ANALISIS KLASIFIKASI SENTIMEN PREDIKSI RATING APLIKASI APPLE’S APPSTORE DENGAN MENGGUNAKAN METODE ALGORITMA RANDOM FOREST

ARMYKA PRATAMA HARAHAP, NPM 2108100008 (2025) ANALISIS KLASIFIKASI SENTIMEN PREDIKSI RATING APLIKASI APPLE’S APPSTORE DENGAN MENGGUNAKAN METODE ALGORITMA RANDOM FOREST. Tugas_Akhir(Artikel) Building of Informatics, Technology and Science (BITS), 6 (4). pp. 2371-2379. ISSN 2685-3310(e-ISSN) 2684-8910(p-ISSN)

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Abstract

Pengguna aplikasi Apple’s App Store semakin meluas di kalangan pengguna smartphone. Namun, tanggapan pengguna terhadap aplikasi ini sangat bervariasi. Selain itu, perkembangan terus-menerus dalam menambah fitur dan kemampuan pengeditan telah membawa kompleksitas penggunaan aplikasi ini semakin meningkat. Penelitian ini bertujuan untuk menganalisis sentimen pengguna Aplikasi Pada Apple’s App Store melalui ulasan yang terdapat di Google Play Store menggunakan metode Random Forest. Metode ini dipilih untuk mengidentifikasi dan mengelompokkan tanggapan pengguna ke dalam kategori positif dan negatif secara efisien. Dataset yang digunakan dalam penelitian ini mencakup 5000 ulasan, mencerminkan keragaman pendapat dari pengguna yang berpartisipasi aktif. Tahapan preprocessing data melibatkan proses cleaning, case folding, tokenisasi, stopword removal, dan lemmatisasi untuk memastikan kualitas data yang baik sebelum dilakukan analisis sentimen. Selanjutnya, pembobotan kata dilakukan dengan metode TF-IDF untuk memberikan nilai bobot pada kata-kata yang mempengaruhi sentimen pengguna. Hasil penelitian menunjukkan bahwa metode Random Forest memberikan tingkat akurasi yang tinggi dalam menganalisis sentimen pengguna aplikasi Apple’s App Store, dengan akurasi sebesar 86%, presisi 89%, recall 81%, dan f1-score 85%. Penelitian ini memberikan pemahaman lebih lanjut terkait tanggapan pengguna terhadap aplikasi Apple’s App Store, serta menegaskan keberhasilan metode Random Forest dalam menangani analisis sentimen pada dataset ulasan pengguna di Google Play Store. Kata kunci: Analisis Sentimen, Aplikasi Apple’s App Store, Google Play Store, Metode Random Forest ==================================================================================================== Users of Apple's App Store applications are increasingly widespread among smartphone users. However, user responses to these apps vary widely. In addition, continuous developments in adding features and editing capabilities have led to the increasing complexity of using these applications. This research aims to analyze the sentiment of application users on Apple's App Store through reviews on the Google Play Store using the Random Forest method. This method was chosen to efficiently identify and group user responses into positive and negative categories. The dataset used in this study includes 5000 reviews, reflecting the diversity of opinions from actively participating users. The data preprocessing stage involves cleaning, case folding, tokenization, stopword removal, and lemmatization to ensure good data quality before sentiment analysis is carried out. Next, word weighting is carried out using the TF-IDF method to assign weight values to words that influence user sentiment. The research results show that the Random Forest method provides a high level of accuracy in analyzing user sentiment for Apple's App Store applications, with an accuracy of 86%, precision of 89%, recall of 81%, and f1-score of 85%. This research provides further understanding regarding user responses to Apple's App Store applications, and confirms the success of the Random Forest method in handling sentiment analysis on user review datasets on the Google Play Store. Keywords: Sentiment Analysis; Apple's App Store Application, Google Play Store, Random Forest Method

Item Type:	Article
Uncontrolled Keywords:	Analisis Sentimen, Aplikasi Apple’s App Store, Google Play Store, Metode Random Forest ==============Sentiment Analysis; Apple's App Store Application, Google Play Store, Random Forest Method
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General) Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4450 Databases
Divisions:	Fakultas Sains Dan Teknologi > Teknologi Informasi
Depositing User:	Unnamed user with email repository@ulb.ac.id
Date Deposited:	16 Oct 2025 04:02
Last Modified:	16 Oct 2025 04:02
URI:	http://repository.ulb.ac.id/id/eprint/1815

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