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All Journal Jurnal Ilmu Pertanian Indonesia FORUM STATISTIKA DAN KOMPUTASI Media Statistika Disease Prevention and Public Health Journal JURNAL PETERNAKAN Sosioinforma JUITA : Jurnal Informatika International Journal of Advances in Intelligent Informatics BAREKENG: Jurnal Ilmu Matematika dan Terapan J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika Jambura Journal of Mathematics ComTech: Computer, Mathematics and Engineering Applications J-ADIMAS (Jurnal Pengabdian kepada Masyarakat) JURNAL AKUNTANSI KEUANGAN DAN MANAJEMEN Jurnal Pendidikan dan Kebudayaan Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Jurnal Natural Xplore: Journal of Statistics Jurnal Pengabdian Masyarakat dan Lingkungan PRAGMATIS Jurnal Manajemen dan Bisnis Jurnal Medika Veterinaria Scientific Journal of Informatics Indonesian Journal of Statistics and Its Applications Jurnal Info Kesehatan
Indahwati
Department Of Statistics, Faculty Of Mathematics And Natural Science, IPB University, Bogor, Indonesia
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AUTOREGRESSIVE MOVING AVERAGE (ARMA) MODEL FOR DETECTING SPATIAL DEPENDENCE IN INDONESIAN INFANT MORTALITY DATA Ray Sastri; Khairil Anwar Notodiputro; _ Indahwati
FORUM STATISTIKA DAN KOMPUTASI Vol. 20 No. 2 (2015)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (543.39 KB)

Abstract

Infant mortality is an important indicator that must to be monitored seriously. The infant mortality is associated with several determinants, such as the infant’s characteristics, maternal and fertility factors, housing condition, geographical area, and policy. It can also be influenced by the presence of spatial dependence between regency in Indonesia. This is due to the social and economic activity in one regency depend on social and economic activity in other regency, especially with neighboring area. Infant mortality data obtained from Indonesian Demographic and Health Survey (IDHS) published by Statistic Indonesia (BPS). In BPS’s publication, data is always sorted by regency code from the smallest to the largest. Therefore, the closeness of the regency code refers to the closeness of the regency itself. the infant mortality data by regency could be analogized as time series data. So that, the relationship between regency can be seen using Autoregressive Moving Average (ARMA) model. If the parameter at ARMA is significant, we can conclude that there is a spatial dependence on the infant mortality in Indonesia. This paper will focus on discussing whether there is a spatial dependenc in Indonesia’s Infant Mortality Data using ARMA approach. The result is the Autocorrelation Function (ACF) showed a significant effect until lag 3, and Partial Autocorrelation Function (PACF) showed a significant effect until lag 1. Based on Bayesian Information Criterion (BIC), the AR(1) fitted the model well. It shows that the probability of infant mortality in one regency is affected by probability of infant mortality in neighboring regency.Key words : ARMA, spatial dependence, infant mortality, IDHS
COMPARISON OF LOW BIRTH WEIGHT RATE ESTIMATES BASED ON DIFFERENT AGGREGATE LEVELS DATA USING LOGISTIC REGRESSION MODEL Antonius Benny Setyawan; Khairil Anwar Notodiputro; _ Indahwati
FORUM STATISTIKA DAN KOMPUTASI Vol. 20 No. 2 (2015)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Abstract

Low Birth-Weight (LBW) is defined as a birth weight of a live-born infant of less than 2.500 grams regardless of gestational age. Case of LBW is associated with infant mortality, infant morbidity, inhibited growth and slow cognitive development, also chronic diseases in later life. It is vital because with high LBW rate the generation hardly grow into its full potential. There are many risk factors, whether direct or indirect, can cause a birth as a high risk of Low Birth Weight case. These factors are genetics, obstetrics, nutrition intakes, diseases, toxic exposures, pregnancy care and social factors. With these factors measured, statistical modelling can be used to estimate rate on group level or probability on individual level of the Low Birth Weight event. As the case is a binary response, Logistic Regression Model is commonly used.Data of LBW case and the risk factors came from Indonesian Demographic and Health Survey (IDHS) 2012. Published national rate of LBW was 7.3% with provincial rates fell between 4.7-15.7 %. Although the national rate was considered low, the wide variation of provincial rates showed that the problem was not handled so well. However, these rates cannot be measured yearly due to 5 year period of the survey. With the availability of risk factors data a model can be built to estimate the LBW rates. But, another problem for the model is the case when aggregate level data is available instead of individual level data. So, the purpose of this study was to compare models based on different aggregate levels and theirs estimated provincial rates. Comparison was done among individual birth level, mother level, household level and census block (cluster) level. Models from three former levels were quite similar with adequate significant parameters, while cluster level model was resulted only a few significant parameters. But instead, LBW rate estimates from cluster level model were the closest to the direct estimates. But the variance of these estimates was still higher than the other models.Key words : Low Birth-Weight, IDHS, Logistic Regression, GLM, Aggregate Data
SMALL AREA ESTIMATION OF LITERACY RATES ON SUB-DISTRICT LEVEL IN DISTRICT OF DONGGALA WITH HIERARCHICAL BAYES METHOD Rifki Hamdani; Budi Susetyo; _ Indahwati
FORUM STATISTIKA DAN KOMPUTASI Vol. 20 No. 2 (2015)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Abstract

Literacy Rate (LR) is defined as percentage of population aged over 15 with ability to read and write. LR, as one of people welfare indicators, is a measurement of educational development. The indicator, as a measurement of government performance on education, can be measured if all variables related is available. Statistics Indonesia (BPS) each year calculated LR based on National Socio-Economic Survey (SUSENAS) with estimation available only on provincial level and district level. Along with establishment of autonomous regional policy, where regional government had greater power to manage its own region, availability of LR on lower levels to monitor educational development is necessary. Due to sampling design of SUSENAS, accommodated only estimation on district level, will give high variance if used to estimate on lower sub-district level, although still unbiased. Modelling LR was done with Logit-Normal approach, because LR data followed Binomial Distribution. Good estimators from inadequate sample size can be obtained with method of Small Area Estimation (SAE). Hierarchical Bayes (HB) method is one of SAE methods which are proven to give good estimate on binomial distributed data as LR. Estimation on sub-district level in District of Donggala with HB method gave better result compared to the direct estimation with lower Mean Square Error (MSE).Key words : Small Area Estimation, Literacy Rate, Hierarchical Bayes, Logit-Normal Model
SMALL AREA ESTIMATION FOR ESTIMATING THE NUMBER OF INFANT MORTALITY USING MIXED EFFECTS ZERO INFLATED POISSON MODEL Arie Anggreyani; _ Indahwati; Anang Kurnia
FORUM STATISTIKA DAN KOMPUTASI Vol. 20 No. 2 (2015)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Abstract

Demographic and Health Survey Indonesia (DHSI) is a national designed survey to provide information regarding birth rate, mortality rate, family planning and health. DHSI was conducted by BPS in cooperation with National Population and Family Planning Institution (BKKBN), Indonesia Ministry of Health (KEMENKES) and USAID. Based on the publication of DHSI 2012, the infant mortality rate for a period of five years before survey conducted is 32 for 1000 birth lives. In this paper, Small Area Estimation (SAE) is used to estimate the number of infant mortality in districts of West Java. SAE is a special model of Generalized Linear Mixed Models (GLMM). In this case, the incidence of infant mortality is a Poisson distribution which has equdispersion assumption. The methods to handle overdispersion are binomial negative and quasi-likelihood model. Based on the analysis results, quasi-likelihood model is the best model to overcome overdispersion problem. However, after checking the residual assumptions, still resulted that residuals of model formed two normal distributions. So as to resolve the issue used Mixed Effect Zero Inflated Poisson (ZIP) Model. The basic model of the small area estimation used basic area level model. Mean square error (MSE) which based on bootstrap method is used to measure the accuracy of small area estimates.Keywords : SAE, GLMM, Mixed Effect ZIP Model, Bootstrap
PROPORSI KEMISKINAN DI KABUPATEN BOGOR Titin Suhartini; Kusman Sadik; Indahwati Indahwati
Sosio Informa Vol 1 No 2 (2015): Sosio Informa
Publisher : Politeknik Kesejahteraan Sosial

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33007/inf.v1i2.144

Abstract

Kemiskinan merupakan salah satu permasalahan mendasar yang menjadi pusat perhatian pemerintahIndonesia. Aspek penting untuk mendukung strategi penanggulangan kemiskinan adalah ketersediaandata dan informasi yang akurat. Penelitian ini bertujuan untuk menduga proporsi status kemiskinan rumahtangga pada tingkat kecamatan di Kabupaten Bogor dan mengidentifikasi sumber/jenis pekerjaan rumahtangga. Metode yang disusun berdasarkan pendugaan langsung dengan asumsi metode sampel acaksederhana untuk memperoleh penduga proporsi dan berdasarkan tabulasi silang untuk mengetahui latarbelakang jenis pekerjaan yang berdampak pada kemiskinan. Penelitian ini menggunakan data sekunderberupa Survei Sosial Ekonomi Nasional (Susenas) dengan variabel terpilih. Badan Pusat Statistik memilikiprogram pengumpulan data melalui sensus dan survei. Survei tersebut menggunakan metode rancanganpenarikan sampel yang kompleks. Hasil penelitian menunjukkan bahwa rumah tangga miskin di KabupatenProporsi Kemiskinan di Kabupaten Bogor, Titin Suhartini, Kusman Sadik, dan Indahwati 161Bogor sebesar 6,84%. 31,08% rumah tangga miskin berasal dari jenis pekerjaan pertanian tanaman pangan.Hanya 24 kecamatan yang dapat dilakukan pendugaan proporsi status kemiskinan rumah tangga.Pendugaanproporsi rumah tangga miskin terbesar berada di kecamatan Nanggung yaitu sebesar 45%. Untuk mengatasiketerbatasan pendugaan yang dilakukan terhadap 16 kecamatan lainnya dapat menggunakan alternatifmetode pendugaan area kecil.Kata Kunci: pendugaan, proporsi, rumah tangga.
Overdispersion study of poisson and zero-inflated poisson regression for some characteristics of the data on lamda, n, p Lili Puspita Rahayu; Kusman Sadik; Indahwati Indahwati
International Journal of Advances in Intelligent Informatics Vol 2, No 3 (2016): November 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v2i3.73

Abstract

Poisson distribution is one of discrete distribution that is often used in modeling of rare events. The data obtained in form of counts with non-negative integers. One of analysis that is used in modeling count data is Poisson regression. Deviation of assumption that often occurs in the Poisson regression is overdispersion. Cause of overdispersion is an excess zero probability on the response variable. Solving model that be used to overcome of overdispersion is zero-inflated Poisson (ZIP) regression. The research aimed to develop a study of overdispersion for Poisson and ZIP regression on some characteristics of the data. Overdispersion on some characteristics of the data that were studied in this research are simulated by combining the parameter of Poisson distribution (λ), zero probability (p), and sample size (n) on the response variable then comparing the Poisson and ZIP regression models. Overdispersion study on data simulation showed that the larger λ, n, and p, the better is the model of ZIP than Poisson regression. The results of this simulation are also strengthened by the exploration of Pearson residual in Poisson and ZIP regression.
Backwards Stepwise Binary Logistic Regression for Determination Population Growth Rate Factor in Java Island Khusnia Nurul Khikmah; Indahwati Indahwati; Anwar Fitrianto; Erfiani Erfiani; Reni Amelia
Jambura Journal of Mathematics Vol 4, No 2: July 2022
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1332.021 KB) | DOI: 10.34312/jjom.v4i2.13529

Abstract

The high population growth rate can impact various fields due to several factors. Some of the impacts of this high rate are high poverty rates, unemployment, consumption levels, inequality in education figures, gender empowerment index, and increasingly narrow land or area. Therefore, research on the rate of population growth using data on poverty, unemployment, consumption levels, education rates, gender empowerment index, and area makes sense. This data was taken from the official website of the Central Statistics Agency for six provinces on the island of Java, Indonesia. The data used contains missing data so that the missing data is presumed by using the k-nearest neighbour method. The estimated missing data values were modelled using binary logistic regression. Variables that significantly affect the rate of population growth, namely the level of consumption, gender empowerment index, and area, are obtained using the backward stepwise method and are selected based on the smallest Aikakes criterion information value or the one with the most excellent accuracy rate. 
PENERAPAN METODE KLASIFIKASI RANDOM FOREST DALAM MENGIDENTIFIKASI FAKTOR PENTING PENILAIAN MUTU PENDIDIKAN Aditya Ramadhan; Budi Susetyo; Indahwati
Jurnal Pendidikan dan Kebudayaan Vol. 4 No. 2 (2019)
Publisher : Badan Standar, Kurikulum, dan Asesmen Pendidikan, Kemendikbudristek

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24832/jpnk.v4i2.1327

Abstract

National Education Standards serves as the basis of education development strategy based on the result of evaluation the implementation of education. The evaluation is implemented through accreditation and national exam. The objective of this study is to analyze the score of computer-based national exam based on accreditation scores per items of instrument by applying multiclass random forest classification modeling. The research used Computer-Based National Exam data in 2018 and accreditation data from the year of 2017 and 2018. This study employed random forest for multiclass classification. The results showed that, based on the evaluation model, classification accuration value in multiclass random forest was 83.49%. In addition, this model produces important variables in classifying the average value of computer-based national examination, i.e., items laboratory conditions (x71, x68, x69, x67), electrical installation (x62), infrastructure (x64), canteen (x83), laboratory (x55), special service officers (x56), certified teachers (x39), library staff (x54), head of administration (x51), literacy activities for students (x33), use of textbooks (x14), and community/partner collaboration in education management (x96). Based on the indicators of important variables, National Education Standards that have important role are facility and infrastructure standards, educator and educational staff standards, and graduate competence standards. Therefore, improving the quality of education can be done by improving school facilities, the competency of teacher and education staff, and graduate competency. Abstak Standar Nasional Pendidikan (SNP) berfungsi sebagai dasar strategi pengembangan pendidikan berdasarkan hasil evaluasi pelaksanaan pendidikan. Evaluasi pelaksanaan pendidikan dilaksanakan melalui akreditasi dan ujian nasional (UN). Tujuan penelitian ini untuk menganalisis nilai ujian nasional berbasis komputer (UNBK) berdasarkan skor akreditasi per butir instrumen dengan menerapkan pemodelan klasifikasi random forest multikelas. Penelitian ini menggunakan data UNBK tahun 2018 dan data hasil akreditasi tahun 2017 dan 2018. Metode penelitian yang digunakan adalah pemodelan klasifikasi random forest multikelas. Hasil penelitian menunjukkan bahwa, pertama, berdasarkan evaluasi model, nilai akurasi klasifikasi dalam pemodelan klasifikasi random forest multikelas sebesar 83.49%. Kedua, model ini menghasilkan tingkat kepentingan variabel prediktor (butir-butir instrumen akreditasi) dalam mengklasifikasikan nilai rataan UNBK yakni kondisi laboratorium (x71, x68, x69, x67), instalansi listrik (x62), prasarana (x64), kantin (x83), kondisi laboran (x55), petugas layanan khusus (x56), guru tersertifikat (x39), tenaga perpustakaan (x54), kepala administrasi (x51), kegiatan literasi S/M bagi peserta didik (x33), penggunaan buku teks (x14), dan kerja sama masyarakat/mitra dalam pengelolaan pendidikan (x96). Berdasarkan indikator variabel penting tersebut, SNP yang memiliki peran penting adalah Standar Sarana dan Prasarana, Standar Pendidik dan Tenaga Kependidikan, dan Standar Kompetensi Lulusan. Oleh karena itu, peningkatan mutu pendidikan dapat dilakukan dengan meningkatkan sarana dan prasarana, kompetensi pendidik dan tenaga kependidikan, serta kompetensi lulusan.Â
Teknik Oversampling Pada Regresi Logistik Ordinal Dalam Menduga Faktor Yang Memengaruhi Risiko Penyebaran Zona Covid-19 di Kabupaten Garut Ghina Fauziah; Indahwati; Erfiani; Anwar Fitrianto; Reni Amelia
J STATISTIKA: Jurnal Imiah Teori dan Aplikasi Statistika Vol 15 No 2 (2022): Jurnal Ilmiah Teori dan Aplikasi Statistika
Publisher : Fakultas Sains dan Teknologi Univ. PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/jstat.vol15.no2.a5596

Abstract

Covid-19 merupakan penyakit yang disebabkan oleh infeksi virus SARS-CoV-2, pertama kali masuk ke Indonesia pada awal tahun 2020. Adanya wabah covid-19 menyebabkan setiap daerah yang ada di Indonesia khususnya kabupaten Garut harus terbagi ke dalam beberapa risiko zona covid-19 sesuai dengan kondisi dari suatu daerah tersebut. Beberapa faktor yang dapat memengaruhi suatu daerah masuk pada risiko zona tertentu dapat ditentukan berdasarkan jumlah kasus positif covid-19, kasus suspek, kasus kontak erat, jumlah desa, dan kepadatan penduduk daerah tersebut. Regresi logistik ordinal merupakan salah satu analisis regresi yang digunakan untuk menganalisa hubungan antara variabel prediktor dan variabel respon, dimana variabel respon tersebut bersifat kategorik dengan skala ordinal. Oleh karena itu, maka digunakan regresi logistik ordinal untuk mengetahui faktor apa saja yang memberikan pengaruh terhadap pembagian risiko zona covid-19 di kabupaten Garut pada bulan Juli tahun 2021. Sebelum melakukan pemodelan regresi logistik ordinal dilakukan terlebih dahulu proses teknik resampling dengan metode oversampling untuk menangani data yang tidak seimbang pada peubah respon. Berdasarkan pemodelan hasil dari pemodelan serta pengujian secara parsial, didapatkan bahwa peubah bebas yang memiliki pengaruh terhadap risiko zona covid-19 di kabupaten Garut yaitu jumlah desa, kepadatan penduduk, kasus suspek, dan kasus konfirmasi positif dengan nilai akurasi sebesar 85.71%.
Faktor – Faktor yang Memengaruhi Permasalahan Stunting di Jawa Barat Menggunakan Regresi Logistik Biner Silmi Annisa Rizki Manaf; Erfiani; Indahwati; Anwar Fitrianto; Reni Amelia
J STATISTIKA: Jurnal Imiah Teori dan Aplikasi Statistika Vol 15 No 2 (2022): Jurnal Ilmiah Teori dan Aplikasi Statistika
Publisher : Fakultas Sains dan Teknologi Univ. PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/jstat.vol15.no2.a5654

Abstract

Salah satu bentuk akibat dari kurangnya asupan gizi kronis pada balita adalah stunting. Stunting merupakan permasalahan kesehatan yang saat ini sedang digencarkan untuk diturunkan angka prevalensinya. Permasalahan kesehatan ini berhubungan erat pada pertumbuhan tinggi badan yang lebih rendah dengan anak seusianya. Berdasarkan data Kemenkes per Agustus 2021, Provinsi Jawa Barat menduduki posisi pertama dengan angka balita stunting paling tinggi di Indonesia. Penelitian ini bertujuan untuk mengetahui faktor apa saja yang memengaruhi terjadinya stunting pada balita dan memodelkan dengan metode regresi logistik biner untuk wilayah Jawa Barat. Metode ini dapat menunjukkan faktor yang memengaruhi berdasarkan peubah yang signifikan. Regresi logistik biner akan memodelkan hubungan antara satu atau beberapa peubah prediktor dengan peubah respon yang kategorik. Peubah respon didefinisikan sebagai persentase angka balita stunting dan dibagi kedalam dua kategori yakni tinggi dan rendah. Pengategorian kelas didasarkan pada nilai median pada persentase angka balita stunting. Unit penelitian menuju pada 27 wilayah Kabupaten/Kota di Jawa Barat. Hasil analisis menunjukkan dari 11 peubah prediktor, setelah dilakukan pemodelan terdapat 3 peubah yang berpengaruh signifikan pada taraf nyata 0,10 yakni imunisasi dasar lengkap, tempat pengelolaan makanan yang memenuhi syarat kesehatan, dan penduduk miskin. Model yang terpilih berdasarkan nilai akurasi seimbang terbesar dibandingkan model lainnya yakni dihasilkan nilai akurasi seimbang sebesar 81,59%.
Co-Authors A. A., Muftih Aditya Ramadhan Agus Mohamad Soleh Agustini , Ni Ketut Yulia Agustini, Ni Ketut Yulia Aji Hamim Wigena Akbar Rizki Aliu, Mufthi Alwi ALIU, MUFTIH ALWI Amelia, Reni Amin, Yudi Fathul Anang Kurnia Anik Djuraidah Antonius Benny Setyawan Ari Handayani Arie Anggreyani Aristawidya, Rafika Assyifa Lala Pratiwi Hamid Aunuddin . Bagus Sartono Budi Susetyo Cahyani Oktarina Chrisinta, Debora Daswati, Oktaviyani Dea Fisyahri Akhilah Putri Dian Kusumaningrum Erfiani Erfiani Erfiani Erfiani Erfiani Etis Sunandi Farit Mochamad Afendi Farit Mohamad Afendi Fatimah Fatimah Fira Nurahmah Al Aminy Fitrianto, Anwar Fulazzaky, Tahira Ghina Fauziah Hanifa Izzati Hari Wijayanto Harismahyanti A., Andi Hasanah, Lailatul I Gusti Putu Purnaba I Made Sumertajaya Iin Maena Indah, Yunna Mentari Irawan Irawan Jaya, Eddy Santosa Julianti, Elisa D Kamil, Farid Ikram Karunia, Nia Khairil Anwar Notodiputro Khikmah, Khusnia Nurul Kholidiah, Kholidiah Khusnia Nurul Khikmah Kusman Sadik Latifah, Leli Lestari, Nila Lili Puspita Rahayu Miranti, Ita Miranti, Ita Mohammad Masjkur Mualifah, Laily Nissa Mualifah, Laily Nissa Atul Muhammad Nur Aidi Naima Rakhsyanda Narindria, Yasmin Nadhiva Nurul Fadhilah Panjaitan, Intan Juliana Puput Cahya Ambarwati Putra, Stefanus Morgan Setyadi Perdana Putri, Christiana Anggraeni Ramdani, Indri Rasyid, Baharun Ray Sastri Regan, Regan Reni Amelia Reni Amelia Reza, Charolina Therezia Rifki Hamdani Rindy Anggun Pertiwi Salvina Salvina Silmi Annisa Rizki Manaf Siti Hafsah Siwi Haryu Pramesti Tahira Fulazzaky Tina Aris Perhati Titin Agustina Titin Suhartini Titin Suhartini, Titin Utami Dyah Syafitri Vera Maya Santi Vitona, Desi Wahyudi Setyo Yenni Angraini Yuniarty, Titin Zulkarnain, Rizky _ Aunuddin