Platform digital, termasuk aplikasi mobile, mempunyai peran penting dalam gigeconomy, yaitu sebagai media gig worker dalam berinteraksi dengan penyedia layanantenaga kerja. Aplikasi mobile berbasis gig economy semakin diminati masyarakat.Peningkatan jumlah pengguna mengakibatkan meningkatnya jumlah unduhan dan ulasanyang diberikan. Namun, semakin banyak ulasan menyulitkan pengembang dalammemahami informasi yang terdapat dalam ulasan. Selain itu, satu ulasan dapat memilikiberbagai informasi. Untuk mengatasi masalah tersebut, penelitian ini mengusulkan modelyang dapat mengkategorikan konten dan sentimen ulasan menggunakan teknikpembelajaran mesin. Algoritme Support Vector Machine (SVM), Multinomial NaïveBayes, Complement Naïve Bayes, Binary Relevance, Classifier Chain, dan Label powerset digunakan pada penelitian ini. Hasil dari penelitian didapatkan algoritme SVMsebagai algoritme terbaik, baik pada klasifikasi sentimen ulasan maupun kategorisasiulasan.

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Digital platforms, including mobile applications, have an important role in gig economy,

as a gig worker platform in interacting with labor service providers. Gig economy based

mobile applications are increasingly in demand by the public. An increase in the number

of users results in an increase in the number of downloads and reviews provided.

However, the number of reviews makes it difficult for developers to understand the

information contained in reviews. In addition, one review can have a variety of

information. To overcome this problem, this study proposes a model that can categorize

content and sentiment reviews using machine learning techniques. Support Vector

Machine (SVM), Multinomial Naïve Bayes, Complement Naïve Bayes, Binary

Relevance, Classifier Chain, and Label power sets were used in this study. The results of

the study obtained the SVM algorithm as the best algorithm, both in the classification of

sentiment reviews and review categorization.