Pub Date : 2023-06-30DOI: 10.21108/ijoict.v9i1.713
Revelin Angger Saputra
The capital market industry in Indonesia is developing in a better direction so that the growth of new investors is also increasing. Until the end of February 2021, operational data from the Indonesian Stock Exchange (IDX) and data from the Indonesian Central Securities Depository (KSEI) recorded that the number of new capital market investors had increased by 16.35% or 634,350 investors, from the previous 3,880,753 investors. to 4,515,103 investors. The development of the capital market industry in Indonesia, which has increased investor interest in investing, is expected to mobilize public funds to support national economic development. Some companies that are familiar to the community are BCA, BNI, BRI and MANDIRI. This study attempts to forecast banking stock prices on the LQ45 index, using the Long Short-Term Memory (LSTM) method. LSTM is one of the Recurrent Neural Networks (RNN) which has good accuracy in predictions. The identified fields are Close, Open, RSI, MACD and MA. The evaluation method used in this prediction system is MAPE in the form of percent output.
{"title":"A STOCK PREDICTION SYSTEM USING TEKNIKAL INDICATORS WITH THE LSTM METHOD","authors":"Revelin Angger Saputra","doi":"10.21108/ijoict.v9i1.713","DOIUrl":"https://doi.org/10.21108/ijoict.v9i1.713","url":null,"abstract":"The capital market industry in Indonesia is developing in a better direction so that the growth of new investors is also increasing. Until the end of February 2021, operational data from the Indonesian Stock Exchange (IDX) and data from the Indonesian Central Securities Depository (KSEI) recorded that the number of new capital market investors had increased by 16.35% or 634,350 investors, from the previous 3,880,753 investors. to 4,515,103 investors. The development of the capital market industry in Indonesia, which has increased investor interest in investing, is expected to mobilize public funds to support national economic development. Some companies that are familiar to the community are BCA, BNI, BRI and MANDIRI. This study attempts to forecast banking stock prices on the LQ45 index, using the Long Short-Term Memory (LSTM) method. LSTM is one of the Recurrent Neural Networks (RNN) which has good accuracy in predictions. The identified fields are Close, Open, RSI, MACD and MA. The evaluation method used in this prediction system is MAPE in the form of percent output.","PeriodicalId":488588,"journal":{"name":"International journal on information and communication technology","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135857627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-18DOI: 10.21108/ijoict.v9i1.698
Gharyni Nurkhair Mulyono, Deni Saepudin, Aniq Atiqi Rohmawati
A portfolio is a collection of investment financial assets managed by financial institutions or individuals. In investment activities, investors expect minimal loss risk and optimal stock portfolio weight to get maximum profit. Investors can monitor changes in stock index values to compare portfolio performance. This research has discussed how to build a portfolio based on stock datasets with the LQ45 index using return predictions from the artificial neural network (ANN) method with semi-absolute deviation (SAD). Furthermore, the portfolio is optimized by looking for weights that match it. After that, a comparison of portfolio performance was carried out using the Sharpe ratio (SR) method between the semi-absolute deviation (SAD) portfolio and the portfolio resulting from the formation of the equal weight (EW) portfolio. Portfolio performance with ANN prediction and SAD is better than equal-weight portfolios in terms of mean return, standard deviation, and sharpe ratio for portfolios with few stocks, namely 2 and 3 stocks. In addition, a portfolio with a higher number of stocks can make the portfolio value from the ANN close prediction algorithm process and the selection of weights based on SAD is better than portfolios with equal weight for each list of stocks in the portfolio.
{"title":"Portfolio Optimization Based on Return Prediction and Semi Absolute Deviation (SAD)","authors":"Gharyni Nurkhair Mulyono, Deni Saepudin, Aniq Atiqi Rohmawati","doi":"10.21108/ijoict.v9i1.698","DOIUrl":"https://doi.org/10.21108/ijoict.v9i1.698","url":null,"abstract":"A portfolio is a collection of investment financial assets managed by financial institutions or individuals. In investment activities, investors expect minimal loss risk and optimal stock portfolio weight to get maximum profit. Investors can monitor changes in stock index values to compare portfolio performance. This research has discussed how to build a portfolio based on stock datasets with the LQ45 index using return predictions from the artificial neural network (ANN) method with semi-absolute deviation (SAD). Furthermore, the portfolio is optimized by looking for weights that match it. After that, a comparison of portfolio performance was carried out using the Sharpe ratio (SR) method between the semi-absolute deviation (SAD) portfolio and the portfolio resulting from the formation of the equal weight (EW) portfolio. Portfolio performance with ANN prediction and SAD is better than equal-weight portfolios in terms of mean return, standard deviation, and sharpe ratio for portfolios with few stocks, namely 2 and 3 stocks. In addition, a portfolio with a higher number of stocks can make the portfolio value from the ANN close prediction algorithm process and the selection of weights based on SAD is better than portfolios with equal weight for each list of stocks in the portfolio.","PeriodicalId":488588,"journal":{"name":"International journal on information and communication technology","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135471324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-09DOI: 10.21108/ijoict.v9i1.688
Nadja Adryana, Niken Cahyani, Erwid Jadied
Memory acquisition process is one of digital forensics act. There are several tools that support memory acquisition process. At this time, there is a feature named secure mode that can caused crash or error in memory acquisition tools system and caused the tools to be unusable, also the loss of the computer memory. This research is focusing on analyzing the acquisition tools that has error or crash when the device that is being used for memory acquisition is in secure mode. The analysis is being carried out using static code analysis method, which is one of the techniques of reverse engineering, using IDA. This study aims to find the cause of the crash or error in memory acquisition tools. The purpose of this study is to be useful for digital forensic tester in understanding the potential risk of the secure mode impact in acquisition process. The results of this study indicate that different operating system and different kernel which runs in the device are the reasons that memory acquisition tools cannot run properly on VSM environment being turned on.
{"title":"Static Code Analysis on The Effect of Virtual Secure Mode on Memory Acquisition with IDA","authors":"Nadja Adryana, Niken Cahyani, Erwid Jadied","doi":"10.21108/ijoict.v9i1.688","DOIUrl":"https://doi.org/10.21108/ijoict.v9i1.688","url":null,"abstract":"Memory acquisition process is one of digital forensics act. There are several tools that support memory acquisition process. At this time, there is a feature named secure mode that can caused crash or error in memory acquisition tools system and caused the tools to be unusable, also the loss of the computer memory. This research is focusing on analyzing the acquisition tools that has error or crash when the device that is being used for memory acquisition is in secure mode. The analysis is being carried out using static code analysis method, which is one of the techniques of reverse engineering, using IDA. This study aims to find the cause of the crash or error in memory acquisition tools. The purpose of this study is to be useful for digital forensic tester in understanding the potential risk of the secure mode impact in acquisition process. The results of this study indicate that different operating system and different kernel which runs in the device are the reasons that memory acquisition tools cannot run properly on VSM environment being turned on.","PeriodicalId":488588,"journal":{"name":"International journal on information and communication technology","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135215201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-03DOI: 10.21108/ijoict.v8i2.681
None Muhammad Kiko Aulia Reiki, Yuliant Sibaroni, Erwin Budi Setiawan
The relocation of the State Capital to “Nusantaraâ€, which was inaugurated with the enactment of UU No. 3 of 2022, is a significant project that has sparked polemics among Indonesian citizens. Many people expressed their opinions and thoughts regarding the relocation of the State Capital on Twitter. This tendency of public opinion needs to be identified with sentiment analysis. In sentiment analysis, term weighting is an essential component to obtain optimal accuracy. Various people are trying to modify the existing term weighting to increase the performance and accuracy of the model. One of them is icf-based or tf-bin.icf, which combines inverse category frequency (ICF) and relevance frequency (RF). This study compares the tf-idf, tf-rf, and tf-bin.icf term weighting with the SVM classification method on the new State Capital of Indonesia topic. The tf-idf weighting results are still the best compared to the tf-bin.icf and tf-rf term weights, with an accuracy score of 88.0% a 1,3% difference with tf-bin.icf term weighting.
{"title":"Comparison of Term Weighting Methods in Sentiment Analysis of the New State Capital of Indonesia with the SVM Method","authors":"None Muhammad Kiko Aulia Reiki, Yuliant Sibaroni, Erwin Budi Setiawan","doi":"10.21108/ijoict.v8i2.681","DOIUrl":"https://doi.org/10.21108/ijoict.v8i2.681","url":null,"abstract":"The relocation of the State Capital to “Nusantaraâ€, which was inaugurated with the enactment of UU No. 3 of 2022, is a significant project that has sparked polemics among Indonesian citizens. Many people expressed their opinions and thoughts regarding the relocation of the State Capital on Twitter. This tendency of public opinion needs to be identified with sentiment analysis. In sentiment analysis, term weighting is an essential component to obtain optimal accuracy. Various people are trying to modify the existing term weighting to increase the performance and accuracy of the model. One of them is icf-based or tf-bin.icf, which combines inverse category frequency (ICF) and relevance frequency (RF). This study compares the tf-idf, tf-rf, and tf-bin.icf term weighting with the SVM classification method on the new State Capital of Indonesia topic. The tf-idf weighting results are still the best compared to the tf-bin.icf and tf-rf term weights, with an accuracy score of 88.0% a 1,3% difference with tf-bin.icf term weighting.","PeriodicalId":488588,"journal":{"name":"International journal on information and communication technology","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135654905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-03DOI: 10.21108/ijoict.v8i2.682
Alifia Shafira
The increasing use of technology makes it easier for information media such as news to be disseminated and does not demand possibilities, there is a lot of hoax news spreading. Twitter is one of the media most frequently used by the public to access and disseminate information. This research will focus on detecting Indonesian language COVID-19 news taken from Twitter. Detection of hoax news can be assisted by using sentiment analysis, one of the uses of classification text. Support Vector Machine (SVM) can be used to perform sentiment analysis tasks. After getting the sentiment analysis results, the hoax detection process will use the Bag of Words. Bag of Words is a collection of word dictionaries for weighting words to determine specific labels. The built SVM model succeeded in classifying tweet repliessentiment with an average accuracy of 83.17% with a threshold of 35%. At the same time, the hoax detection process gets the best accuracy of 62.5% with a threshold of -5 or -6.
科技的日益普及使得信息媒体如新闻的传播变得更加容易，在不要求可能性的情况下，出现了大量的假新闻传播。Twitter是公众获取和传播信息最常用的媒体之一。这项研究将侧重于检测从Twitter上获取的印尼语COVID-19新闻。使用情感分析(分类文本的用途之一)可以辅助检测恶作剧新闻。支持向量机(SVM)可以用来执行情感分析任务。在获得情感分析结果后，恶作剧检测过程将使用词袋。Bag of Words是一个单词字典集合，用于对单词进行加权以确定特定的标签。所建立的SVM模型对tweet回复信息进行分类，平均准确率为83.17%，阈值为35%。同时，在阈值为-5或-6时，恶作剧检测过程的准确率达到62.5%。
{"title":"Hoax COVID-19 News Detection Based on Sentiment Analysis in Indonesian using Support Vector Machine (SVM) Method","authors":"Alifia Shafira","doi":"10.21108/ijoict.v8i2.682","DOIUrl":"https://doi.org/10.21108/ijoict.v8i2.682","url":null,"abstract":"The increasing use of technology makes it easier for information media such as news to be disseminated and does not demand possibilities, there is a lot of hoax news spreading. Twitter is one of the media most frequently used by the public to access and disseminate information. This research will focus on detecting Indonesian language COVID-19 news taken from Twitter. Detection of hoax news can be assisted by using sentiment analysis, one of the uses of classification text. Support Vector Machine (SVM) can be used to perform sentiment analysis tasks. After getting the sentiment analysis results, the hoax detection process will use the Bag of Words. Bag of Words is a collection of word dictionaries for weighting words to determine specific labels. The built SVM model succeeded in classifying tweet repliessentiment with an average accuracy of 83.17% with a threshold of 35%. At the same time, the hoax detection process gets the best accuracy of 62.5% with a threshold of -5 or -6.","PeriodicalId":488588,"journal":{"name":"International journal on information and communication technology","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135654906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：