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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan INTEGRITAS : Jurnal Pengabdian Jurnal Statistika dan Aplikasinya International Journal of Applied Science and Sustainable Development (IJASSD) STATISTIKA
Faroh Ladayya
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Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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ANALYZING OPEN UNEMPLOYMENT RATE IN JAVA USING PENALIZED SPLINE NONPARAMETRIC REGRESSION Vera Maya Santi; Nia Rahayu Ningsih; Faroh Ladayya
International Journal of Applied Science and Sustainable Development (IJASSD) Vol. 4 No. 2 (2022): International Journal of Applied Science and Sustainable Development (IJASSD)
Publisher : Lembaga Penelitian dan `Pengabdian Kepada Masyarakat (LPPM)

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

Abstract

In regression analysis, there are three regression curve approach methods: parametric approach, semiparametric approach, and nonparametric approach. One of the estimation methods in nonparametric regression is spline regression with parameter estimation methods, namely smoothing, truncated, and penalized. Penalized spline estimation controls the smoothness of the curve so that the curve avoids stiffness and overfitting and does not require assumptions. This study aims to analyze the open unemployment rate in Java, which has the highest open unemployment rate in Indonesia, where studies using this approach have never been conducted. The study's results resulted in an additive Mean Square Error (MSE) of 4.137 with a coefficient of determination of 44.58%, indicating that explanatory variables of 44.58% could explain the open unemployment rate. Based on the parameter significance test, the factors that significantly effect the open unemployment rate are the dependency ratio, the GDP growth rate, senior high school gross enrollment, percentage of the poor population, and population growth rate.
MULTILEVEL REGRESSION WITH MAXIMUM LIKELIHOOD AND RESTRICTED MAXIMUM LIKELIHOOD METHOD IN ANALYZING INDONESIAN READING LITERACY SCORES Vera Maya Santi; Rifa Kamilia; Faroh Ladayya
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 4 (2022): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (442.412 KB) | DOI: 10.30598/barekengvol16iss4pp1423-1432

Abstract

The multilevel regression model is a development of the linear regression model that can be used to analyze data that has a hierarchical structure. The problem with this data structure is that individuals in the same group tend to have the same characteristics, so the observations at lower levels are not independent. Education research often produces a hierarchical structure, one of which is PISA data, where students as level-1 nested within schools as level-2. In the PISA 2018 survey, reading literacy is the main focus. The data are sourced from the Organisation for Economic Co-operation and Development (OECD). The survey results show that the reading literacy scores of Indonesian students have decreased, thus placing Indonesia at 74th out of 79 countries. However, it is still very rare to research the reading literacy of Indonesian students' using a multilevel regression model. This study aims to apply a multilevel regression model to determine the factors influencing Indonesian reading literacy scores in PISA 2018 survey data. The results of this study indicate that the factors that influence response variable are gender, grade level, mother's education, facilities at home, age at school entry, student discipline behavior at school, and failing grade, while at the school level are the type of school and school location. The magnitude variance of student reading literacy scores can be explained by the explanatory variables the student level is 11,42% and the school level is 60,66%, while the rest is explained by another factor outside the study.
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.
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.
Pemodelan ARIMA Intervensi Untuk Meramalkan Harga Minyak Mentah Dunia Indah Lestari; Bagus Sumargo; Faroh Ladayya
Statistika Vol. 22 No. 2 (2022): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v22i2.1593

Abstract

Harga minyak mentah dunia mengalami penurunan yang sangat signifikan karena adanya suatu intervensi, yaitu pandemi COVID-19. Peramalan harga minyak mentah dunia penting dilakukan untuk memberikan informasi terkait fluktuasi harga minyak mentah karena adanya ketidakpastian harga akibat adanya intervensi. Metode untuk memodelkan dan meramalkan data deret waktu yang dipengaruhi oleh intervensi adalah metode analisis intervensi. Penelitian ini bertujuan untuk mendapatkan model intervensi terbaik dan hasil peramalan harga minyak mentah dunia menggunakan model intervensi terbaik. Tahapan analisis intervensi yaitu membagi data menjadi data sebelum intervensi dan data saat intervensi sampai data terakhir. Data sebelum intervensi digunakan untuk pemodelan ARIMA. Sisaan pada model ARIMA berdasarkan data sebelum intervensi tersebut digunakan untuk identifikasi orde intervensi. Selanjutnya adalah melakukan pendugaan parameter, uji diagnostik, dan melakukan peramalan. Hasil penelitian menunjukkan bahwa model intervensi terbaik adalah model intervensi ARIMA(0,2,2), yang artinya pembedaan data harga minyak mentah dunia dilakukan sebanyak 2 kali, data suatu periode dipengaruhi oleh nilai sisaan 2 periode sebelumnya, serta dipengaruhi oleh orde intervensi b=0, s=0, r=1. Hasil peramalan harga minyak mentah dunia menggunakan model intervensi terbaik menghasilkan harga minyak mentah dunia yang cenderung konstan dengan harga berkisar antara 78 sampai 86 dollar AS per barel dengan MAPE yaitu 9,29%, artinya kemampuan model dalam melakukan peramalan sangat baik. Kata Kunci: Model intervensi, ARIMA, harga minyak mentah dunia, COVID-19
Pengembangan Kompetensi Guru Sekolah Dasar Melalui Telaah Miskonsepsi Materi Operasi Bilangan Pecahan Devi Eka Wardani Meganingtyas; Faroh Ladayya; Bhayu Phermana Sachty Muktar; Rizka Anjani Azzahra; Sitta Aliya Dj
INTEGRITAS : Jurnal Pengabdian Vol 7 No 2 (2023): AGUSTUS - DESEMBER
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat - Universitas Abdurachman Saleh Situbondo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36841/integritas.v7i2.3688

Abstract

Salah satu permasalahan konsep dasar matematika yang ada di sekolah dasar adalah kesulitan dalam memahami konsep (miskonsepsi) materi operasi bilangan, khususnya bilangan pecahan. Oleh karena itu, diperlukan kegiatan kepada masyarakat dalam bentuk pelatihan kepada guru Sekolah Dasar (SD) untuk meningkatkan kompetensi guru sehingga dapat meminimalisir miskonsepsi yang dialami siswa. Pelaksanaan kegiatan dilakukan di wilayah Kota Bekasi dengan bekerja sama dengan mitra KKG Jatiasih Keota Bekasi. Berdasarkan hasil pengamatan guru, masih terdapat banyak miskonsepsi yang ditemui di sekolahnya terkait konsep materi bilangan pecahan dan operasinya.
Co-Authors Bagus Sumargo Bhayu Phermana Sachty Muktar Bintang Mahesa Wardana Dania Siregar Devi Eka Wardani Meganingtyas Hilmy Dzaky Muchtar Indah Lestari Naufal Zhafran Albaqi Nia Rahayu Ningsih Rifa Kamilia Rizka Anjani Azzahra Sitta Aliya Dj Vera Maya Santi Wiligis Eka Pranoto