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Ceddi Journal of Information System and Technology (JST)
Published by Yayasan Cendekiawan Inovasi Digital Indonesia
ISSN : 2829808X     EISSN : 28296575     DOI : https://doi.org/10.56134/jst.v1i1.1
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
Ceddi Journal of Information System and Technology (JST) is a peer-reviewed journal that publishes articles through fair and transparent quality control. We understand that authors need facilities for their papers, whereas readers expect reliable information from these journals. Therefore, our editorial team and reviewers strive to maintain quality and ethics in the authorship and publishing of all articles. In principle, we strive to provide the best service for the research community around the world. We hope this journal can be a new source of insight and inspiration for future research. Ceddi Journal of Information System and Technology (JST) publish the best articles the results of research on issues of concern, the latest and the trend internationally. Submitted papers must be written in English at title and abstract of paper for the initial review stage by editors and further review process by minimum of two reviewers. The scope of the journal includes: - Information Systems - Web Application - Computer Network - Mobile Application - Game Development - Decision Support System - Big Data - E-Commerce - Cloud Computing - Data Mining
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 5 Documents
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Search results for , issue "Vol. 2 No. 2 (2023): December" : 5 Documents clear
Sentiment Analysis Regarding Kanjuruan Stadium Polemics Based on Public Opinion Through Twitter Social Media with SVM Classifier Method Ghama Wellyandi; Bayhaqy, Achmad Bayhaqy; Chandra; Efit Afandi; Rimah Abu Achmed
Ceddi Journal of Information System and Technology (JST) Vol. 2 No. 2 (2023): December
Publisher : Yayasan Cendekiawan Digital Indonesia (CEDDI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56134/jst.v2i2.18

Abstract

Football fans are individuals who promote, motivate, and inspire football. Players of football clubs have both positive and negative fanaticism in both the real world and social media, especially on Twitter. Twitter is one of the communication media. Attracting people worldwide, Twitter saw a record increase in global users, with 313 million monthly active users in 2016 alone; the majority accessed Twitter through mobile devices, accounting for 82 percent of users. Due to the multitude of users tweeting, the latest news and comments become significant worldwide. What happens becomes the main topic, and comments received from many users trigger trending topics on Twitter. This research aims to develop a classification model to predict whether tweets from stadium events are positive or negative from fan perspectives. The classification model is based on a Twitter dataset, and sentiment analysis of tweets was conducted using the Support Vector Machine (SVM) algorithm. The next step involved preprocessing, including case-folding, cleansing, translation to English, and sentiment labeling using VADER. Subsequently, in the preprocessing step 2, tokenization, stopwords, and stemming were applied. For modeling, classic algorithms such as Naïve Bayes and Support Vector Machine were used. The highest accuracy, 87.77%, was achieved using the Support Vector Machine (SVM) algorithm.
Python-Powered Precision: Unraveling Consumer Price Index Trends in Makassar City through a Duel of Long Short-Term Memory and Gated Recurrent Unit Models Abd. Rahman; First Wanita; Rose Arisha; Aditya Halim Perdana Kusuma; Azhary, Zulmy
Ceddi Journal of Information System and Technology (JST) Vol. 2 No. 2 (2023): December
Publisher : Yayasan Cendekiawan Digital Indonesia (CEDDI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56134/jst.v2i2.44

Abstract

This research aims to carry out a predictive analysis of the Consumer Price Index in the city of Makassar to anticipate possible impacts on inflation and deflation in the future. The Consumer Price Index is an indicator that can be used as a basis for measuring changes in the prices of goods and services purchased by consumers which have an impact on inflation in a region. The CPI is very useful for knowing the level of increase in prices, services, and income, as well as measuring the amount of production costs. This data was obtained through the official website of the Central Statistics Agency (BPS) for the Makassar city area. The methods used in this research are Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). The results of this research show that based on analysis and testing, the LSTM model has an MAE of 1.0849 and the GRU model has an MAE of 0.9915, which shows that there is no significant difference between the two methods and both methods can work very well, however, The lowest error value was obtained in the GRU model using a 70:30 dataset ratio, 9 number of sequences, 16 neurons in hidden layer 1 and 32 neurons in hidden layer 2, and 1000 number of epochs.
Digital Forensic Evidence Analysis In Revealing Defamation On Social Media (Twitter) Using The Static Forensics Method Reski Badillah; Andi Yulia Muniar; Abd. Rahman; Febri Hidayat Saputra; Mansyur; Supriadi Sahibu
Ceddi Journal of Information System and Technology (JST) Vol. 2 No. 2 (2023): December
Publisher : Yayasan Cendekiawan Digital Indonesia (CEDDI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56134/jst.v2i2.45

Abstract

This research addresses the persistent challenge of defamation, notably prevalent on the Twitter platform, where the discovery of digital evidence is hampered by robust privacy protections. The study aims to investigate and identify digital evidence in defamation cases on Twitter, focusing on optimizing the evidence discovery process. Employing static forensics to prevent data alterations during acquisition from devices associated with defamation, the research successfully uncovered various digital evidence, including text from deleted comments, usernames, emails, and deleted image files linked to defamation. Out of the initial 28 reported data instances, 22 pieces of evidence were identified, resulting in an impressive 79% accuracy rate. The investigative procedures align with the chain of custody, ensuring the reliability of the collected evidence. This study not only contributes valuable insights into digital evidence discovery in online defamation cases but also highlights the efficacy of static forensics as a method. These findings provide a foundation for robust digital forensic practices, crucial for addressing challenges posed by online defamation on social media platforms.
Application of the Adaptive Boosting Method to Increase the Accuracy of Classification of Type Two Diabetes Mellitus Patients Using the Decision Tree Algorithm Hao Chieh Chiua; Robbi Rahim; Mahmud Mustapa; Kamaruddin; Akbar Hendra; Asnimar; Abigail, Omita
Ceddi Journal of Information System and Technology (JST) Vol. 2 No. 2 (2023): December
Publisher : Yayasan Cendekiawan Digital Indonesia (CEDDI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56134/jst.v2i2.47

Abstract

One of the data mining processes that is often used in machine learning is the data classification process. A decision tree is a classification algorithm that has the advantage of being easy to visualize because of its simple structure. However, the decision tree algorithm is quite susceptible to incorrect classification calculations due to the presence of noise in the data or imbalance in the data, which can reduce the overall level of accuracy. Therefore, the decision tree algorithm should be combined with other methods that can increase the accuracy of classification performance. Machine Learning is used through an artificial intelligence approach to solve problems or carry out optimization. Adaptive Boosting is used to optimize classification calculations. This study aims to examine the performance of Adaptive Boosting in the process of classifying second-degree diabetes mellitus patients using the Decision Tree algorithm. Diabetes mellitus is known as a chronic condition of the human body, the cause of which is an increase in the body's blood sugar levels because the body is unable to produce insulin or is unable to utilize insulin effectively, which is usually referred to as hyperglycemia.. By using a 60:40 data split, the Decision Tree algorithm produces an accuracy value of 95.71%, while the Adaptive Boosting-based Decision Tree results reach a value of 98.99%.
Implementation Of Fuzzy Multi-Criteria Decision Making To Design An Expert System For Prediction Of Digestive Diseases At Dogs Tisna Kusuma, Irene; Kusnadi, Adhi; Adline Twince Tobing, Fenina
Ceddi Journal of Information System and Technology (JST) Vol. 2 No. 2 (2023): December
Publisher : Yayasan Cendekiawan Digital Indonesia (CEDDI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56134/jst.v2i2.50

Abstract

A dog is a kind of animal that is often looked after at home. Except for being known as a human's friend, a dog is also able to be trained to do many things. The age of the dog depends on the type of race. If it's fed well, exercises, and visits the doctor regularly, a dog can live longer. Dogs are very sensitive to certain diseases; one of them is intestinal disease. An expert system of a dog's digestion is built for the dog's lover so that they know exactly what's disease, which relies on the appearance of the symptoms. The symptoms of the different intestinal diseases look almost similar. For this reason, we need an appropriate method to get a precise result. Fuzzy multiple-criteria decision-making can help make the right decision that has some consideration points. This method is also equipped with optimal ultimate selections, which leads to a very precise result. The goal of this application is to know the right kind of disease and its therapy. This study has been validated by matching the results of the application with the doctor's diagnosis. The compatibility level is 80%.

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