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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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MODEL REGRESI DATA PANEL DINAMIS DENGAN ESTIMASI PARAMETER ARELLANO-BOND PADA PERTUMBUHAN EKONOMI DI INDONESIA Muhammad Emir Wicaksono; Di Asih I Maruddani; Iut Tri Utami
Jurnal Gaussian Vol 12, No 2 (2023): 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.12.2.266-275

Abstract

Economic growth is one of factor for knowing rate of income in some country and knowing the rate of income from the indicator the value of Gross Domestic Product (GDP). The factor to be expected that affected GDP are Human Development Index (HDI), Foreign Investment, Domestic Investment, inflation, export net, Labour Participation Rate, and Government Spending. The research to determine a model and short-term effect also long-term effect from the variable that suspected to affect economic growth in Indonesia. The research does with dynamic panel data model defined as model involved lag from dependent variable as their independent variable. Usage of lag on the model caused of estimation with Ordinary Least Square (OLS) produced bias and inconsistent estimation. Generalized Method of Moment (GMM) Arellano-Bond estimation which is the parameter estimation with first differencing and instrumental variable method used to clear the solution of OLS produced bias and inconsistent estimation. The research produced model from variable influence to economic growth in Indonesia, HDI and Government Spending. Short-term effect from HDI for GDP has increased 2,410332 percent and long-term effect has increased 18,7610975 percent. Short-term effect from Government Spending for GDP has decreased 0,1025608 percent and long-term effect has decreased 0,798293831 percent.
PENENTUAN PORTOFOLIO OPTIMAL DENGAN METODE MULTI INDEX MODEL DAN PENGUKURAN RISIKO DENGAN EXPECTED SHORTFALL (Studi Kasus: Kelompok Saham LQ45 Periode Januari 2017 - Desember 2021) Wanda Zulfa Fauziah; Tatik Widiharih; Di Asih I Maruddani
Jurnal Gaussian Vol 12, No 2 (2023): 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.12.2.209-220

Abstract

Various methods have been applied to determine the optimal portfolio, one of which is Multi Index Model. MIM is a method that uses more than one factors that affects stock price movements, this study uses ICI and exchange rate factors. Risk measurement is very important in financial analysis because almost all of them contain elements of risk. One form of risk measure that’s relatively popular in financial risk analysis is Value at Risk. VaR has a disadvantage because it only measures the percentile of the loss distribution without considering losses that exceed VaR and VaR isn’t coherent (it doesn’t fulfill the property of subadditivity). The risk measure used to overcome the weakness of VaR is Expected Shortfall. The results of the study using MIM method obtained the optimal portfolio consisting of BBRI (45.777%), PTPP (2.952%), and UNTR (51.271%) which provide a profit rate of 0.383%. The calculation results show that with a 95% confidence level, ES and VaR values obtained are 26.639% and 11.210%, respectively. ES value will be more precise in the context of a portfolio so that the maximum loss that will be received by the optimal portfolio investor that has been formed one month ahead is 26.639%. 
ANALISIS PORTOFOLIO OPTIMAL MENGGUNAKAN MODEL INDEKS TUNGGAL DAN PENGUKURAN VALUE AT RISK DENGAN SIMULASI MONTE CARLO (Studi Kasus: Exchange Traded Fund di Bursa Efek Indonesia Periode Januari 2021 – Juni 2022) Vian Rizeki Alif Priyantono; Di Asih I Maruddani; Iut Tri Utami
Jurnal Gaussian Vol 12, No 2 (2023): 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.12.2.158-165

Abstract

Exchange Traded Fund is one of the investment instruments on the Indonesia Stock Exchange. One way to minimize investment risk is to form an optimal portfolio. This research uses a single index model method in the formation of the optimal portfolio because it has a simpler calculation than other methods, while to measure the Value at Risk (VaR) of the optimal portfolio using Monte Carlo Simulation. The Monte Carlo simulation assumes that the portfolio returns are normally distributed. This research uses ETF data for the period January 2021 to June 2022. The results show that of the seven ETFs sampled, only two ETFs are included in the optimal portfolio, that is XISR (Premiere ETF Sri-Kehati) and XIIT (Premiere ETF IDX-30). Of the two ETFs included in the optimal portfolio, the XISR ETF has a weight of 47.29% while the XIIT ETF has a weight of 52.71% in the formed portfolio, with the VaR estimation in the next month after investing in the optimal portfolio with a 95% confidence level is IDR 58,334,796.00 from the initial capital of IDR 1,000,000,000.00.
IMPLEMENTASI GRIDSEARCHCV PADA SUPPORT VECTOR REGRESSION (SVR) UNTUK PERAMALAN HARGA SAHAM Aanisah Waliy Ishlah; Sudarno Sudarno; Puspita Kartikasari
Jurnal Gaussian Vol 12, No 2 (2023): 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.12.2.276-286

Abstract

Stock is a sign of the capital participation of a person or authority in a company (PT). PT Anabatic Technologies Tbk (ATIC) is one of the service providers and IT consultants that is included in the technology sector, which is a new sector in the IDX-IC classification. ATIC stock trading was temporarily suspended due to a significant increase in cumulative prices. This indicates that stock prices tend to be volatile and non-linear. The Support Vector Regression (SVR) method can be used to predict stock prices. SVR is able to solve non-linear data problems by using kernel functions so it can overcome overfitting problems and will give good performance. The SVR problem is difficult to determine the optimal hyperparameters, so this research uses grid search cross validation (GridSearchCV). In this research, ATIC’s daily closing price data was used with 1007 training data and 252 testing data. The results show that the best model is SVR with a linear kernel and the hyperparameters used are Cost  and epsilon . The linear kernel SVR model produces a MSE of 0,001237173; SMAPE of 0,1167301; and  = 0,9206643
PERAMALAN INDEKS JAKARTA ISLAMIC INDEX (JII) DENGAN PENDEKATAN REGRESI PARAMETRIK LINIER SEDERHANA DAN REGRESI NONPARAMETRIK KERNEL DILENGKAPI GUI R-SHINY Rahmadia Fitri; Suparti Suparti; Puspita Kartikasari
Jurnal Gaussian Vol 12, No 2 (2023): 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.12.2.221-230

Abstract

Investment in Islamic stocks in Indonesia has increased from 2019 to 2021. One of the references for investors in monitoring Islamic stock price movements is the Jakarta Islamic Index_(JII).  The_purpose_of_this_research_is_to model the index (JII) using nonparametric kernel regression.  The kernel_functions_used_in nonparametric regression are Gaussian, Uniform, Triangle, and Epanechnikov._The research data-is-divided-into-In-Sample-data-for the period January-2010-to-December 2020 and-Out-Sample-data.for the_period_January_2021_to_December_2021. The_best_model_is selected based_on_the smallest MSE-value-obtained by the Triangle kernel regression with an optimum bandwidth (h) of 48,  2.  The R2 value is 0.897.  Based on the criteria for the R2 value, it-can-be-stated that_the_best model_is_a strong model_with a proportion of_the influence-of-the-previous index-on-the.current index value of-89.7%, and-there-maining_10.3%_is_influenced_by_other_factors.-The best model forecasting ability can be seen from the MAPE data out sample value of 3.04%, which is less than 10%, meaning that the performance of the kernel model in predicting the JII index is very good.  This research uses R software which is equipped with R-Shiny GUI to help with data processing.
PEMODELAN DAN PREDIKSI HARGA SAHAM PERUSAHAAN FAST MOVING CUSTOMER GOODS MENGGUNAKAN VECTOR AUTOREGRESSIVE WITH EXOGENOUS VARIABLES (VARX) Marya Magdalena Simanjuntak; Tarno Tarno; Puspita Kartikasari
Jurnal Gaussian Vol 12, No 2 (2023): 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.12.2.166-177

Abstract

The increase in the population of Indonesia causes consumption to increase. This has made the FMCG (Fast Moving Consumer Goods) industry in Indonesia grow rapidly and occupy the second largest proportion of market capitalization thereby attracting investors to invest. One way to choose the best stocks to invest is by modeling. Modeling is carried out on the share price of companies with large capitalization, namely Mayora Indah, Indofood CBP, and Siantar Top. One of the factors that influence a company's stock price is the stock price of a competitor, namely Unilever and Buyung Poetra. Therefore, to predict and determine the relationship between stocks, the VARX (Vector Autoregressive with Exogenous Variables) method is used. The data period in this study starts from January 4, 2021 to January 14, 2022 with the results of the analysis, namely VARX (1) is the model obtained for prediction. The errors from the model meet the white noise and multinormal assumptions. The SMAPE value of the Mayora, Indofood CBP, and Siantar Top variables is below 10% which means the model has very good predictive ability. In addition, the prediction results show that Indofood's share price is more stable than other stocks. 
PERAMALAN PADA RUNTUN WAKTU DENGAN POLA TREND MENGGUNAKAN SSA-LRF Diah Safitri; Gunardi Gunardi; Nanang Susyanto; winita Sulandari
Jurnal Gaussian Vol 12, No 2 (2023): 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.12.2.296-303

Abstract

Singular Spectrum Analysis-Linear Recurrent Formulae (SSA-LRF) is a forecasting method that starts by decomposing time series data into several independent and interpretable components. SSA-LRF does not have any assumptions that must be fulfilled thus it is more flexible to use. In this research, an empirical study of time series forecasting that has a trend data pattern will be carried out using SSA-LRF without difference transformation and with difference transformation. A difference transformation is performed because the data has a trend pattern. Although there are no assumptions that must be met in forecasting using SSA-LRF, it is expected that difference transformation will produce better forecasting accuracy than without difference transformation process. There are three data used in this research. The first is data from Wei's book (2006), this data is called series W8 and is a simulation data. The second data is the number of railway passengers in the Java region. The third data is Mauna Loa atmospheric CO2 concentration data obtained from R software. Forecasting using SSA-LRF without difference transformation and with difference transformation on all three data resulted in accurate forecasting values, and difference transformation improved the accuracy values
K-NEAREST NEIGHBOR DENGAN ADAPTIVE BOOSTING DAN SYNTHETIC MINORITY OVERSAMPLING TECHNIQUE UNTUK KLASIFIKASI DATA TIDAK SEIMBANG Ria Sulistyo Yuliani; Agus Rusgiyono; Rukun Santoso
Jurnal Gaussian Vol 12, No 2 (2023): 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.12.2.231-241

Abstract

Breast cancer is non-skin cancer that is caused by several factors, including glandular ducts, cells, and breast support tissue, except for the skin of the breast. Breast cancer if not treated immediately will be fatal for the sufferer, so early detection of breast cancer is important for the patient's safety. The success of breast cancer detection depends on the right diagnosis. Measurement of the accuracy of a breast cancer diagnosis can be assisted by statistical methods, namely classification. K-Nearest Neighbor is a classification algorithm based on the nearest neighbor that is easy to implement. In the classification process, there are several problems including when faced with imbalanced data. Imbalanced data can cause classification algorithms to tend to focus on the majority class. Data imbalance can be overcome by using Synthetic Minority Oversampling Technique (SMOTE). Ensemble methods can be applied to improve the performance of imbalanced data classification, one of which is Adaptive Boosting. This study applies K-Nearest Neighbor combined with Adaptive Boosting and SMOTE for handling imbalanced data classification. The results of this study are, SMOTE can handle the problem of imbalanced data and the application of K-Nearest Neighbor with Adaptive Boosting can produce an accuracy of 80%, a sensitivity of 83,33%, a specificity of 66,67%, and a G-Mean value of 74,54%. So it can be concluded that K-Nearest Neighbor combined with Adaptive Boosting and SMOTE can be applied for handling imbalanced data classification. 
ANALISIS k-MEDOIDS DENGAN VALIDASI INDEKS PADA IPM DAERAH 3T DI INDONESIA Maria Dafrosa Doi; Agus Rusgiyono; Triastuti Wuryandari
Jurnal Gaussian Vol 12, No 2 (2023): 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.12.2.178-188

Abstract

Human development is a development paradigm that places humans as the main target of all development activities, namely controling over resources, improving health and improving education. The Human Development Index (HDI) in Indonesia varies in each district, especially in the 3T areas. The 3T area is an area that is classified as underdeveloped, remote and outermost in terms of economy, health, education and infrastructure. The k-Medoids method is a partitional clustering method for grouping several objects into clusters. This clustering algorithm uses the medoid as the center of the cluster, so it is robust to data containing outliers. This study aims to classify the 3T regions in Indonesia based on the Human Development Index to find out which areas require more attention from the government in optimizing the Human Development Index numbers. The size of object similarity is calculated by using the Euclidean distance and Manhattan distance, for the selection of the best number of clusters, internal cluster validation, such as Calinski – Harabasz index, Gamma Index, and Silhouette index. The result of this study showed that the best cluster were four by using Euclidean distance measurement, having Calinski – Harabasz index  score of 37.15764, Gamma index score of 0.7821181, and Silhouette index score of 0.3354435.
PENERAPAN MODEL LEAST SQUARE SUPPORT VECTOR MACHINE (LSSVM) UNTUK PERAMALAN KASUS COVID-19 DI INDONESIA Lutfi Ardining Tyas; I Made Tirta; Yuliani Setia Dewi
Jurnal Gaussian Vol 12, No 2 (2023): 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.12.2.304-313

Abstract

Forecasting is about predicting the future based on historical data and any information that might affects the forecasts. This article applies the LSSVM model to forecast Covid-19 cases in Indonesia. The purpose of this study is to find out how the LSSVM model applied and the model performances for forecasting Covid-19 cases in Indonesia, using time series data and the factors that influence it, as input features. The factor data used in this study are mobility data and daily fully vaccinated data. The research has three main objectives; first, calculate the correlation between confirmed cases data and past data (lag) of mobility and vaccination. Second, is the selection of input features based on the highest correlation coefficient value of each variable. Third, do LSSVM modeling and Covid-19 case forecasting with the optimal model. RBF kernel and grid-search algorithm with 10-fold cross-validation are used to tune model parameters. The results show that the LSSVM model provides good performance for Covid-19 forecasting and the optimal LSSVM model for forecasting Covid-19 cases in Indonesia is using time lag 14 for the mobility factor and time lag 24 for the vaccination factor.

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