DEWANTORO, AEHMAD (2025) KLASIFIKASI OPINI PENGGUNA MAHASISWA TERHADAP PINJAMAN ONLINE DENGAN MEMBANDINGKAN ALGORITMA RANDOM FOREST DAN SUPPORT VECTOR MACHINE (SVM). S1 thesis, Universitas Mercu Buana Jakarta.
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Abstract
This study aims to classify user opinions on online loan applications using a machine learning approach. The data used was obtained from a questionnaire distributed to students, which contained responses to online loan services on a Likert scale. These opinions were then processed and labeled into two categories, namely positive and negative. The methods used in this study are Support Vector Machine (SVM) and Random Forest (RF). Both models were trained and tested to compare their performance. Based on the evaluation results, the Random Forest algorithm demonstrated superior performance with an average accuracy, precision, recall, and F1-score of 88%, compared to SVM, which only achieved an average of 82%. Additionally, word cloud visualization was used to identify dominant words in positive and negative opinions. The study findings indicate that opinion classification using machine learning algorithms can serve as an effective analytical tool for understanding users' perceptions of online lending applications. This approach can provide valuable insights for application developers, universities, and policymakers. Keywords: opinion classification, online loans, machine learning, Random Forest, Support Vector Machine, word cloud Penelitian ini bertujuan untuk mengklasifikasikan opini pengguna terhadap aplikasi pinjaman online menggunakan pendekatan machine learning. Data yang digunakan diperoleh dari kuesioner yang disebarkan kepada mahasiswa, yang berisi tanggapan terhadap layanan pinjaman online dalam skala Likert. Opini tersebut kemudian diproses dan dilabeli menjadi dua kategori, yaitu positif dan negatif. Metode yang digunakan dalam penelitian ini adalah Support Vector Machine (SVM) dan Random Forest (RF). Kedua model dilatih dan diuji untuk dibandingkan performanya. Berdasarkan hasil evaluasi, algoritma Random Forest menunjukkan performa yang lebih unggul dengan Rata - rata berdasarkan akurasi, presisi, recall, F1-score sebesar 88%, dibandingkan SVM yang hanya memiliki Rata - rata 82%. Selain itu, visualisasi word cloud digunakan untuk mengidentifikasi kata-kata dominan dalam opini positif dan negatif. Hasil penelitian menunjukkan bahwa klasifikasi opini menggunakan algoritma machine learning dapat menjadi alat bantu analisis yang efektif dalam memahami persepsi pengguna terhadap aplikasi pinjaman online. Pendekatan ini dapat memberikan wawasan berharga bagi pengembang aplikasi, pihak universitas, maupun pemangku kebijakan. Kata Kunci: klasifikasi opini, pinjaman online, machine learning, Random Forest, Support Vector Machine, word cloud
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