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Department of Statistic, Faculty of Science and Mathematics , Universitas Diponegoro Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro Gedung F lt.3 Tembalang Semarang 50275
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Jurnal Gaussian
Published by Universitas Diponegoro
ISSN : -     EISSN : 23392541     DOI : -
Core Subject : Education,
Other
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.
Arjuna Subject : -
Articles 733 Documents
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PEMODELAN PENGELUARAN PER KAPITA DAN PERSENTASE PENDUDUK MISKIN DI JAWA TENGAH MENGGUNAKAN REGRESI BIRESPON SPLINE TRUNCATED Merinda Pangestikasari; Rita Rahmawati; Dwi Ispriyanti
Jurnal Gaussian Vol 7, No 2 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (481.114 KB) | DOI: 10.14710/j.gauss.v7i2.26649

Abstract

The Central Bureau of Statistics states that the average per capita spending (Y1) of Central Java Community in 2016 is around 27.808 rupiah per day. This value is still considered low, because it covers all the needs of an individual's life. The low expenditure per capita indicates the low level of welfare. Another indicator that can be used to measure community welfare is the percentage of poverty (Y2). Through this variable can be known how proportion of people who still difficult to meet their needs. Many factors are suspected to affect welfare, one of which is the average variable of school length (X). This study aims to get the best model and know the goodness of the model. Approach is done by nonparametric regression that is regres biresponse spline truncated. Nonparametric approach is done when data function does not show certain pattern. The best spline truncated biresponse model is highly dependent on determining the order and location of the optimal knot point that has a minimum Mean Square Error (MSE) value. In this study, the best model is obtained when order of Y1 is 2 and order of Y2 is 2 with five knots. The location of the knot point obtained is 7,05; 7,17; 7,32; 9,82 and 10,29 with MSE value of 662634,2. The goodness of the model is measured based on R-Square and MAPE, R-Square=43,21%, means the variance of response variables that can be explained by the predictor variable are 43,21% while the rest is influenced by other variables and MAPE=14,25%. Based on the value of MAPE can be said that the model had a good performance. Keywords: Welfare, Expenditure, Percentage of Povery, Birespon Spline, Truncated, MSE
ANALISIS DESAIN FAKTORIAL FRAKSIONAL 2k-p DENGAN METODE LENTH Gian Kusuma Diah Tantri; Tatik Widiharih; Triastuti Wuryandari
Jurnal Gaussian Vol 4, No 3 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (713.049 KB) | DOI: 10.14710/j.gauss.v4i3.9432

Abstract

Rancangan faktorial fraksional banyak digunakan dalam percobaan terutama di bidang industri karena dapat menentukan pengaruh faktor utama dan interaksi terhadap respon. Rancangan yang melibatkan k buah faktor dengan dua taraf dan menggunakan 2-p fraksi dari percobaan faktorial lengkap disebut rancangan faktorial fraksional 2k-p. Penentuan faktor signifikan jika data yang diamati tanpa pengulangan dapat diuji dengan menggunakan metode Lenth. Penelitian ini bertujuan untuk menentukan penaksir dan statistik uji untuk mendapatkan faktor signifikan dengan metode Lenth, serta menentukan perbedaan dalam penggunaan metode Lenth dengan metode klasik. Kasus yang digunakan adalah rancangan faktorial fraksional 25-1 dengan faktor A, B, C, D, E. Hasil pengujian dengan metode Lenth diperoleh nilai estimasi S0 dan  sebagai penaksir awal dan akhir. Nilai Margin Error dan Simultan Margin Error sebagai batas kesalahan dalam penentuan faktor signifikan. Faktor yang berpengaruh terhadap respon adalah faktor B dan C. Apabila diuji dengan metode klasik diperoleh faktor yang berpengaruh terhadap respon adalah faktor B, C, D, E, AB, AC, dan BC, sehingga dapat dikatakan bahwa metode klasik lebih sensitif daripada metode Lenth. Kata kunci: Faktorial, fraksional, tanpa pengulangan, plot probabilitas normal, metode Lenth
KOMPUTASI METODE EXPONENTIALLY WEIGHTED MOVING AVERAGE UNTUK PENGENDALIAN KUALITAS PROSES PRODUKSIMENGGUNAKAN GUI MATLAB (STUDI KASUS : PT Djarum Kudus SKT Brak Megawon III) Iyan Antono; Rukun Santoso; Yuciana Wilandari
Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (777.354 KB) | DOI: 10.14710/j.gauss.v5i4.14724

Abstract

Control chart is one of tools for quality control of production.  control chart is one of tool that can be used to control the quality of production for variable data such as weight of product. However, there is a weakness of   control chart, which is sensitivless in detecting small shift of the mean process. Exponentially Weighted Moving Average (EWMA) control chart is one of the quality control tool that can improve the weakness of  control chart. EWMA control chart has a weight smoothing parameter (λ) which makes EWMA control chart more sensitive in detecting small shifts the process mean. Each production data will be weighted and past production data will be affected by present production data. EWMA control chart will be used to make a control chart by weight of cigarette data in Brak Megawon III PT Djarum Kudus. In this study, will be established to assist in the GUI Matlab computational EWMA methods chart controller to control the quality of production at PT Djarum Kudus.In this study showed that the most optimum weight refiner which is at a value of 0.6.Keyword : EWMA, Smoothing weight (λ), GUI, Weight of cigarette
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (454.872 KB) | DOI: 10.14710/j.gauss.v3i2.5900

Abstract

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
PERAMALAN BEBAN PUNCAK PEMAKAIAN LISTRIK DI AREA SEMARANG DENGAN METODE HYBRID ARIMA (AUTOREGRESSIVE INTEGRATED MOVING AVERAGE)-ANFIS (ADAPTIVE NEURO FUZZY INFERENCE SYSTEM) (Studi Kasus di PT PLN (Persero) Distribusi Jawa Tengah dan DIY) Kristiana, Ana; Wilandari, Yuciana; Prahutama, Alan
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (639.505 KB) | DOI: 10.14710/j.gauss.v4i4.10125

Abstract

Electricity become one of the basic needs in society, so that the demand level for electricity even bigger as more complex activities in society. In order to fulfill the needs of electricity in Indonesia, PT PLN have to do electrical peak load forecasting to prevent electrical crisis. In this research, we use hybrid ARIMA-ANFIS methods to forecast daily peak load of electricity in Semarang period December 2014 until January 2015. The use of hybrid ARIMA-ANFIS is to capture both linear and nonlinear patterns in the data, because sometimes time series data can contain both linear and nonlinear patterns. Since ARIMA can not deal with nonlinear patterns while ANFIS is not able to handle both linear and nonlinear patterns alone. The accuracy of the model was measured by symmetric MAPE (sMAPE) criteria, in which the best model chosen is the model with the smallest sMAPE value. The results showed that the hybrid ARIMA-ANFIS model that used to predict the daily peak load electricity in Semarang during the period of December 2014 until January 2015, comes from combination between SARIMA (0,1,1)(0,1,1)7 model and residual forecasting with ANFIS model using first lag input, Gaussian membership function in 3 clusters. Keywords: Electricity, Electrical peak load forecasting, ARIMA, ANFIS, Hybrid ARIMA-ANFIS.
PELATIHAN FEED FORWARD NEURAL NETWORK MENGGUNAKAN ALGORITMA GENETIKA DENGAN METODE SELEKSI TURNAMEN UNTUK DATA TIME SERIES David Yuliandar; Budi Warsito; Hasbi Yasin
Jurnal Gaussian Vol 1, No 1 (2012): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (484.511 KB) | DOI: 10.14710/j.gauss.v1i1.574

Abstract

ABSTRAK Pemodelan time series seringkali dikaitkan dengan proses peramalan suatu nilai karakteristik tertentu pada periode mendatang. Salah satu metode peramalan yang berkembang saat ini adalah menggunakan artificial neural network atau yang lebih dikenal dengan neural network.Penggunaan neural network dalam peramalan time series dapat menjadi solusi yang baik, namun yang menjadi masalah adalah arsitektur jaringan dan pemilihan metode pelatihan yang tepat. Salah satu pilihan yang mungkin adalah menggunakan algoritma genetika. Algoritma genetika adalah suatu algoritma pencarian stokastik berdasarkan cara kerja melalui mekanisme seleksi alam dan genetik yang bertujuan untuk mendapatkan solusi dari suatu masalah. Algoritma ini dapat digunakan sebagai metode pembelajaran dalam melatih model feed forward neural network. Penerapan algoritma genetika dan neural network untuk peramalan time series bertujuan untuk mendapatkan bobot-bobot yang optimum dengan meminimumkan error. Dari hasil pelatihan dan pengujian pada data kurs Dolar Australia terhadap Rupiah didapatkan nilai RMSE sebesar 117.3599 dan 82.4917. Model ini baik untuk digunakan karena memberikan hasil prediksi yang cukup akurat yang ditunjukkan oleh kedekatan target dengan output.
ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI INDEKS PEMBANGUNAN MANUSIA (IPM) MENGGUNAKAN METODE REGRESI LOGISTIK ORDINAL DAN REGRESI PROBIT ORDINAL (Studi Kasus Kabupaten/Kota di Jawa Tengah Tahun 2014) Nurmalasari, Ratih; Ispriyanti, Dwi; Sudarno, Sudarno
Jurnal Gaussian Vol 6, No 1 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (886.236 KB) | DOI: 10.14710/j.gauss.v6i1.14774

Abstract

Human Development Index (HDI) is one of the most important indicator to observe another dimensions of human development. The HDI is a measurement for achievement levels of the quality of human development. This study analyze HDI in the Districts/Cities of Central Java in 2014. The Central Java’s HDI data is categorized as low, medium, and high. The HDI presumed to be affected by many factors, such as high school participation rates, middle school graduates percentage, percentage of household with clean water access, numbers of health facility, open unemployment rate,and labour force participation rate. This study used the ordinal logistic regression and the ordinal probit regression as its statical analysis method. The result showed that factors affecting HDI in the Districts/Cities of Central Java in 2014 are percentage of household with clean water access and numbers of health facility. To evaluate the performance of ordinal logistic regression and the ordinal probit regression, researcher uses classification accuracy and AIC. Based on reasearch classification accuracy and AIC of each methods, the result showed that both the ordinal logistic regression and the ordinal probit regression has good result in analyzing factors affecting Human Development Index in the Districts/Cities of Central Java in 2014.Keywords: HDI, Ordinal Logistic Regression, Ordinal Probit Regression, Classification Accuracy, AIC
KLASIFIKASI TINGKAT KELUARGA SEJAHTERA DENGAN MENGGUNAKAN METODE REGRESI LOGISTIK ORDINAL DAN FUZZY K-NEAREST NEIGHBOR (STUDI KASUS KABUPATEN TEMANGGUNG TAHUN 2013) Dini Puspita; Suparti Suparti; Yuciana Wilandari
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (400.874 KB) | DOI: 10.14710/j.gauss.v3i4.8075

Abstract

Indonesian is a country that have a lot of people, its about 250 millions people. Each of they have a family. Family is a group of person who have relationship and responsibility for each other. The characteristic of family is very important in relationship with society. A lot of requirement must to be have in family. Ownership requirement in family can be figure of that family. In case, accuracy of classification about prosperity family in Kabupaten Temanggung 2013th will be analysed, in BKKBN is have 5 level of prosperity family, there are pra prosperity family, prosperity family 1, prosperity family 2, prosperity family 3, and prosperity family 3 plus. Regression Logistics Ordinal method and Fuzzy K-Nearest Neighbor (FK-NN) method be use for analysis this minithesis. From the analysis regression logistics ordinal accuracy of classification have value 80,47%, and FK-NN have value 87,60%. Both of the value accuracy of classification can get conclusion regression logistics ordinal method have a less value than FK-NN. So FK-NN method is a best method for level of prosperity family in Kabupaten Temanggung 2013th.Keywords : Prosperity Family, Regression Logistics Ordinal, Fuzzy K-Nearest Neighbor (FK-NN)
PREDIKSI INFLASI BEBERAPA KOTA DI JAWA TENGAH TAHUN 2014 MENGGUNAKAN METODE VECTOR AUTOREGRESSIVE (VAR) Utami, Tika Nur Resa; Rusgiyono, Agus; Sugito, Sugito
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (679.258 KB) | DOI: 10.14710/j.gauss.v4i4.10240

Abstract

Inflation is a situation where there is an increase in the general price level. Inflation for goods and services purchased by consumers is measured by changes in the Indeks Harga Konsumen (IHK). Determination of the amount, type and quality of commodities in the package of goods and services in the IHK is based on the Survey Biaya Hidup (SBH). In Central Java, there are only four cities covered in the implementation of SBH, namely Purwokerto, Solo, Semarang, and Tegal. It was the underlying researchers took the four cities. In this case, researchers taken for the period of 2009-2013. Inflation Purwokerto, Solo, Semarang, and Tegal is a multivariate time series  that show activity for a certain period. One method to analyze multivariate time series is Vector Autoregressive (VAR). VAR method is one of the multivariate time series analysis of variables that can be used to predict and assess the relationship between variables. Inflation researchers predict that by 2014 the four cities using VAR (1). Chosen VAR (1) is based on the results of some tests. VAR (1) have the optimal lag value, there is no correlation between the residual lag, and the value Root Mean Square Error (RMSE) is smaller than the other models.                                                                                      Keywords: Inflation, IHK, SBH, Multivariate Time Series, Forecasting, Vector Autoregressive (VAR).
PERBANDINGAN ANALISIS FAKTOR KLASIK DAN ANALISIS FAKTOR ROBUST UNTUK DATA INFLASI KELOMPOK BAHAN MAKANAN DI JAWA TENGAH Erna Puspitasari; Moch. Abdul Mukid; Sudarno Sudarno
Jurnal Gaussian Vol 3, No 3 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (467.852 KB) | DOI: 10.14710/j.gauss.v3i3.6445

Abstract

Factor analysis is a statistical method used to describe a set of variables based on common dimensions. Factor analysis that is often used is the classical factor analysis with principal components method. Classical factor analysis can not work properly if the data contained many outliers. In order factor analysis remains optimal in explaining a set of variables even in conditions of data containing many outliers, we need a robust estimator. Through factor analysis is expected to obtain robust high accuracy analysis results for data containing many outliers. Estimator fast-MCD is one of the robust estimator that aims to get the smallest determinant of the covariance matrix. Robust factor analysis with fast-MCD method in this thesis is applied to explain the many subgroups of food at food inflation rate in Central Java into a more modest dimensions. The total proportion of the data variance can be explained by factors that are formed through a robust method of factor analysis in foodstuffs inflation data in Central Java that is equal to 72.9 percent larger than the classical factor analysis method which generates at 53.5 percent. This suggests that a more robust factor analysis method is able to cope with food inflation data in Central Java group containing outliers of the classical factor analysis method.

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