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All Journal UNP Journal of Statistics and Data Science
Hanifah Nazhiroh
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Computer Science & IT Decision Sciences, Operations Research & Management Mathematics Social Sciences

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Structural Equation Modeling Partial Least Square (SEM-PLS) Untuk Membandingkan Kondisi Public Speaking Anxiety Mahasiswa Soshum dan Saintek Sabina Chairun Najwa; Natasya Dwi Ovalingga; Hanifah Nazhiroh; Rizki Akbar; Fadhilah Fitri
UNP Journal of Statistics and Data Science Vol. 1 No. 5 (2023): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol1-iss5/132

Abstract

Public speaking is a communication skill to deliver opinion or massage to the audience. Public speaking anxiety, caused by various factors. Social and science students have differences in culture and learning systems. Therefore, students in both educational clusters have their own ways of overcoming communication barriers. This study aimed to identify factors that influence public speaking anxiety in social and science students at Padang State University. The method used is the Structural Equation Model Partial Least Square (SEM-PLS) to understand the influential factors in more detail and minimize analysis errors caused by missing values and multicollinearity due to diverse samples. The results of the analysis are path diagrams for structural models and outer loading tables. If the < value is 0.7, then recalculation is carried out so that a new model is formed. The feasibility of the social science family model was obtained 35% and the scientific science family was 36.5%. The effect of latent or exogenous variables in this study is weak. Social students have higher levels of speech anxiety than science students. This is influenced by humiliation, unfamiliar role, and negative result factors. In science students, the influencing factors are humiliation, preparation, and unfamiliar Role.
PT.Telkom (Tbk) Stock Price Forecasting Using Long Short Term Memory (LSTM) hanifah nazhiroh; Dina Fitria; Dony Permana; Zilrahmi
UNP Journal of Statistics and Data Science Vol. 2 No. 4 (2024): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol2-iss4/223

Abstract

The movement of the share price of PT Telkom (Tbk) fluctuates so it is necessary to do a forecasting analysis. Forecasting the share price of PT Telkom (Tbk) can be done using the Long Short Term Memory (LSTM) method. LSTM is a development of the Recurrent Neural Network (RNN) method. In this study using PT.Telkom (Tbk) stock price data for 2018-2023 and PT.Telkom (Tbk) stock price data after Covid-19 (20121-2023). The purpose of this research is to determine the movement of PT.Telkom (Tbk) stock prices in 2024, to find out the difference in forecasting using PT.Telkom (Tbk) 2018-2023 stock price data with PT.Telkom (Tbk) stock price data after covid-19 2021-2023, and to determine the level of accuracy of forecasting PT.Telkom (Tbk) stock prices using the LSTM method. The results showed that both data have a small MAPE value. to forecast the share price of PT.Telkom for 1 year, PT.Telkom (Tbk) share price data for 2018-2023 is used which has more data to analyze long-term forecasting. From the analysis results obtained MAPE of 1.016% with the optimal parameter combination of neuron 4, batch size 64, and epoch 80. The results of forecasting the share price of PT.telkom (Tbk) in 2024 experienced very rapid fluctuations with an average share price of PT.Telkom (Tbk) in 2024 Rp 4,668 / sheet.
Co-Authors Dina Fitria Dony Permana Fadhilah Fitri Natasya Dwi Ovalingga Rizki Akbar Sabina Chairun Najwa Zilrahmi, Zilrahmi