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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Sistemasi: Jurnal Sistem Informasi Jurnal Varian ESTIMASI: Journal of Statistics and Its Application Indonesian Journal of Statistics and Its Applications
Nirwan Ilyas
Department Of Statistics, Hasanuddin University, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Mathematics Social Sciences Other

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Penggunaan Metode Copula Gaussian untuk Menentukan Nilai Value at Risk Investasi Saham pada Bank BCA dan Bank BRI Palungan, Kevin Ekarinaldo; Kalondeng, Anisa; Ilyas, Nirwan
ESTIMASI: Journal of Statistics and Its Application Vol. 6, No. 2, Juli, 2025 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v6i2.35960

Abstract

Investment is capital for one or more assets over a long period of time to obtain profits. Besides being able to provide profits, stock investment also contains an element of risk. Therefore, risk measurement needs to be done so that the risk is within a controlled level so as to reduce the occurrence of investment losses. This study uses the Gaussian Copula to calculate Value at Risk on the closing price data of PT. Bank Central Asia Tbk and PT. Bank Rakyat Indonesia Tbk for the period January 02, 2020 to December 30, 2022. For the Kendall's correlation value τ=0.3307 produces a Pearson correlation value of ρ=0.4965 which is also used as an estimate of the Copula Gaussian parameter. The results of the VaR calculation on a portfolio with a weight of 50% shares of PT Bank Central Asia Tbk and 50% shares of PT Bank Rakyat Indonesia Tbk average VaR at the 95% confidence level of -0.0269 means that if investors invest their funds by 50% in PT Bank Central Asia Tbk shares and 50% in PT Bank Rakyat Indonesia Tbk shares there is a risk that the maximum loss is 2.69% of the invested funds.
Data Balancing Approach Using Combine Sampling on Sentiment Analysis With K-Nearest Neighbor Kondy, Evlyn Pricilia; Siswanto, Siswanto; Ilyas, Nirwan
Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i5.4013

Abstract

One of the topics that has been discussed on twitter is the rules regarding the removal of masks. However, there's a chance that the data from Twitter contains unequal data classes. An unequal amount of data can cause the classification process to malfunction. Combining under- and oversampling techniques is known as combine sampling, and it is a data-balancing strategy. The research's data consists of Indonesian tweets using the hashtag "The Policy of Removing Masks." In this study, the classification approach was K-Nearest Neighbor, while the oversampling and undersampling techniques were SMOTE and Tomek Links. The purpose of this research is to classify sentiment using the K-Nearest Neighbor algorithm and to use combine sampling to balance the amount of training data in the two classes that are not yet balanced. 234 training data with a positive sentiment and 652 training data with a negative sentiment were obtained after the data was divided. Due to an imbalance in the quantity of training data between the two classes, the positive class's data is minor and the negative class's data is major. The quantity of training data are 613 in the positive class and 613 in the negative class obtained following the combine sampling. Following the balancing of data between the two classes, sentiment classification was performed, yielding accuracy of 60.4%, precision of 78.5%, and recall of 65%. The reason for the accuracy number of 60.4% is because machine learning misinterpreted a tweet regarding Indonesia's mask removal policy, leading to incorrect classification.
Comparison of Negative Binomial Regression Model and Geographically Weighted Poisson Regression on Infant Mortality Rate in South Sulawesi Province Siswanto, Siswanto; Saputra R, Edy; Sunusi, Nurtiti; Ilyas, Nirwan
Indonesian Journal of Statistics and Applications Vol 6 No 2 (2022)
Publisher : Statistics and Data Science Program Study, IPB University, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v6i2p170-179

Abstract

The number of infant mortality cases is an important indicator to assess the quality of a country's public health. A number of studies argue that the case of infant mortality has a close relation to the living area condition and the social status of the parents. Indirectly, the quality of life of babies in a country will impact the nation's quality of life in general. Therefore, many efforts are required to reduce the infant mortality in Indonesia. One of the steps that could be done to overcome this issue is to analyze the causative factors. The statistical method that has been developed for data analysis taking into account current spatial factors is the Geographically Weighted Poisson Regression (GWPR) with a weighted Bisquare kernel function. Based on the partial estimation with the GWPR model, there are seven groups based on significant variables that affect the number of infant deaths in South Sulawesi Province. Of the seven groups formed, the first group is the Selayar Islands where all variables have a significant effect. This needs to be a concern for the South Sulawesi provincial government to improve facilities and infrastructure in the Selayar Islands, of course the location which is very far from the city center can affect access to drug reception, medical personnel and so on. Based on the results of the analysis of the factors that affect the number of infant deaths in South Sulawesi Province using a negative binomial regression approach and GWPR with a bisquare kernel weighting, it can be concluded that the GWPR model used is the best for analyzing the number of infant deaths in South Sulawesi Province because it has an AIC value. The smallest is 167.668.
Co-Authors Amni, Wa Ode Sitti Amni Andi Kresna Jaya Andi Kresna Jaya Andi Sitti Fahmi Riyanti Hufaini Anisa Anisa Anisa Anisa Anisa Kalondeng Anna Islamiyati Asnidar Asnidar Bunga Aprilia Edy Saputra Rusdi Grace Oktavia Yusuf Herdiani, Erna Tri Kalondeng, Anisa Kondy, Evlyn Pricilia Muhammad Fadil Musdalifah Musdalifah NURTITI SUNUSI Nurul Ikhsani Palungan, Kevin Ekarinaldo Raupong, Raupong Raupong, Raupong S Siswanto Saputra R, Edy Siswanto Siswanto