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All Journal Business Economic, Communication, and Social Sciences Journal (BECOSS) Engineering, Mathematics and Computer Science Journal (EMACS)
Annisa Putri Ramadhanty
Politeknik Statistika STIS
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering Mathematics

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Penentuan Faktor-Faktor yang Mempengaruhi Tingkat Fertilitas Di Indonesia Tahun 2017 Dengan Metode Multiple Classification Analysis (Analisis Data SDKI 2017) Vivy Maharani; Annisa Putri Ramadhanty; Galang Madya Putra; Iqbal Mukti Pratama; Risni Julaeni Yuhan
Business Economic, Communication, and Social Sciences (BECOSS) Journal Vol. 2 No. 3 (2020): BECOSS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/becossjournal.v2i3.6478

Abstract

Fertility is the ability to produce offspring associated with female fertility. The desired condition is for the population to grow in balance as a prerequisite for achieving a population without growth, where fertility, mortality rates are declining, and distribution is more evenly distributed. To achieve a Balanced Growing Population Condition (PTS), a total fertility rate (TFR) of 2.1 per woman is expected in 2015. However, based on the results of the 2017 IDHS fertility rate in Indonesia is 2.4. This has not met the desired conditions to achieve the Balanced Growing Population (PTS) condition. For this problem, it is necessary to do further research to find out the factors that affect the level of fertility or the number of children born to women. In this study, researchers used the Multiple Classification Analysis (MCA) method to determine the factors that influence the number of births. The results and discussion show that a mother who knows her ovulation cycle and / or lives in a city has an average number of children who are smaller than a mother who does not know her ovulation cycle and / or resides in the village. This happens because a mother who knows her ovulation cycle is more able to control the incidence of pregnancy compared to a mother who does not know her ovulation cycle.
Faktor-Faktor yang Mempengaruhi Persentase Penduduk Miskin di Indonesia Tahun 2015-2018 Menggunakan Regresi Data Panel Ian Tryaldi Halim; Annisa Putri Ramadhanty; Dewi Retno Oscarini; Galang Madya Putra; Helen Fricylya Br Tobing; Rani Nooraeni
Engineering, MAthematics and Computer Science (EMACS) Journal Vol. 2 No. 2 (2020): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v2i2.6368

Abstract

Indonesia as a country rich in natural resources has not been able to make it as a country that is free from poverty. The percentage of poor people in Indonesia is still high, is still less efficient, the government's policy in alleviating poverty. This can be seen from the increase in the human development index, gross domestic product and the number of health facilities that are not counted by reducing the percentage of the poor population. The purpose of this study is to describe the percentage of poor people in Indonesia and to analyze the factors that influence the percentage of poor people in Indonesia. This study uses panel data regression analysis using the Random Effect Model (REM) method. The results showed the regional gross domestic product and the level of openness significantly open to the percentage of Indonesia's poor population. While the human development index and the amount of health development are not significant to the percentage of poor people in Indonesia. From the results of this study, Indonesia can optimize employment opportunities that can be released so that it can improve the state of the country. This implementation is expected to increase the number of poor people in Indonesia which can be significant.
Klasifikasi Status Desa/Kelurahan DIY (Yogyakarta) Menggunakan Model Decision Tree (Studi Kasus Data Praktik Kerja Lapangan Politeknik Statistika STIS Tahun 2020) Apriliansyah Mahmud; Ana Pangestika; Annisa Putri Ramadhanty; Galang Madya Putra; Galuh Sri Natungga Dewi Susilo Putri; Rani Nooraeni
Engineering, MAthematics and Computer Science (EMACS) Journal Vol. 3 No. 1 (2021): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v3i1.6787

Abstract

Status desa/kelurahan menjadi sebuah hal yang penting guna mengetahui perkembangan pembangunan yang ada pada desa/kelurahan tersebut serta dalam melakukan evaluasi terkait kebijakan yang telah dibuat mengenai infrastruktur. Badan Pusat Statistik (BPS) telah melakukan proses klasifikasi dengan metode skoring. Oleh karena itu pada penelitian ini akan mengimplementasikan model decision tree dikarenakan indicator klasifikasi status desa/kalurahan yang digunakan BPS belum mengikuti perkembangan zaman. Dalam penelitian ini menggunakan data hasil Praktik Kerja Lapangan (PKL) Politeknik Statistika STIS tahun 2020 yang dilaksanakan di D.I.Yogyakarta. Diharapkan penelitian ini dapat menjadi metode alternatif untuk mengganti metode yang sudah ada. Hasil penelitian menunjukkan bahwa dari 438 desa/kelurahan model decision tree mampu mengklasifikasi secara benar 392 desa/kelurahan sesuai dengan status desa/kelurahan sebelumnya. Model ini memiliki tingkat kebaikan model (specificity) sebesar 90.32%, presisi model (precision) sebesar 87.5%, sensitivitas model (recall) sebesar 88.42%, serta F1 Score sebesar 87.95%.
Co-Authors Ana Pangestika Apriliansyah Mahmud Dewi Retno Oscarini Galang Madya Putra Galuh Sri Natungga Dewi Susilo Putri Helen Fricylya Br Tobing Ian Tryaldi Halim Iqbal Mukti Pratama Nooraeni, Rani Risni Julaeni Yuhan Vivy Maharani