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Tatik Widiharih
Departemen Statistika, Fakultas Sains dan Matematika, Undip
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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 Sentimen Pada Perusahaan Penyedia Jasa Logistik J&T Menggunakan Algoritma Multinomial Naive Bayes dan Support Vector Machine Helmi Aulia Rahman; Rukun Santoso; Tatik Widiharih
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.12.2.242-253

Abstract

Online shopping is a way to a faster and easier process of buying things or needs for people these days. Logistic services are essential in the process of buying things online, for they will be the one who ship the package to the buyer. PT. Global Jet Express or J&T is one of many logistics service provider company that are available in Indonesia. J&T has a Twitter account which is used for communicating with their customers. Opinions that were posted by J&T consumers on Twitter could be used as a data to do sentiment analysis which the purpose is to extract information that are told by people in Twitter about J&T. Data crawling was done for 15.000 tweets that were posted during the period of 4th to 10th of July 2022, duplicated tweets and those who has the exact same contents were removed resulting the data reduced to 2500 tweets. Tweets will be divided into two class; positive class and negative class Some classification methods are commonly used in text classification, such as Random Forest, Decision Tree, Naïve Bayes Classifier, Support Vector Machine etc. Data in this research will be classified using Multinomial Naïve Bayes and Support Vector Machine to compare their accuracy, the reason for the comparison is these methods have significant difference in their concept complexity. Multinomial Naïve Bayes classify data by finding the greatest conditional probability value, whilst Support Vector Machine classify data by finding the best hyperplane to divide into two class. Multinomial Naïve Bayes has the accuracy of 72,80% and Support Vector Machine has the accuracy of 82,40%. Based on their accuracy, Support Vector Machine has the best performance in classifying public opinions about J&T on Twitter.
Co-Authors Di Asih I Maruddani Helmi Aulia Rahman Rukun Santoso Wanda Zulfa Fauziah