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Open access Jurnal SINTA 5

SENTIMENT ANALYSIS OF ONLINE LOAN APPLICATIONS BASED ON PLAY STORE REVIEWS USING THE NAÏVE BAYES METHOD AND SUPPORT VECTOR MACHINE (CASE STUDY: ADAKAMI AND EASYCASH)

ANALISIS SENTIMEN APLIKASI PEMINJAMAN ONLINE BERDASARKAN ULASAN PADA PLAY STORE MENGGUNAKAN METODE NAÏVE BAYES DAN SUPPORT VECTOR MACHINE (STUDI KASUS: ADAKAMI DAN EASYCASH)

AnoaTIK: Jurnal Teknologi Informasi dan Komputer · Vol. 2 No. 2 (2024) · pp. 121–128

DOI
10.33772/anoatik.v2i2.71
ISSN
2987-7652
Published
2024-12-10
Publisher
Program Studi Ilmu Komputer FMIPA-UHO
Language
ind
License
creativecommons.org/licenses/by-sa/4.0

Abstract

English

This research aims to analyze the sentiment of online lending applications based on reviews on the Google Play Store using the Naïve Bayes and Support Vector Machine methods and determine which online lending applications are more trustworthy. AdaKami is an online lending application under the auspices of PT Pemfinaan Digital Indonesia. EasyCash is an online lending application which is a financial technology company owned by PT. Indonesia Fintopia Technology which provides a digital financial service portal, especially online lending. However, to determine whether this online lending application is reliable or trustworthy, it requires a collection of information that comes from previous user experience. The Naïve Bayes and Support Vector Machine methods are used to analyze loan application sentiment based on relevant review data which is processed using the Python programming language with Google Colabs as a tool for carrying out the research stage. The research results show that the Naïve Bayes and Support Vector Machine methods can be applied in analyzing the sentiment of online lending applications and based on the results of application analysis using the Naïve Bayes Adakami method, it is more trusted by previous users because it produces 95% positive review data and the Easycash application produces positive review data of 95%. 93% and the results using the Adakami Support Vector Machine method produced positive review data of 91% and the Easycash application produced positive review data of 83%.review data while the Easycash application produces 93% positive review data.

Bahasa Indonesia

Penelitian ini bertujuan menganalisis sentimen aplikasi peminjaman online berdasarkan ulasan pada Google Play Store menggunakan metode Naïve Bayes dan Support Vector Machine serta menentukan aplikasi peminjaman online mana yang lebih terpercaya. AdaKami merupakan aplikasi peminjaman online yang berada dalam naungan PT.Pembiayaan Digital Indonesia. EasyCash merupakan aplikasi peminjaman online yang merupakan perusahaan finansial teknologi milik PT.Indonesia Fintopia Technology yang menyediakan portal layanan keuangan digital khususnya peminjaman online. Namun untuk menentukan apakah aplikasi peminjaman online ini andal atau terpercaya, dibutuhkan kumpulan informasi yang berasal dari pengalaman pengguna sebelumnya. Metode Naïve Bayes dan Support Vector Machine digunakan untuk menganalisis sentimen aplikasi peminjaman berdasarkan data ulasan relevan yang diproses menggunakan bahasa pemrograman python dengan google colabs sebagai tools untuk melakukan tahap penelitian. Hasil Penelitian menunjukan bahwa metode Naïve Bayes dan Support Vector Machine dapat diterapkan dalam menganalisis sentimen aplikasi peminjaman online dan berdasarkan hasil analisis aplikasi menggunakan metode Naïve Bayes Adakami lebih terpercaya oleh pengguna sebelumnya dikarenakan menghasilkan data ulasan positif sebesar 95% dan aplikasi Easycash menghasilkan data ulasan positif sebesar 93% serta hasil menggunakan metode Support Vector Machine Adakami menghasilkan data ulasan positif sebesar 91% dan aplikasi Easycash menghasilkan data ulasan positif sebesar 83%.

Keywords

Collaboration network of the authors

39 authors · 87 co-authorship links · 2 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universitas Halu Oleo
  • Universitas Gadjah Mada
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Ferdinand Murni HamunduUniversitas Halu Oleo4
Gusti Arviana RahmanUniversitas Halu Oleo3
Achirul Slamet SugiartoAffiliation unknown3
La Ode SaidiAffiliation unknown3
Ferdinand Murni HamunduAffiliation unknown3
Muhammad ArfanAffiliation unknown2
Dian Safitri DurukaUniversitas Halu Oleo2
Abdullah Malik Fajar ThahaUniversitas Halu Oleo2
Juliarni PogasangUniversitas Halu Oleo2
Herdi BudimanUniversitas Halu Oleo2
Juang JulianUniversitas Halu Oleo2
Putu Nara Kusuma PrasanjayaUniversitas Halu Oleo2
Wa Ode IrmayadaniUniversitas Halu Oleo1
Jumadil NangiUniversitas Halu Oleo1
Umu KhabibahUniversitas Halu Oleo1
Muhammad RialUniversitas Halu Oleo1
Laode SaidiUniversitas Halu Oleo1
GunawanUniversitas Halu Oleo1
La Ode Muhammad Ilham SetiawanUniversitas Halu Oleo1
Auni TiftazaniUniversitas Halu Oleo1
Amal AnugraUniversitas Halu Oleo1
Fazlul Rachmat MubbaraqUniversitas Halu Oleo1
Ilham Julian EfendiUniversitas Halu Oleo1
WindaAffiliation unknown1
Sri Ayu LestariAffiliation unknown1
Muh Istikmal HusainAffiliation unknown1
GunawanAffiliation unknown1
Wasda Bil HusnaAffiliation unknown1
Herdi BudimanAffiliation unknown1
SahrulAffiliation unknown1
Edi WinarkoUniversitas Gadjah Mada1
Reza PulunganUniversitas Gadjah Mada1
Ahmad Hoirul BasoriAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://anoatik.uho.ac.id/index.php/atik/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">SENTIMENT ANALYSIS OF ONLINE LOAN APPLICATIONS BASED ON PLAY STORE REVIEWS USING THE NAÏVE BAYES METHOD AND SUPPORT VECTOR MACHINE (CASE STUDY: ADAKAMI AND EASYCASH)</dc:title>
	<dc:title xml:lang="id-ID">ANALISIS SENTIMEN APLIKASI PEMINJAMAN ONLINE BERDASARKAN ULASAN PADA PLAY STORE MENGGUNAKAN METODE NAÏVE BAYES DAN SUPPORT VECTOR MACHINE (STUDI KASUS : ADAKAMI DAN EASYCASH)</dc:title>
	<dc:creator>Abdillah Sam Mongkito, La Ode Muhammad Hafidz</dc:creator>
	<dc:creator>Ransi, Natalis</dc:creator>
	<dc:creator>Surimi, La</dc:creator>
	<dc:creator>Tenriawaru, Andi</dc:creator>
	<dc:creator>Gunawan, Gunawan</dc:creator>
	<dc:creator>Wijaya Rauf, Budi</dc:creator>
	<dc:subject xml:lang="id-ID">AdaKami</dc:subject>
	<dc:subject xml:lang="id-ID">Easycash</dc:subject>
	<dc:subject xml:lang="id-ID">Analisis Sentimen</dc:subject>
	<dc:subject xml:lang="id-ID">Naive Bayes</dc:subject>
	<dc:subject xml:lang="id-ID">Support Vector Machine</dc:subject>
	<dc:subject xml:lang="en-US">AdaKami</dc:subject>
	<dc:subject xml:lang="en-US">Easycash</dc:subject>
	<dc:subject xml:lang="en-US">Sentiment Analysis</dc:subject>
	<dc:subject xml:lang="en-US">Naive Bayes</dc:subject>
	<dc:subject xml:lang="en-US">Support Vector Machine</dc:subject>
	<dc:description xml:lang="en-US">This research aims to analyze the sentiment of online lending applications based on reviews on the Google Play Store using the Naïve Bayes and Support Vector Machine methods and determine which online lending applications are more trustworthy. AdaKami is an online lending application under the auspices of PT Pemfinaan Digital Indonesia. EasyCash is an online lending application which is a financial technology company owned by PT. Indonesia Fintopia Technology which provides a digital financial service portal, especially online lending. However, to determine whether this online lending application is reliable or trustworthy, it requires a collection of information that comes from previous user experience. 
The Naïve Bayes and Support Vector Machine methods are used to analyze loan application sentiment based on relevant review data which is processed using the Python programming language with Google Colabs as a tool for carrying out the research stage. 
The research results show that the Naïve Bayes and Support Vector Machine methods can be applied in analyzing the sentiment of online lending applications and based on the results of application analysis using the Naïve Bayes Adakami method, it is more trusted by previous users because it produces 95% positive review data and the Easycash application produces positive review data of 95%. 93% and the results using the Adakami Support Vector Machine method produced positive review data of 91% and the Easycash application produced positive review data of 83%.review data while the Easycash application produces 93% positive review data.</dc:description>
	<dc:description xml:lang="id-ID">Penelitian ini bertujuan menganalisis sentimen aplikasi peminjaman online berdasarkan ulasan pada Google Play Store menggunakan metode Naïve Bayes dan Support Vector Machine serta menentukan aplikasi peminjaman online mana yang lebih terpercaya. AdaKami merupakan aplikasi peminjaman online yang berada dalam naungan PT.Pembiayaan Digital Indonesia. EasyCash merupakan aplikasi peminjaman online yang merupakan perusahaan finansial teknologi milik PT.Indonesia Fintopia Technology yang menyediakan portal layanan keuangan digital khususnya peminjaman online. Namun untuk menentukan apakah aplikasi peminjaman online ini andal atau terpercaya, dibutuhkan kumpulan informasi yang berasal dari pengalaman pengguna sebelumnya.
Metode Naïve Bayes dan Support Vector Machine digunakan untuk menganalisis sentimen aplikasi peminjaman berdasarkan data ulasan relevan yang diproses menggunakan bahasa pemrograman python  dengan google colabs sebagai tools untuk melakukan tahap penelitian.
Hasil Penelitian menunjukan bahwa metode Naïve Bayes dan Support Vector Machine dapat diterapkan dalam menganalisis sentimen aplikasi peminjaman online dan berdasarkan hasil analisis aplikasi menggunakan metode Naïve Bayes Adakami lebih terpercaya oleh pengguna sebelumnya dikarenakan menghasilkan data ulasan positif sebesar 95% dan aplikasi Easycash menghasilkan data ulasan positif sebesar 93% serta hasil menggunakan metode Support Vector Machine Adakami menghasilkan data ulasan positif sebesar 91% dan aplikasi Easycash menghasilkan data ulasan positif sebesar 83%.</dc:description>
	<dc:publisher xml:lang="id-ID">Program Studi Ilmu Komputer FMIPA-UHO</dc:publisher>
	<dc:date>2024-12-10</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://anoatik.uho.ac.id/index.php/atik/article/view/71</dc:identifier>
	<dc:identifier>10.33772/anoatik.v2i2.71</dc:identifier>
	<dc:source xml:lang="en-US">AnoaTIK: Jurnal Teknologi Informasi &amp; Komputer; Vol. 2 No. 2 (2024): Desember 2024; 121-128</dc:source>
	<dc:source xml:lang="id-ID">AnoaTIK: Jurnal Teknologi Informasi dan Komputer; Vol 2 No 2 (2024): Desember 2024; 121-128</dc:source>
	<dc:source>2987-7652</dc:source>
	<dc:source>10.33772/anoatik.v2i2</dc:source>
	<dc:language>ind</dc:language>
	<dc:relation>https://anoatik.uho.ac.id/index.php/atik/article/view/71/28</dc:relation>
	<dc:rights xml:lang="id-ID">Hak Cipta (c) 2024 La Ode Muhammad Hafidz Abdillah Sam Mongkito, Natalis Ransi, La Surimi, Andi Tenriawaru, Gunawan Gunawan, Budi Wijaya Rauf</dc:rights>
	<dc:rights xml:lang="id-ID">https://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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