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Erna Piantari
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jcs@upi.edu
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Department of Computer Science Education, Universitas Pendidikan Indonesia, Jl. Setiabudhi 229, Bandung, Indonesia
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INDONESIA
Journal of Computers for Society
Published by Universitas Pendidikan Indonesia
ISSN : -     EISSN : 27234088     DOI : https://doi.org/10.17509/jcs.v5i2
Core Subject : Science, Education, Engineering,
Computer Science & IT Engineering Library & Information Science Mathematics
The Journal invites original articles and not simultaneously submitted to another journal or conference. The whole spectrum of computer science are welcome, which includes, but is not limited to - Artificial Intelligence, IoT and Robotics - Data Analysis and Big Data - Multimedia and Design, - Software Engineering, - Computer Networking, - Information System, and - Applications of computer science in education, agriculture, government, smart city, bioinformatics, astrophysics, simulation and modelling, etc.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 6 Documents
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Search results for , issue "Vol 1, No 1 (2020): JCS: June 2020" : 6 Documents clear
Hamming Code at Marker-Based Augmented Reality on the Android Platform for Teaching Philosophy of Batik Wiwid Widyanto; Rosa Ariani Sukamto; Alejandro Rosales Pérez; Isma Widiaty
Journal of Computers for Society Vol 1, No 1 (2020): JCS: June 2020
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/jcs.v1i1.24943

Abstract

Batik is one of the many cultural heritage of Indonesia that has high artistic value and has become the hallmark of a nation that must be preserved. Augmented reality (AR) is a technology that adds virtual objects into the real world. This technology could provide new ways of delivering information to users, a more interactive way. In this research, a system of marker-based AR tracking used the introduce batik was made. One type of augmented reality is AR marker-based tracking. To track markers, several there are steps that must be performed on every frame received from the camera android smartphone. Phase tracking in this study includes of conversion of images from the camera frame into a grayscale image, detects the contour, perspective transformations, and decoding algorithm using Hamming Code. Test-based results by author, tracking markers on this system could properly track 100% of each marker in normal circumstances, within a certain range depending on the size of the marker and at an angle of 45°. Test-based results by author, tracking markers on this system could properly track 100% of each marker in normal circumstances, within a certain range depending on the size of the marker and at an angle of 45°.
Prediction of Diarrhea Sufferers in Bandung with Seasonal Autoregressive Integrated Moving Average (SARIMA) Cacuk Jati Pangestu; Erna Piantari; Munir Munir
Journal of Computers for Society Vol 1, No 1 (2020): JCS: June 2020
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/jcs.v1i1.25375

Abstract

Diarrhea is the second disease that causes death in children in the world. Every year, around 1.7 million cases of diarrhea are found and cause around 525,000 deaths in children under the age of five in the world. Proper analysis of health service data can help predict epidemics, cure, and disease, and improve quality of life and avoid preventable deaths. This research is aimed at predicting diarrhea sufferers in the future by using Seasonal Autoregressive Integrated Moving Average (SARIMA) and Seasonal Autoregressive Integrated Moving Average with explanatory X (SARIMAX) by involving climate factors in the form of average temperature and average humidity. The data used are data of diarrhea sufferers and climate in 2010-2019 in the city of Bandung. The result shows that there is not significant relation between temperature or humidity and the diarrhea cases. However, the SARIMA model had performed better than the SARIMAX model with the addition of climate factors to predict the diarrhea case in Bandung. The predictive accuracy of the SARIMA model obtained is 78.6%.
Web e-Learning Component Analysis: A Metamodel Narti Prihartini
Journal of Computers for Society Vol 1, No 1 (2020): JCS: June 2020
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/jcs.v1i1.25511

Abstract

Research focus on learning technologies currently classified into three main themes included pedagogical aspect, underpinning technology, and organizational issues. In particular, there is an expand research on how to explore learning technologies in order to support the communication and collaboration by increasing focus on relevant pedagogical and organizational issues. Concerns that appear in every research focus can be related to determine the specific component of web e-learning. Analysis of specific component then required to describe all aspects that support the web e-learning implementation. Final result from this analysis is initialization of web e-learning component in logical and technical along with it relations by proposed a web e-learning component metamodel.
A Text Mining Implementation Based on Twitter Data to Analyse Information Regarding Corona Virus in Indonesia Enda Esyudha Pratama; Rizqia Lestika Atmi
Journal of Computers for Society Vol 1, No 1 (2020): JCS: June 2020
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/jcs.v1i1.25502

Abstract

CORONA virus outbreak (COVID-19) began to infect almost all countries in early 2020 including Indonesia. Since its distribution, various information has been spread in the community from various sources, one of them is social media. Various terms also appear on social media related to the corona virus. This study analyzes related terms that emerge from social media-based. The data used was sourced from Twitter in the past month where the data processed was text data. The method used is text mining. Text Mining is a method used to extract important information from a group of texts. From the results of the research conducted, there are several terms or information that tend to appear frequently on social media, namely “PSBB”, “new normal”, “karantina”, and “juru bicara Dr. Reisa”.
Natural Language Processing and Levenshtein Distance for Generating Error Identification Typed Questions on TOEFL Lala Septem Riza; Faisal Syaiful Anwar; Eka Fitrajaya Rahman; Cep Ubad Abdullah; Shah Nazir
Journal of Computers for Society Vol 1, No 1 (2020): JCS: June 2020
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/jcs.v1i1.24940

Abstract

Test of English as a Foreign Language (TOEFL) is one of the evaluations requiring good quality of the questions so that they can reflect the English abilities of the test takers. However, it cannot be denied that making such questions with good quality is time consuming. In fact, the use of computer technology is able to reduce the time spent in making such questions. This study, therefore, develops a model to generate error identification typed questions automatically from news articles. Questions from the sentences on news sites are created by utilizing Natural Language Processing, Levenshtein Distance, and Heuristics. This model consists of several stages: (1) data collection; (2) preprocessing; (3) part of speech (POS) tagging; (4) POS similarity; (5) choosing question candidates based on ranking; (6) determining underline and heuristics; (7) determining a distractor. Testing ten different news articles from various websites, the system has produced some error identification typed questions. The main contributions of this study are that (i) it can be used as an alternative tool for generating error identification typed questions on TOEFL from news articles; (ii) it can generate many questions easily and automatically; and (iii) the question quality are maintained as historical questions of TOEFL.
Software Bug Prioritization in Beta Testing Using Machine Learning Techniques Anum Waqar
Journal of Computers for Society Vol 1, No 1 (2020): JCS: June 2020
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/jcs.v1i1.25355

Abstract

Testing in Software Development Life Cycle is one of the most crucial activities. Bug prioritization has been a manual process for long. Our paper provides a methodology for ease of bug prioritization in beta testing phase. In the methodology, data from various bug reports is supplied into a model and, through machine learning, the model outputs fairly accurate bug priority based on historical data.

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