Sentiment Classification Twitter of LRT, MRT, and Transjakarta Transportation using Support Vector Machine

Kristiyanti, Dinar Ajeng; Aulianita, Rizki; Putri, Dwi Andini; Utami, Lilyani Asri; Agustini, Fajar; Alfianti, Zulia Imami

doi:10.1109/icsintesa56431.2022.10041651

Cited by 3 publications

(4 citation statements)

References 20 publications

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“…Anemia can be detected with a complete blood count (CBC). A test called complete blood count (CBC) is used to evaluate red, white and platelet counts in the blood [18]. This examination can find many diseases and problems as well as determine general health.…”

Section: Methodsmentioning

confidence: 99%

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Data Mining Techniques for Predictive Classification of Anemia Disease Subtypes

Setiawan,

Amalia,

Prasetiawan

2024

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Anemia, characterized by insufficient red blood cells or reduced hemoglobin, hinders oxygen transport in the body. Understanding the different types of anemia is vital to tailor effective prevention and treatment. This research explores the role of data mining in predicting and classifying anemia types, focusing on complete blood count (CBC) and demographic data. Data mining is the key to building models that help healthcare professionals diagnose and treat anemia. Employing the cross-industry standard process for data mining (CRISP-DM), with its six phases, facilitates this effort. Our study compared Naïve Bayes, J48 Decision Tree, and Random Forest algorithms using RapidMiner tools, evaluating precision, mean recall, and mean precision. The J48 decision tree outperformed the others, highlighting the importance of algorithm choice in anemia classification models. Furthermore, our analysis identified renal disease-related and chronic anemia as the most prevalent types, with greater occurrence among females. Recognizing gender disparities in the prevalence of anemia informs customized healthcare decisions. Understanding demographic factors in specific types of anemia is crucial to effective care strategies.

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Section: Methodsmentioning

confidence: 99%

“…This study uses data mining techniques and the CRISP-DM framework to predict the classification [17], [18] of the different forms of anemia. The Naive Bayes, J48 Decision Tree, and Random Forest algorithms will be contrasted.…”

Section: Business Understanding Phasementioning

confidence: 99%

Data Mining Techniques for Predictive Classification of Anemia Disease Subtypes

Setiawan,

Amalia,

Prasetiawan

2024

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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“…This sentiment analysis is based on the machine learning approach and Support Vector Machine (SVM) has been chosen as the classifier. SVM has proven to be able to produce good performance in sentiment classification problems [8] [9] [10] [11]. The objective of the research is to explore the capability of SVM in the classification of Terengganu"s tourist spot reviews after the Covid-19 pandemic using TikTok data.…”

Section: Introductionmentioning

confidence: 99%

Post Pandemic Tourism: Sentiment Analysis using Support Vector Machine Based on TikTok Data

Sabri,

Subki,

Bahrin

et al. 2024

IJACSA

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The tourism industry is one of the hard hit businesses during the Covid-19 pandemic and has been struggling for backup ever since. However, nowadays the industry has started to bloom again with the lifting of all of the restrictions of Covid-19. This research aims to analyze the sentiments of the tourists using the Support Vector Machine (SVM) algorithm to know their views on the tourist spots after the pandemic. The scope of the research covers the state of Terengganu which is popularly known for its islands and unique culture on the east coast of Malaysia. TikTok data has been used as the source of data as social media currently has become one of the top mediums for reviewing, selling and promoting products and services. The objective of the research is to explore the SVM algorithm in the sentiment classification of tourist spots in Terengganu. This research is expected to help the Tourism Terengganu to improve their tourist spots and their services. The phases of the research include collecting data from TikTok, data pre-processing, data labelling, feature extraction, model creation using SVM, graphical user interface development and performance evaluation. The evaluation results showed that the performance of the SVM classifier model was good and reliable, with 90.68% accuracy. The future work would be collecting more data from TikTok regularly to further improve the accuracy of the algorithm.

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“…Contrast collectives against popular uprisings: local authorities mobilize not only residents, but also local authorities actively promote citizens ' actions (Panagiotopoulos et al, 2014). Also, Twitter produces a lot of data that may be related to people's opinions of a company (Oetama, 2019;Kristiyanti et al, 2022). Twitter's social network proves that the distrust of citizens could have a negative impact on bilateral security cooperation.…”

Section: Introductionmentioning

confidence: 99%

The Use of Twitter by Government: Engaging the Secure Based Feeling on Economic Investment

Alam

Istikhoroh

Putranto

2023

JKP

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Indonesia witnessed a very rapid growth of 12.58 per cent in the tourism sector in 2018, compared with an average world growth of only 5.6 per cent, as well as an ASEAN growth of 7 per cent. To make tourism become one of the most important sectors for Indonesia in terms of increasing government revenue and improving the efficiency and competitiveness of citizens on international markets. Of course, a lot needs to be prepared to achieve this goal. One of them is a safe community-based feeling. Because Indonesia is currently developing and requires significant domestic investment, security in Indonesia is a focus that needs to be improved. And in order to be able to understand a sense of community security, the government must have a relationship between the government and the community. This study aims to know e-government service quality implementation by the Ministry of Agrarian Affairs and Spatial Planning via twitter. The method used in this study is a content analysis of Twitter posts from the Ministry of Agrarian Affairs and Spatial Planning of the Republic of Indonesia for a period of 1 year, from 1 January 2019 to 31 December 2019. Based on the findings and discussion, it is concluded that secure based feeling towards investment can be achieved through social media, one of which is Twitter by providing a more understanding related to land rights or land that will be used in investment.

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Sentiment Classification Twitter of LRT, MRT, and Transjakarta Transportation using Support Vector Machine

Cited by 3 publications

References 20 publications

Data Mining Techniques for Predictive Classification of Anemia Disease Subtypes

Data Mining Techniques for Predictive Classification of Anemia Disease Subtypes

Post Pandemic Tourism: Sentiment Analysis using Support Vector Machine Based on TikTok Data

The Use of Twitter by Government: Engaging the Secure Based Feeling on Economic Investment

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