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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 7 Documents
 1 
Search results for , issue "Vol. 7 No. 1 (2021): June 2021" : 7 Documents clear
Comparative Analysis of QoE Multipath TCP Congestion Control LIA, CUBIC, and WVEGAS on Video Streaming Siti Amatullah Karimah; Fiqqih Maulana Susanto; Aji G. Putrada
International Journal on Information and Communication Technology (IJoICT) Vol. 7 No. 1 (2021): June 2021
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v7i1.534

Abstract

Transmission Control Protocol (TCP) is a type of protocol that allows a collection of computers to communicate and exchange data within a network. Nowadays electronic devices such as tablets, personal computers and smartphones can use more than one network at the same time, but this is not supported by the characteristics of TCP which can only use one path on the network. To solve this condition there are several new generations of standardized network protocols. Multipath TCP is a development of TCP, Multipath which is a new generation network protocol that allows traffic to use multiple paths in the network. In addition to being able to use multiple paths on multipath TCP, there are several congestion control algorithms including LIA, CUBIC and WVEGAS congestion control algorithms. Tests conducted in this study were to compare the performance of congestion control LIA, CUBIC and WVEGAS to improve the quality of video streaming. From the test results, CUBIC is better than WVEGAS and LIA because the QoS and QoE video streaming test for CUBIC in all testing environment has better results than others.
Safety Requirements Analysis using Misuse Cases Method Ryo Alif Ramadhan; Dana Sulistyo Kusumo; Jati Hiliamsyah Husen
International Journal on Information and Communication Technology (IJoICT) Vol. 7 No. 1 (2021): June 2021
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v7i1.543

Abstract

Safety requirements analysis is an activity inside software requirements engineering that focuses on finding and solving safety gaps inside a software product. One method to do safety requirements analysis is misuse cases, a technique adopted from the security analysis method. Misuse cases provide a safety analysis approach which allows detailed steps from different stakeholders' perspective. In this research, we evaluate the misuse cases method's understandability by implementing it to analyze safety requirements for an electric car's autopilot system. We assessed the developed models using the walkthrough method. We found differences between how the model understood from someone with experience in software development and those who don't.
Neural Network on Stock Prediction using the Stock Prices Feature and Indonesian Financial News Titles Nur Ghaniaviyanto Ramadhan; Imelda Atastina
International Journal on Information and Communication Technology (IJoICT) Vol. 7 No. 1 (2021): June 2021
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v7i1.544

Abstract

Stocks are the most popular investments among entrepreneurs or other investors. When investing in stocks these investors tend to learn how to invest stocks correctly and when is the right time. For the problem of how to invest shares correctly can be used a variety of basic theories that already exist, but for the problem when the right time needs further learning. In this paper will purpose about stock price prediction using stock data indicators and financial headline data in Bahasa Indonesia. The machine learning model used is a multi-layer perceptron neural network (MLP-NN) with the highest accuracy produced by 80%.
Performance Comparison of Several Range-based Techniques for Indoor Localization Based on RSSI Dwi Joko Suroso; Farid Yuli Martin Adiyatma; Ahmad Eko Kurniawan; Panarat Cherntanomwong
International Journal on Information and Communication Technology (IJoICT) Vol. 7 No. 1 (2021): June 2021
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v7i1.550

Abstract

The classical rang-based technique for position estimation is still reliably used for indoor localization. Trilateration and multilateration, which include three or more references to locate the indoor object, are two common examples. These techniques use at least three intersection-locations of the references' distance and conclude that the intersection is the object's position. However, some challenges have appeared when using a simple power-to-distance parameter, i.e., received signal strength indicator (RSSI). RSSI is known for its fluctuated values when used as the localization parameter. The improvement of classical range-based has been proposed, namely min-max and iRingLA algorithms. These algorithms or methods use the approximation in a bounding-box and rings for min-max and iRingLA, respectively. This paper discusses the comparison performance of min-max and iRingLA with multilateration as the classical method. We found that min-max gives the best performance, and in some positions, iRingLA gives the best accuracy error. Hence, the approximation method can be promising for indoor localization, especially when using a simple and straightforward RSSI parameter.
Toxic Comment Classification on Social Media Using Support Vector Machine and Chi Square Feature Selection Nadhia Azzahra; Danang Murdiansyah; Kemas Lhaksmana
International Journal on Information and Communication Technology (IJoICT) Vol. 7 No. 1 (2021): June 2021
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v7i1.552

Abstract

The use of social media in society continues to increase over time and the ease of access and familiarity of social media then make it easier for an irresponsible user to do unethical things such as spreading hatred, defamation, radicalism, pornography so on. Although there are regulations that govern all the activities on social media. However, the regulations are still not working effectively. In this study, we conducted a classification of toxic comments containing unethical matters using the SVM method with TF-IDF as the feature extraction and Chi Square as the feature selection. The best performance result based on the experiment that has been carried out is by using the SVM model with a linear kernel, without implementing Chi Square, and using stemming and stopwords removal with the F1 − Score equal to 76.57%.
Forecasting the COVID-19 Increment Rate in DKI Jakarta Using Non-Robust STL Decomposition and SARIMA Model Rosmelina Deliani Satrisna; Aniq A. Rohmawati; Siti Sa’adah
International Journal on Information and Communication Technology (IJoICT) Vol. 7 No. 1 (2021): June 2021
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v7i1.554

Abstract

The Corona virus known as COVID-19 was first present in Wuhan, China at this time has troubled many countries and its spread is very fast and wide. Data on daily confirmed COVID-19 cases were collected from the DKI Jakarta province between early May 2020 and late January 2021. The daily increase in confirmed COVID-19 cases has a percentage of the value of increase in total cases. In this study, modeling and analysis of forecasting the increment rate in daily number of new cases COVID-19 DKI Jakarta was carried out using the Seasonal-Trend Loess (STL) Decomposition and Seasonal Autoregressive Integrated Moving Average (SARIMA) models. STL Decomposition is a form of algorithm developed to help decompose a Time Series, and techniques considering seasonal and non-stationary observation. The results of the best forecasting accuracy are proven by STL-ARIMA, there are MAPE and MSE which only have an error value of 0.15. This proposed approach can be used for consideration for the DKI Jakarta government in making policies for handling COVID-19, as well as for the public to adhere to health protocols.
Classification of Dengue Hemorrhagic Fever (DHF) Spread in Bandung using Hybrid Naïve Bayes, K-Nearest Neighbor, and Artificial Neural Network Methods Fatri Nurul Inayah; Sri Suryani Prasetiyowati; Yuliant Sibaroni
International Journal on Information and Communication Technology (IJoICT) Vol. 7 No. 1 (2021): June 2021
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v7i1.562

Abstract

Dengue fever is a dangerous disease caused by the dengue virus. One of the factors causing dengue fever is due to the place where you live in the tropics, so that cases of dengue fever in Indonesia, especially in the Bandung Regency area, will continue to show high numbers. Therefore, information is needed on the spread of this disease by requiring the accuracy and speed of diagnosis as early prevention. In terms of compiling this information, classification techniques can be done using a combination of methods Naïve Bayes, K-Nearest Neighbor(KNN), and Artificial Neural Network(ANN) to build predictions of the classification of dengue fever, and the data used in this Final Project are dataset affected by the spread of dengue fever in Bandung regency in the 2012-2018 period. The hybrid classifier results can improve accuracy with the voting method with an accuracy level of 90% in the classification of dengue fever.

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