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Stock Price Modeling with Geometric Brownian Motion and Value with Risk PT Ciputra Development TBK Amri Muhaimin; Trimono Trimono
Nusantara Science and Technology Proceedings 7st International Seminar of Research Month 2022
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2023.3329

Financial sector investment is an activity that attracts a lot of public interest. One of them is investing funds in purchasing the company’s shares. Profit received from stock investment activity can be seen from the value of stock returns. While, if the previous stock returns to Normal distribution, the future stock price can be predicted by Geometric Brownian Motion Method. Based on the stock price prediction, can also be measured an estimated value of the investment risk. The result of data processing shows that the stock price prediction of PT. Ciputra Development Tbk period December 1, 2016, until January 31, 2017, has very good accuracy, based on the value of MAPE 1.98191%. Further, the Value Risk Method of Monte Carlo Simulation with ? = 5% significance level was used to measure the share investment risk of PT.Ciputra Development Tbk. Thus, this method is only useful if it can be used to predict accurately. Therefore, backtesting is needed. Based on the processing obtained data, backtesting generates the value of violation ratio at 0, it means that at significance level ? = 5%, the Value at Risk Method of Monte Carlo Simulation can be used at all levels of probability violation.

A Simple Data Sentiment Analysis using Bjorka phenomenon on Twitter Prismahardi Aji Riyantoko; Amri Muhaimin
Nusantara Science and Technology Proceedings 7st International Seminar of Research Month 2022
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2023.3353

Social media is one of the means used by netizens to access, share and discuss the latest and hottest news issues. Twitter as one of the social media is a platform that in real-time is often chosen to communicate that matter. Through sentiment analysis with the text method mining on Twitter, we can understand how people describe and express their perceptions of obesity both positively and negatively nor neutral. This analysis is important to see the extent to which social media such as Twitter is used today. Those are one of the instruments for disseminating information data security in Indonesia. Research objectives for identifying sentiment analysis on related Twitter the Bjorka phenomenon in Indonesia using the text mining method. The type of research is cross-sectional. This research plan was chosen because of the data taken from Twitter in the last four-month time series (June 2022 - October 2022). The result of web scraping on Twitter is 998 Indonesian tweets. Taking data using the Twitter Scraping extension pack and analyzing using Python 3.7.2. Based on the results of sentiment analysis tweets got a neutral sentiment of 744 (75%) tweets, followed by negative sentiment of as much as 175 (18%) tweets and positive sentiment by the number 75 (8%) of a total of 994 tweets. The conclusion was presented the modelling in based on the topic, and we got three topic most relevant terms for topic 0, 1, or 2 with 35,3%, 33%, 31,7% of tokens, respectively.

Intermittent Data Forecasting using Kernel Support Vector Regression Amri Muhaimin; Endah Setyowati; Kartika Maulida H; Allan Ruhui Fatma Sari
Nusantara Science and Technology Proceedings 8th International Seminar of Research Month 2023
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2024.4105

Forecasting involves making future estimates. Forecasting methods are commonly employed to predict stock prices, monetary distribution, and weather conditions. To generate accurate forecasts, it is crucial that the data used is consistent, comprehensive, and unchanging. Some data can be readily predicted, while some poses a considerable challenge. An illustration of this is found in discontinuous data, which is notably hard to forecast. Discontinuous data is marked by frequent instances of zero values due to sporadic events. For instance, when tracking the sales of aircraft or other products, sales do not transpire daily, causing recorded data to often register as zero. Various techniques have been explored to handle this kind of data. In this particular study, the chosen method is support vector regression. This method is capable of predicting discontinuous data with a quality level of 1.004, which is lower than traditional approaches like exponential smoothing.

Sentiment Analysis in Social Media: Case Study in Indonesia Amri Muhaimin; Tresna Maulana Fahrudin; Syifa Syarifah Alamiyah; Heidy Arviani; Ade Kusuma; Allan Ruhui Fatmah Sari; Angela Lisanthoni
Nusantara Science and Technology Proceedings 8th International Seminar of Research Month 2023
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2024.4106

Stunting is a problem that currently requires special attention in Indonesia. The stunting rate in 2022 will drop to 21.6% and for the future, the government has set a target of up to 14% in 2024. There have been many government efforts in implementing programs to reduce stunting rates. However, not everything runs optimally. Rapid technological developments and freedom of expression in the internet world produce review text data that can be analyzed for evaluation. This study aims to analyze the text data of Twitter users' reviews on stunting. The method used is a text-mining approach and topic modeling based on Latent Dirichlet Allocation (LDA). The results show that negative sentiment dominates by 60.6%, positive sentiment by 31.5%, and neutral by 7.9%. In addition, this research shows that 'anak', 'turun', 'angka', 'cegah' and 'gizi' are among the words that often appear on the topic of stunting.

Forecasting The Number of Traffic Accidents in Purbalingga Regency on 2023 Using Time Series Model Trimono; Amri Muhaimin; Nabilah Selayanti
Nusantara Science and Technology Proceedings 8th International Seminar of Research Month 2023
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2024.4168

Accident data from Satlantas Purbalingga Regency shows that in 2022 there is an increase in the number of traffic accidents in the Purbalingga Regency. In the future, the impact of accidents is predicted to be bigger so it is necessary to forecasting. Forecasting is one of the most important elements in decision making, because effective or not a decision generally depends on several factors that can not be seen at the time the decision was taken. In this time study the possible time series model is ARMA (2,2), ARMA (2,1), ARMA (1,2), ARMA (1,1), AR (2), AR (1), MA (2), MA (1). However, after testing, the model used is ARMA (1,1). This model is used because it meets all the assumption requirements that are parameter significant, residual independent test, residual normality test, and the smallest Mean Square Error value. According to data forecasting results the highest number of crashes existed in January of 97 accidents and the lowest in December amounted to 93 accidents, So the necessary action from the relevant agencies to cope with the increasing number of traffic accidents in the Purbalingga Regency.

Stock Price Modeling with Geometric Brownian Motion and Value with Risk PT Ciputra Development TBK Amri Muhaimin; Trimono Trimono
Nusantara Science and Technology Proceedings 7st International Seminar of Research Month 2022
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2023.3329

Financial sector investment is an activity that attracts a lot of public interest. One of them is investing funds in purchasing the company’s shares. Profit received from stock investment activity can be seen from the value of stock returns. While, if the previous stock returns to Normal distribution, the future stock price can be predicted by Geometric Brownian Motion Method. Based on the stock price prediction, can also be measured an estimated value of the investment risk. The result of data processing shows that the stock price prediction of PT. Ciputra Development Tbk period December 1, 2016, until January 31, 2017, has very good accuracy, based on the value of MAPE 1.98191%. Further, the Value Risk Method of Monte Carlo Simulation with ? = 5% significance level was used to measure the share investment risk of PT.Ciputra Development Tbk. Thus, this method is only useful if it can be used to predict accurately. Therefore, backtesting is needed. Based on the processing obtained data, backtesting generates the value of violation ratio at 0, it means that at significance level ? = 5%, the Value at Risk Method of Monte Carlo Simulation can be used at all levels of probability violation.

A Simple Data Sentiment Analysis using Bjorka phenomenon on Twitter Prismahardi Aji Riyantoko; Amri Muhaimin
Nusantara Science and Technology Proceedings 7st International Seminar of Research Month 2022
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2023.3353

Social media is one of the means used by netizens to access, share and discuss the latest and hottest news issues. Twitter as one of the social media is a platform that in real-time is often chosen to communicate that matter. Through sentiment analysis with the text method mining on Twitter, we can understand how people describe and express their perceptions of obesity both positively and negatively nor neutral. This analysis is important to see the extent to which social media such as Twitter is used today. Those are one of the instruments for disseminating information data security in Indonesia. Research objectives for identifying sentiment analysis on related Twitter the Bjorka phenomenon in Indonesia using the text mining method. The type of research is cross-sectional. This research plan was chosen because of the data taken from Twitter in the last four-month time series (June 2022 - October 2022). The result of web scraping on Twitter is 998 Indonesian tweets. Taking data using the Twitter Scraping extension pack and analyzing using Python 3.7.2. Based on the results of sentiment analysis tweets got a neutral sentiment of 744 (75%) tweets, followed by negative sentiment of as much as 175 (18%) tweets and positive sentiment by the number 75 (8%) of a total of 994 tweets. The conclusion was presented the modelling in based on the topic, and we got three topic most relevant terms for topic 0, 1, or 2 with 35,3%, 33%, 31,7% of tokens, respectively.

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