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INDONESIA
IJoICT (International Journal on Information and Communication Technology)
Published by Universitas Telkom
ISSN : -     EISSN : 23565462     DOI : -
Core Subject : Science,
Computer Science & IT
International Journal on Information and Communication Technology (IJoICT) is a peer-reviewed journal in the field of computing that published twice a year; scheduled in December and June.
Arjuna Subject : -
Articles 6 Documents
 1 
Search results for , issue "Vol. 8 No. 2 (2022): December 2022" : 6 Documents clear
Hybrid Hybrid wavelet and entropy features to monitor happy hypoxia based on photoplethysmogram signals Ayub Ginting
International Journal on Information and Communication Technology (IJoICT) Vol. 8 No. 2 (2022): December 2022
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v8i2.629

Abstract

Happy hypoxia is a condition where patients experience decreasing oxygen saturation in their brains. In worst cases, Happy hypoxia can reduce the patient's consciousness and even death. Covid-19 has increased cases of happy hypoxia. Several studies have been conducted to detect the happy hypoxia. Existing research projects generally use photo plethysmography signals. However, the results show that the accuracy of happy hypoxia detection is still low. This study provides a solution to the above problems, by proposing a happy hypoxia detection system based on entropy and Discrete Wavele Transform (DWT) features that are combined with a classifier based on K Nearest Neighbor (KNN). The method used in this research is as below Hybrid Wavelet and Entropy Features method.Experiments on the proposed system have been carried out using data on Covid-19 patients from Haji Adam Malik Hospital in Medan.The experimental results show that the system proposed has an accuracy of 87%, sensitivity of 90% and specificity of 85
Recommender System Based on Matrix Factorization on Twitter Using Random Forest (Case Study: Movies on Netflix) Bagas Teguh Imani; Erwin Budi Setiawan
International Journal on Information and Communication Technology (IJoICT) Vol. 8 No. 2 (2022): December 2022
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v8i2.655

Abstract

In this day and age, there is a lot of entertainment that can be done, one of which is watching movies using the Netflix platform. When you want to watch, sometimes users can be confused about which movies to watch according to their tastes and interests, which requires a solution, namely by using a recommendation system. The recommendation system is a system that emerged as a solution to provide information by learning data from users with previously stored data items. One of the recommendation system techniques is Collaborative Filtering. By using Collaborative Filtering, this study will focus on using Matrix Factorization-based because it is considered more efficient and allows the incorporation of additional information in the data. This study will use the Random Forest algorithm to improve the results of good predictions. In this study, a recommendation system based on Matrix Factorization on Twitter will be made using Random Forest in a case study of films on Netflix. The experimental results have shown that the use of the system gets a Mean Absolute Error (MAE) value of 0.7641 to 0.8496 and a Root mean squared error (RMSE) of 1.0359 to 1.1935.
The Analysis of Support Vector Machine (SVM) on Monthly Covid-19 Case Classification Rifaldo Sitepu
International Journal on Information and Communication Technology (IJoICT) Vol. 8 No. 2 (2022): December 2022
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v8i2.671

Abstract

Covid-19 is disease caused by the new corona virus called Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The effect of this virus usually causes infection on respiratory system. Covid-19 was rapidly spread globally. Experts said that the factor that caused this to spread rapidly is human mobility. Therefore, several countries create new rules so that it can suppress the spreading of this disease, by prohibiting a large scale gathering, keeping away distance with each other, mandatory rule of using mask, and the prohibition for the entry of their country. This research proposes a performance analysis of Support Vector Machine (SVM) to classify the monthly data of covid-19. The data used in this research is a series of covid-19 data of towns in Bandung from November 2020 until December 2021. From conducting this research It is found that the best accuracy was found on December 2021 with the accuracy of 100%, followed by July and August with the accuracy of 97%, and October with the value of 90%. We can conclude that Support Vector Machine (SVM), is a good method on classifying the monthly covid-19 data.
Estimation of Ordinary Kriging Method with Jackknife Technique on Rainfall Data in Malang Raya Novia Nur Rohma
International Journal on Information and Communication Technology (IJoICT) Vol. 8 No. 2 (2022): December 2022
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v8i2.678

Abstract

Geostatistics is a science that focuses on spatial data. In geostatistics, there is an estimation method to handle variables whose values ​​vary with the change in location or place, which are called regionalized variables. The estimation method used to handle regionalized variables is called the kriging method. In the ordinary kriging method it is necessary to take into account the semivariogram. Rain is a process of falling water from the clouds to the earth. Rain is measured through rainfall. The purpose of this study was to determine estimation of the ordinary kriging method on normally distributed data and abnormally distributed data, and determine the best semivariogram. The data used is monthly rainfall data in Malang Raya for the period January 2016 to December 2016. From the monthly rainfall dataset, the data are normally distributed in January, February, March, April, May, June, August, September, October, November and December 2016, while the data are not normally distributed in July. Ordinary kriging with Jackknife method can be used to analyze data with normal distribution and data with abnormal distribution.
Comparison of Term Weighting Methods in Sentiment Analysis of the New State Capital of Indonesia with the SVM Method Muhammad Kiko Aulia Reiki; Yuliant Sibaroni; Erwin Budi Setiawan
International Journal on Information and Communication Technology (IJoICT) Vol. 8 No. 2 (2022): December 2022
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v8i2.681

Abstract

The relocation of the State Capital to “Nusantara”, 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.
Hoax COVID-19 News Detection Based on Sentiment Analysis in Indonesian using Support Vector Machine (SVM) Method Alifia Shafira
International Journal on Information and Communication Technology (IJoICT) Vol. 8 No. 2 (2022): December 2022
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v8i2.682

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.

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