Jurnal Gaussian
Jurnal Gaussian terbit 4 (empat) kali dalam setahun setiap kali periode wisuda. Jurnal ini memuat tulisan ilmiah tentang hasil-hasil penelitian, kajian ilmiah, analisis dan pemecahan permasalahan yang berkaitan dengan Statistika yang berasal dari skripsi mahasiswa S1 Departemen Statistika FSM UNDIP.
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<![CDATA[PERAMALAN BEBAN PEMAKAIAN LISTRIK JAWA TENGAH DAN DAERAH ISTIMEWA YOGYAKARTA DENGAN MENGGUNAKAN HYBRID AUTOREGRESIVE INTEGRATED MOVING AVERAGE – NEURAL NETWORK ]]>
<![CDATA[Berta Elvionita Fitriani]]>;
<![CDATA[Dwi Ispriyanti]]>;
<![CDATA[Alan Prahutama]]>
<![CDATA[ Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v4i4.10128
<![CDATA[Excessive use of electronic devices in household and industry has made the demand of nation’s electrical power increase significantly these days. As a corporation that aim to provide national electrical power, Perusahaan Listrik Negara (PLN) that distributes electrical power to Central Java and Yogyakarta has to be able to provide an economical and reliable system of electrical power provider. This study aimed to forecast data of electrical power usage in Central Java and Yogyakarta for the next 30 days. There were three forecasting methods used in this study; Neural Networks and Hybrid ARIMA-NN. The data used in this study was electrical power usage data in January 2014 - November 2014 in Central Java and Yogyakarta. The accuracy of the study was measured based on MSE criteria where the best model chosen was the model that has lowest MSE value. According to the result of the analysis, using Neural Networks model to forecast electrical power usage for the next 30 days has better forecasting result than Hybrid ARIMA-NN model.Key Word : electrical power usage, forecasting of electrical power usage, ARIMA, NN, hybrid ARIMA-NN]]>
<![CDATA[ANALISIS INDEKS HARGA SAHAM GABUNGAN (IHSG) DENGAN MENGGUNAKAN MODEL REGRESI KERNEL ]]>
<![CDATA[Icha Puspitasari]]>;
<![CDATA[Suparti Suparti]]>;
<![CDATA[Yuciana Wilandari]]>
<![CDATA[ Jurnal Gaussian Vol 1, No 1 (2012): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v1i1.577
<![CDATA[Saham merupakaninvestasi yang banyak dipilih para investor, salah satu indikator yang menunjukkan pergerakan harga saham adalah Indeks Harga Saham Gabungan (IHSG). IHSG merupakan data runtun waktu sehingga untuk menganalisisnya dapat menggunakan metode runtun waktu klasik. Namun dengan metode tersebut banyak asumsi yang harus dipenuhi, sehingga diperlukan metode alternatif salah satunya metode regresi nonparametrik karena dalam model regresi nonparametrik tidak ada asumsi khusus sehingga model ini merupakan metode alternatif yang dapat digunakan dalam analisis IHSG. Dalam makalah ini dibandingkan nilai MSE yang dihasilkan dari analisis runtun waktu klasik, regresi parametrik linier sederhana dan regresi nonparametrik kernel. Data IHSG yang digunakan adalah periode minggu pertama Januari 2011 sampai dengan minggu ke empat Februari 2012. Data tersebut merupakan data closing price saham mingguan pada periode perdagangan terakhir. Hasil perbandingan nilai MSE dari dataIHSG yang sering fluktuatif pada tiga analisis didapatkan nilai MSE terkecil adalah pada analisis menggunakan regresi nonparametrik kernel dengan fungsi triangle dan badwidth h sebesar 58.2 dengan nilai MSE = 6987.787. Model terbaik tersebut dapat digunakan untuk memprediksikan nilai IHSG selanjutnya.]]>
<![CDATA[ANALISIS FAKTOR-FAKTOR PRODUKSI PERIKANAN TANGKAP PERAIRAN UMUM DARATAN DI JAWA TENGAH MENGGUNAKAN REGRESI BERGANDA DAN MODEL DURBIN SPASIAL ]]>
<![CDATA[Puji Retnowati]]>;
<![CDATA[Rita Rahmawati]]>;
<![CDATA[Agus Rusgiyono]]>
<![CDATA[ Jurnal Gaussian Vol 6, No 1 (2017): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v6i1.16131
<![CDATA[Indonesia’s inland openwater is the second largest in Asia after China. It’s estimated Indonesia’s inland openwater capture fisheries potential reached 3.034.934 tons per year. Central Java is one of the provinces that have great potential in the field of fisheries. In this study will be discussed about the factors suspected to affect inland openwater capture fisheries production. The method used are multiple regression analysis with maximum likelihood estimation and spatial durbin models. Spatial durbin models is the development of linear regression which location factors are also considered. The results of spatial dependences shows there is spatial dependence in the inland openwater capture fisheries production variable, fisheries establishments variables and the number of boats variable. So spatial durbin models can be used for analysis. In spatial durbin models, variables that significantly influence inland openwater capture fisheries production is the number of fishing gear, the number of boats, and the number of fishing trip with coefficient of determination (R2) of 0,9054. While in the multiple regression analysis showed that the only number of fishing trip variable that significantly, where the value of the coefficient of determination (R2) is 0,857. Thus better spatial durbin models used to analyze inland openwater capture fisheries production, in addition more significant variables also have the coefficient of determination (R2) that is greater than the multiple regression analysis.Keywords: inland openwater capture fisheries production, maximum likelihood, spatial durbin model.]]>
<![CDATA[Peramalan Laju Inflasi dan Nilai Tukar Rupiah Terhadap Dolar Amerika Menggunakan Model Vector Autoregressive (VAR) ]]>
<![CDATA[Fitrian Fariz Ichsandi]]>;
<![CDATA[Rita Rahmawati]]>;
<![CDATA[Yuciana Wilandari]]>
<![CDATA[ Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v3i4.8078
<![CDATA[Vector Autoregressive Method (VAR) is a simultaneous equation model has several endogeneous variables. In the VAR Model each variable endogeneous is explained by lag from own value and lag from the other variable. Equation of VAR generally use to forecast. In this final task VAR model was applied to find the forecasting value of inflation rate in Indonesia and the US dollar exchange rates. Testing in VAR models includes stationarity test, granger causality test and white noise test. Based on the analysis showed that inflation variable and US dollar exchange rates variable are both experiencing differencing first lag so as mentions for both variables become d_inflasi and d_kurs. The best lag for VAR model is lag 3 for each model. Forecasting for 5 periods refers to indicate that inflation rate fluctuated is stable at the average rate 0,33% while the US dollar exchange rates tended to decrease on 4 periode and increase on periode to 5 with an average exchange rate is Rp. 10.018,76.Keywords: inflation, US dollar exchange rates, VAR]]>
<![CDATA[KLASIFIKASI KELOMPOK RUMAH TANGGA DI KABUPATEN BLORA MENGGUNAKAN MULTIVARIATE ADAPTIVE REGRESSION SPLINE (MARS) DAN FUZZY K-NEAREST NEIGHBOR (FK-NN) ]]>
<![CDATA[Yani Puspita Kristiani]]>;
<![CDATA[Diah Safitri]]>;
<![CDATA[Dwi Ispriyanti]]>
<![CDATA[ Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v4i4.10243
<![CDATA[Good classification method will result on less classification error. Classification method developed rapidly. Two of the existing classification methods are Multivariate Adaptive Regression Spline (MARS) and Fuzzy K-Nearest Neighbor (FK-NN). This research aims to compare the classification of poor household and prosperous household based on per capita income which has been converted according to the poverty line between MARS and FK-NN method. This research used secondary data in the form of result of National Economy and Social Survey (SUSENAS) in Blora subdistrict in 2014. The result of the classification was evaluated using APER. The best classification result using MARS method is by using the combination of BF= 76, MI= 3, MO= 1 because it will result on the smallest Generalized Cross Validation (GCV) and the APER is 10,119 %. The best classification result using FK-NN method is by using K=9 because it will result on the smallest error and the APER is 9,523 %. The APER calculation shows that the classification of household in Blora subdistrict using FK-NN method is better than using MARS method. Keywords: Classification, MARS, FK-NN, APER, SUSENAS, Blora]]>
<![CDATA[PERBANDINGAN ANALISIS KLASIFIKASI MENGGUNAKAN METODE K-NEAREST NEIGHBOR (K-NN) DAN MULTIVARIATE ADAPTIVE REGRESSION SPLINE (MARS) PADA DATA AKREDITASI SEKOLAH DASAR NEGERI DI KOTA SEMARANG ]]>
<![CDATA[Bisri Merluarini]]>;
<![CDATA[Diah Safitri]]>;
<![CDATA[Abdul Hoyyi]]>
<![CDATA[ Jurnal Gaussian Vol 3, No 3 (2014): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v3i3.6441
<![CDATA[Classification methods have been developed and two of the existing are K-Nearest Neighbor (K-NN) and Multivariate Adaptive Regression Spline (MARS). The purpose of this research is comparing the classification of public elementary school accreditation in Semarang city with K-NN and MARS methods. This research using accreditation data with the result of eight accreditation components in public elementary school that has A accreditation (group 1) and B accreditation (group 2) in Semarang city. To evaluate the classification method used test statistic Press’s Q, APER, specificity, and sensitivity. The best classification results of the K-NN method is when using K=5 because it produces the smallest error rate and obtained information that the correct classification data are 159 and the misclassification data are 9. The best classification result of the MARS method is when using combination BF=32, MI=2, MO=1 because it produces the smallest Generalized Cross Validation (GCV) and obtained information that the correct classification data are 164 and the misclassification data are 4. Based on analyze result, Press’s Q showed that both methods are good as classification or statistically significant to classify the public elementary school in Semarang city based of the accreditation. APER, specificity, and sensitivity showed that classify of public elementary school accreditation in Semarang city using MARS method is better than K-NN method.]]>
<![CDATA[PERBANDINGAN METODE K–MEANS DAN SELF ORGANIZING MAP (STUDI KASUS: PENGELOMPOKAN KABUPATEN/KOTA DI JAWA TENGAH BERDASARKAN INDIKATOR INDEKS PEMBANGUNAN MANUSIA 2015) ]]>
<![CDATA[Rachmah Dewi Kusumah]]>;
<![CDATA[Budi Warsito]]>;
<![CDATA[Moch. Abdul Mukid]]>
<![CDATA[ Jurnal Gaussian Vol 6, No 3 (2017): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v6i3.19346
<![CDATA[Cluster analysis is a process of separating the objects into groups, so that the objects that belong to the same group are similar to each other and different from the other objects in another group. In this study used two method to classify data of district / city in Central Java based on indicators of Human Development Index (HDI) 2015 are K-Means and Self Organizing Map (SOM) with the number of groups as much as two to seven. Furthermore, the results of both methods were compared using the Davies-Bouldin Index (DBI) values to determine which method is better. Based on the research that has been conducted found that the K-Means (K=4) method works better than SOM (K=2) to classify district / city in Central Java based on indicators of Human Development Index (HDI) as evidenced by the value of the Davies-Bouldin Index (DBI) on K-Means (K=4) of 0.786 is smaller than the value at SOM (K=2) Davies-Bouldin Index (DBI) which is equal to 0.893. Keywords: clustering, HDI, K-Means, SOM, DBI]]>
<![CDATA[PEMODELAN TINGKAT PENGANGGURAN TERBUKA DI JAWA TENGAH MENGGUNAKAN REGRESI SPLINE ]]>
<![CDATA[Seta Satria Utama]]>;
<![CDATA[Suparti Suparti]]>;
<![CDATA[Rita Rahmawati]]>
<![CDATA[ Jurnal Gaussian Vol 4, No 1 (2015): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v4i1.8151
<![CDATA[Unemployment is one of the employment problems facing Indonesia. Central Java Province is one of the provinces with a high enough unemployment. The main indicators used to measure the unemployment rate in the labor force that is unemployed. Based on research Arianie (2012) labor force participation rate significantly affect the unemployment rate and based on research Sari (2012) the gross enrollment ratio significantly affects the rate of open unemployment. Therefore, in this study using the two predictor variables with the labor force participation rate as X1 and gross enrollment rate as X2. This study aimed to explore the model of open unemployment rate in the Province of Central Java. The method used is the method of spline regression. Spline regression has the ability to adapt more effectively to the data patterns up or down dramatically with the help of dots knots. Determination of the optimal point knots are very influential in determining the best spline models. The best spline models are models that have a minimum GCV (Generalized Cross Validation) Value. Best spline models for the analysis of the data rate of unemployment in Central Java Province is the spline regression model when order X1 is 2 and order X2 is 4 and large number of knots in the X1 is 1 knot at the point 68.02394 and X2 is 3 knots at the point 82.13, 87.19, and 87.65 with GCV value of 1.732746. Keywords: Rate of Open Unemployment, Spline Regression, GCV]]>
<![CDATA[KOMPUTASI METODE SAW DAN TOPSIS MENGGUNAKAN GUI MATLAB UNTUK PEMILIHAN JENIS OBJEK WISATA TERBAIK (Studi Kasus : Pesona Wisata Jawa Tengah) ]]>
<![CDATA[Rima Nurlita Sari]]>;
<![CDATA[Rukun Santoso]]>;
<![CDATA[Hasbi Yasin]]>
<![CDATA[ Jurnal Gaussian Vol 5, No 2 (2016): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v5i2.11851
<![CDATA[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]]>
<![CDATA[REGRESI SPLINE SEBAGAI ALTERNATIF DALAM PEMODELAN KURS RUPIAH TERHADAP DOLAR AMERIKA SERIKAT ]]>
<![CDATA[Sulton Syafii Katijaya]]>;
<![CDATA[Suparti Suparti]]>;
<![CDATA[Sudarno Sudarno]]>
<![CDATA[ Jurnal Gaussian Vol 2, No 3 (2013): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v2i3.3668
<![CDATA[Exchange rate is the ratio of value or price of the currency between two countries. Many factors are thought to affect change in the inflation rate, the activity balance of payments, interest rate differentials, the relative level of income, government control and expectations. Therefore the method that can be used to analyze the exchange rate is needed such as the classical time series analysis (parametric). However the fluctuated data rate doesn’t occupy the assumption of stationarity often. Another alternative for this study is the spline regression. Spline is a nonparametric regression that doesn’t hold any assumption of regression curves. Spline regression has high flexibility and ability to estimate the data behavior which is likely to be different at every point of the interval, with the help of knots. The best model depends on the determination of the optimal point knots, that is has a minimum value of Generalized Cross Validation (GCV). Using data daily exchange rate of the rupiah against the dollar in the period of January 2, 2012 until October 15, 2012, the best spline model in this study is when using 2 to 3 order of approaching knots point, those points are 9512, 9517 and 9522 with the GCV = 1036.38.]]>
