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All Journal Jambura Journal of Mathematics Emerging Statistics and Data Science Journal
PRASMONO, AMIMAH SHABRINA PUTRI
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Computer Science & IT Mathematics

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Analisis Regresi Berganda pada Faktor-Faktor yang Mempengaruhi Kinerja Fisik Preservasi Jalan dan Jembatan Di Provinsi Sumatera Selatan: Analisis Regresi Berganda PRASMONO, AMIMAH SHABRINA PUTRI; Atina Ahdika
Emerging Statistics and Data Science Journal Vol. 1 No. 1 (2023): Emerging Statistics and Data Science Journal
Publisher : Statistics Department, Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/esds.vol1.iss.1.art6

Abstract

Maksud penelitian ini ialah menganalisis faktor-faktor yang mempengaruhi Kinerja Fisik Preservasi Jalan dan Jembatan di Provinsi Sumatera Selatan. Dalam penelitian ini terdapat variabel rencana fisik dan realisasi fisik yang menjadi variabel independen, dan variabel kinerja fisik Balai Besar Pelaksanaan Jalan Nasional (BBPJN) Provinsi Sumatera Selatan merupakan variabel dependen. Tipe penelitian yang dipakai ialah penelitian deskriptif dengan pendekatan korelasi. Tipe data yang dipakai ialah data sekunder berbentuk Laporan Progress Mingguan Preservasi Jalan dan Jembatan Wilayah IA (Sumatera) TA 2021 yang diperoleh dari Subdirektorat Preservasi Jalan dan Jembatan Wilayah IA. Metode analisis data yang diterapkan ialah analisis Regresi Linear Berganda. Didapatkan hasil pengolahan data dari regresi linear berganda yaitu. Dari persamaan yang diperoeh bahwa variabel Rencana Fisik dan Realisasi Fisik memiliki pengaruh yang signifikan dalam mempengaruhi Kinerja Fisik BBPJN Sumatera Selatan Tahun 2021. Didapatkan koefisien determinasi sebesar 0.9096. Artinya, variabel rencana fisik dan realisasi fisik mampu menjelaskan variabel kinerja fisik sejumlah 90.69%, sementara itu sisanya sejumlah 9.31% diterangkan sama faktor lain yang tidak dituturkan dalam model.
The Childfree Phenomenon in Indonesia: An Analysis of Sentiments on YouTube Video Comments Prasmono, Amimah Shabrina Putri; Kartikasari, Mujiati Dwi
Jambura Journal of Mathematics Vol 6, No 1: February 2024
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v6i1.23591

Abstract

Childfree is a condition in which a person or couple decides not to have children in marriage. Childfree became popular in Indonesia when YouTuber and influencer Gita Savitri uploaded an Instagram story about it. This brought many pros and cons among the people towards the freedom to have children. Many TV broadcasts and YouTube videos cover this phenomenon. Several YouTube channels that broadcast this phenomenon are Menjadi Manusia and Analisa Channel. We collect YouTube comment data using web scraping techniques. From September 2021 to September 2022, 674 sample data points were obtained from two YouTube videos. Data is labelled (positive, negative, and neutral) using the Indonesian language lexicon approach as well as the Support Vector Machine (SVM) and Random Forest algorithms to determine the best model for classifying YouTube comments. The purpose of this research is to understand the public's perception of childfree and to compare the accuracy and AUC values of the two methods. Based on the results of the analysis, 128 comments are classified as positive, the remaining 39 comments are classified as neutral, and 503 comments are classified as negative. This shows that that the commentators on YouTube do not support or give a negative stigma to people who adhere to childfree. The solution to the balanced data problem for each sentiment class uses the random oversampling (ROS) approach. The RBF kernel SVM classification algorithm is a suitable method for classifying commentary data with an accuracy of 98.01% and an AUC of 98.58%, while the Random Forest algorithm only obtains an accuracy of 94.37% and an AUC of 95.87%.
Co-Authors Atina Ahdika Kartikasari, Mujiati Dwi