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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.
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Articles 693 Documents
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PENGGUNAAN SELEKSI FITUR CHI-SQUARE DAN ALGORITMA MULTINOMIAL NAÏVE BAYES UNTUK ANALISIS SENTIMEN PELANGGGAN TOKOPEDIA Tri Ernayanti; Mustafid Mustafid; Agus Rusgiyono; Arief Rachman Hakim
Jurnal Gaussian Vol 11, No 4 (2022): 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.11.4.562-571

Abstract

E-commerce is a medium for online shopping that is popular among the public. Ease of access for all internet users and the completeness of products offered by e-commerce are new alternatives in meeting the needs of the community. This causes stiff competition in the e-commerce, so e-commerce need to carry out the right marketing strategy in order to compete in obtaining, retaining, and partnering with customers, one of which is by reviewing aspects of customer satisfaction. Tokopedia is an e-commerce buying and selling online that connects sellers and buyers throughout Indonesia for free. In this study, an analysis of Tokopedia's customer sentiment was carried out with the Multinomial Naïve Bayes classification. Algorithm Multinomial Nave Bayes is a model development of the Nave Bayes. The difference lies in the selection of data, if Naïve Bayes uses a Gaussian that is suitable for continue, while Multinomial Naïve Bayes is suitable for discrete data such as the number of words in a document. Multinomial Naïve Bayes is the simplest method of probability classification but is sensitive to feature selection, so the amount of data is determined by the results of Chi-Square.Multinomial Naïve Bayes is used to classify customer opinions that are positive and negative so that they can form customer satisfaction factors Tokopedia, while the Chi-Square used to measure the level of feature dependence with class (positive and negative) so as to eliminate disturbing features in the classification process. Classification performance results using Multinomial Naïve Bayes without Chi-Square obtained accuracy and kappa statistics of 88% and 75.95%, while using Chi-Square obtained accuracy and kappa statistics of 95% and 89.99%, respectively. This means that Multinomial Naïve Bayes has quite effective performance and results in analyzing Tokopedia customer satisfaction sentiment and the use of Chi-Square for feature selection can improve the accuracy of the classification process. 
PREDIKSI TINGKAT TEMPERATUR KOTA SEMARANG MENGGUNAKAN METODE LONG SHORT-TERM MEMORY (LSTM) Rahmatul Akbar; Rukun Santoso; Budi Warsito
Jurnal Gaussian Vol 11, No 4 (2022): 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.11.4.572-579

Abstract

Temperature is one of the most important attributes of climate, temperature affects life in many different ways such as in agriculture, aviation, energy, and life in general. Temperature prediction is needed to make the right step to prevent the negative impact of climate change. Long Short-Term Memory (LSTM) is the method that can predict time series data, using the unique design of neural networks, LSTM can help to prevent vanishing gradient from happening which allows LSTM model to use more data from the past to predict the future. Hyperparameters like LSTM unit, epochs, and batch size are used to make the best model, the best model is the one with the lowest loss function. This research used climate data from 1 January 2019 until 31 December 2021 consist of 1096 data in total. The best prediction in this research is made by the model with 70% training data, 0,009 learning rate, 128 LSTM unit, 16 batch size, and 100 epochs with the lowest loss function of 0,013, this model gives MAPE value of 1,896016% and RMSE value of 0,725.
PERBANDINGAN MODEL KLASIFIKASI RANDOM FOREST DENGAN RESAMPLING DAN TANPA RESAMPLING PADA PASIEN PENDERITA GAGAL JANTUNG Rizwan Arisandi
Jurnal Gaussian Vol 12, No 1 (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.12.1.136-145

Abstract

Cardiovascular disease that causes heart failure is one of the diseases with the highest mortality rate in the world. Therefore, there is a need for an accurate model to classify heart failure based on clinical information and the lifestyle of patients with the disease, as an alternative solution in administering appropriate drugs. This study compared the classification model of living and deceased heart failure patients based on clinical information and patient lifestyle using the random forest method when using resampling techniques and not using resampling techniques. The results obtained from this study are that the Random Forest model with a combination of the SMOTE and Edited Nearest Neighbors methods is the best model for classifying someone with heart failure as alive or dead. The Random Forest model with a combination of the SMOTE and Edited Nearest Neighbors methods has a high level of classification accuracy in the evaluation model that focuses on recall, namely rf_model_smoteenn can classify 82.96% of patients with living status and 90% of patients with death status.
PENERAPAN METODE POISSON EXPONENTIALLY WEIGHTED MOVING AVERAGE (PEWMA) UNTUK MEMBUAT BAGAN PENGENDALI VARIABEL BERDISTRIBUSI POISSON Nida Adelia; Mustafid Mustafid; Dwi Ispriyanti
Jurnal Gaussian Vol 12, No 1 (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.12.1.71-80

Abstract

Airplane is a mode of transportation that has an accident risk. Aircraft accidents are recorded to occur almost every year in Indonesia. The Poisson distribution is used to model the number of aircraft accidents that occur each year because they have a fixed time and independent. Statistical quality control is applied as a method to monitor the number of fatal aircraft accidents in Indonesia that are within control limits. One method to carry out quality control is to use a control chart. This study aims to apply the Poisson Exponentially Weighted Moving Average (PEWMA) method to create a control chart with a case study of the number of fatal airplane accidents in Indonesia from 1962 to 2021 with a Poisson distribution. The EWMA control chart is used to monitor the average or process variability and is considered effective in detecting small shifts in the process (the shift is said to be small if the shift is less than 1.5σ). The calculation of Average Run Length (ARL) is performed to test the performance of the PEWMA control chart. Control charts with smaller out-of-control ARLs are considered superior and can detect process shifts more quickly than other control charts. Based on the results of the calculation of the ARL value, it was found that the weight of 0.3 is the optimal weight with the smallest ARL value of 1.138 which is able to describe the state of the data on fatal aircraft accidents in Indonesia. The control chart with the optimal weight shows the data on fatal aircraft accidents in Indonesia that are tolerated equal to one.
PERBANDINGAN MODEL ARIMA DENGAN MODEL NONPARAMETRIK POLINOMIAL LOKAL DAN SPLINE TRUNCATED UNTUK PERAMALAN HARGA MINYAK MENTAH WEST TEXAS INTERMEDIATE (WTI) DILENGKAPI GUI R Salsabila Rizkia Gusman; Suparti Suparti; Agus Rusgiyono
Jurnal Gaussian Vol 12, No 1 (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.12.1.20-29

Abstract

Crude oil as one of the most important natural resources experiences price fluctuations from time to time, even the spot price of West Texas Intermediate (WTI) world crude oil on 20th April 2020 reached -36,98 USD/barrel due to the Covid-19 pandemic. WTI oil price data was modeled using the ARIMA method, local polynomial, and spline truncated nonparametric regression then compared and obtained the best model and formed R Graphical User Interface (GUI). The ARIMA model and nonparametric time series models can be used to model time series data, but in the ARIMA model there are assumptions that must be fulfilled. Nonparametric time series models, which include local polynomial model and truncated spline do not need to fulfill these assumptions. The ARIMA model obtained did not fulfilled the assumptions of normality and residual homoscedasticity, so the modeling was stopped and modeling was only carried out using nonparametric regression methods. Based on the minimum MSE criteria, the best nonparametric model was obtained, namely nonparametric truncated spline model with degrees 3 and 3 knot points which was categorized as a strong model based on R-squared in sample value and having a very good forecasting ability based on MAPE out sample value.
PENERAPAN TEXT MINING DAN FUZZY C-MEANS CLUSTERING UNTUK IDENTIFIKASI KELUHAN UTAMA PELANGGAN PDAM TIRTA MOEDAL KOTA SEMARANG Genisia Pramestiloka Aulia; Tatik Widiharih; Iut Tri Utami
Jurnal Gaussian Vol 12, No 1 (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.12.1.126-135

Abstract

Customer complaints can be handled effectively by identifying the main complaints that cause customers to be dissatisfied. Many customer complaints cause difficulty for PDAM Tirta Moedal Semarang to identify problems, which are frequently the primary complaints of customers. Grouping complaints that have similarities using Fuzzy C-Means Clustering will make the identification of the main customer complaints easier. Fuzzy C-Means uses fuzzy models, allows data to be a member of all formed clusters with membership level between 0-1. Fuzzy C-Means Clustering can also introduce more flexible patterns and show results in more accurate cluster placement. Text mining is used to convert textual data into numerical data. Customer complaints received through all contacts in PDAM Tirta Moedal Semarang from October–December 2021 were used as data. The clustering process forms 6 clusters,with the number of clusters tried being 3, 4, 5, and 6, which are seen by the smallest Xie-Beni Index. The main complaints from PDAM Tirta Moedal Semarang customer that seen through Word cloud in each cluster are that the water stops running in clusters 1 and 6 and the pipes leak in clusters 4 and 5. Complaints in clusters 2 and 3 are complaints related to water meters and water flow.
PENGUKURAN NILAI RISIKO PORTOFOLIO SAHAM PADA INDEKS LQ45 DI BIDANG TELEKOMUNIKASI MENGUNAKAN METODE KOPULA CLAYTON Salsabila Syifa Binsanno; Sudarno Sudarno; Di Asih I Maruddani
Jurnal Gaussian Vol 12, No 1 (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.12.1.81-91

Abstract

The characteristic of copula is non strict on certain distribution assumptions, can explain nonlinier relationship, and easily construct distribution through the marginals that do not need to come from the same distribution family. Copula will be useful for stock data that has price charts fluctuate rapidly and risk will always follow in investing. The relation between risk and copula in this study is to calculate the risk value in the stock portfolio using VaR with the generation of Monte Carlo simulation through Clayton copula on four companies engaged in telecommunications sector, namely EXCL.JK (PT XL Axiata Tbk), TLKM.JK (PT Telekomunikasi Indonesia Tbk), TOWR.JK (PT Sarana Menara Nusantara Tbk), and TBIG.JK (PT Tower Bersama Infrastructure Tbk) for period 2 January 2020 to 31 December 2021. This study resulted that the selected stock portfolios are EXCL and TBIG which had the highest risk value of -0,062741 at 99% confidence level, so when an investor will invest Rp100.000.000,00 the maximum estimated risk is Rp.6.274.100 within one day.
PEMODELAN HARGA SAHAM PERUSAHAAN PROPERTI DAN REAL ESTATE MENGGUNAKAN REGRESI LONGITUDINAL SPLINE TRUNCATED DILENGKAPI GUI R Nurina Salma Alfiyyah; Suparti Suparti; Sugito Sugito
Jurnal Gaussian Vol 12, No 1 (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.12.1.42-51

Abstract

Stocks are one of the most popular financial instruments traded in the capital market. One of stock prices fluctuate up and down due to the influence of several factors, one of which is inflation. Stocks in the property and real estate sectors are important indicators to determine the level of a country economy. Data on several stock prices is one case of longitudinal data in economic field. The data is divided into 2 parts, namely in sample data from January 2016 to October 2020 and out sample data from November 2020 to December 2021. In this study, longitudinal stock price data is modeling using nonparametric spline truncated. The best spline truncated model is determined by the order and the optimal number of knot points based on the minimum Generalized Cross Validation value. Spline truncated nonparametric regression modeling for longitudinal data in this study is equipped with Graphical User Interface (GUI) that can facilitate the data processing. The results of the analysis show that the best longitudinal spline truncated regression model obtained on 2nd order with 5 knot points. 95.04% value of  indicates the model is a strong model. In the evaluation of the best model, the MAPE data out sample value is 16.45%. It indicates the model has good forecasting ability.
ESTIMASI RISIKO PORTOFOLIO SAHAM PERUSAHAAN PERKEBUNAN DI BURSA EFEK INDONESIA MENGGUNAKAN VALUE AT RISK NON-NORMAL Aulia Ikhsan; Tatang Sutisna; Siti Widiati
Jurnal Gaussian Vol 12, No 1 (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.12.1.146-158

Abstract

Stock investment portfolio aims to minimize the investment risk. However, problems of the portfolio formation are determining funds allocation for each stock and measuring its risk. Fund allocation is determined using the Mean-Variance Efficient Portfolio method, while risk measurement is carried out using Value at Risk (VaR). Nevertheless, problem on VaR is determining a fit distribution which would be involved to obtain quantile values at certain probability. This study discusses way of funds allocation determination and VaR value calculation that is aimed to analyze their impact in estimating the VaR value. The study used stock price return rate data of plantation companies listed on Indonesia Stock Exchange such as Astra Agro Lestari Tbk. (AALI), BISI International Tbk. (BISI), and PP London Sumatra Indonesia Tbk. (LSIP). The result showed BISI stock has high volatility so that its funds allocation is relatively smaller. The distribution identified for portfolio return rate is Logistics Distribution with the estimated parameters  0.0001187447 and 0.008810698. Portfolio VaR value at the 95% confidence level is -0.02582382. We conclude the determination of funds allocation does not minimize risk and the calculation of VaR with distributions do not match the data result a relatively higher VaR value.
PENDEKATAN MODEL KMV MERTON UNTUK PENGUKURAN NILAI RISIKO KREDIT OBLIGASI EXPECTED DEFAULT FREQUENCY (EDF) DILENGKAPI GUI R Agil Setyo Anggoro; Mustafid Mustafid; Puspita Kartikasari
Jurnal Gaussian Vol 12, No 1 (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.12.1.92-103

Abstract

Bonds are debt securities from the issuer to bondholders with a promise to pay off the principal and the coupon at maturity. Bond investing can generate income while also posing investment risks. One of the risks connected with bond investing is credit risk, which might manifest as a firm collapsing (default). The KMV Merton model approach is one method of measuring bond credit risk. This Merton KMV model computes the Expected Default Frequency (EDF), which is the likelihood of a firm failing in the following years or years. The data processing system using the Graphical User Interface (GUI) can facilitate the analysis process by implementing the Shiny Package in the R studio program. This research case makes use of up to 48 months of monthly corporate asset data from January 2018 to December 2021. The results obtained the value of Expected Default Frequency (EDF) in each company, namely PT Bank Mandiri Tbk obtained a value of 0% and PT Bank Rakyat Indonesia Tbk obtained a value of 1,406668E-113%. Because PT Bank Rakyat Indonesia Tbk's percentage return is higher than that of PT Bank Mandiri Tbk, investors would be better off investing in bonds at PT Bank Mandiri Tbk.

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