P2P Lending Sentiment Analysis in Indonesian Online News

Suryono, Ryan Randy; Budi, Indra

doi:10.2991/aisr.k.200424.006

Cited by 11 publications

(8 citation statements)

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“…Most of the negative reviews commented on the slow transfer or approval of funds, collecting debt, and the difficulty of registering. In the second experiment, we looked at the sentiment on Indonesian Online News on the topic of P2P lending ( Suryono and Budi, 2019 ). From 1100 news articles, we found that the number of articles with positive values was more than those with negative values.…”

Section: Methodsmentioning

confidence: 99%

“…For this reason, China provides an explanation of the regulation of illegal fundraising ( Liu et al., 2018 ). Our research suggests how to build a system for analyzing social media or online news to identify issues and trends about fintech practice ( Suryono and Budi, 2019 ) ( Pohan et al., 2019 ) ( Pengnate and Riggins, 2020 ).…”

Section: The Relationship Between the Findings And P2p Lending Practicesmentioning

confidence: 99%

“…Further research could propose a supervisory standard for technology and regulation. Currently, several studies to see the analytical sentiment of P2P products have been carried out by previous researchers ( Suryono and Budi, 2019 ) ( Pohan et al., 2019 ) ( Pengnate and Riggins, 2020 ). It is better if the social media data can be processed by the OJK, SWI and the Ministry of ICT when determining policies and supervision in creating consumer protection.…”

Section: Conclusion and Recommendationmentioning

confidence: 99%

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Detection of fintech P2P lending issues in Indonesia

Suryono

Budi

Purwandari

2021

Heliyon

Self Cite

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Financial technology (fintech) is a growing industry in Indonesia, supported by advances in the technological infrastructure. At the end of 2019, the Financial Services Authority (OJK), the financial authority in Indonesia, recorded 164 registered and licensed fintech (P2P lending) companies. However, since early 2018, the Investment Alert Task Force (SWI) and the Ministry of Communication and Information Technology have blocked 1,350 illegal fintech platforms. Illegal fintech lending practices have mechanisms beyond the responsibility and authority of the OJK, including the risk of collection and distribution of personal data. The essence of this study is to discuss the landscape of fintech P2P lending in Indonesia from Indonesian Online News data, explore cases of fintech p2p lending in Indonesia, and understand the rules and policies. Qualitative research with a case study approach and Focus Group Discussion techniques were used to obtain data from 4 stakeholders in the Fintech P2P Lending Industry in Indonesia. VOS Viewer software is used to build keywords from Indonesian Online News collections, NVIVO 12 qualitative software is used to assist data analysis. The research found the keyword clusters most frequently discussed in the Indonesian Online News collection and five case themes such as public awareness about P2P lending (user understanding), data leakage, and restriction of data access, including personal data protection, personal data fraud, illegal fintech lending, and Product marketing ethics.

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Section: Methodsmentioning

confidence: 99%

Section: The Relationship Between the Findings And P2p Lending Practicesmentioning

confidence: 99%

Section: Conclusion and Recommendationmentioning

confidence: 99%

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Detection of fintech P2P lending issues in Indonesia

Suryono

Budi

Purwandari

2021

Heliyon

Self Cite

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“…Analisis sentimen adalah proses memahami dan mengolah data tekstual secara otomatis untuk mendapatkan informasi [5]. Analisis sentimen dilakukan untuk mendeteksi opini terhadap suatu subjek dan objek (misalnya individu, organisasi ataupun produk) dalam sebuah kumpulan data [6].…”

Pendeteksian Sarkasme pada Proses Analisis Sentimen Menggunakan Random Forest Classifier

Alita

Isnain

2020

komputasi

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Kalimat sindiran atau sarkasme masih sering digunakan oleh kalangan publik untuk mengungkapkan maksud isi hati dan pikiran baik itu yang disampaikan secara langsng maupun tidak langsung. Sarkasme dilakukan untuk menyindir dan menyakiti hati seseorang dengan menggunakan bahasa atau kata yang didalamnya mengandung kata positif tetapi maknanya negatif sehingga sering sekali terjadi opini salah diklasifikasikan. Penelitian ini melakukan kombinasi antara proses sentimen analisis dengan deteksi sarkasme untuk pengklasifikasian opini yang terdapat pada Twitter. Proses analisis sentimen dilakukan dengan tahapan preprocessing dan ekstraksi fitur dan diklasifikan dengan menggunakan metode Support Vector Machine dilanjutkan dengan proses pendeteksian sarkasme yang dilakukan tahapan ekstraksi fitur dengan 4 set fitur yaitu sentiment related, punctuation-relate, lexical and syntactic, dan pattern-relate dan diklasifikasikan dengan menggunakan metode Random Forest Classifier. Hasil penelitian ini didapatkan peningkatan nilai rata-rata akurasi sebesar 16,61 %, nilai presisi sebesar 5,45 %, nilai recall sebesar 9,64% dan kenaikan nilai F1score sebesar 11,27% dengan jumlah data sebanyak 2.027 dengan rincian data dengan label positif berjumlah 1023, data dengan label negatif berjumlah 587 dan data dengan label netral berjumlah 462. Data sarkasme didapatkan dari tweet dengan label positif yang kemudian diberikan label sarkasme atau tidak sarkasme dan didapat hasil label dengan jumlah keseluruhan berlabel sarkasme berjumlah 354 dan tidak sarkasme berjumlah 669.

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“…Twitter is an online social networking and microblogging service that allows users to send and read text-based messages of up to 140 characters, known as Tweets [4]. Twitter is an online social networking and microblogging service that allows users to send and read textbased messages of up to 140 characters, known as Tweet [5] [6].…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis Of Government Policy On Corona Case Using Naive Bayes Algorithm

Isnain

Marga

Alita

2021

Indonesian J. Comput. Cybern. Syst.

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The Indonesian government has enforced the New Normal rule in maintaining economic stabilization and also restraining the spread of the virus during the Covid 19 pandemic. This has become a hot topic of conversation on social media Twitter, many people think positive and negative.The research conducted is a representation of text mining and text processing using machine learning using the Naive Bayes Classifier classification method, the objective of the analysis is to determine whether public sentiment towards the New Normal policy is positive or negative, and also as a basis for measuring the performance of the TF-IDF feature extraction and N-gram in machine learning uses the Naive Bayes method.The results of this study resulted in the accuracy rate of the Naive Bayes method with the TF-IDF feature selection. The total accuracy was 81% with a Precision value of 78%, Recall 91%, and f1-Score 84%. The highest results were obtained from the use of the Naive Bayes and Trigram algorithm parameters, namely 84%, namely 84% Precision, 86% Recall, and 85% f1-Score. The Naive Bayes algorithm with the use of the trigram type N-Gram feature extraction shows a fairly good performance in the process of classifying public tweet data.

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P2P Lending Sentiment Analysis in Indonesian Online News

Cited by 11 publications

References 0 publications

Detection of fintech P2P lending issues in Indonesia

Detection of fintech P2P lending issues in Indonesia

Pendeteksian Sarkasme pada Proses Analisis Sentimen Menggunakan Random Forest Classifier

Sentiment Analysis Of Government Policy On Corona Case Using Naive Bayes Algorithm

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