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All Journal Jurnal Gaussian Journal of Mathematics Education and Science
Deby Fakhriyana, Deby
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Education Mathematics Other

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PERBANDINGAN MODEL ARCH/GARCH MODEL ARIMA DAN MODEL FUNGSI TRANSFER (Studi Kasus Indeks Harga Saham Gabngan dan Harga Minyak Mentah Dunia Tahun 2013 sampai 2015) Fakhriyana, Deby; Hoyyi, Abdul; Widiharih, Tatik
Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (597.137 KB) | DOI: 10.14710/j.gauss.v5i4.14720

Abstract

Indonesian Composite Index is a value that used to measure the combined performance of shares listed in stock market. Price of crude oil is one of the factors that affect Indonesian Composite Index. If the prices of crude oil is increasing, it will be responsed by Indonesian goverment directly with also increasing the fuel prices, that will have an impact on Indonesian Composite Index. ARIMA  and transfer function are methods of modeling time series data and it have assumption that the residual models have to be homogen. To overcome violations of those assumption, this study continue to modelling ARCH/GARCH with ARIMA and transfer function approach. The data used in this study are daily of Indonesian Composite Index and West Texas Intermediate (WTI) crude oil prices data from 2013 to 2015. This study gained two models, the first is ARIMA (1,1,[3]) which variance model of ARCH(1), it’s AIC value is equal to 7707,4287. The second is transfer fuction model (1,0,0) which noise model ARMA(0,[1,3) as well as variance model ARCH(1), it’s AIC value equal to 7689,18984. The best model is the one that has smallest AIC value. From this study can be concluded that the best of ARCH/GARCH model is ARCH/GARCH model with transfer function approach. Keywords : Indonesian Composite Index, crude oil prices, ARIMA, transfer function, ARCH/GARCH
PENENTUAN VALUE AT RISK (VAR) PADA PORTOFOLIO BIVARIAT DENGAN PENDEKATAN COPULA GUMBEL Febriani, Karina; Tarno, Tarno; Fakhriyana, Deby
Jurnal Gaussian Vol 13, No 1 (2024): 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.13.1.79-87

Abstract

One way to minimize risk in stock investment is stock portfolio. Value at Risk (VaR) is a calculation method that can be used to estimate the risk of a stock portfolio. VaR can be measured by parametric and non-parametric approaches. Calculation of VaR with Monte Carlo simulation assumes the data is normally distributed. Stock return data generally has high volatility so that the residual variance of the model is not constant (heteroscedasticity) and not normally distributed. The ARIMA-GARCH model can be used to solve heteroscedasticity problems. Copula is a tool used to model the combined distribution of residuals from the ARIMA-GARCH model which does not require normality assumptions. Gumbel's copula is copula that has the best sensitivity to high risk. This study uses stock data of PT Bukit Asam Tbk (PTBA) and PT Chandra Asri Petrochemical Tbk (TPIA) for the period April 1 2020 – December 1 2022. The initial step of this research is model stock returns using the ARIMA-GARCH method and then calculate portfolio VaR using the Gumbel’s copula. The results showed that the best model for PTBA is ARIMA(2,0,2) GARCH(1,1) and for TPIA is ARIMA(1,0,0) GARCH (1,1). At the 95% confidence level, the portfolio risk is 2,41%.
IDENTIFIKASI POLA PERILAKU REMAJA DENGAN PATH ANALYSIS Saadah, Ardiana Alifatus; Fakhriyana, Deby; Hersugondo, Hersugondo
Jurnal Gaussian Vol 12, No 4 (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.12.4.499-508

Abstract

Globalization has an impact on cultural changes in Indonesia. Apart from positive impacts, globalization also has several negative impacts. The decreasing level of politeness in today's teenagers is part of a cultural change that we cannot ignore. Teenagers in this era are reportedly paying less attention to how to act and behave politely. Politeness is the practical application of good manners and etiquette. To improve polite behavior in teenagers, it is important to know factors that might influence polite behavior. This study used psychological theory developed by Ajzen and Fishbein, the Theory of Reasoned Action. Model in this theory consists of four variables, namely attitude, subjective norm, behavioral intention and behavior. Analytical method used in this research is path analysis. Based on the test results, the attitude variable has an effective influence on increasing the polite behavior variable in teenagers. This is because attitude variable not only influence behavioral variable directly, but also indirectly through behavioral intention variable. Furthermore, the increase in polite behavior is significantly influenced by behavioral intentions. Model combination is able to explain 63.06% of the data diversity, while the rest is explained by other variables and error.
PENGENDALIAN KUALITAS PUPUK NITROGEN, PHOSPAT, KALIUM (NPK) PELANGI FUSION DI PT PUPUK KALIMANTAN TIMUR MENGGUNAKAN PETA KENDALI MEWMA & MEWMV Grace, Bungan Tcania Paulina; Kartikasari, Puspita; Fakhriyana, Deby
Jurnal Gaussian Vol 15, No 1 (2026): 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.15.1.110-120

Abstract

The NPK Pelangi Fusion fertilizer is one of the main products manufactured by PT Pupuk Kalimantan Timur. NPK fertilizer is a blend of various plant nutrients, primarily Nitrogen, Phospat, and Kalium, aimed at enhancing crop yields. However, deviations from the NPK fertilizer specifications can lead to inconsistent plant growth and low harvest yields. Statistical Process Control (SPC) is a method used to process data and monitor production processes using statistical techniques, with the goal of detecting changes in process performance through the use of control charts. In this study, the Multivariate Exponentially Weighted Moving Variance (MEWMV) control chart is used to monitor the variance of the production process due to its optimal performance in detecting small variance shifts. Additionally, the Multivariate Exponentially Weighted Moving Average (MEWMA) control chart is used to monitor the mean of the NPK Pelangi Fusion production process, as it quickly detects subtle shifts in variance. The analysis results indicate that the optimal weighting for the MEWMV control chart is ω=0.2 and λ=0.4, resulting in an Average Run Length (ARL) of 370. Similarly, the optimal weighting for the MEWMA control chart is λ=0.06, with an upper control limit of H=9.80 and an ARL of 200. This study concludes that the mean and variance of the multivariate production process have been effectively controlled.
PENERAPAN METODE RANDOM FOREST UNTUK ANALISIS SENTIMEN PENGGUNA APLIKASI BANK DIGITAL SEABANK Bangun, Fenansia Clara Hana; Mustafid, Mustafid; Fakhriyana, Deby
Jurnal Gaussian Vol 15, No 1 (2026): 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.15.1.36-45

Abstract

Sentiment analysis on reviews of an application becomes an option to see users responses to the service of that particular application. Random Forest is one of the classification modeling techniques that originate from a combination of Decision Trees, providing the final result based on majority voting. This research aims to improve the performance of sentiment classification on customer reviews of Seabank, one of the most widely used digital banking services in Indonesia, by utilizing the Random Forest algorithm. The study involves sentiment analysis of user reviews on the Seabank application, collected from 15,000 reviews on Google Playstore. The review features available on Google Playstore are used as a means to convey opinions as user feedback for an application. Random Forest is trained to classify reviews into 3 sentiment classes: positive, neutral, and negative. Based on the research conducted with model evaluation using Confusion Matrix, an accuracy value of 94.1% was obtained, indicating that Random Forest's accuracy in classifying Seabank customer reviews is 94.1%. This demonstrates the effectiveness of using Random Forest in text review classification due to its high accuracy value.
Co-Authors Abdul Hoyyi Bangun, Fenansia Clara Hana Febriani, Karina Grace, Bungan Tcania Paulina Hersugondo Hersugondo Kartini, Alif Yuanita Mustafid Mustafid Puspita Kartikasari Saadah, Ardiana Alifatus Tarno Tarno Tatik Widiharih