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All Journal JISICOM (Journal of Information System, Infomatics and Computing)
Okki Setyawan
Universitas Nusa Mandiri
Computer Science & IT

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KLASIFIKASI TINGKAT KEPARAHAN SERANGAN JARINGAN KOMPUTER DENGAN METODE MACHINE LEARNING Okki Setyawan; Angge Firizkiansah; Ahmad Nuryanto
Journal of Information System, Informatics and Computing Vol 5 No 1 (2021): JISICOM : Volume 5, Nomor 1, Juni 2021
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisicom.v5i1.443

Abstract

Computer networks are currently developing very rapidly, so that many electronic devices are connected to the internet, but the security system adopted by these devices must be qualified so they are not vulnerable to threats and dangers. Researchers want to find out how severe the threat of an attack is detected by a firewall using data records from a company, using machine learning, namely K-Nearest Neighbors, Decission Tree. Classification of the severity of a computer network security system is usually called the severity level. In this study, the limitation of the seriousness level of the attack was divided into 3 parts from the highest level, namely critical, high and medium. The processed dataset is logging into the firewall as many as 5999 with 23 columns or features. The best of the three methods are K-Nearest Neighbors getting 100% accuracy and Decission Tree getting 100% accuracy . With the results of this data processing, the machine learning method is very suitable to be used to classify the severity of computer network attacks
Sentiment analysis for event-based stock price predictions using bidirectional long short term memory Okki Setyawan; Hilman F. Pardede
Journal of Information System, Informatics and Computing Vol 6 No 1 (2022): JISICOM: June 2022
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisicom.v6i1.772

Abstract

The stock market has become an important role in the economy and attracts the attention of investors because it also generates funds and makes investment decisions for companies and investors. The most important thing investors do before investing is to look at information about the capital market in various media, both social media and news portals circulating on the internet to see stock movements that often change due to fluctuations. Sentiment analysis carried out in this study uses a public dataset from the dataset portal at kaggle.com, namely the DJIA (Dow Jones Industrial Average) dataset in the form of events (timeseries) which contains data from a collection of news headlines from redditnews.com as many as 1990 lines of data and has a class label. to go up or down, then trained with deep learning models, namely BiLSTM and LSTM with variations of hyperparameter tuning, and compared with other models including SVM, KNN, Logistic Regression, Naïve Bayes Multinomial. From this research, the best results were obtained with the Deep BiLSTM model with 1 hidden layers, Epoch 20 and batch size 16 resulting in an accuracy score of 0.8677 and F1 score 0.8674 also AUC 0.8671, this study is an improvement from previous research using the LSTM model with an accuracy rate of 0.5212. F1 Score 0.6762 and AUC 0.6211.
Co-Authors Ahmad Nuryanto Angge Firizkiansah Hilman F. Pardede