Analisis Sentimen Aplikasi Youtube di Google Play Store Menggunakan Machine Learning
DOI:
https://doi.org/10.30865/resolusi.v4i4.1750Keywords:
Youtube; Sentiment Analysis; Google Play Store; Machine LearningAbstract
YouTube users can create, watch, and share videos for free. Interaction between viewers occurs through the comment feature, which can be positive or negative. The frequent appearance of negative comments on the youtube application on the google play store can have an effect on these accounts. But to find out how much negative comments on the account are needed, an SVM algorithm is needed. This study aims to determine the sentiment towards the youtube application on the google play store using Machine Learning with the SVM algorithm. The data taken is 4996 review data which is then preprocessed so that the remaining data becomes 4993 data that can be processed. Data labelling is done automatically based on the review rating score. The results of data labelling are divided into 3 classes, namely positive classes as many as 1083, negative classes as many as 3365 and neutral as many as 545. Classification and evaluation are carried out using the SVM method. Based on the training and testing data comparison value of 9: 1, the results obtained an accuracy rate of 75% then negative class precision of 76% and negative class recall of 97% and K-Fold Cross Validation testing using a value of K = 10 with an average accuracy of 0.75 or 75%.
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References
P. Arsi and R. Waluyo, “Analisis Sentimen Wacana Pemindahan Ibu Kota Indonesia Menggunakan Algoritma Support Vector Machine (SVM),” J. Teknol. Inf. dan Ilmu Komput., vol. 8, no. 1, p. 147, 2021, doi: 10.25126/jtiik.0813944.
Friska Aditia Indriyani, Ahmad Fauzi, and Sutan Faisal, “Analisis sentimen aplikasi tiktok menggunakan algoritma naïve bayes dan support vector machine,” TEKNOSAINS J. Sains, Teknol. dan Inform., vol. 10, no. 2, pp. 176–184, 2023, doi: 10.37373/tekno.v10i2.419.
S. A. Aaputra, Didi Rosiyadi, Windu Gata, and Syepry Maulana Husain, “Sentiment Analysis Analysis of E-Wallet Sentiments on Google Play Using the Naive Bayes Algorithm Based on Particle Swarm Optimization,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 3, no. 3, pp. 377–382, 2019, doi: 10.29207/resti.v3i3.1118.
A. I. Tanggraeni and M. N. N. Sitokdana, “Analisis Sentimen Aplikasi E-Government pada Google Play Menggunakan Algoritma Naïve Bayes,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 9, no. 2, pp. 785–795, 2022, doi: 10.35957/jatisi.v9i2.1835.
M. Hudha, E. Supriyati, and T. Listyorini, “Analisis Sentimen Pengguna Youtube Terhadap Tayangan #Matanajwamenantiterawan Dengan Metode Naïve Bayes Classifier,” JIKO (Jurnal Inform. dan Komputer), vol. 5, no. 1, pp. 1–6, 2022, doi: 10.33387/jiko.v5i1.3376.
M. K. Khoirul Insan, U. Hayati, and O. Nurdiawan, “Analisis Sentimen Aplikasi Brimo Pada Ulasan Pengguna Di Google Play Menggunakan Algoritma Naive Bayes,” JATI (Jurnal Mhs. Tek. Inform., vol. 7, no. 1, pp. 478–483, 2023, doi: 10.36040/jati.v7i1.6373.
M. D. Hendriyanto, A. A. Ridha, and U. Enri, “Analisis Sentimen Ulasan Aplikasi Mola Pada Google Play Store Menggunakan Algoritma Support Vector Machine,” INTECOMS J. Inf. Technol. Comput. Sci., vol. 5, no. 1, pp. 1–7, 2022, doi: 10.31539/intecoms.v5i1.3708.
Karsito and S. Susanti, “Klasifikasi Kelayakan Peserta Pengajuan Kredit Rumah Dengan Algoritma Naïve Bayes Di Perumahan Azzura Residencia,” J. Teknol. Pelita Bangsa, vol. 9, pp. 43–48, 2019.
Y. Septiani, E. Aribbe, and R. Diansyah, “ANALISIS KUALITAS LAYANAN SISTEM INFORMASI AKADEMIK UNIVERSITAS ABDURRAB TERHADAP KEPUASAN PENGGUNA MENGGUNAKAN METODE SEVQUAL (Studi Kasus?: Mahasiswa Universitas Abdurrab Pekanbaru),” J. Teknol. Dan Open Source, vol. 3, no. 1, pp. 131–143, 2020, doi: 10.36378/jtos.v3i1.560.
H. Mukhtar, J. Al Amien, and M. A. Rucyat, “Filtering Spam Email menggunakan Algoritma Naïve Bayes,” J. CoSciTech (Computer Sci. Inf. Technol., vol. 3, no. 1, pp. 9–19, 2022, doi: 10.37859/coscitech.v3i1.3652.
F. Alghifari and D. Juardi, “Penerapan Data Mining Pada Penjualan Makanan Dan Minuman Menggunakan Metode Algoritma Naïve Bayes,” J. Ilm. Inform., vol. 9, no. 02, pp. 75–81, 2021, doi: 10.33884/jif.v9i02.3755.
N. Faridhotul Hidayah, K. Paranita Kartika R., and S. Nur Budiman, “Penerapan Metode Naive Bayes Dalam Analisis Sentimen Aplikasi Sentuh Tanahku Pada Google Play,” JATI (Jurnal Mhs. Tek. Inform., vol. 6, no. 2, pp. 679–683, 2022, doi: 10.36040/jati.v6i2.5610.
P. M. Nirmala Dharmapatni and N. L. P. Merawati, “Penerapan Algoritma Support Vector Machine Dalam Sentimen Analisis Terkait Kenaikan Tarif BPJS Kesehatan,” J. Bumigora Inf. Technol., vol. 2, no. 2, pp. 105–112, 2020, doi: 10.30812/bite.v2i2.904.
D. Darwis, E. S. Pratiwi, and A. F. O. Pasaribu, “Penerapan Algoritma Svm Untuk Analisis Sentimen Pada Data Twitter Komisi Pemberantasan Korupsi Republik Indonesia,” Edutic - Sci. J. Informatics Educ., vol. 7, no. 1, pp. 1–11, 2020, doi: 10.21107/edutic.v7i1.8779.
Y. Femilia Nugraini, R. Rohmat Saedudin, and R. Andreswari, “Implementasi Data Mining Dalam Kasus Mental Health Pada Sosial Media Twitter Menggunakan Metode Naive Bayes Implementation of Data Mining in the Case of Mental Health on Social Media Twitter Using Naive Bayes Method,” e-Proceeding Eng., vol. 8, no. 5, pp. 9260–9265, 2021.
P. P. A. Arsya Monica Pravina, Imam Cholissodin, “Analisis Sentimen Tentang Opini Maskapai Penerbangan pada Dokumen Twitter Menggunakan Algoritme Support Vector Machine ( SVM ),” vol. 3, no. 3, pp. 2789–2797, 2019.
R. Ardhani et al., “ANALISIS SENTIMEN TERHADAP LAYANAN APLIKASI GRAB INDONESIA MENGGUNAKAN METODE NAÏVE BAYES,” vol. 8, no. 1, pp. 303–309, 2024.
I. S. K. Idris, Y. A. Mustofa, and I. A. Salihi, “Analisis Sentimen Terhadap Penggunaan Aplikasi Shopee Mengunakan Algoritma Support Vector Machine (SVM),” Jambura J. Electr. Electron. Eng., vol. 5, no. 1, pp. 32–35, 2023, doi: 10.37905/jjeee.v5i1.16830.
S. M. Fani, R. Santoso, and S. Suparti, “Penerapan Text Mining Untuk Melakukan Clustering Data Tweet Akun Blibli Pada Media Sosial Twitter Menggunakan K-Means Clustering,” J. Gaussian, vol. 10, no. 4, pp. 583–593, 2021, doi: 10.14710/j.gauss.v10i4.30409.
S. N. Hakim, “ANALISIS SENTIMEN PERSEPSI PENGGUNA MYINDIHOME MENGGUNAKAN METODE SUPPORT VECTOR MACHINE (SVM) DAN NAÏVE BAYES CLASSIFIER (NBC) TUGAS,” p. 6, 2021.
D. A. Agustina, S. Subanti, and E. Zukhronah, “Implementasi Text Mining Pada Analisis Sentimen Pengguna Twitter Terhadap Marketplace di Indonesia Menggunakan Algoritma Support Vector Machine,” Indones. J. Appl. Stat., vol. 3, no. 2, p. 109, 2021, doi: 10.13057/ijas.v3i2.44337.
R. Risnantoyo, A. Nugroho, and K. Mandara, “Sentiment Analysis on Corona Virus Pandemic Using Machine Learning Algorithm,” J. Informatics Telecommun. Eng., vol. 4, no. 1, pp. 86–96, 2020, doi: 10.31289/jite.v4i1.3798.
A. FATIHIN, “Analisis Sentimen Terhadap Ulasan Aplikasi Mobile Menggunakan Metode Support Vector Machine (Svm) Dan Pendekatan Lexicon Based,” p. 103, 2022.
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