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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,
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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.
Arjuna Subject : -
Articles 733 Documents
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PENGUKURAN VALUE AT-RISK PADA PORTOFOLIO OBLIGASI DENGAN METODE VARIAN-KOVARIAN Khoirul Anam; Di Asih I Maruddani; Puspita Kartikasari
Jurnal Gaussian Vol 9, No 4 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v9i4.29012

Abstract

A bond is investment instrument that is basically a debt investment. The profit gained in investing will be comparable with the risk. An investor must pay attention to the size of the risk in choosing bonds. Value at-Risk (VaR) is a risk measurement instruments for measure the maximum loss of asset or portfolio over a spesicif time interval for a given confidence level under normal market conditions. The purpose of this paper is to explain VaR measurement on bond portfolio using variance-covariance method and prove that method is valid to estimate VaR’s model using likelihood ratio. Variance covariance method was chosen because giving lower estimate potential volatility of asset or portfolio than historical simulation and Monte-Carlo simulation. This article use goverment bonds with code FR0053, FR0061, FR0073, FR0074 and portfolio combination. Normality test of return asset and portfolio are required before calculating VaR values. The result of this paper for confidence level 95% showed that bond portfolio FR0053 with FR0061 have a smaller value with VaR values 2,28% of the total market value. It was concluded that VaR bond portfolio are smaller than VaR single asset. Verification test estimate that VaR values using variance-covariance is valid at confidence level 95%.
PERAMALAN JUMLAH PENUMPANG PESAWAT DI BANDARA INTERNASIONAL AHMAD YANI DENGAN METODE HOLT WINTER’S EXPONENTIAL SMOOTHING DAN METODE EXPONENTIAL SMOOTHING EVENT BASED Sofiana Sofiana; Suparti Suparti; Arief Rachman Hakim; Iut Triutami
Jurnal Gaussian Vol 9, No 4 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v9i4.29448

Abstract

Forecasting the number of airplane passengers can be a consideration for the airline at Ahmad Yani International Airport related with addition of extra flight. The number of airplane passengers can be influenced by certain seasonal or special events. The seasonal influences can be known through historical data patterns and if there is a seasonal pattern, the Holt Winter’s Exponential Smoothing method can be used. Exponential Smoothing Event Based (ESEB) forecasting method can be use to see the special events that effect the number of airplane passengers at Ahmad Yani International Airport. After compared, the Holt Winter’s Exponential Smoothing method is a better method of forecasting the number of airplane passengers at Ahmad Yani International Airport because it has a smaller error value, namely the MSE value and the MAPE value than the Exponential Smoothing Event Based (ESEB)method. The MAPE and MSE values be produced from the best method each of  5,644139% and 619,998,718 .Keywords : Airplane Passengers, Seasonal Pattern, Special Event, Exponential Smoothing Event Based , Holt Winter’s Exponential Smoothing.
HISTORICAL SIMULATION UNTUK MENGHITUNG VALUE AT RISK PADA PORTOFOLIO OPTIMAL BERDASARKAN SINGLE INDEX MODEL MENGGUNAKAN GUI MATLAB (Studi Kasus: Kelompok Saham JII Periode Juni - November 2017) Tresno Sayekti Nuryanto; Alan Prahutama; Abdul Hoyyi
Jurnal Gaussian Vol 7, No 4 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v7i4.28869

Abstract

The essence of investment is a placement of a number of funds at one time in hope of gaining profits in the future. One of the most traded forms of investment is stocks. When investing in stocks, investors often run the risk of loss. This loss risk can be overcome by forming a portfolio consisting of several shares. To form an optimal portfolio, investors must first determine an efficient portfolio that produces a certain level of profit with the lowest risk, or a certain level of risk with the highest level of profit. One method for determining the optimal portfolio is to use the Single Index Model method. Whereas to calculate Value at Risk (VaR) using the Historical Simulation method. In this study, researcher used data from the daily closing price of shares incorporated in the Jakarta Islamic Index (JII) stock group in the period of June - November 2017. The shares which will be used were 9 shares in the JII stock group. According to the research result, there are three stocks that go into an optimal portfolio that is SMGR, UNTR, and KLBF with the value of each of its shares respectively by 48,54%, 46,18%, and 5,28%. While the value of the Value at Risk with initial capital of Rp100.000.000, 1 day holding period and a trust level of 95% for optimal portfolio and each stock that goes into optimal portfolio amounted Rp2.090.283, Rp2.258.600, Rp3.403.000, and Rp2.564.200. Keywords: Share, Portofolio, Single Index Model, Value at Risk, Historical Simulation, JII.
MODEL REGRESI DATA PANEL UNTUK MENGETAHUI FAKTOR YANG MEMPENGARUHI TINGKAT KEMISKINAN DI PULAU MADURA Artanti Indrasetianingsih; Tutik Khalimatul Wasik
Jurnal Gaussian Vol 9, No 3 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v9i3.28925

Abstract

Poverty arises when a person or group of people is unable to meet the level of economic prosperity which is considered a minimum requirement of a certain standard of living or poverty is understood as a state of lack of money and goods to ensure survival. Panel data regression is the development of regression analysis which is a combination of time series data and cross section data. Panel data regression is usually used to make observations of data that is examined continuously for several periods. The purpose of this study is to determine the factors that influence the level of poverty in Madura Island in the period 2008 - 2017. In this study the variables used in this study are life expectancy (X1), average length of school (X2), level open unemployment (X3), and labor force participation (X4) with the Comman Effect Model (CEM) approach, Fixed Effect Model and Random Effect Model (REM). To choose the best model from the three is the chow test, the hausman test and the breusch-pagan test. In this study, the best model chosen was the Fixed Effect Model. Keywords: CEM, Fixed Effect Model, Data Panel Regression, REM, Poverty level.
PENGEMBANGAN ESTIMASI PARAMETER PADA METODE EXPONENTIAL SMOOTHING HOLT-WINTERS ADDITIVE MENGGUNAKAN METODE OPTIMASI GOLDEN SECTION Al Qarani, Muhammad Aqajahs; Santoso, Rukun; Safitri, Diah
Jurnal Gaussian Vol 7, No 4 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v7i4.28861

Abstract

Forecasting is an activity to estimate what will happen in the future, one method that can be used is Exponential Smoothing. In this study used the smoothing method of Exponential Smoothing Holt-Winters Additive with three parameters that can be used for prediction of time series data that has trend patterns and seasonal patterns. The problem that arises in this method is to determine the optimum parameter to minimize the forecast error value. This study uses the Golden Section optimization method to estimate the optimum parameters that minimize the MAPE value. The data used is data on foreign tourists who use accommodation services in Yogyakarta from the period January 2009 to December 2016 that have trend patterns and additive seasonal patterns. In simplifying the optimization calculation process, a syntax using RStudio is arranged which contains the Golden Section algorithm to determine the combination that has the optimum parameters. In this optimization there are two treshold error, namely 0.001 and 0.00001. The results showed that the parameter estimator with the Golden Section method for the treshold error of 0.001 obtained MAPE of 18,96732% and for treshold error of 0.00001 MAPE was 18,96536%. This value is in the same MAPE criteria which is 10% ─ 20% (good) so that the selection of the best model is determined based on minimal iteration. Therefore the weighting parameter value used is the result of optimization with ε ≤ 0.001, then from the selected model it is used to predict the number of foreign tourists using accommodation services in Yogyakarta in the next 12 months.
PERBANDINGAN FUZZY TIME SERIES DENGAN METODE CHEN DAN METODE S. R. SINGH (Studi Kasus : Nilai Impor di Jawa Tengah Periode Januari 2014 – Desember 2019) Rachim, Febyani; Tarno, Tarno; Sugito, Sugito
Jurnal Gaussian Vol 9, No 3 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v9i3.28912

Abstract

Import is one of the efforts of an area to meet the needs of its population in order to stabilize prices and maintain stock availability. The value of imports in Central Java throughout 2016 amounted to 8811.05 Million US Dollars. The value of imports in Central Java is the top 10 in all provinces in Indonesia with a percentage of 6.50%. Import data in Central Java is included in the time series data category. To maintain the stability of imports in Central Java, it is deemed necessary to make a plan based on a statistical model. One of the time series models that can be applied is the fuzzy time series model with the Chen method approach and the S. R. Singh method because the method is suitable for cyclical patterned data with monthly time periods such as Import data in Central Java. Important concepts in the preparation of the model are fuzzy sets, membership functions, set basic operators, fuzzy variables, universe sets and domains. The fuzzy time series modeling procedure is carried out through several stages, namely the determination of universe discourse which is divided into several intervals, then defines the fuzzy set so that it can be performed fuzzification. After that the fuzzy logical relations and fuzzy logical group relations are determined. The accuracy calculation in both methods uses symmetric Mean Absolute Percentage Error (sMAPE). In this study the sMAPE value obtained in the Fuzzy Time Series Chen method of 10.95% means that it shows good forecasting ability. While the sMAPE value on the Fuzzy Time Series method of S. R. Singh method by 5.50% shows very good forecasting ability. It can be concluded that the sMAPE value in the S. R. Singh fuzzy time series method is better than the Chen method.Keywords: Import value, fuzzy time series , Chen, S. R. Singh, sMAPE
ANALISIS WEB USAGE MINING MENGGUNAKAN METODE MODIFIED GUSTAFSON – KESSEL CLUSTERING DAN ASSOCIATION RULE PADA WEBSITE UNIVERSITAS DIPONEGORO Kurniawati, Galuh Nurvinda; Santoso, Rukun; Sugito, Sugito
Jurnal Gaussian Vol 9, No 4 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v9i4.29446

Abstract

The comprehension of web visitors patterns are needed to develop website in an optimal fashion. The visitor pattern contained in the web log file of Diponegoro University’s website is clustered by Modified Gustafson-Kessel method. In general, this method produces two until six clusters. Two kinds of results are outlined in this paper. The first is the result contains two clusters, and the second is containing three clusters. In the first result, the visitors are divided into information seekers of student capacity and Engineering Faculty. In the second result, the visitors are divided into information seekers of Medicine Faculty, student admission and Engineering Faculty.  
PENGENDALIAN KUALITAS PRODUK MINO DI HOME INDUSTRY “SARANG SARI” BANYUMAS Winahyu Handayani; Tatik Widiharih; Budi Warsito
Jurnal Gaussian Vol 6, No 4 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v6i4.30386

Abstract

Mino is Banyumas’s signature souvenir that is fancied by the public. High competitiveness makes mino manufacturers are prosecuted to improve the quality of their products. One of the ways to ascertain whether a product has a good quality is by looking at the number of defective products, the less the number of defective products the better the quality. The objective of the study is to minimize broken and burnt products and also size faultiness of the mino. Control Charts   and R are used to view defectiveness data from mino’s diameter and mino’s weight respectively, where as Control Chart p is used to see the data of burnt and broken mino. Furthermore, the value of process capability (Cpk) used to review whether the process is considered capable or not capable. The result and analysis at “Sarang Sari” Nopia and Mino’s Home Industry Banyumas show attribute data in the form of broken and burned defects is restrained after eliminating seven observations data. Thereupon, the variable data in the form of mino’s weight data is restrained after omitting the three observations data with Cpk value is 1.1180, and for mino’s diameter data process has been restrained with Cpk value of 0.9559. Factors that are affecting mino’s defectiveness are equipment, method and measurement. Meanwhile, the profit value of this mino home industry business is Rp 9.276.110 per month. Keywords: Mino, Chart Control, Process Capability, Economic Analysis
ANALISIS SENTIMEN PEMINDAHAN IBU KOTA NEGARA DENGAN KLASIFIKASI NAÏVE BAYES UNTUK MODEL BERNOULLI DAN MULTINOMIAL Nabila Surya Wardani; Alan Prahutama; Puspita Kartikasari
Jurnal Gaussian Vol 9, No 3 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v9i3.27963

Abstract

Text mining is a variation on a field called data mining that tries to find interesting patterns from large databases. Indonesian President affirmed that the capital would be moved to East Kalimantan on August 26, 2019. That planning would receive pros and cons from public. Sentiment analysis is part of text mining that typically involves taking data from opinion, comment, or response. Sentiment analysis is the choice to do on this topic to get results about the public’s opinion. As the most used social media in Indonesia, Youtube is able to be data source by crawling the comments on a video uploaded by Kompas TV channel. Those comments were crawled on October 15, 2019, and selected 1500 latest comments (August 26 – October 12, 2019). The selected comments get transformed by using data pre-processing technique that involves case folding, removing mention, unescaping HTML, removing numbers, removing punctuation, text normalization, stripping whitespace, stopwords removal, tokenizing, and stemming. Labeling of sentiment class uses the sentiment scoring technique. The number of negative comments is 849, while the number of positive comments is 651. The ratio between training data and testing data is 80%: 20%. The classification method used to do sentiment analysis is the Naive Bayes Classifier for Bernoulli and Multinomial model. Bernoulli model only uses occurrence information, whereas the multinomial model keeps track of multiple occurrences. The results show that Bernoulli Naïve Bayes has a 93,45% level of sensitivity (recall) and Multinomial Naïve Bayes has a 90,19% level of sensitivity (recall). It means that both Bernoulli and Multinomial have a good result for this research.  
ANALISIS MODEL ANTREAN NON-POISSON DAN UKURAN KINERJA SISTEM BERBASIS GUI WEB INTERAKTIF MENGGUNAKAN R-SHINY (Studi Kasus: Bus di Terminal Penggaron Kota Semarang) Devi Wijayanti; Sugito Sugito; Hasbi Yasin
Jurnal Gaussian Vol 9, No 4 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v9i4.29010

Abstract

Since September 1, 2018, The Semarang City Government has diverted intercity bus stop within the province from Terboyo Terminal to Penggaron Terminal, resulting in an imbalance of movement and capacity of the Penggaron Terminal which causes queue of bus. Non-Poisson queue is a queue model in which the arrival and service distribution do not have a Poisson distribution or do not have an Exponential distribution. The study was conducted on buses entering the Penggaron Bus Station with the destination of Jepara, Kedungjati, Juwangi, Yogyakarta, Kudus/Pati/Lasem, Pekalongan/Tegal, and Purwokerto/Purworejo. The main goal of this project is to identify the queue model of Non-Poisson and calculate the measure of system performance using the GUI R. One of the programs in R that can create an interactive web-based GUI (Graphical User Interface) is R-Shiny. R-Shiny is a toolkit of R programs that can be used to create online programs. The distribution test obtained using the EasyFit program. The bus queue model of Jepara is (DAGUM/GEV/4):(GD/∞/∞), the queue model of Kedungjati is (GPD/ DAGUM/1):(GD/∞/∞), the queue model of Juwangi is (GEV/ GEV/1):(GD/∞/∞), the queue model of Yogyakarta is (DAGUM/ DAGUM/1) : (GD/∞/∞), the queue model of Kudus/Pati/Lasem is (DAGUM/GEV/1):(GD/∞/∞), the queue model of Pekalongan/Tegal is (GEV/DAGUM/1):(GD/∞/∞), and the queue model of Purwokerto/Purworejo is (GPD/DAGUM/1) : (GD/∞/∞). 

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