A Comparison Study of Search Strategy on Collecting Twitter Data for Drug Adverse Reaction

Laksito, Arif Dwi; Kusrini, Kusrini; Sismoro, Heri; Rahmawati, Fita; Yusa, Mochammad

doi:10.1109/isemantic.2018.8549746

Cited by 8 publications

(3 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This study uses data from Twitter. Strategy on collecting Twitter data has been reviewed by Laksito, et al [12]. To begin the process, the data is labeled manually according to their class or category.…”

Section: Methodsmentioning

confidence: 99%

Twitter Data Analysis Using Machine Learning To Evaluate Community Compliance in Preventing the Spread of Covid-19

Wibowo¹,

Mahardika²,

Kusrini³

2020

2020 2nd International Conference on Cybernetics and Intelligent System (ICORIS)

View full text Add to dashboard Cite

The Covid-19 pandemic that has hit the world, including Indonesia, has forced the government to take policies to prevent the spread of this deadly virus. One of the efforts is socialization to gain people's awareness to keep their distance and stay home. One of the media that can be used for this purpose is Twitter. However, even socialization efforts have been carried out, the spread of Covid-19 cases still has not yet decreased. Many aspects have to be evaluated to fix the situation. One of them is to evaluate the level of community compliance compared to the spread of Covid-19. This study aims to determine the level of community compliance to stay at home and its correlation with the number of positive cases of Covid-19 in Indonesia. This study takes the data from the tweets of the Indonesian people. The algorithms used are logistic regression and random forest combined with the ensemble algorithm, namely bagging. Twitter data taken are those that contain the word covid19, tetapdirumah, stayathome, mudik, psbb; location in Indonesia within a period of March 3 rd to July 7 th 2020. The data obtained from 705 tweets shows that noncompliance community has increased starting mid-June 2020 which is in line with the increasing data trend on the Covid-19 cases in Indonesia.

show abstract

Section: Methodsmentioning

confidence: 99%

Twitter Data Analysis Using Machine Learning To Evaluate Community Compliance in Preventing the Spread of Covid-19

Wibowo¹,

Mahardika²,

Kusrini³

2020

2020 2nd International Conference on Cybernetics and Intelligent System (ICORIS)

View full text Add to dashboard Cite

show abstract

“…Pengguna menggunakan Facebook untuk menyampaikan komentar mereka kepada masyarakat umum. Jumlah pengguna Facebook telah mencapai 2,45 milyar orang di seluruh dunia (Laksito: 2018). Komentar yang disampaikan bisa dalam bentuk berita, opini, argumen, dan beberapa jenis kalimat lainnya (Cathuryedi: 2018).…”

Section: Pendahuluanunclassified

Analisis Sentimen Komentar Facebook Berbasis Lexicon Dan Support Vector Machine

Kurniasari¹

2020

SAINTEKBU

View full text Add to dashboard Cite

Facebook adalah salah satu media sosial yang sering digunakan. Terutama pada pandemi co-19 saat ini. Banyak sekali sentimen publik yang beredar, terutama di Facebook dalam bentuk komentar atas informasi yang ada tentang covid-19 yang menantang untuk dianalisis untuk beberapa tujuan. Teknik NLP (Natural Language Processing) yang terdiri dari casefolding, tokenizing, filtering dan stemming dapat digunakan dalam kasus ini. Studi ini berfokus pada pengembangan analisis sentimen di Facebook menggunakan Lexicon dan Support Vector Machine. Data Lexicon yang diperoleh memiliki akurasi lebih rendah daripada menggunakan Support Vector Machine.

show abstract

“…Suharno [9] used the social media crawling technique for his research in decision making. While other research implemented a crawling technique for automatic data collection using Twitter's Developer API [10] [11]. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol.…”

Section: Introductionmentioning

confidence: 99%

The Hybrid Recommender System of the Indonesian Online Market Products using IMDb weight rating and TF-IDF

Johari

Laksito

2021

RESTI

View full text Add to dashboard Cite

Today, consumers are faced with an abundance of information on the internet; accordingly, it is hard for them to reach the vital information they need. One of the reasonable solutions in modern society is implementing information filtering. Some researchers implemented a recommender system as filtering to increase customers’ experience in social media and e-commerce. This research focuses on the combination of two methods in the recommender system, that is, demographic and content-based filtering, commonly it is called hybrid filtering. In this research, item products are collected using the data crawling method from the big three e-commerce in Indonesia (Shopee, Tokopedia, and Bukalapak). This experiment has been implemented in the web application using the Flask framework to generate products’ recommended items. This research employs the IMDb weight rating formula to get the best score lists and TF-IDF with Cosine similarity to create the similarity between products to produce related items.

show abstract

A Comparison Study of Search Strategy on Collecting Twitter Data for Drug Adverse Reaction

Cited by 8 publications

References 5 publications

Twitter Data Analysis Using Machine Learning To Evaluate Community Compliance in Preventing the Spread of Covid-19

Twitter Data Analysis Using Machine Learning To Evaluate Community Compliance in Preventing the Spread of Covid-19

Analisis Sentimen Komentar Facebook Berbasis Lexicon Dan Support Vector Machine

The Hybrid Recommender System of the Indonesian Online Market Products using IMDb weight rating and TF-IDF

Contact Info

Product

Resources

About