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[ANALISIS INDEKS HARGA SAHAM GABUNGAN DAN FAKTOR PENGARUHNYA MENGGUNAKAN PEMODELAN REGRESI SEMIPARAMETRIK KERNEL DILENGKAPI GUI-R ]]>
<![CDATA[Arnisa Melani Kahar]]>;
<![CDATA[Suparti Suparti]]>;
<![CDATA[Arief Rachman Hakim]]>
<![CDATA[ Jurnal Gaussian Vol 12, No 1 (2023): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.12.1.30-41
<![CDATA[Composite Stock Price Index (IDX) shows the movement of stock prices used by investors to determine their investment strategy. IDX movement is influenced by macroeconomic factors such as money supply and inflation, so regression analysis is used to determine the relationship between the variables. Based on the scatterplot, money supply is known as a parametric predictor variable as it has a linier line patterned scatterplot and inflation is a nonparametric predictor variable as it has a random patterned scatterplot, so semiparametric regression modelling is used for the analysis. Kernel regression was chosen to analyze the nonparametric component based on the random patterned scatterplot of inflation. This study aims to obtain the results of semiparametric kernel regression modelling analysis and to create a GUI to be applied to the analysis as a development of previous similar studies that still done based on CLI. This study uses monthly data from January 2013 to December 2020 with the proportion of in sample and out sample data distribution 87,5%:12,5%. Based on the smallest MSE value as the best model criteria, semiparametric regression model with triangle kernel function is the best model obtained with optimal bandwidth=3.24, which means the model is strong and which means that the forecasting results are very accurate. GUI has been created according to the needs of the modelling analysis implementation.]]>
<![CDATA[GLUE VALUE AT RISK UNTUK MENGUKUR RISIKO PADA PORTOFOLIO OPTIMAL DENGAN METODE MULTI INDEX MODEL ]]>
<![CDATA[Nur Khofifah]]>;
<![CDATA[Agus Rusgiyono]]>;
<![CDATA[Di Asih I Maruddani]]>
<![CDATA[ Jurnal Gaussian Vol 12, No 1 (2023): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.12.1.116-125
<![CDATA[Creating a portfolio is one method of reducing risk. One of the best portfolio decisions is made by Multi Index Model. Multi Index Model is a method that makes use of multiple variables that impact stock returns. Before making an investment, risk measurement must be considered. Calculation of risk on a portfolio will be more accurate if it is calculated using Glue Value at Risk, because it satisfies the property of subadditivity, which is one of the coherence properties of a risk measure that reflects the idea that risk can reduce by diversification. The stocks used in this study are 4 stocks that are members of SRI-KEHATI stock group in the period January 2017 – December 2021. The factors used are Composite Stock Price Index (JCI), and Rupiah to USD exchange rate. According to the study's findings, the best portfolio consist of four stocks: BBRI (Bank Rakyat Indonesia Tbk.) (17.82%), KLBF (Kalbe Farma Tbk.) (56.66%), UNTR (United Tractors Tbk.) (24.13%), and WIKA (Wijaya Karya Tbk.) (1.39%). The confidence levels of and , the distortion function height is and are used, the GlueVaR value for the stock portfolio is 10.476%. ]]>
<![CDATA[PEMODELAN INDEKS HARGA PERDAGANGAN BESAR (IHPB) SEKTOR EKSPOR MENGGUNAKAN ARFIMA-GARCH ]]>
<![CDATA[Gandhes Linggar Winanti]]>;
<![CDATA[Dwi Ispriyanti]]>;
<![CDATA[Sugito Sugito]]>
<![CDATA[ Jurnal Gaussian Vol 12, No 1 (2023): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.12.1.52-60
<![CDATA[Indonesia's price index serves as a barometer for the nation's economic condition. One of the Indonesia’s price index is Wholesale Price Index (WPI). WPI is a price index that tracks the average change in wholesale prices over time. Time series analysis can be used for forecasting because WPI is one of the time series data. WPI is long memory, which is a condition in which data from different time periods have a high link despite being separated by a large amount of time. The Autoregressive Fractional Integrated Moving Average (ARFIMA) model can be used to overcome this feature when modeling time series data. The assumption of constant error variance is not fulfilled in the IHPB data analysis, indicating that the data is heteroscedastic. The GARCH (Generalized Auto Regressive Conditional Heteroscedasticity) model is one of the models used to overcome heteroscedasticity. The data used is the export sector of WPI from January 2003 to June 2021. The best model for forecasting WPI is ARFIMA(1,b,2) – GARCH(1,1) with b=0,7345333, and MAPE value is 3,150875%.]]>
<![CDATA[PENERAPAN DIAGRAM PENGENDALI NONPARAMETRIK EXPONENTIALLY WEIGHTED MOVING AVERAGE SIGN UNTUK ANALISIS PERGERAKAN HARGA SAHAM SEKTOR PROPERTI ]]>
<![CDATA[Radian Lukman]]>;
<![CDATA[Mustafid Mustafid]]>;
<![CDATA[Sugito Sugito]]>
<![CDATA[ Jurnal Gaussian Vol 12, No 1 (2023): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.12.1.1-9
<![CDATA[Stocks are evidence of equity participation in a company. Investors need to know the quality of stock prices so that they can minimize losses when investing. Technical analysis can be used by investors to decide when to buy or sell a stock. One of the technical analysis that can be used on stock prices is using quality control. Control charts can be used to make decisions in stock trading activities. The Exponentially Weighted Moving Average control chart is very useful for detecting small shifts such as in financial data. The assumption that must be fulfilled in using the EWMA control chart is that the data is normally distributed. The non-fulfillment of the normal distribution assumption causes the EWMA control chart produces plots that are far from the control limits. This problem can be solved using the nonparametric EWMA Sign control chart. The construction of the nonparametric EWMA Sign control chart on stock prices is expected to overcome the limitations of the standard EWMA control chart and provide a signal to investors to know the best time to trade stocks. The data used in this study is the daily closing price data of PT Bumi Serpong Damai Tbk on March 1, 2021 to March 4, 2022 with a total of 250 data. The nonparametric EWMA Sign control chart shows that the daily closing price data is out of control because it produces plots that are spread out non-randomly and shows a relatively similar pattern.]]>
<![CDATA[PERAMALAN JUMLAH PENUMPANG KERETA API DI PULAU JAWA MENGGUNAKAN METODE HOLT WINTERS EXPONENTIAL SMOOTHING DAN FUZZY TIME SERIES MARKOV CHAIN ]]>
<![CDATA[Santa Agata Mendila]]>;
<![CDATA[Iut Tri Utami]]>;
<![CDATA[Puspita Kartikasari]]>
<![CDATA[ Jurnal Gaussian Vol 12, No 1 (2023): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.12.1.104-115
<![CDATA[One of the public transportation choices by the public is the train. The number of train passengers on the island of Java often increases and decreases in certain months. PT.KAI can monitor the number of train passengers by forecasting. Forecasting aims to predict the number of train passengers so that PT.KAI is ready to provide the best service. This study uses monthly data on the number of train passengers on Java Island from January 2015 to February 2020. This study uses multiplicative holt winters exponential smoothing and fuzzy time series markov chain. The multiplicative Holt Winters exponential smoothing method is used on data that contains trend and seasonal elements that experience data fluctuations simultaneously. The fuzzy time series markov chain method is a combination of the fuzzy time series with the markov chain which aims to obtain the greatest probability using the transition probability matrix. Based on the analysis results, it can be concluded that the multiplicative holt winters exponential smoothing method is better at predicting the number of train passengers on Java Island because the value of sMAPE multiplicative holt winters exponential smoothing is smaller, it is 3,0643% and the sMAPE fuzzy time series markov chain value is 5,2955%.]]>
<![CDATA[PERAMALAN PENDAPATAN BULANAN MENGGUNAKAN FUZZY TIME SERIES CHEN ORDE TINGGI ]]>
<![CDATA[Muhammad Rizky Yuliyanto]]>;
<![CDATA[Triastuti Wuryandari]]>;
<![CDATA[Iut Tri Utami]]>
<![CDATA[ Jurnal Gaussian Vol 12, No 1 (2023): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.12.1.61-70
<![CDATA[Cooperatives need consideration in the making of business strategy decisions. Forecasting can assist cooperatives in deciding on their business strategy. This study used n-orde Fuzzy Time Series Chen. n-orde Fuzzy Time Series Chen captures data patterns formed by two or more historical data in each period called fuzzy logic relation (FLR). The pattern of FLR is used to be projected in forecasting future conditions. This study used 2-orde, 3-orde, and 4-orde with 1-orde as the comparison. This study used data on the monthly revenue of the Employee Cooperative of PT. Telekomunikasi Indonesia Semarang Region for the period of January 2019 to May 2022 to predict revenue for the period of June and July 2022. This study used symmetric Mean Absolute Percentage Error (sMAPE) in calculating the forecasting error rate. 1-orde, 2-orde, 3-orde, and 4-orde of Fuzzy Time Series Chen produced different forecasting results for the period of June and July 2022. 1-orde has sMAPE value of 23.15% (good enough forecasting), 2-orde and 3-orde have sMAPE value of 10.06% (good forecasting), and 4-orde has sMAPE value of 4.52% (very good forecasting). This study showed that the larger orde used in Fuzzy Time Series Chen, the lower forecasting error rate.]]>
<![CDATA[PENERAPAN METODE FUZZY TIME SERIES MENGGUNAKAN PARTICLE SWARM OPTIMIZATION ALGORITHM UNTUK PERAMALAN INDEKS SAHAM LQ45 ]]>
<![CDATA[Arya Despa Ihsanuddin]]>;
<![CDATA[Dwi Ispriyanti]]>;
<![CDATA[Tarno Tarno]]>
<![CDATA[ Jurnal Gaussian Vol 12, No 1 (2023): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.12.1.10-19
<![CDATA[Stocks have a volatile nature and it is difficult to predict the ups and downs. Therefore, stock data forecasting is done by investors to get a picture of future results. Fuzzy Time Series is a time series method that is suitable for forecasting fluctuating stock data because it does not require the fulfillment of assumptions such as normality and stationarity, but the Fuzzy Time Series method has weaknesses in determining intervals. So that in this study, interval optimization will be carried out on Fuzzy Time Series with Particle Swarm Optimization algorithm to predict LQ45 stock index data, Particle Swarm Optimization algorithm is used because it produces more optimal interval values compared to other optimization methods such as Genetic Algorithm. The data to be used is the closing price of the LQ45 stock index on January 5, 2020 to December 26, 2021. Forecasting using the Fuzzy Time Series method produces a SMAPE value of 1.53%, then after optimization using the Particle Swarm Optimization algorithm, the SMAPE value decreases to 1, 27%. Therefore, it can be concluded that optimization using Particle Swarm Optimization on Fuzzy Time Series produces a more optimal forecasting value. ]]>
<![CDATA[ANALISIS VOLATILITAS BITCOIN MENGGUNAKAN MODEL ARCH DAN GARCH ]]>
<![CDATA[Dheanisa Widyanti]]>;
<![CDATA[sudarno sudarno]]>;
<![CDATA[Tatik widiharih]]>
<![CDATA[ Jurnal Gaussian Vol 12, No 2 (2023): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.12.2.254-265
<![CDATA[The popularity of Bitcoin increased significantly in 2021. Bitcoin is considered to deliver high returns in a relatively short period, indicating that bitcoin has high volatility. Data with high volatility usually violates the Autoregresstive IntegratedinMovinginAverage (ARIMA)in homoscedasticity assumption. The Autoregressive Conditional Heteroscedasticity (ARCH) and General Autoregressive Conditional Heteroscedasticity (GARCH) model is often used to overcome the problem of heteroscedasticity in thelARIMA model. The ARCH and GARCH models canfbe used to model thefvolatilityfof data. This Research uses ARCH and GARCH models to overcome the heteroscedasticity problem caused by the high volatility of Bitcoin data for the period 30th June 2018 to 30th June 2022. The results of this study suggest that there might be a heteroscedasticity problem in Bitcoin data. The bestffiimodel for Bitcoin data ismiARIMA(1,0,[4])-GARCH(1,1) with an AIC value of -1,4263 at a 95% confidence level]]>
<![CDATA[PERBANDINGAN GULUD REGRESSION DAN PRINCIPAL COMPONENT REGRESSION (PCR) TERHADAP PEMODELAN INDEKS PEMBANGUNAN MANUSIA PROVINSI JAWA TIMUR ]]>
<![CDATA[Raihandika Ari Indhova]]>;
<![CDATA[Suparti Suparti]]>;
<![CDATA[Arief Rachman Hakim]]>
<![CDATA[ Jurnal Gaussian Vol 12, No 2 (2023): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.12.2.199-208
<![CDATA[Human resources is valuable asset in a country. Human Development Index (HDI) becomes important indicator of quality of human resources in an area. HDI value is affected by a variety of factors that are strongly related to each other so they cause multicollinearity. This observation aims to deal with multicollinearity optimally by comparing Gulud Regression to Component Regression in modeling factors that affect East Java HDI in 2020. Data that are used in this observation are East Java HDI in 2020 (Y), Life Expectancy (X1), Infant Mortality Rate (X2), Mean Years of Schooling (X3), Expected Years of Schooling (X4), Open-Unemployment Rate (X5), Average Household Expenditure per Capita (X6), and Labor Force Participation Rate (X7). Based on MSE value, the Gulud Regression method is better than Principal Component Regression (PCR) method in dealing with multicollinearity problem. Based on adjusted score that is 0,954, feasibility test of the best model of Gulud Regression method is a strong model.]]>
<![CDATA[PERBANDINGAN METODE LVQ DAN BACKPROPAGATION UNTUK KLASIFIKASI STATUS GIZI ANAK DI KECAMATAN SANGKUP ]]>
<![CDATA[Alamri, Fahima]]>;
<![CDATA[Ningsih, Setia]]>;
<![CDATA[Djakaria, Ismail]]>;
<![CDATA[Wungguli, Djihad]]>;
<![CDATA[K. Hasan, Isran]]>
<![CDATA[ Jurnal Gaussian Vol 12, No 3 (2023): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.12.3.314-321
<![CDATA[The problem of children nutrition isi still a problem in various regions in Indonesia. Poor or poor nutrition of children is influenced by several factors, namely insufficient food intake and infectious diseases. Undernutrition or poor nutrition can be known from the nutritional status assessment obtained from classifying the nutrional status of children. Classification is a part of data mining that is often used to classify data based on certain data or variables. This study aims to compare the classification of the nutritional status of children using data mining with the learning vector quantization (LVQ) and backpropagation methods. Test were carried out using a comparasion ratio of training and testing data, namely 75% and 25%. From the research results, LVQ is superior with an accuracy of 95.12% and backpropagation of 80.49%.]]>
