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Jurnal Gaussian

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Published by Universitas Diponegoro

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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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Search results for , issue "Vol 8, No 3 (2019): Jurnal Gaussian" : 11 Documents clear

Analisis Kesehatan Bank Menggunakan Local Mean K-Nearest Neighbor dan Multi Local Means K-Harmonic Nearest Neighbor Alwi Assegaf; Moch. Abdul Mukid; Abdul Hoyyi
Jurnal Gaussian Vol 8, No 3 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The classification method continues to develop in order to get more accurate classification results than before. The purpose of the research is comparing the two k-Nearest Neighbor (KNN) methods that have been developed, namely the Local Mean k-Nearest Neighbor (LMKNN) and Multi Local Means k-Harmonic Nearest Neighbor (MLM-KHNN) by taking a case study of listed bank financial statements and financial statements complete recorded at Bank Indonesia in 2017. LMKNN is a method that aims to improve classification performance and reduce the influence of outliers, and MLM-KHNN is a method that aims to reduce sensitivity to a single value. This study uses seven indicators to measure the soundness of a bank, including the Capital Adequacy Ratio, Non Performing Loans, Loan to Deposit Ratio, Return on Assets, Return on Equity, Net Interest Margin, and Operating Expenses on Operational Income with a classification of bank health status is very good (class 1), good (class 2), quite good (class 3) and poor (class 4). The measure of the accuracy of the classification results used is the Apparent Error Rate (APER). The best classification results of the LMKNN method are in the proportion of 80% training data and 20% test data with k=7 which produces the smallest APER 0,0556 and an accuracy of 94,44%, while the best classification results of the MLM-KHNN method are in the proportion of 80% training data and 20% test data with k=3 which produces the smallest APER 0,1667 and an accuracy of 83,33%. Based on APER calculation shows that the LMKNN method is better than MLM-KHNN in classifying the health status of banks in Indonesia.Keywords: Classification, Local Mean k-Nearest Neighbor (LMKNN), Multi Local Means k-Harmonic Nearest Neighbor (MLM-KHNN), Measure of accuracy of classification

PENENTUAN FAKTOR-FAKTOR YANG MEMPENGARUHI INTENSITAS CURAH HUJAN DENGAN ANALISIS DISKRIMINAN GANDA DAN REGRESI LOGISTIK MULTINOMIAL (Studi Kasus: Data Curah Hujan Kota Semarang dari Stasiun Meteorologi Maritim Tanjung Emas Periode Oktober 2018 – Maret 2019) Shella Faiz Rohmana; Agus Rusgiyono; Sugito Sugito
Jurnal Gaussian Vol 8, No 3 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Meteorologist develop rainfall forecasting methods to obtain better and more accurate rainfall information. One of them is the research of grid data and the method of grouping rainfall. According to BMKG, rainfall is classified into light, medium, and heavy rain. This study aims to determine the factors that influencing rainfall grouping using multiple discriminant analysis with a stepwise selection method. This study uses the daily climate data of Semarang City for period of October 2018 to March 2019. Based on its partial F value, the wind speed variable is eliminated so the significant variable on rainfall grouping are air temperature, air humidity, and wind direction. This analysis produces discriminant scores obtained from linear combinations between discriminant weights and observation values of significant independent variable. The classification procedure is based on the discriminant score each observations compared to cutting score resulted in classification accuracy of 62.89%. Multinomial logistic regression analysis is used to determine the effect of independent variables on rainfall intensity using the odds ratio. This analysis produces an estimate of the conditional probability of each group using significant independent variables are air temperature, air humidity, wind speed, and wind direction. The classification procedure is based on the largest conditional probability value between rainfall groups resulted in classification accuracy of 69.80%. Keywords: multiple discriminant analysis, multinomial logistic regresion, classification accuracy, rainfall

IMPLEMENTASI ALGORITMA MODIFIED GUSTAFSON-KESSEL UNTUK CLUSTERING TWEETS PADA AKUN TWITTER LAZADA INDONESIA Ratna Kencana Putri; Budi Warsito; Mustafid Mustafid
Jurnal Gaussian Vol 8, No 3 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Online social media is a new kind of media which is steadily growing and has become publicly popular. Due to its ability to spread informations rapidly and its easiness to access for internet users, social media provides new alternative to conduct advertising and product segmentation. Twitter is one of the most favored social media with 19.5 million users in Indonesia to the date. In this research, the application of text mining to cluster tweets from the @LazadaID Twitter account is done using the Modified Gustafson-Kessel clustering algorithm. The clustering process is executed five times with the number of cluster starts from two to six cluster. The results of this research indicate that the optimum number of clusters formed based on the Partition Coefficient and Classification Entropy validation index are three clusters. Those three clusters are tweets containing electronic stuff offers, discounts, and prize quizes. Tweets with the most retweets and likes are prize quiz tweets. PT Lazada Indonesia could use this kind of tweet to conduct advertising on social media Twitter because the prize quiz tweets are liked by the @LazadaID Twitter account followers.Keywords: Twitter, advertising, Lazada Indonesia, Gustafson-Kessel Clustering algorithm, validation index

ANALISIS VARIANSI PADA RANCANGAN BUJUR SANGKAR YOUDEN DENGAN DUA DATA HILANG Amalina Sari Dewi; Tatik Widiharih; Rita Rahmawati
Jurnal Gaussian Vol 8, No 3 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Youden Square Design (YSD) is an incomplete latin square design with at least one row/column which can’t run in an experiment. In this research we took 5x4 YSD (one column is not runned in an experiment). This design has a balance characteristic from a balanced incomplete block design where all treatments appears with the same number in each row. Missing data can occur in YSD. In this discussion, YSD with two missing data was used. Missing data is estimated by an iterative method then we arrange analysis of variance and LSD test. Analysis of variance with two missing data in YSD is calculated by adjusting the treatment sum of squares with it’s bias value and the total degrees of freedom and error degrees of freedom are substracted by two. LSD test is carried out if the treatment has a significant effect to the response. To clarify the discussion in YSD, example of application in the field of industry is given by observing the effect of the assembly method to the length of assembly time of X component. The assembly method has an effect to the length of assembly time of X component and if the missing data are and so the suggested assembly method is E method because it has the fastest average assembly time. Keywords: YSD, Missing Data, Analysis of Variance, LSD Test

PERAMALAN JUMLAH WISATAWAN YANG BERKUNJUNG KE OBJEK WISATA DI JAWA TENGAH MENGGUNAKAN VARIASI KALENDER ISLAM REGARIMA Jesica, Haniela Puja; Ispriyanti, Dwi; Tarno, Tarno
Jurnal Gaussian Vol 8, No 3 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Tourism is one of the most strategically controlled areas that have been developed.The number of tourists in Central Java is constantly rising in the month of Eid Al-Fitr caused by holiday and mudik to hometown. The shift of the Eid Al-Fitr month on the data will form a seasonal pattern with an unequal period, then called moving holiday effect.One of the calendar variationsare often used to remove the moving holiday effect is RegARIMA model. RegARIMA is a combination of the linier regression and ARIMA, which a weight was used as a regression variable and error of regression model was used a variable in the ARIMA process. Based on the analysis carried out on the monthly number of tourists visiting tourist attractions in Central Java data for the period January 2011 to December 2017, the RegARIMA (1,1,1) (0,0,1)12model as the best model because it have the lowest AIC value than other model. The forecasting results in 2018 shows an increase on number of tourists data on June 2018 which coincided with the Eid Al-Fitr holiday on 15 June 2018. sMAPE value is 23,298%.Keyowrds:Time Series, Tourists, RegARIMA, Moving Holiday Effect

PERBANDINGAN KINERJA MUTUAL K-NEAREST NEIGHBOR (MKNN) DAN K-NEAREST NEIGHBOR (KNN) DALAM ANALISIS KLASIFIKASI KELAYAKAN KREDIT Annisa Sugesti; Moch. Abdul Mukid; Tarno Tarno
Jurnal Gaussian Vol 8, No 3 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Credit feasibility analysis is important for lenders to avoid the risk among the increasement of credit applications. This analysis can be carried out by the classification technique. Classification technique used in this research is instance-based classification. These techniques tend to be simple, but are very dependent on the determination of K values. K is number of nearest neighbor considered for class classification of new data. A small value of K is very sensitive to outliers. This weakness can be overcome using an algorithm that is able to handle outliers, one of them is Mutual K-Nearest Neighbor (MKNN). MKNN removes outliers first, then predicts new observation classes based on the majority class of their mutual nearest neighbors. The algorithm will be compared with KNN without outliers. The model is evaluated by 10-fold cross validation and the classification performance is measured by Gemoetric-Mean of sensitivity and specificity. Based on the analysis the optimal value of K is 9 for MKNN and 3 for KNN, with the highest G-Mean produced by KNN is equal to 0.718, meanwhile G-Mean produced by MKNN is 0.702. The best alternative to classifying credit feasibility in this study is K-Nearest Neighbor (KNN) algorithm with K=3.Keywords: Classification, Credit, MKNN, KNN, G-Mean.

PEMODELAN REGRESI POISSON BIVARIAT PADA JUMLAH KEMATIAN IBU HAMIL DAN NIFAS DI JAWA TENGAH TAHUN 2017 Arbella Maharani Putri; Alan Prahutama; Budi Warsito
Jurnal Gaussian Vol 8, No 3 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The maternal mortality rate is one of the indicators that determine the prosperity level of society in a country. Most of the maternal mortality caused by pregnancy maternal mortality and postpartum maternal mortality. Central Java is one of the provinces with the biggest number of pregnancy maternal mortality and postpartum maternal mortality in Indonesia. The number of pregnancy maternal mortality and postpartum maternal mortality follow Poisson Distribution and it has a significant correlation. Therefore, the writer analyzed factor that influences the number of pregnancy maternal mortality and postpartum maternal mortality using Univariate and Bivariate Poisson Regression method. Results from this study obtained that in the Univariate Poisson Regression variables that significantly influence pregnancy maternal mortality and postpartum maternal mortality are the percentage of pregnant women implementing K1 (X1), percentage of childbirth women that has puerperal health service (X6) and percentage of household with clean and healthy behavior (X7). In the Bivariate Poisson, the best model is the second model which assuming that covariance is an equation.Keywords: Pregnancy of Maternal Mortality, Postpartum Maternal Mortality, Bivariate Poisson Regression.

REGRESI ROBUST ESTIMASI-M DENGAN PEMBOBOT ANDREW, PEMBOBOT RAMSAY DAN PEMBOBOT WELSCH MENGGUNAKAN SOFTWARE R Aulia Desy Deria; Abdul Hoyyi; Mustafid Mustafid
Jurnal Gaussian Vol 8, No 3 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Robust regression is one of the regression methods that robust from effect of outliers. For the regression with the parameter estimation used Ordinary Least Squares (OLS), outliers can caused assumption violation, so the estimator obtained became bias and inefficient. As a solution, robust regression M-estimation with Andrew, Ramsay and Welsch weight function can be used to overcome the presence of outliers. The aim of this study was to develop a model for case study of poverty in Central Java 2017 influenced by the number of unemployment, population, school participation rate, Human Development Index (HDI), and inflation. The result of estimation using OLS show that there is violation of heteroskedasticity caused by the presence outliers. Applied robust regression to case study proves robust regression can solve outliers and improve parameter estimation. The best robust regression model is robust regression M-estimation with Andrew weight function. The influence value of predictor variables to poverty is 92,7714% and MSE value is 370,8817. Keywords: Outliers, Robust Regression, M-Estimator, Andrew, Ramsay, Welsch

PEMILIHAN PERUMAHAN TERFAVORIT MENGGUNAKAN METODE VIKOR DAN TOPSIS DENGAN GUI MATLAB (Studi Kasus: Perumahan Mijen Semarang) Alika Ramadhani; Rukun Santoso; Rita Rahmawati
Jurnal Gaussian Vol 8, No 3 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The increase in the population of Semarang has an impact on the increasing demand for residential housing. Unfortunately, the limitations of the area became an obstacle in Semarang to develop residential areas. This development of residential housing in Semarang leads to suburban such as Mijen. The method that can be used to choose favorite housing is Visekriterijumsko Kompromisno Rangiranje (VIKOR) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Both methods can be applied to solve Multiple Criteria Decision Making (MCDM) issue. This study has 8 alternatives of residential housing in Mijen with 5 criteria such as Price, Payment Method, Building Specifications, Housing Facilities, and Location. This research was design with Graphical User Interface (GUI) Matrix Laboratory (MATLAB) as computing tool. VIKOR and TOPSIS method on this research, obtained the same result that the most favorite residential housing is A5. Keywords: Housing, SPK, VIKOR, TOPSIS, GUI

PEMODELAN REGRESI HURDLE POISSON DALAM MENGATASI EXCESS ZEROS UNTUK KASUS PENYAKIT TETANUS NEONATORUM PADA NEONATAL DI JAWA TIMUR Cylvia Evasari Margaretha; Dwi Ispriyanti; Tatik Widiharih
Jurnal Gaussian Vol 8, No 3 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Tetanus Neonatorum is one of the infectious diseases that occur in newborns caused by Clostridium Tetani bacteria through cuts or scratches. The number of Tetanus Neonatorum cases in East Java Province in 2017 is discrete data Poisson distribution with a proportion of zero value of 73,7 percent. The amount of zero value data can result in overdispersion where the variance is greater than the mean. To overcome this problem, Hurdle Poisson regression model is a solution. To estimation of regression parameters for Hurdle Poisson regression is using the Maximum Likelihood Estimation (MLE) method and Broyden Fletcher Goldfarb Shanno (BFGS) iteration. Hurdle Poisson regression produces predictor variables that affect the number of Tetanus Neonatorum cases in East Java Province in the logit model are the percentage of pregnant women administered the K4 program, population density per and in the truncated Poisson model are the percentage of labor assisted by health workers the percentage of pregnant women administered the K4 program, population density per with the Akaike Information Criterion (AIC) value of 78,422.Keywords: Tetanus Neonatorum, Excess Zeros, Overdispersion, Hurdle Poisson Regression

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Jawa tengah

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