Pemodelan Faktor-Faktor yang Mempengaruhi Persentase Pencurian Kendaraan Bermotor (Curanmor) di Jawa Timur Menggunakan Regresi Nonparametrik Spline Truncated

Jawa Timur merupakan salah satu provinsi di Indonesia dengan jumlah kejahatan yang cukup tinggi setelah Sumatra Utara dan DKI Jakarta. Data Kepolisian Negara Daerah Jawa Timur menyatakan bahwa jumlah kejahatan di Jawa Timur sebanyak 29.960 kasus, dengan kasus terbanyak di setiap kabupaten/kota adalah pencurian kendaraan bermotor (curanmor). Pada penelitian ini memodelkan persentase pencurian kendaraan bermotor (curanmor) di Jawa Timur dengan 4 variabel yang diduga berpengaruh. Metode yang dipilih adalah regresi nonparametrik spline truncated. Metode tersebut dipilih karena spline merupakan metode yang fleksibel dan pada model ini cenderung mencari sendiri estimasi data. Dalam pemodelan ini terdapat titik knot. Pemilihan titik knot optimum dilakukan dengan cara memilih nilai GCV paling minimum. Berdasarkan penelitian yang telah dilakukan semua variabel prediktor berpengaruh terhadap pencurian kendaraan bermotor (curanmor), yaitu tingkat pengangguran terbuka, kepadatan penduduk, persentase penduduk miskin, dan persentase penduduk yang tidak pernah sekolah, dengan nilai koefisien determinasi sebesar 97.42 %

Comparisons of two quantile regression smoothers

The paper compares the small-sample properties of two non-parametric quantile regression estimators. The first is based on constrained B-spline smoothing (COBS) and the other is based on a variation and slight extension of a running interval smoother, which apparently has not been studied via simulations. The motivation for this paper stems from the Well Elderly 2 study, a portion of which was aimed at understanding the association between the cortisol awakening response and two measures of stress. COBS indicated what appeared be an usual form of curvature. The modified running interval smoother gave a strikingly different estimate, which raised the issue of how it compares to COBS in terms of mean squared error and bias as well as its ability to avoid a spurious indication of curvature. R functions for applying the methods were used in conjunction with default settings for the various optional arguments. The results indicate that the modified running interval smoother has practical value. Manipulation of the optional arguments might impact the relative merits of the two methods, but the extent to which this is the case remains unknown.Comment: 18 pp, 5 figure

Pemodelan Faktor-Faktor yang Mempengaruhi Angka Prevalensi Kusta di Jawa Timur dengan Menggunakan Regresi Nonparametrik Spline Truncated

Penyakit kusta atau lepra merupakan sebuah infeksi menular yang disebabkan oleh bakteri Mycobacterium leprae. Menurut laporan World Health Organization (WHO) pada tahun 2018, di tahun 2017 Indonesia menempati posisi ketiga setelah India dan Brazil sebagai negara dengan jumlah  penderita kusta terbanyak di dunia. Pada tahun 2017, Jawa Timur menempati posisi pertama di Indonesia dengan jumlah penderita kusta terbanyak di Indonesia dengan jumlah 4.183 jiwa. Batas angka prevalensi kusta yang ingin dicapai Indonesia adalah 1. Sedangkan di Provinsi Jawa Timur, masih terdapat 10 Kabupaten dari 38 Kabupaten atau Kota yang memiliki angka prevalensi di atas 1.  Metode analisis yang digunakan untuk mengetahui faktor-faktor yang mempengaruhi angka prevalensi kusta yaitu Regresi Nonparametrik Spline Truncated karena pola hubungan antara angka prevalensi kusta dengan masing-masing variabel prediktor yang didapatkan tidak membentuk suatu pola tertentu. Berdasarkan model yang diperoleh, hasilnya adalah semua variabel prediktor berpengaruh signifikan terhadap angka prevalensi kusta, yaitu persentase rumah tangga berPHBS, persentase sarana air bersih, persentase jamban sehat, persentase penduduk miskin, dan persentase puskesmas per 100.000 penduduk dengan nilai koefisien determinasi sebesar 95,34%

PEMODELAN PRODUKTIVITAS PADI MENGGUNAKAN REGRESI SEMIPARAMETRIK SPLINE TRUNCATED

Indonesia termasuk 10 besar negara produktivitas padi terbesar didunia dengan rata-rata produksi mencapai 77,96 juta ton atau berkontribusi sebesar 10,28% terhadap total produksi padi dunia, namun pada lima tahun terakhir posisinya menurun. Berdasarkan penelitian yang dilakukan penyebab penurunan produksi padi ini Luas Lahan (Ha), Jumlah Pupuk (ton) dan curah hujan (Mm). Berdasarkan hasil scatterplot yang dilakukan variabel prediktor yang mempengaruhi produksi padi, sebagian memiliki pola tertentu dan sebagian lagi tidak memiliki pola tertentu (acak), sehingga model regresi terbaik untuk dapat memodelkan produksi padi menggunakan model regresi semiparametrik menggunakan estimator spline truncated. Salah satu metode yang digunakan untuk memilih titik knot optimal adalah dengan menggunakan metode GCV (generalized Cross Validation). Berdasarkan analisis yang dilakukan diketahui bahwa nilai GCV minimum terdapat pada model spline truncated dengan kombinasi knot (2,3,1), Sehingga, dapat disimpulkan bahwa model regresi semiparametrik spline truncated yang paling baik adalah spline dengan kombinasi knot dengan jumlah parameter model sebanyak 12 sudah termasuk ????0 (konstanta). Selanjutnya hasil estimasi dan observasi produktivitas padi dapat dimodelkan dengan cukup baik menggunakan model regresi semiparametrik spline truncated, karena nilai hasil estimasi dan observasi cukup dekat satu dengan yang lain, artinya hasil estimasi hampir mendekati nilai observasi

Pemodelan Regresi Nonparametrik Data Longitudinal Menggunakan Polinomial Lokal (Studi Kasus: Harga Penutupan Saham Pada Kelompok Harga Saham Periode Januari 2012 – April 2015)

Stocks are securities that can be bought or sold by individuals or institutions as a sign of participating or possessing a company in the amount of its proportions. From the lens of market capitalization values, stocks are divided into 3 groups: large capitalization (Big-Cap), medium capitalization (Mid-Cap) and small capitalization (Small-Cap). Longitudinal data is observation which is conducted as n subjects that are independent to each subject observed repeatedly in different periods dependently. Smoothing technique used to estimate the nonparametric regression model in longitudinal data is local polynomial estimator. Local polynomial estimator can be obtained by WLS (Weighted Least Square) methods. Local polynomial estimator is very dependent on optimal bandwidth. Determination of the optimal bandwidth can be obtained by using GCV (Generalized Cross Validation) method. Among the Gaussian kernel, Triangle kernel, Epanechnikov kernel and Biweight kernel, it is obtained the best model using Gaussian kernel. Based on the application of the model simultaneously, it is obtained coefficient of determination of 97,80174% and MSE values of 0,03053464. Using Gaussian kernel, MAPE out sample of data is obtained as 11,74493%

Pemodelan Regresi Spline Truncated Untuk Data Longitudinal ( Studi Kasus : Harga Saham Bulanan Pada Kelompok Saham Perbankan Periode Januari 2009 – Desember 2015 )

Stocks are securities that can be bought and sold by individuals or institutions as a sign of ownership of any person nor bussines entity within a company. From the value of market capitalization, the stock is divided into 3 groups: large capitalization (big-cap), medium capitalization (mid-cap), and small capitalization (small-cap). The stocks has been fluctuated up and down because of several factors, one of them is inflation. Longitudinal data are observations made of n subjects that mutually independent with each subject which observed repeatedly in different period of time mutually dependent. Modelling longitudinal data of stock prices do with truncated spline nonparametric regression approach. The best model of spline depends on the determination of the optimal knot points which has minimum value of Generalized Cross Validation (GCV). The best of truncated spline regression is spline order 2 with 3 knot points for each of the subjects on longitudinal data. By using the model, the value of MAPE for each subject is 29,93% for PT Bank Mandiri (Persero) Tbk., 16,67% for PT Bank Bukopin Tbk., and 12,99% for PT Bank Bumi Arta Tbk.

Sparse LS-SVMs with L0-norm minimization

This is an electronic version of the paper presented at the 19th European Symposium on Artificial Neural Networks, held in Bruges on 2011Least-Squares Support Vector Machines (LS-SVMs) have been successfully applied in many classification and regression tasks. Their main drawback is the lack of sparseness of the final models. Thus, a procedure to sparsify LS-SVMs is a frequent desideratum. In this paper, we adapt to the LS-SVM case a recent work for sparsifying classical SVM classifiers, which is based on an iterative approximation to the L0-norm. Experiments on real-world classification and regression datasets illustrate that this adaptation achieves very sparse models, without significant loss of accuracy compared to standard LS-SVMs or SVMs

Pemodelan Regresi Nonparametrik Menggunakan Pendekatan Polinomial Lokal pada Beban Listrik di Kota Semarang

Semarang is the provincial capital of Central Java, with infrastructure and economic's growth was high. The phenomenon of power outages that occurred in Semarang, certainly disrupted economic development in Semarang. Large electrical energy consumed by industrial-scale consumers and households in the San Francisco area, monitored or recorded automatically and presented into a historical data load power consumption. Therefore, this study modeling the load power consumption at a time when not influenced by the use of electrical load (t-1)-th. Modeling using nonparametric regression approach with Local polynomial. In this study, the kernel used is a Gaussian kernel. In local polynomial modeling, determined optimum bandwidth. One of the optimum bandwidth determination using the Generalized Cross Validation (GCV). GCV values obtained amounted to 1425.726 with a minimum bandwidth of 394. Modelling generate local polynomial of order 2 with MSE value of 1408.672

ESTIMASI MODEL REGRESI SEMIPARAMETRIK SPLINE TRUNCATED MENGGUNAKAN METODE MAXIMUM LIKELIHOOD ESTIMATION (MLE)

Regression modeling with a semiparametric approach is a combination of two approaches, namely the parametric regression approach and the nonparametric regression approach. The semiparametric regression model can be used if the response variable has a known relationship pattern with one or more of the predictor variables used, but with the other predictor variables the relationship pattern cannot be known with certainty. The purpose of this research is to examine the estimation form of the semiparametric spline truncated regression model. Suppose that random error is assumed to be independent, identical, and normally distributed with zero mean and variance , then using this assumption, we can estimate the semiparametric spline truncated regression model using the Maximum Likelihood Estimation (MLE) method.  Based on the results, the estimation results of the semiparametric spline truncated regression model were obtained  p=(inv(M'M)) M'y