAN HARGA BATU BARA ACUAN MENGGUNAKAN METODE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE DAN FUNGSI TRANSFER Suci Pujiani Prahesti; Itasia Dina Sulvianti; Yenni Angraini
Xplore: Journal of Statistics Vol. 12 No. 1 (2023): Vol. 12 No. 1 (2023)
Publisher : Department of Statistics, IPB

Indonesia as one of the largest coal producing countries in the world has an important role in coal global demand. Currently, most countries in Europe are turning to coal as a source of electricity. This is due to Covid-19 pandemic and the conflict between Russia and Ukraine which endangers energy sources. Therefore, forecasting coal prices in the future is needed to determine the right policy in dealing with the large demand for coal. Coal price fluctuation are influenced by several factors such as the prices of the other commodities instance natural gas price. The natural gas price factor will be modeled in coal price forecasting using the transfer function method as the input series. This study compares the ARIMA and Transfer Function in coal price forecasting. The results showed that MAPE values of ARIMA and transfer function method are 23,14% and 17,66%. Based on MAPE values that forecasting using the transfer function method has a better ability than ARIMA method in forecasting coal prices.

APLIKASI MODEL ARIMA GARCH DALAM PERAMALAN DATA NILAI TUKAR RUPIAH TERHADAP DOLAR TAHUN 2017-2022 Nickyta Shavira Maharani; Yenni Angraini; Mahesa Ahmad Rahmawan; Oktaviani Aisyah Putri; Steven Kurniawan; Tias Amalia Safitri; Akbar Rizki; Wiwik Andriyani Lestari Ningsih; Nabila Ghoni Trisno Hidayatulloh; Andika Putri Ratnasari
Jurnal Matematika Sains dan Teknologi Vol. 24 No. 1 (2023)
Publisher : LPPM Universitas Terbuka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33830/jmst.v24i1.4875.2023

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%.

Co-Authors Aam Alamudi Akbar Rizki Akbar Rizki Andika Putri Ratnasari Andika Putri Ratnasari Anistia Iswari ASEP SAEFUDDIN Bagus Sartono Berliana Apriyanti Dian Kusumaningrum Eka Dewi Pertiwi Else Virdiani Fira Nurahmah Al Aminy Fitrianto, Anwar Hari Wijayanto I Made Sumertajaya Indahwati Indahwati Itasia Dina Sulvianti Jamaluddin Rabbani Harahap Kenia Maulidia Kusman Sadik Lia Ratih Kusuma Dewi Mahesa Ahmad Rahmawan Mohammad Abror Gustiansyah Mohammad Masjkur Nabila Ghoni Trisno Hidayatulloh Nabila Ghoni Trisno Hidayatulloh Nickyta Shavira Maharani Oksi Al Hadi Oktaviani Aisyah Putri Putri Zainal Raffael Julio Roger Roa Riana Riskinandini Steven Kurniawan Suci Pujiani Prahesti Tendi Ferdian Diputra Tias Amalia Safitri Utami Dyah Syafitri Wiwik Andriyani Lestari Ningsih Wiwik Andriyani Lestari Ningsih

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