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INDONESIA
Jurnal Informatika
Published by Universitas Ahmad Dahlan
ISSN : 19780524     EISSN : 25286374     DOI : 10.26555
Core Subject : Science,
Computer Science & IT
Arjuna Subject : -
Articles 5 Documents
 1 
Search results for , issue "Vol 15, No 1 (2021): January 2021" : 5 Documents clear
Comparison of machine learning for sentiment analysis in detecting anxiety based on social media data Shoffan Saifullah; Yuli Fauziyah; Agus Sasmito Aribowo
Jurnal Informatika Vol 15, No 1 (2021): January 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v15i1.a20111

Abstract

All groups of people felt the impact of the COVID-19 pandemic. This situation triggers anxiety, which is bad for everyone. The government's role is very influential in solving these problems with its work program. It also has many pros and cons that cause public anxiety. For that, it is necessary to detect anxiety to improve government programs that can increase public expectations. This study applies machine learning to detecting anxiety based on social media comments regarding government programs to deal with this pandemic. This concept will adopt a sentiment analysis in detecting anxiety based on positive and negative comments from netizens. The machine learning methods implemented include K-NN, Bernoulli, Decision Tree Classifier, Support Vector Classifier, Random Forest, and XG-boost. The data sample used is the result of crawling YouTube comments. The data used amounted to 4862 comments consisting of negative and positive data with 3211 and 1651. Negative data identify anxiety, while positive data identifies hope (not anxious). Machine learning is processed based on feature extraction of count-vectorization and TF-IDF. The results showed that the sentiment data amounted to 3889 and 973 in testing, and training with the greatest accuracy was the random forest with feature extraction of vectorization count and TF-IDF of 84.99% and 82.63%, respectively. The best precision test is K-NN, while the best recall is XG-Boost. Thus, Random Forest is the best accurate to detect someone's anxiety based-on data from social media.
Transformation of the generalized chaotic system into the discrete-time complex domain Nina Volianska; Roman Voliansky; Oleksandr Sadovoi
Jurnal Informatika Vol 15, No 1 (2021): January 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v15i1.a20222

Abstract

The paper deals with the development of the backgrounds for the design and implementation of secured communications by using systems with chaotic dynamics. Such backgrounds allow us to perform the stable transformation of a nonlinear object into a simpler form and formulate the nonlinearities simplification optimization algorithm. This algorithm is based on the optimization problem's solution, which makes it possible to define polynomial order, approximation terms, and breakpoints. Usage of proposed algorithms is one of the ways to simplify known chaotic system models without neglecting their unique properties and features. We prove our approach by considering simplifying the Mackey-Glass system and transforming it into a discrete-time complex domain. This example shows that the transformed system produces chaotic oscillations with twice-increased highest Lyapunov exponent. This fact can be considered as improving the unpredictability of the transformed system, and thus it makes background to make highly non-intercepted and undecoded transmission channels.
Performance measurement of the relationship between students' learning with lecturers' characteristics as supervisors based on fuzzy-based assessment Mulyanto Mulyanto; Bedi Suprapty; Arief Bramanto Wicaksono Putra; Achmad Fanany Onnilita Gaffar
Jurnal Informatika Vol 15, No 1 (2021): January 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v15i1.a17389

Abstract

In addition to the focus of research selected as a Final Project material, the selection of lecturers as student's supervisor becomes very important. The lecturer's competence related to the focus of student research and the supervising style of lecturers is also very influential on the final results. Measurement of style appropriateness between students' learning styles and supervising lecturers' styles can benchmark the quality of the final project's implementation, especially higher education institutions. This study has applied fuzzy-based assessment to build objective perceptions of students' learning characteristics and lecturers' characteristics (Visual (V), Auditory (A), Kinesthetic (K)) as supervisors through questionnaire processing that has designed in such away. Hence, it is suitable for this study. The measuring technique of the percentage of overlapping areas under the curves and the correlation test between a pair of curves have been used as performance measurement metrics. In general, the study results indicate a significant level of coverage adequacy for all research variables regarding existing conditions. It means that the process of Final Project activities in terms of students' and lecturers' learning characteristics as supervisors and their distribution is at a reasonable level (88.38%). It has also been shown by the results of the correlation test of the appropriateness of choice, both supervisors selected by students (0.8657) and students chosen by lecturers (0.9897) who are at a very significant level of similarity. Correlation tests conducted for similarities between students' and lecturers' learning characteristics as supervisors show almost no significant correlation between them (0.4064).
Reality of the internet and social media addiction in Indonesian students Nasy`an Taufiq Al Ghifari; Akhmadi Surawijaya; Fitra Arifiansyah; Agus Komarudin; Denny Hidayat Tri Nugroho; Dimitri Mahayana
Jurnal Informatika Vol 15, No 1 (2021): January 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v15i1.a18951

Abstract

The use of the Internet and social media today is inseparable from the life of modern society. This can lead to an addiction to the Internet and social media. This research aims to answer whether the phenomenon of Internet and social media addiction is a scientific reality or not in Indonesia, especially in Indonesian Students who are undergoing adaptation of the learning process from offline to online due to the Covid-19 pandemic situation. Data collection was conducted with a survey of 2002 respondents. Before the questionnaire was distributed, a validity test and reliability test with Alpha Cronbach's were conducted, and the results showed that all questions on the questionnaire were valid and reliable. Based on the survey results, 20.18% of respondents experienced mild addiction, 4.85% of respondents experienced moderate addiction, and 0.45% of respondents experienced severe addiction to Internets. While the survey results for social media addiction were 14.99% of respondents experienced mild addiction, 4.7% of respondents experienced moderate addiction, and 0.45% of respondents experienced severe addiction. Judging by the philosophy of science, Internet and Social Media Addiction are said to be science and not pseudoscience because it has fulfilled the characteristics of science that is logical, empirical, and can be falsified. There needs to be special attention from the Indonesians about the addiction to the Internet and social media so that this addiction can be anticipated and the inflicted symptoms can be minimized.
Hierarchical long short-term memory for action recognition based on 3D skeleton joints from Kinect sensor Nur Awal Hidayanto; Adhi Prahara; Riky Dwi Puriyanto
Jurnal Informatika Vol 15, No 1 (2021): January 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v15i1.a20106

Abstract

Action recognition has been used in a wide range of applications such as human-computer interaction, intelligent video surveillance systems, video summarization, and robotics. Recognizing action is important for intelligent agents to understand, learn and interact with the environment. The recent technology that allows the acquisition of RGB+D and 3D skeleton data and a deep learning model's development significantly increases the action recognition model's performance. In this research, hierarchical Long Sort-Term Memory is proposed to recognize action based on 3D skeleton joints from Kinect sensor. The model uses the 3D axis of skeleton joints and groups each joint in the axis into parts, namely, spine, left and right arm, left and right hand, and left and right leg. To fit the hierarchically structured layers of LSTM, the parts are concatenated into spine, arms, hands, and legs and then concatenated into the body. The model crosses the body in each axis into a single final body and fed to the final layer to classify the action. The performance is measured using cross-view and cross-subject evaluation and achieves accuracy 0.854 and 0.837, respectively, from the 10 action classes of the NTU RGB+D dataset.

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