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All Journal Jurnal Gaussian Jurnal Statistika Universitas Muhammadiyah Semarang J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika

Hasbi Yasin

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Author-ID : 452055

Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Other

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ORDINARY KRIGING DALAM ESTIMASI CURAH HUJAN DI KOTA SEMARANG Ahmat Dhani Riau Bahtiyar; Abdul Hoyyi; Hasbi Yasin
Jurnal Gaussian Vol 3, No 2 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

In a measurement of rainfall data, not all points are gauges because of a limitation. Given these limitations, a method is needed to estimate a value for points that are not measurable. Kriging as geostatistical analysis used in the estimation of a value in a point which is not sampled based sample points in the surrounding areas by taking into account the spatial correlation using a spatial weighting, where the correlation is shown by the variogram. Ordinary Kriging is the most widely used. By using the experimental variogram were compared with some theoretical variogram (Exponential, Gaussian, Spherical) selected one of the best semivariogram models to estimate the value that want to find. In this study, conducted rainfall estimates in Semarang in February where the result obtained is the value of rainfall each district and village

GEOGRAPHICALLY WEIGHTED REGRESSION PRINCIPAL COMPONENT ANALYSIS (GWRPCA) PADA PEMODELAN PENDAPATAN ASLI DAERAH DI JAWA TENGAH Nurmalita Sari; Hasbi Yasin; Alan Prahutama
Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Linear Regression Analysis is a method for modeling the relation between a response variable with two or more independent variables. Geographically Weighted Regression (GWR) is a development of the regression model where each observation location has different regression parameter values because of the effects of spatial heterogenity. Regression Principal Component Analysis (PCA) is a combination of PCA and are used to remove the effect of multicolinearity in regression. Geographically Weighted Regression Principal Component Analysis (GWRPCA) is a combination of PCA and GWR if spatial heterogenity and local multicolinearity occured. Estimation parameters for the GWR and GWRPCA using Weighted Least Square (WLS). Weighting use fixed gaussian kernel function through selection of the optimum bandwidth is 0,08321242 with minimum Cross Validation (CV) is 3,009035. There are some variables in PCA that affect locally-generated revenue in Central Java on 2012 and 2013, which can be represented by PC1 that explained the total variance data about 71,4%. GWRPCA is a better model for modeling locally-generated revenue for the districts and cities in Central Java than RPCA because it has the the smallest Akaike Information Criterion (AIC) and the largest R2. Keywords : Spatial Heterogenity, Local Multicolinearity, Principal Component Analysis, Geographically Weighted Regression Principal Component Analysis.

PEMODELAN PERSENTASE BALITA GIZI BURUK DI JAWA TENGAH DENGAN PENDEKATAN GEOGRAPHICALLY WEIGHTED REGRESSION PRINCIPAL COMPONENTS ANALYSIS (GWRPCA) Novika Pratnyaningrum; Hasbi Yasin; Abdul Hoyyi
Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Geographically Weighted Regression Principal Components Analysis (GWRPCA) is a combination of method of Principal Components Analysis (PCA) and Geographically Weighted Regression (GWR). PCA is used to eliminate the multicollinearity effect in regression analysis. GWR is a local form of regression and a statistical method used to analyze the spatial data. In GWRPCA predictor variables is a principal components of the PCA result. Estimates of parameters of the GWRPCA model can use Weighted Least Square (WLS). Selection of the optimum bandwidth use Cross Validation (CV) method. Conformance testing PCA regression and GWRPCA models approximated by the F distribution, while the partial identification of the model parameters using the t distribution. In PCA obtained variables that affect the percentage of severe children malnutrition in Central Java in 2012 can be represented or replaced with PC1 and PC2 which can explain the total variance of data is 78.43%. Application GWRPCA models at the percentage of severe children malnutrition in Central Java in 2012 showed every regency locations have different model with global coefficient of determination is 0.6313309 and the largest local coefficient of determination is 0.72793026 present in Batang regency, while the smallest local coefficient of determination is 0.03519539 present in Sukoharjo regency. Keywords : Severe Malnutrition, Multicollinearity, Geographically Weighted Regression Principal Components Analysis, Weighted Least Square,Coefficient of Determination.

PREDIKSI CURAH HUJAN DENGAN METODE KALMAN FILTER (Studi Kasus di Kota Semarang Tahun 2012) Tika Dhiyani Mirawati; Hasbi Yasin; Agus Rusgiyono
Jurnal Gaussian Vol 2, No 3 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The rainfall data is very interesting to be studied because it is constitutes one of the biggest factor that influence the climate on a region and human life sector. In this studies, the rainfall prediction is utilized by Kalman Filter method. The implementation of Kalman Filter analysis in this research is used for modelling and forecasting rainfall in Semarang city. This method provide a recursive solution to minimize error. Kalman Filter consists of state equation and observation equation. The forecasting result in 2012 showed that the prediction is close to the current data whereas in 2013 it increase which the maximum rainfall is 406 mm happening in February and the minimum rainfall is 35 mm happening in July. Overall, the average rainfall in 2013 at Semarang city is 196,25 mm

PERAMALAN INDEKS HARGA KONSUMEN 4 KOTA DI JAWA TENGAH MENGGUNAKAN MODEL GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) Lina Irawati; Tarno Tarno; Hasbi Yasin
Jurnal Gaussian Vol 4, No 3 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Generalized Space Time Autoregressive (GSTAR) models are generalization of the Space Time Autoregressive (STAR) models which has the data characteristics of time series and location linkages (space-time). GSTAR is more flexible when faced with the locations that have heterogeneous characteristics. The purposes of this research are to get the best GSTAR model and the forecasting results of Consumer Price Index (CPI) data in Purwokerto, Solo, Semarang and Tegal. The best model obtained is GSTAR (11) I(1) using cross correlation normalization weight because it generated white noise and multivariate normal residuals with average value of MAPE 3,93% and RMSE 10,02. The best GSTAR model explained that CPI of Purwokerto is only affected by times before, it does not affect to other cities but can be affecting to other cities. Otherwise, CPI of Surakarta, Semarang and Tegal are affecting each others. Keywords: GSTAR, Space Time, Consumer Price Index, MAPE, RMSE

KOMPUTASI METODE SAW DAN TOPSIS MENGGUNAKAN GUI MATLAB UNTUK PEMILIHAN JENIS OBJEK WISATA TERBAIK (Studi Kasus : Pesona Wisata Jawa Tengah) Rima Nurlita Sari; Rukun Santoso; Hasbi Yasin
Jurnal Gaussian Vol 5, No 2 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Multi-Attribute Decision Making (MADM) is a method of decision-making to establish the best alternative from a number of alternatives based on certain criteria. Some of the methods that can be used to solve MADM problems are Simple Additive Weighting (SAW) Method and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). SAW works by finding the sum of the weighted performance rating for each alternative in all criteria. While TOPSIS uses the principle that the alternative selected must have the shortest distance from the positive ideal solution and the farthest from the negative ideal solution. Both of these methods were applied in making the selection of the best tourist attractions in Central Java. There are 15 tourist attractions and 7 criteria: location, infrastructure, beauty, atmosphere, tourist interest, promotion, and cost. This primary research employed a questionnaire that passed the questionnaire testing, namely its validity and reliability test. The result of this study shows that the best type of tourism according to the government is temple tour. While water sports tourism is favored by tourism observers. As for college students, the preferred tourist destination is religious tourism. This study also produced a GUI Matlab programming application that can help users in performing data processing using SAW and TOPSIS to select the best attraction in Central Java. Keywords: MADM, SAW, TOPSIS, GUI, tourism

PENENTUAN MODEL ANTRIAN DAN PENGUKURAN KINERJA PELAYANAN PLASA TELKOM PAHLAWAN SEMARANG Ilham Indra Bakti Al-Irsyad; Sugito Sugito; Hasbi Yasin
Jurnal Gaussian Vol 4, No 3 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Plasa Telkom Pahlawan is a place of ministry-owned PT Telkom provided to serve customers of Telkom. To serve its customers, Plasa Telkom Pahlawan operates several kind of services, they are Customer Service, Cashier, Quick Service, Sales and in November 2014 operated new kind of service, it was Flexi Upgrade. As a provider of facility services, the problem of queues is a problem that is absolutely the case and must be considered. Queue situation occurs because the number of customers at a facility of service exceed the capacity available to perform such services. At Plasa Telkom Pahlawan queuing occurs in five different kinds of services. The best queueing models in Customer Service is (M/G/6):(GD:∞:∞) based on simulation level of aspiration, while the best model of Cashier and Quick Service are (M/M/2):(GD:∞:∞), for Sales is (M/M/1):(GD:∞:∞). Especially for Flexi Upgrade, the best model based on simulation level of aspiration is (G/G/6):(GD:∞:∞). From the analyzed model can be concluded that the queueing system available in Plasa Telkom Pahlawan Service is optimal.Keywords : Queuing system, Plasa Telkom Pahlawan, Customer Service, Cashier, Quick Service, Sales, Flexi Upgrade.

ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI LAJU PERTUMBUHAN PENDUDUK KOTA SEMARANG TAHUN 2011 MENGGUNAKAN GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION Catra Aditya Wisnu Aji; Moch. Abdul Mukid; Hasbi Yasin
Jurnal Gaussian Vol 3, No 2 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Geographically Weighted Logistic Regression (GWLR) is a local form of logistic regression where geographical factors considered and it is assumed that the Bernoulli distribution of data used to analyze spatial data from non-stationary processes. This research will determine the factors that affect the Population Growth Rate (PGR) in the Semarang city using logistic regression and GWLR with a weighting function of bisquare kernel and gaussian kernel. The result showed that GWLR model with a weighting function of bisquare kernel better than logistic regression model and GWLR model with a weighting function of gaussian kernel because it has the smallest AIC value and classification accuracy is 87,5%. Factor that have significant effect is the number of couples of childbearing age in the Semarang city.

ESTIMASI KANDUNGAN HASIL TAMBANG MENGGUNAKAN ORDINARY INDICATOR KRIGING Aldila Abid Awali; Hasbi Yasin; Rita Rahmawati
Jurnal Gaussian Vol 2, No 1 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Kriging is a geostatistical analysis of the data used to estimate the value that represents a no sample point based sample point in the surrounding by considering the spatial correlation in the data. Kriging is an interpolation method that generates unbiased predictions or estimations and has a minimum error. Indicator kriging is an estimation method that does not require the assumption of normality of data and can also be used to treat data that have a significant outlier. The indicator kriging that based on the principle of ordinary kriging also called ordinary indicator kriging. In this case study conducted Morowali estimated iron content in Central Sulawesi using ordinary indicator kriging method. The data used in the form of data coordinate point and iron content. The results obtained are presented probability value locations that fall within the zone of potential and non potential with the value the error variance. Based on the analysis to obtain a plot depicting the location of the entry in the zones of potential iron mine on the abscissa coordinate (7150–7210), the ordinate (54180–54540), and the depth ranges (440–500) meters and also the coordinates of the abscissa (7710–8130), the ordinate (54800–54960), and depths ranging from (327–342) meters.

PEMODELAN GENERAL REGRESSION NEURAL NETWORK (GRNN) PADA DATA RETURN INDEKS HARGA SAHAM EURO 50 Rezzy Eko Caraka; Hasbi Yasin; Alan Prahutama
Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

General Regression Neural Network (GRNN) merupakan salah satu model jaringan radial basis yang digunakan untuk pendekatan suatu fungsi. Model GRNN termasuk model jaringan syaraf tiruan dengan solusi yang cepat, karena tidak diperlukan iterasi yang besar pada estimasi bobot-bobotnya. Model ini memiliki arsitektur jaringan yang baku, dimana jumlah unit pada pattern layer sesuai dengan jumlah data input. Salah satu aplikasi GRNN adalah untuk memprediksi nilai return saham dari indeks Euro 50 CFD (Contract For Difference). Indeks Euro 50 CFD (Contract For Difference) digunakan sebagai patokan harga saham dari 50 perusahaan terbesar di zona Eropa. Para investor melakukan investasi di saham indeks Euro 50 CFD (Contract For Difference) dengan harapan mendapatkan kembali keuntungan yang sesuai dengan apa yang telah di investasikannya. Dengan menggunakan model GRNN diperoleh bahwa nilai RMSE dan R2 untuk data training sebesar 0,00095 dan 99,19%. Untuk data testing diperoleh nilai RMSE dan R2 sebesar 0,00725 dan 98,46%. Berdasarkan nilai prediksi return saham dua belas hari ke depan diperoleh kerugian tertinggi atau capital loss pada 15 Desember 2014 sebesar 5,583188% dan profit tertinggi atau capital gain pada tanggal 10 Desember 2014 sebesar 2,267641% Kata Kunci: GRNN, Jaringan Syaraf Tiruan, Return Saham, Indeks Euro 50, Kerugian Tertinggi, Profit Tertinggi, Prediksi

Co-Authors Abdul Hoyyi Agus Rusgiyono Ahmat Dhani Riau Bahtiyar Ajeng Arum Sari Alan Prahutama Aldila Abid Awali Alfiyatun Rohmaniyah Alvita Rachma Devi Andreas Pedo Baluk Arief Rachman Hakim Budi Warsito Cakra Kurniawan Catra Aditya Wisnu Aji Christa Monica David Yuliandar Depy Veronica Diah Safitri Dody Apriliawan Dwi Ispriyanti Ely Fitria Rifkhatussa'diyah Erfan Sofha Firda Shintia Dewi Ilham Indra Bakti Al-Irsyad Indra Satria Indra Satria Ira Puspita Sari Ishlahul Kamal Johan Adi Wicaksana Khusnul Yeni Widiyanti Laily Nadhifah Lenti Agustina Lianasari Tambunan Lina Irawati Mamuroh Mamuroh Mega Fitria Andriyani Moch. Abdul Mukid Muh Najib Hilmi Muhammad Hilman Rizki Abdullah Muryanto Muryanto Muryanto Muryanto Novian Trianggara Novika Pratnyaningrum Nurmalita Sari Nursihan Nursihan Putri Aulia Netra Putri Aulia Wahyuningsih Rahafattri Ariya Fauzannissa Rahma Kurnia Widyawati Rahma Nurfiani Pradita Restu Dewi Kusumo Astuti Rezzy Eko Caraka Rima Nurlita Sari Rita Rahmawati Rizky Amanda Rukun Santoso Safitri Daruyani Sarita Budiyani Purnamasari Sudarno Sudarno SUGITO Sugito Sugito Suparti Suparti Syaifudin Karyadi Tarno Tarno Tatik Widiharih Tika Dhiyani Mirawati Triastuti Wuryandari Vita Dwi Rachmawati Wayaning Apsari Yuciana Wilandari

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