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All Journal MUST: Journal of Mathematics Education, Science and Technology Jurnal Statistika dan Aplikasinya
Dania Siregar
Universitas Negeri Jakarta
Agriculture, Biological Sciences & Forestry Computer Science & IT Economics, Econometrics & Finance Education Mathematics Social Sciences Other

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Peramalan Indeks Saham LQ45 pada Masa Pandemi COVID-19 Menggunakan Analisis Intervensi Sherina Arthariani Zukrianto; Widyanti Rahayu; Dania Siregar
Jurnal Statistika dan Aplikasinya Vol 5 No 2 (2021): Jurnal Statistika dan Aplikasinya
Publisher : Program Studi Statistika FMIPA UNJ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.05213

Abstract

Analisis intervensi merupakan metode pemodelan deret waktu yang dipengaruhi oleh suatu peristiwa yang menyebabkan data deret waktu mengalami fluktuatif. Metode analisis intervensi memiliki tujuan untuk mengukur besar dan lamanya efek dari suatu intervensi pada data deret waktu. Terdapat dua jenis variabel analisis intervensi, yaitu fungsi step dan fungsi pulse. Tujuan penelitian ini untuk memodelkan dan meramalkan model intervensi fungsi step pada indeks saham LQ45 dengan waktu intervensi yang diketahui. Deret waktu LQ45 dipengaruhi oleh suatu intervensi, yaitu pandemi COVID-19. Prosedur dalam melakukan metode analisis intervensi diawali dengan mengelompokkan data menjadi dua kelompok, yaitu data sebelum intervensi dan data saat intervensi sampai data terakhir. Data sebelum intervensi digunakan untuk pemodelan ARIMA. Model ARIMA yang didapatkan dari data sebelum terjadinya intervensi digunakan sebagai informasi untuk melakukan identifikasi orde intervensi. Selanjutnya dilakukan estimasi parameter dan pemeriksaan uji asumsi white noise serta uji asumsi berdistribusi normal. Model intervensi yang telah memenuhi kedua asumsi tersebut dapat digunakan untuk peramalan. Peramalan dari indeks saham LQ45 menghasilkan nilai indeks saham LQ45 yang cenderung konstan dan berkisar pada level indeks saham sebesar 883 – 884. Hasil peramalan indeks saham LQ45 sudah sangat baik dengan nilai galat sebesar 7%.
Analisis Sentimen pada Program Transportasi Publik JakLingko dengan Metode Support Vector Machine Faroh Ladayya; Dania Siregar; Wiligis Eka Pranoto; Hilmy Dzaky Muchtar
Jurnal Statistika dan Aplikasinya Vol 6 No 2 (2022): Jurnal Statistika dan Aplikasinya
Publisher : Program Studi Statistika FMIPA UNJ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.06221

Abstract

As a metropolitan city with high mobility, public transportation plays an important role in facilitating economic, business and government activities in DKI Jakarta. DKI Jakarta provincial government launched the JakLingko program to create an integrated, convenient, efficient, and affordable public transportation system. Knowledge of public opinion can help improve the service quality of the JakLingko program. The use of social media is becoming very popular nowadays. Through social media, anyone can easily express their opinion about an issue. It is used to obtain objective and latest public opinion. Sentiment analysis is a method that can be used to analyze public opinion. Through sentiment analysis whose data was collected from Twitter, it can be seen how the public opinion toward JakLingko program. In this study, public sentiment will be classified into positive sentiment or negative sentiment. As for the classification, the Support Vector Machine (SVM) algorithm is used.
ANALYZING READING LITERACY SCORE OF INDONESIAN STUDENTS USING LINEAR MIXED MODELS Vera Maya Santi; Syifa Azzahra; Dania Siregar
MUST: Journal of Mathematics Education, Science and Technology Vol 7 No 2 (2022): DECEMBER
Publisher : Universitas Muhammadiyah Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30651/must.v7i2.14420

Abstract

The linear mixed models is a development of the linear model which includes both fixed and random effects in the model. Random effect in the model is used to model complex data that has a grouping structure. The grouping structure can occur because the same observations are measured repeatedly or each observation is measured only once but these observations have some form of group structure. Students who participate in the Program for International Student Assessment (PISA) are nested in several schools, so the PISA data structure is quite complex and requires a more in-depth analysis. Quantitative studies on PISA, especially in reading literacy, are still rarely done. The purpose of this study is to determine what factors effect the Indonesian student’s PISA reading literacy scores using a linear mixed model approach with school being used as a random effect in the model. The findings of the study are that the factors that affects Indonesian student’s PISA reading literacy scores are the class being taken, gender, mother's highest education, facilities at home, school entry age, student discipline and failed a grade. The result of the estimation of random effect variance which is not equal to zero indicates that there is a random effect from the student’s school on PISA reading literacy scores. Based on model diagnostics and parameter testing, it was concluded that the model obtained is fitted in modeling Indonesian student’s PISA reading literacy scores.
Penerapan Metode Support Vector Machines (SVM) dan Metode Naïve Bayes Classifier (NBC) dalam Analisis Sentimen Publik terhadap Konsep Child-free di Media Sosial Twitter Dania Siregar; Faroh Ladayya; Naufal Zhafran Albaqi; Bintang Mahesa Wardana
Jurnal Statistika dan Aplikasinya Vol 7 No 1 (2023): Jurnal Statistika dan Aplikasinya
Publisher : Program Studi Statistika FMIPA UNJ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.07109

Abstract

Child-free is a concept in which a person chooses not to have children or places and situations that are without the presence of a child. Along with the rapid flow of information, this child-free concept began to be discussed virally, especially on Indonesian social media, such as Twitter. Sentiment analysis is the mining of all people’s expressions and views on a phenomenon or product online in the form of text. Through a large sample collection of opinions and expressions, we can capture the voices or views of society, understand the dynamics that are taking place, and even know the extent to which the issue begins to touch aspects of people’s social life. This study aims to conduct sentiment analysis by comparing the performance of two different methods used to classify people’s views in the form of text data crawled tweets from Twitter. The two methods compared are Support Vector Machines (SVM) and Naive Bayes Classifier (NBC). Another purpose of this study is to provide an overview of public sentiment on social media Twitter about the concept of child-free. The results of this study showed that the data experienced an imbalance so to overcome this problem, SMOTE is used, SMOTE managed to increase the sensitivity of the prediction of minor data. The classification method that produces the best prediction on test data using the F1-weighted average criterion is SMOTE-SVM with a value of 60.45%. The opinions that support child-free mostly have to do with parents' unpreparedness to take care of children, while opinions that reject child-free think that it is contrary to religious advice and child-free decisions will make it difficult for old age because no one takes care of them.
Co-Authors Bintang Mahesa Wardana Faroh Ladayya Hilmy Dzaky Muchtar Naufal Zhafran Albaqi Sherina Arthariani Zukrianto Syifa Azzahra Vera Maya Santi Widyanti Rahayu Wiligis Eka Pranoto