Garuda - Garba Rujukan Digital

PROPORSI KEMISKINAN DI KABUPATEN BOGOR Suhartini, Titin; Sadik, Kusman; Indahwati, Indahwati
Sosio Informa Vol 1, No 2 (2015): Sosio Informa
Publisher : Puslitbangkesos

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

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.

Modeling of Malaria Prevalence in Indonesia with Geographically Weighted Regression Miranti, Ita; Djuraidah, Anik; Indahwati, Indahwati
Kes Mas: Jurnal Fakultas Kesehatan Masyarakat Vol 9, No 2 (2015): Kes Mas: Jurnal Fakultas Kesehatan Masyarakat
Publisher : Universitas Ahmad Dahlan

Malaria is a public health problem that can lead to death, especially in high-risk groups i.e. infants, toddlers and pregnant women. This disease is still endemic in most parts of Indonesia. The relation of location factor between regions with the surrounding region was assumed to give the effect of spatial variability in the prevalence of malaria in the region. It would lead to the prevalence of malaria modeling using classical regression methods become less precise due to the assumption of homogeneity of variance was not met. It could be overcome by Geographically Weighted Regression (GWR) modeling. In GWR analysis, the selection weighting function was one determinant of the analysis results. GWR analysis resulted on the prevalence of malaria in Indonesia, GWR model with bisquare kernel weighting function had a better value of R2 and AIC than GWR models with gaussian kernel weighting function.

Analisis dan Perancangan Aplikasi Bee-Messenger Sebagai Media-Media Instant Messanging di Universitas Bina Nusantara Jaya, Eddy Santosa; Irawan, Irawan; Regan, Regan; Indahwati, Indahwati
ComTech: Computer, Mathematics and Engineering Applications Vol 1, No 2 (2010): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v1i2.2673

Technology today has progressed at a rapid rate, as well as with instant messaging growing into all aspects of life, even more so in the advancement of communication technology. By using the VoIP feature, users can communicate by voice via the Internet. Messenger as a communication medium that is fast, accurate and low cost is expected to meet the need for a practical communication media. The purpose of this study is to analyze and design a messaging application called Bee-Messenger which will be used as a medium of instant messaging in Bina Nusantara University, and also analyze the needs of users with feature-contained in commonly used messenger applications. Research methods used are literature review, field studies, and field observations. Evaluation is based on the comparison of Bee-Messenger features with the same features found in Yahoo Messenger and MSN Messenger. Bee-Messenger can also activate YM and MSN messaging accounts simultaneously in a single application that gives users a practical application messaging.

MODELING OF MALARIA PREVALENCE IN INDONESIA WITH GEOGRAPHICALLY WEIGHTED REGRESSION Miranti, Ita; Djuraidah, Anik; Indahwati, Indahwati
Kes Mas: Jurnal Fakultas Kesehatan Masyarakat Vol 9, No 2 (2015): Kes Mas: Jurnal Fakultas Kesehatan Masyarakat
Publisher : Universitas Ahmad Dahlan

Malaria is a public health problem that can lead to death, especially in high-risk groups i.e. infants, toddlers and pregnant women. This disease is still endemic in most parts of Indonesia. The relation of location factor between regions with the surrounding region was assumed to give the effect of spatial variability in the prevalence of malaria in the region. It would lead to the prevalence of malaria modeling using classical regression methods become less precise due to the assumption of homogeneity of variance was not met. It could be overcome by Geographically Weighted Regression (GWR) modeling. In GWR analysis, the selection weighting function was one determinant of the analysis results. GWR analysis resulted on the prevalence of malaria in Indonesia, GWR model with bisquare kernel weighting function had a better value of R2 and AIC than GWR models with gaussian kernel weighting function.

UTILIZATION OF STUDENTâ€™S T DISTRIBUTION TO HANDLE OUTLIERS IN TECHNICAL EFFICIENCY MEASUREMENT Zulkarnain, Rizky; Djuraidah, Anik; Sumertajaya, I Made; Indahwati, Indahwati
MEDIA STATISTIKA Vol 14, No 1 (2021): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.14.1.56-67

Stochastic frontier analysis (SFA) is the favorite method for measuring technical efficiency. SFA decomposes the error term into noise and inefficiency components. The noise component is generally assumed to have a normal distribution, while the inefficiency component is assumed to have half normal distribution. However, in the presence of outliers, the normality assumption of noise is not sufficient and can produce implausible technical efficiency scores. This paper aims to explore the use of Studentâ€™s t distribution for handling outliers in technical efficiency measurement. The model was applied in paddy rice production in East Java. Output variable was the quantity of production, while the input variables were land, seed, fertilizer, labor and capital. To link the output and inputs, Cobb-Douglas or Translog production functions was chosen using likelihood ratio test, where the parameters were estimated using maximum simulated likelihood. Furthermore, the technical efficiency scores were calculated using Jondrow method. The results showed that Studentâ€™s t distribution for noise can reduce the outliers in technical efficiency scores. Studentâ€™s t distribution revised the extremely high technical efficiency scores downward and the extremely low technical efficiency scores upward. The performance of model was improved after the outliers were handled, indicated by smaller AIC value.

APLIKASI REGRESI LOGISTIK ORDINAL MULTILEVEL UNTUK PEMODELAN DAN KLASIFIKASI HURUF MUTU MATA KULIAH METODE STATISTIKA Iin Maena; Indahwati .; Dian Kusumaningrum
FORUM STATISTIKA DAN KOMPUTASI Vol. 15 No. 2 (2010)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (450.765 KB)

Statistical Methods (STK211) is an interdept course under coordination of Statistic Departement Faculty of Mathematics and Natural Science, Bogor Agricultural University (BAU). The final grade received by student who follow Statistical Methods is measurement in ordinal scale, that is A, B, C, D and E. In the 2008/2009 academic year there are 7 parallel classes in the Faculty of Mathematics and Natural Science, BAU. By considering the hierarchical structure contained in the score of student achievement data, the student (first level) is nested in a parallel class (second level), hence this study used multilevel ordinal logistic regression analysis to model the final score of Statistical Methods with the factors that influence it. Explanatory variables that significantly affect the final score of Statistical Methods are the GPA of TPB (student’s first year of college) and gender, with the variability of the intercepts across parallel classes in the logit function as 1.184. Percentage classification accuracy obtained by using multilevel ordinal logistic regression model was 56.85%Keywords : hierarchical, multilevel modeling, multilevel ordinal logistic regression, classification

KAJIAN SIMULASI KETAKNORMALAN PENGARUH ACAK DAN BANYAKNYA DERET DATA LONGITUDINAL DALAM PEMODELAN BERSAMA (JOINT MODELING) (Simulation Study of Random Effects Nonnormality and Number of Longitudinal Data Series in Joint Modeling) Indahwati .; Aunuddin .; Khairil Anwar Notodiputro; I Gusti Putu Purnaba
FORUM STATISTIKA DAN KOMPUTASI Vol. 16 No. 2 (2011)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (335.827 KB)

Joint modeling is intended to model longitudinal response process that affect the other primary response based on assumption that both processes induced by the same random effects. One of the assumptions that must be met in joint modeling is normality of random effects and intra-subject error. The simulation results show that the robustness of parameter estimates of joint model to the assumption of random effects normality can be achieved by increasing the frequency of longitudinal observations. Keywords: longitudinal data, joint modeling, robust

GENERALIZED VARIANCE FUNCTIONS FOR BINOMIAL VARIABLES IN STRATIFIED TWO-STAGE SAMPLING Ari Handayani; Aunuddin .; Indahwati .
FORUM STATISTIKA DAN KOMPUTASI Vol. 10 No. 1 (2005)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (552.919 KB)

This empirical study evaluates the application of Generalized Variance Functions (GVFs) for binomial variables in the 1998 Indonesian Labor Force Survey. The survey employs stratified two-stage cluster sampling for selecting samples from a population of households. The study covers all provinces in Java to produce estimates at the level of Java Island. The relative variance estimates resulted from the GVF models are compared to the relative variance estimates which are computed directly. The results illustrate that model expressed by logarithmic model log = log c + d log () gives a good approximation to estimate the variances for the nonagricultural employment group, especially for working male category both in urban and rural areas. It is also good for the total employment group differentiated by age group, educational attainment, and employment status. On the other hand, the model gives poor results for the agricultural employment group. Based on the empirical results, the GVF models may not perform particularly well for the common characteristics which have relatively dissimilar deff values to majority of characteristics in the same group, since these characteristics usually come out among all persons in the sample household and often among all households in the sample cluster as well. The success of the GVF technique depends critically on the grouping of the estimates total () and amount of characteristics involved as the observations for fitting the model. Furthermore, observations with relatively large residuals will also determine the performance of goodness-of-fit of the model. Application of GVF technique to obtain an approximate standard error on numerous binomial characteristics in large scale survey should be carried out further using extensive data. The better performance of GVF model may also be accomplished by utilizing, for examples, weighted least squares procedure or robust regression method. Additionally, the data users should be warned that there will inevitably be survey characteristics for which GVF's will give poor results or even no GVF will be appropriate. Keywords : Generalized Variance Functions, Stratified Two-Stage Sampling

TINJAUAN EMPIRIK TERHADAP DUGAAN GALAT BAKU NILAI TENGAH YANG DIHASILKAN PROC SURVEYMEANS Bagus Sartono; . Indahwati; Wahyudi Setyo
FORUM STATISTIKA DAN KOMPUTASI Vol. 11 No. 1 (2006)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (71.212 KB)

Penarikan contoh yang semakin kompleks berimplikasi pada proses perhitungan galat baku dugaan parameter semakin rumit. Kesulitan menentukan galat baku ini sering menyebabkan para analis dan peneliti menggunakan formula yang didasarkan pada teknik penarikan contoh acak sederhana. SAS menyediakan PROC SURVEYMEANS yang menghasilkan galat baku dengan formula yang disesuaikan dengan penarikan contohnya. Penelitian ini menunjukkan secara empiris bahwa galat baku dugaan parameter yang dihasilkan oleh PROC SURVEYMEANS memiliki tingkat akurasi yang baik. Indikasi ini ditunjukkan oleh selang kepercayaan yang tidak memuat nilai parameter sebenarnya mendekati tingkat kesalahan (a) yang digunakan.

PENDEKATAN KEKAR UNTUK MODEL BERSAMA (JOINT MODEL) ATAS DASAR SEBARAN t (A Robust Approach for Joint Model Based on t Distribution) _ Indahwati; _ Aunuddin; Khairil Anwar Notodiputro; I Gusti Putu Purnaba
FORUM STATISTIKA DAN KOMPUTASI Vol. 17 No. 1 (2012)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (423.398 KB)

Existing methods for joint modeling are usually based on normality assumption of random effects and intra subject errors. We propose a joint model based on t distribution of the intra subject errors to improve robustness of the estimation. Our model consists of two submodels: a mixed linear mixed effects model for the longitudinal data, and a generalized linear model for continuous/binary primary response. The proposed method is evaluated by means of simulation studies as well as application to HIV data. Keywords: joint modeling, longitudinal data, robust, t distribution

Title

Found 14 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 14 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 14 Documents
Search