Garuda - Garba Rujukan Digital

Article Per Year (5 Year)

All

Jurnal Gaussian

gaussian
Website

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 693 Documents

4 5 6 7 8

PEMODELAN REGRESI 3-LEVEL DENGAN METODE ITERATIVE GENERALIZED LEAST SQUARE (IGLS) (Studi Kasus: Lamanya pendidikan Anak di Kabupaten Semarang) Amanda Devi Paramitha; Suparti Suparti; Triastuti Wuryandari
Jurnal Gaussian Vol 5, No 1 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

In a research, data was used often hierarchical structure. Hierarchical data is data obtained through multistage sampling from a population with independent variables can be defined within each level and dependent variable can be defined at the lowest level. One analysis that can be used for data with a hierarchical structure is a multilevel regression analysis. The purpose of this final three-level regression analyzes to establish regression models about the length of a child's education in the District of Semarang where the individual level-1 with a factor of gender, lodged at the family level-2 by a factor of the length of father's education and duration of maternal education and nesting on the environment level-3 with factor of residence, number of elementary school the large number of junior high school and the large number of high school. Parameter estimation in 3-level regression models can use several methods, one of which is a method of Iterative Generalized Least Square (IGLS). Of cases the length of education in the district of Semarang indicate that factors influencing factor is the length of father's education and the duration of the mother's education. Keywords : Hierarchical structure, multistage sampling, multilevel regression, Iterative Generalized Least Square.

PENINGKATAN PRODUKTIVITAS BENANG POLYESTER COTTON 45 MELALUI ANALISIS TOTAL QUALITY CONTROL (Studi kasus di PT Panca Bintang Tunggal Sejahtera) Afifah Alrizqi; Yuciana Wilandari; Abdul Hoyyi
Jurnal Gaussian Vol 3, No 3 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

PT Panca Bintang Tunggal Sejahtera is a company which operate in textill and garment. The main product is polyester cotton 45 yarn. In the production activity, still failed product. To determine what factors caused the failure of polyester cotton 45 yarn, used the analysis of Total Quality Control to control devices such as check sheet, stratification, bar chart, control chart, cause and effect diagrams, Pareto charts, and scatter plot. From the results of the check sheet, stratification and histogram obtained the highest type of failure is uneven sliver, which is as much as 1871 kg for a month. From the individual unit control chart, indicated that the activities of the production process there are deviations which are beyond the limits of product controllers that need improvement. A cause and effect diagram result show that the biggest factor causing the failure of the product due to labor factor due to lack of training and supervision. Therefore, the company can make improvements with priority on the labor factor.

ANALISIS TEKNIKAL SAHAM DENGAN INDIKATOR GABUNGAN WEIGHTED MOVING AVERAGE DAN STOCHASTIC OSCILLATOR Yustian Dwi Saputra; Di Asih I Maruddani; Abdul hoyyi
Jurnal Gaussian Vol 8, No 1 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The Stochastic Oscillator which is one of the leading indicators has the disadvantage of opening the gap for false signals. To minimize false signals, the smoothing process is carried out using the Moving Average. Stochastic Oscillator is usually combined with SMA (Simple Moving Average). But SMA has the disadvantage of giving the same weight to all data, even though in reality the data that best reflects the next value is the last data. This makes the basis of weighting the WMA (Weighted Moving Average) method.This study aims to test the combination of Stochastic Oscillator with SMA and WMA and use the best combination to predict the trends that will occur and trading decisions taken from the results of these predictions. The research samples were ANTM, BBRI, and GIAA stocks from November 9 2015 to November 9, 2018.The results show a combination of Stochastic Oscillator and WMA is a better combination of predictions than Stochastic Oscillator and SMA because it has a smaller MSE value. Based on the comparison of signal accuracy based on Overbought and Oversold, the best period of combination of Stochastic Oscillator and WMA is period 25. From the predicted trend that will occur with a combination of Stochastic Oscillator and WMA period 25 a decision is made to buy shares for ANTM shares, sell shares for BBRI shares, and waiting for a buy signal for GIAA shares.Keywords: Stochastic Oscillator, SMA, WMA, Predictions, Trends

PENENTUAN MODEL KEMISKINAN DI JAWA TENGAH DENGAN MULTIVARIATE GEOGRAPHICALLY WEIGHTED REGRESSION (MGWR) Sindy Saputri; Dwi Ispriyanti; Triastuti Wuryandari
Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The problem of poverty is a fundamental problem faced in a number of regions in Indonesia, to determine significant indicators on poverty by taking into account the spatial variation in the province of Central Java can use multivariate models Geographically Weighted Regression (MGWR). In the model MGWR model parameter estimation is obtained by using Weighted Least Square (WLS). Selection of the optimum bandwidth using Cross Validation (CV). The study looked for the best model among MGWR with multivariate regression and create distribution maps counties and cities in the province of Central Java based variables significantly to poverty. The results of testing the suitability of the model shows that there is no influence of spatial factors on the percentage of poor and non-poor in the province of Central Java. Variables expected to affect the percentage of poor people is a variable percentage of expenditures for food, while the percentage of the non-poor is a variable percentage of expenditure on food and the percentage of heads of household education level less than SD. Based on the AIC and the MSE obtained the best model is the model MGWR with AIC value of 44.4603 and MSE 0.454.Keywords: Cross Validation, MGWR, Poverty, Weighted Least Square

KLASIFIKASI KEIKUTSERTAAN KELUARGA DALAM PROGRAM KELUARGA BERENCANA (KB) DI KOTA SEMARANG MENGGUNAKAN METODE MARS DAN FK-NNC Aryono Rahmad Hakim; Diah Safitri; Sugito Sugito
Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Classification method is a statistical method for grouping or classifying data. A good classification method will produce a little bit of misclassification. Classification method has been greatly expanded and two of the existing classification methods are Multivariate Adaptive Regression Spline (MARS) and Fuzzy k-Nearest Neighbor in Every Class (FK-NNC). This study is aimed to compare a classification of Keluarga Berencana participation based on suspected factors that affect them between the methods of MARS and FK-NNC. This study uses secondary data which one is the participation of Keluarga Berencana in Semarang on 2014. Evaluation of errors use an Apparent Error Rate (APER). In the method MARS best classification results is obtained with the combination of BF = 24, MI = 3, MO = 0 for generating a smallest Generalized Cross Validation (GCV) value and the APER is obtained by 19%. While FK-NNC method is obtained the best classification results in k = 3 for generating the greatest accuracy of classification value and APER value is obtained by 22%. Based on APER (Apparent Error Rate) calculation, it shown that the classification of family participation in Keluarga Berencana (KB) programs in Semarang using MARS method is better than FK-NNC method.Keywords: Classification, MARS, FK-NNC, APER, Keluarga Berencana

PEMILIHAN CLUSTER OPTIMUM PADA FUZZY C-MEANS (STUDI KASUS: PENGELOMPOKAN KABUPATEN/KOTA DI PROVINSI JAWA TENGAH BERDASARKAN INDIKATOR INDEKS PEMBANGUNAN MANUSIA) Sarita Budiyani Purnamasari; Hasbi Yasin; Triastuti Wuryandari
Jurnal Gaussian Vol 3, No 3 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

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. One method of clustering is Fuzzy C-Means (FCM). FCM is used because each data in a cluster determined by a degree of membership that have value between 0 and 1. This research use two kinds of distance, Manhattan and Euclidean. To determine the proper distance in clustering district / city in Central Java based on indicators of Human Development Index (HDI), we have to calculate the ratio of the standard deviation, where the smaller value indicates a better clustering. While the optimum number of groups obtained from the minimum value of Xie Beni. Variables that used in this research are the indicators of HDI in 2012 for district / city in Central Java, consists of: Life Expectancy Value (years), Literacy Rate (percent), Average Length of School (years), and Purchasing Power Parity (thousands rupiah). The results from this research are the distance that gives a better quality is Euclidean and the optimum cluster given when the number of cluster is five with the smallest value of Xie Beni is 0,50778.

PERBANDINGAN METODE ARIMA BOX-JENKINS DENGAN ARIMA ENSEMBLE PADA PERAMALAN NILAI IMPOR PROVINSI JAWA TENGAH Riski Arum Pitaloka; Sugito Sugito; Rita Rahmawati
Jurnal Gaussian Vol 8, No 2 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Import is activities to enter goods into the territory of a country, both commercial and non-commercial include goods that will be processed domestically. Import is an important requirement for industry in Central Java. The increase in high import values can cause deficit in the trade balance. Appropriate information about the projected amount of imports is needed so that the government can anticipate a high increase in imports through several policies that can be done. The forecasting method that can be used is ARIMA Box-Jenkins. The development of modeling in the field of time series forecasting shows that forecasting accuracy increases if it results from the merging of several models called ensemble ARIMA. The ensemble method used is averaging and stacking. The data used are monthly import value data in Central Java from January 2010 to December 2018. Modeling time series with Box-Jenkins ARIMA produces two significant models, namely ARIMA (2,1,0) and ARIMA (0,1,1). Both models are combined using the ARIMA ensemble averaging and stacking method. The best model chosen from the ARIMA method and ensemble ARIMA based on the least RMSE value is the ARIMA model (2,1,0) with RMSE value of 185,8892 Keywords: Import, ARIMA, ARIMA Ensemble, Stacking, Averaging

ANALISA FAKTOR-FAKTOR YANG MEMPENGARUHI KEPUTUSAN PEMBELIAN DAN KEPUASAN KONSUMEN PADA LAYANAN INTERNET SPEEDY DI KOTA SEMARANG MENGGUNAKAN PARTIAL LEAST SQUARE (PLS) Bella Cynthia Devi; Abdul Hoyyi; Moch. Abdul Mukid
Jurnal Gaussian Vol 4, No 3 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Persepsi konsumen terhadap tuntutan kebutuhan layanan internet Speedysangat beragam. Terdapat beberapa faktor yang dipertimbangkan konsumen sebelum menggunakan layanan akses internet, faktor tersebut diantaranya harga, merek dan kualitas. Di lain pihak, konsumen akan merasa puas jikalayanan internet Speedy melebihi harapan konsumen. Faktor-faktor yang mempengaruhi keputusan pembelian dan kepuasan layanan internet Speedy diungkapkan secara komprehensif dengan persamaan struktural berbasis komponen, Partial Least Square (PLS). PLS mengestimasi model hubungan antar variabel laten dan antar variabel laten dengan indikatornya. Dari hasil analisis diperoleh kesimpulan bahwa keputusan pembelian layanan internet Speedy dipengaruhi oleh harga, merek dan kualitas, sedangkan kepuasan konsumen dipengaruhi oleh keputusan pembelian dan kualitas. Kata kunci : Partial Least Square, Speedy, keputusan pembelian, kepuasanANALISA FAKTOR-FAKTOR YANG MEMPENGARUHI KEPUTUSAN PEMBELIAN DAN KEPUASAN KONSUMEN PADA LAYANAN INTERNET SPEEDY DI KOTA SEMARANGMENGGUNAKAN PARTIAL LEAST SQUARE (PLS)

PEMILIHAN PENGRAJIN TERBAIK DENGAN METODE ELECTRE DAN TOPSIS MENGGUNAKAN GUI MATLAB (STUDI KASUS : PT. Asaputex Jaya, Tegal) Hafii Risalam; Rita Rahmawati; Suparti Suparti
Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Company is technical unity that aims to produce goods services. One of determinants of succesful company is its human resources or known as employees. PT. Asaputex Jaya is one of company that enganged in the manufacture of sarong. Poor quality of human resources, especially on the production is still an obstacle for PT. Asaputex Jaya. Therefore selection of the best craftsmen need to be done so that production process doesn’t meet any probelms. This research was conducted to determine top craftsmen in sarong production on PT. Asaputex Jaya, and also used for PT. Asaputex Jaya’s human resources management interests. ELECTRE is based on rankings concept through pair comparison between alternatives on the suitable criteria. While TOPSIS can determine the value of preference for each alternative, the concept is simple and easy to understand. There are 8 criteria in the selection of top craftsmen: design, fabrics assembly, merger with filler material, manufacture of sarong, punctuality, statutes of size, tailoring results, and neatness or stitching cleanliness. Through the TOPSIS method selected 10 top craftsmen: 5th, 14th, 15th, 9th, 3rd, 13th,6th, 20th, 18th, and 10th , which then only one top craftsman will be chosen using the ELECTRE method. This study also produced a GUI Matlab programming application that can help users in performing data processing using TOPSIS and ELCTRE to select the best craftsmen on PT. Asaputex Jaya Keyword: SDM, TOPSIS, ELECTRE, GUI Matlab, top craftsmen

ANALISIS RANCANGAN BUJUR SANGKAR GRAECO LATIN Yuyun Naifular; Triastuti Wuryandari; Yuciana Wilandari
Jurnal Gaussian Vol 3, No 1 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The design of the experiment is a test or series of tests, using both descriptive statistics and inferential statistics that aims to transform the input variables into an output which is the response of the experiment. The Graeco Latin Square Design was built to control the diversity of component units of local control experiment of three is a row, column, and Greek letters. Terms the Graeco Latin Square Design is if the rows, columns, Latin letters, and Greek letters have the same level and each Greek letter appears only once in each row, column, and Latin letter. The steps in the analysis of the test Graeco Latin Square Design to test the normality of the error, homogeneity of variance test, determine the degrees of freedom, calculating Sum of Squares and Mean Square every factor. Next calculate the value of F for test row, column, treatments Latin letter, and treatment of Greek letters, draw up a table of variance analysis, and conclude whether there is any effect on the response variance of each source. If there is impact, it is necessary to further test using the Duncan test

Page 6 of 70 | Total Record : 693

4 5 6 7 8

Filter by Year

2012 2023

Filter By Issues

All Issue Vol 12, No 3 (2023): Jurnal Gaussian (Forthcomming Issue) Vol 12, No 2 (2023): Jurnal Gaussian Vol 12, No 1 (2023): Jurnal Gaussian Vol 11, No 4 (2022): Jurnal Gaussian Vol 11, No 3 (2022): Jurnal Gaussian Vol 11, No 2 (2022): Jurnal Gaussian Vol 11, No 1 (2022): Jurnal Gaussian Vol 10, No 4 (2021): Jurnal Gaussian Vol 10, No 3 (2021): Jurnal Gaussian Vol 10, No 2 (2021): Jurnal Gaussian Vol 10, No 1 (2021): Jurnal Gaussian Vol 9, No 4 (2020): Jurnal Gaussian Vol 9, No 3 (2020): Jurnal Gaussian Vol 9, No 2 (2020): Jurnal Gaussian Vol 9, No 1 (2020): Jurnal Gaussian Vol 8, No 4 (2019): Jurnal Gaussian Vol 8, No 3 (2019): Jurnal Gaussian Vol 8, No 2 (2019): Jurnal Gaussian Vol 8, No 1 (2019): Jurnal Gaussian Vol 7, No 4 (2018): Jurnal Gaussian Vol 7, No 3 (2018): Jurnal Gaussian Vol 7, No 2 (2018): Jurnal Gaussian Vol 7, No 1 (2018): Jurnal Gaussian Vol 6, No 4 (2017): Jurnal Gaussian Vol 6, No 3 (2017): Jurnal Gaussian Vol 6, No 2 (2017): Jurnal Gaussian Vol 6, No 1 (2017): Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian Vol 5, No 2 (2016): Jurnal Gaussian Vol 5, No 1 (2016): Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian Vol 4, No 3 (2015): Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian Vol 4, No 1 (2015): Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian Vol 3, No 3 (2014): Jurnal Gaussian Vol 3, No 2 (2014): Jurnal Gaussian Vol 3, No 1 (2014): Jurnal Gaussian Vol 2, No 4 (2013): Jurnal Gaussian Vol 2, No 3 (2013): Jurnal Gaussian Vol 2, No 2 (2013): Jurnal Gaussian Vol 2, No 1 (2013): Jurnal Gaussian Vol 1, No 1 (2012): Jurnal Gaussian More Issue

Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name
-

Contact Email
-

Phone
-

Journal Mail Official
-

Editorial Address
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

Location
Kota semarang,

Jawa tengah

INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name -

Contact Email -

Phone -

Journal Mail Official -

Editorial Address 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

Location Kota semarang, Jawa tengah INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Contact Name
-

Contact Email
-

Phone
-

Journal Mail Official
-

Editorial Address
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

Location
Kota semarang,

Jawa tengah

INDONESIA