RAMADHANI, MAHYA (2026) ANALISIS TINGKAT KEPUASAN NASABAH TERHADAP PELAYANAN TELLER MENGGUNAKAN METODE MACHINE LEARNING (STUDI KASUS: BANK MUAMALAT INDONESIA CABANG JAKARTA WEST). S1 thesis, Universitas Mercu Buana Jakarta - Menteng.
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Abstract
Perkembangan teknologi informasi mendorong pemanfaatan machine learning sebagai pendekatan berbasis data dalam pengambilan keputusan, termasuk analisis kepuasan nasabah perbankan. Penelitian ini bertujuan menganalisis dan memprediksi tingkat kepuasan nasabah terhadap pelayanan teller menggunakan metode machine learning pada PT Bank Muamalat Indonesia Cabang Jakarta West. Data penelitian berasal dari Voice of Customer (VOC) hasil survei pelayanan frontliner yang telah melalui tahap pre-processing berupa cleansing dan normalisasi. Metode klasifikasi yang digunakan meliputi Decision Tree (C4.5), Naive Bayes, dan K-Nearest Neighbor (KNN). Evaluasi model dilakukan menggunakan K-Fold Cross Validation, confusion matrix, serta uji statistik ANOVA untuk menilai stabilitas dan signifikansi performa. Hasil penelitian menunjukkan bahwa Decision Tree menghasilkan performa terbaik dengan F1-score sebesar 80.73%dan konsistensi tinggi. Struktur pohon keputusan menunjukkan bahwa kualitas pelayanan frontliner menjadi faktor utama yang memengaruhi kepuasan nasabah, terutama atribut keramahan, empati, ketulusan, antusias, dan komunikasi. Model ini dapat digunakan sebagai alat klasifikasi dan prediksi kepuasan nasabah guna mendukung peningkatan kualitas pelayanan secara berkelanjutan. The development of information technology encourages the use of machine learning as a datadriven approach for decision making, including customer satisfaction analysis in banking services. This study aims to analyze and predict customer satisfaction levels toward teller services using machine learning methods at PT Bank Muamalat Indonesia, Jakarta West Branch. The dataset is obtained from Voice of Customer (VOC) survey data on frontliner services that have undergone data pre-processing, including cleansing and normalization. Classification methods applied in this study include Decision Tree (C4.5), Naive Bayes, and K-Nearest Neighbor (KNN). Model evaluation is conducted using K-Fold Cross Validation, confusion matrix analysis, and ANOVA statistical testing to assess performance stability and significance. The results show that the Decision Tree model achieves the best performance with an F1-score of 80.73%and high consistency. The decision tree structure indicates that frontliner service quality is the main factor influencing customer satisfaction, particularly friendliness, empathy, sincerity, enthusiasm, and communication attributes. This model can be applied as a classification and prediction tool to support continuous improvement of service quality.
| Item Type: | Thesis (S1) |
|---|---|
| NIM/NIDN Creators: | 41819120012 |
| Uncontrolled Keywords: | kepuasan nasabah, pelayanan teller, machine learning, Decision Tree, klasifikasi customer satisfaction, teller service, machine learning, Decision Tree, classification. |
| Subjects: | 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 003 Systems/Sistem-sistem |
| Divisions: | Fakultas Ilmu Komputer > Sistem Informasi |
| Depositing User: | ARDIFTA DWI AFRIANI |
| Date Deposited: | 20 Feb 2026 01:56 |
| Last Modified: | 20 Feb 2026 01:56 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/101027 |
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