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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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (347.338 KB) | DOI: 10.12928/kesmas.v9i2.2125

Abstract

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.
Analysis of Geographically and Temporally Weighted Regression (GTWR) GRDP of the Construction Sector in Java Island Haryanto, Sugi; Aidi, Muhammad Nur; Djuraidah, Anik
Forum Geografi Vol 33, No 1 (2019): July 2019
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/forgeo.v33i1.7332

Abstract

The construction sector is one of the sectors that have strategic value in the national economy. Economic activity in an area is measured using the Gross Regional Domestic Product (GRDP). The development of economic activities in the construction sector can be seen from the GRDP of the construction sector. The Geographically and Temporally Weighted Regression (GTWR) model is a development of the Geographically Weighted Regression (GWR) model taking into account the diversity of locations and times. This study used secondary data, namely the data of GRDP the construction sector as a response variable and four explanatory variables, namely the number of population, local revenue, area, and the number of construction establishments. The purpose of this study is to determine the factors that influence each regency/municipality and each year observing the GRDP of the construction sector in Java with the GTWR model. GTWR model is more effective to describe the value of GRDP the construction sector of regencies/municipalities in Java Island in 2010-2016. This is indicated by the decrease in values of Root Mean Square Error (RMSE), Mean Absolute Deviation (MAD), and the Mean Absolute Percentage Error (MAPE).
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (347.338 KB) | DOI: 10.12928/kesmas.v9i2.2125

Abstract

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.
PENDEKATAN GEOGRAPHICALLY WEIGHTED ZERO INFLATED POISSON REGRESSION (GWZIPR) DENGAN PEMBOBOT FIXED BISQUARE KERNEL PADA KASUS DIFTERI DI INDONESIA Ismah, Ismah; Sumertajaya, I Made; Djuraidah, Anik; Fitrianto, Anwar
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 14 No 1 (2020): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : MATHEMATIC DEPARTMENT, FACULTY OF MATHEMATICS AND NATURAL SCIENCES, UNIVERSITY OF PATTIMURA

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (679.128 KB) | DOI: 10.30598/barekengvol14iss1pp039-046

Abstract

The number of deaths due to diphtheria is counts data and there is a considerable presence of zeros (excess zeros). Besides, data on the spread of disease are generally geographically oriented or observed in each particular region, which is a type of spatial data. Geographically Weighted Zero Inflated Poisson Regression (GWZIPR), as the development of Geographically Weighted Regression (GWR) and Zero Inflated Poisson (ZIP) models will be used as a model in processing provincial diphtheria data in Indonesia in 2018, with the independent variable percentage of diphtheria cases (X1), percentage of vaccinated numbers (X2) and percentage of the population (X3) in each province in Indonesia. Estimating model parameters uses the method of maximum likelihood estimation. While the weighting function used is fixed bisquare kernel. Data is processed using software R packages lctools. The results were obtained if the model involved all three independent variables, the effect of the three independent variables on the number of deaths due to diphtheria was not significant. This is because there is a strong and significant relationship between independent variables, so that if the model does not involve a variable percentage of the population (population density), the percentage of vaccinated people affects the number of deaths caused by diphtheria significantly in an area. So that the provision of immunization vaccines can reduce the number of deaths caused by diphtheria
UTILIZATION OF STUDENT’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

Abstract

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 Student’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 Student’s t distribution for noise can reduce the outliers in technical efficiency scores. Student’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.
PENDUGAAN AREA KECIL DATA PRODUKTIVITAS TANAMAN PADI DENGAN GEOADDITIVE SMALL AREA MODEL Ardiansyah, Muhlis; Djuraidah, Anik; Kurnia, Anang
Jurnal Penelitian Pertanian Tanaman Pangan Vol 2, No 2 (2018): Agustus 2018
Publisher : Pusat Penelitian dan Pengembangan Tanaman Pangan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2440.908 KB) | DOI: 10.21082/jpptp.v2n2.2018.p101-110

Abstract

Tanaman padi memiliki peran politik sebagai tolak ukur keberhasilan pemerintah di bidang pertanian. Pemerintah daerah membutuhkan data produktivitas tanaman padi hingga level kecamatan untuk mendukung program swasembada pangan. Permasalahannya, BPS tidak dapat menyajikan data produktivitas tanaman padi hingga level kecamatan karena ukuran contoh pada Survei Ubinan tidak representatif untuk penyajian data hingga level kecamatan. Tujuan dari penelitian ini adalah melakukan pendugaan data produktivitas tanaman padi dan produksi beras per kecamatan di Kabupaten Seruyan Provinsi Kalimantan Tengah Tahun 2016. Kabupaten ini dipilih karena memiliki lahan menganggur yang besar mencapai 479ribu hektar. Metode yang diajukan untuk menyelesaikan permasalahan di atas adalah menggunakan Geoadditive Small Area Model. Keakuratan pendugaan akan dievaluasi dengan nilai RMSE (Root Mean Squared Error) menggunakan metode jackknife dengan proses resampling. Hasil penelitian menunjukkan produktivitas tanaman padi di Kabupaten Seruyan memiliki kecenderungan bahwa semakin ke hilir Sungai Seruyan maka produktivitas tanaman padi menjadi semakin besar. Produktivitas padi tertinggi berada di Kecamatan Seruyan Hilir Timur (34.58 ku/ha) dan terendah di Seruyan Hulu (19.93 ku/ha). Hasil dugaan dengan model Geoadditive Small Area  memberikan hasil yang akurat dengan nilai RMSE yang kecil. Dari seluruh kecamatan di Kabupaten Seruyan, hanya empat kecamatan mengalami surplus beras  yaitu Kecamatan Seruyan Hilir Timur, Danau Sembuluh, Seruyan Hulu, dan Suling Tambun sedangkan enam kecamatan lainnya mengalami defisit kebutuhan beras. Secara keceluruhan, Kabupaten Seruyan selama tahun 2016  mengalami defisit kebutuhan beras sebesar 8 236.80 ton.Kata kunci: Produktivitas padi, Geoadditive Small Area Model, Surplus/ defisit beras.
REGRESI TERBOBOTI GEOGRAFIS DENGAN PEMBOBOT KERNEL KUADRAT GANDA UNTUK DATA KEMISKINAN DI KABUPATEN JEMBER Rita Rahmawati; Anik Djuraidah
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 (345.642 KB)

Abstract

The determination of whether  rural areas are considered  poor or no are usually based on  the average cost per capita with a global analysis that needs independent observations and the results are applied to all villages. But it is very likely that poverty would be influenced by space and neighboring areas, so the data between observations are rarely independent. One of the statistical analysis that encounters this spatial problem is Geographically Weighted Regression (GWR), which  gives different weights to each geographical observation. In this paper, the weighting used for the GWR model is kernel bi-square, with its bandwidth values respectively. Optimal bandwidth can be obtained by minimizing the value of cross validation coefficient (CV). The results showed that the GWR model is more effective than the regression to analyze the data on average expenditure per capita in Jember.
Aplikasi model kalibrasi di bidang kimia adalah pemodelan hubungan antara kandungan senyawa aktif yang ditentukan dari High Performance Liquid Chromatography (HPLC) dengan bentuk spektrum  dari spektrometer Fourier Transform Infrared (FTIR). Permasalahan utama dalam kalibrasi adalah multikolinear dan pengamatan pencilan. Regresi Kuadrat Terkecil Parsial (RKTP)  merupakan sebuah teknik prediktif yang mampu mengatasi masalah multikolinearitas.. SIMPLS (Straightforward Implementation PLS) adalah al Ismah .; Aji Hamim Wigena; Anik Djuraidah
FORUM STATISTIKA DAN KOMPUTASI Vol. 14 No. 1 (2009)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Abstract

Aplikasi model kalibrasi di bidang kimia adalah pemodelan hubungan antara kandungan senyawa aktif yang ditentukan dari High Performance Liquid Chromatography (HPLC) dengan bentuk spektrum  dari spektrometer Fourier Transform Infrared (FTIR). Permasalahan utama dalam kalibrasi adalah multikolinear dan pengamatan pencilan. Regresi Kuadrat Terkecil Parsial (RKTP)  merupakan sebuah teknik prediktif yang mampu mengatasi masalah multikolinearitas.. SIMPLS (Straightforward Implementation PLS) adalah algoritma pendugaan RKTP yang  tidak resisten terhadap pengamatan pencilan. Hubert and Brande (2003) mengemukakan algoritma RSIMPLS yang bersifat resisten terhadap pencilan. RSIMPLS dibentuk dari matriks ragam-peragam robust dan regresi linear robust. Pada penelitian ini dilakukan modifikasi fungsi bobot pada  RSIMPLS dengan penduga-M Huber dimana setiap pengamatan akan diberikan nilai bobot kecil  jika jarak robust dan jarak ortogonal pengamatan ke-i melebihi nilai batas yang ditentukan, dan  untuk lainnya. Dengan demikian besar  tidak hanya 0 dan 1, melainkan . Hasil penelitian menunjukkan RMSEP (root mean square error) pada metode modifikasi bobot lebih kecil dibandingkan RSIMPLS
Based on the six indicators provided by the State Ministry for Acceleration Development Backward Regions,  the backward regions were clustered into 4 groups: fairly backward region, backward region, highly backward region, and severely backward region. This clustering used weighted average method. The weakness of this method was that the weight determination on each indicator was decided without distinct reference. Besides, there are many outlier in KNDPT data. The objectives of this research ar Titin Agustin; Anikk Djuraidah
FORUM STATISTIKA DAN KOMPUTASI Vol. 15 No. 1 (2010)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Abstract

Based on the six indicators provided by the State Ministry for Acceleration Development Backward Regions,  the backward regions were clustered into 4 groups: fairly backward region, backward region, highly backward region, and severely backward region. This clustering used weighted average method. The weakness of this method was that the weight determination on each indicator was decided without distinct reference. Besides, there are many outlier in KNDPT data. The objectives of this research are to study the non-hierarchy cluster methods, that is C-Means and Fuzzy C-Means. Both methods have difference on membership value and weighted membership value. The result of this research showed that Fuzzy C-Means was more robust than C-Means.
PENDUGAAN REGESI SEMIPARAMETRIK DENGAN PENDEKATAN MODEL CAMPURAN LINEAR Anik Djuraidah
FORUM STATISTIKA DAN KOMPUTASI Vol. 14 No. 2 (2009)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (479.934 KB)

Abstract

Hubungan fungsional antara respon dengan peubah penjelas pada  regresi linear berganda berbentuk parametrik dengan metode pendugaan parameternya adalah metode kuadrat terkecil. Pada regresi semiparametrik, hubungan  fungsional antara respon dengan peubah penjelas dapat berbentuk parametrik atau nonparametrik. Metode yang banyak digunakan untuk pendugaan regresi semiparametrik adalah algoritma backfitting yang dikemukakan oleh Hastie & Tibshirani (1990). Pada penelitian ini pendugaan regresi parametrik didekati dengan model campuran linear. Keuntungan utama pendekatan  dengan model campuran linear adalah menggunakan metode ML atau REML sehingga memberi kemudahan dalam seleksi model dan penarikan kesimpulan