Comparing Decision Tree and Support Vector Machines in Hospital Satisfaction

Authors

  • Dinda Anggraini Universitas Dian Nuswantoro
  • Indra Gamayanto Universitas Dian Nuswantoro
  • Sasono Wibowo Universitas Dian Nuswantoro

DOI:

https://doi.org/10.30871/jaic.v9i2.9203

Keywords:

Decision Tree, SVM, Visitor Satisfaction, Classification, Hospital Service Optimation

Abstract

Patient satisfaction is a key indicator of hospital service quality. This study compares the performance of Decision Tree and Support Vector Machine (SVM) in classifying patient satisfaction at Harapan Hospital Magelang for service optimization. The dataset, derived from a 2024 survey, consists of 577 samples and 13 predictor variables, covering patient demographics and medical service aspects. Preprocessing includes data cleaning, normalization, encoding, and class balancing using SMOTE. The Decision Tree is applied with gini impurity and max_depth=11, while SVM uses the RBF kernel (C=100, gamma=0.01). Model evaluation metrics include accuracy, precision, recall, F1-score, and ROC-AUC.Results show that Decision Tree outperforms SVM, achieving 86% accuracy vs. 81%. It also has 86% precision and 95% recall for the Dissatisfied category, higher than SVM (93% recall). The McNemar test confirms a statistically significant performance difference (p-value = 0.037). With higher accuracy and interpretability, Decision Tree is recommended as the primary method for hospital patient satisfaction analysis. These findings support the development of an adaptive classification system for Indonesian healthcare data.

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Author Biographies

Dinda Anggraini, Universitas Dian Nuswantoro

Program Studi Sistem Informasi

Indra Gamayanto, Universitas Dian Nuswantoro

Program Studi Sistem Informasi

Sasono Wibowo, Universitas Dian Nuswantoro

Program Studi Sistem Informasi

References

[1] H. Wijaya, A. Rohendi, K. Mulyani, and U. A. R. Sanjaya, “Pengaruh Kepercayaan, Kualitas Pelayanan, dan Kewajaran Harga Terhadap Kepuasan Pasien Di Klinik S, Tangerang Selatan”.

[2] M. I. Sodikin, “Penerapan dan Manfaat Machine Learning di Rumah Sakit,” Multiverse Open Multidiscip. J., vol. 2, no. 2, pp. 262–265, Aug. 2023, doi: 10.57251/multiverse.v2i2.1207.

[3] N. Lelyana, “Analisis Dampak Inovasi Teknologi pada Strategi Manajemen Rumah Sakit,” JISHUM J. Ilmu Sos. Dan Hum., vol. 2, no. 4, pp. 425–446, Jun. 2024, doi: 10.57248/jishum.v2i4.380.

[4] R. Hamdani and A. Darta, “Analisis Tingkat Kepuasan Pasien Di Klinik Pratama Salbiyana Dengan Algoritma C4.5,” J. Sci. Soc. Res., vol. VII, pp. 273–280, 02/24.

[5] A. Gunawan and S. Kom, “Pengantar Sistem Informasi Kesehatan”.

[6] K. Kristiawan and A. Widjaja, “Perbandingan Algoritma Machine Learning dalam Menilai Sebuah Lokasi Toko Ritel,” J. Tek. Inform. Dan Sist. Inf., vol. 7, no. 1, Apr. 2021, doi: 10.28932/jutisi.v7i1.3182.

[7] A. Ulfah, D. Hermina, and N. Huda, “Desain Instrumen Evaluasi Yang Valid Dan Reliabel Dalam Pendidikan Islam Menggunakan Skala Likert,” J. Studi Multidisipliner, vol. 8, no. 12, pp. 855–861, Dec. 2024.

[8] M. D. Purbolaksono, M. Irvan Tantowi, A. Imam Hidayat, and A. Adiwijaya, “Perbandingan Support Vector Machine dan Modified Balanced Random Forest dalam Deteksi Pasien Penyakit Diabetes,” J. RESTI Rekayasa Sist. Dan Teknol. Inf., vol. 5, no. 2, pp. 393–399, Apr. 2021, doi: 10.29207/resti.v5i2.3008.

[9] F. Putra, H. F. Tahiyat, R. M. Ihsan, R. Rahmaddeni, and L. Efrizoni, “Penerapan Algoritma K-Nearest Neighbor Menggunakan Wrapper Sebagai Preprocessing untuk Penentuan Keterangan Berat Badan Manusia: Application of K-Nearest Neighbor Algorithm Using Wrapper as Preprocessing for Determination of Human Weight Information,” Malcom Indones. J. Mach. Learn. Comput. Sci., vol. 4, no. 1, pp. 273–281, Jan. 2024, doi: 10.57152/malcom.v4i1.1085.

[10] E. Siallagan, I. Parlina, and D. Suhendro, “Model Aturan Tingkat Kepuasan Pasien Terhadap Pelayanan Puskesmas Menggunakan Algoritma C4.5,” Data Sci., vol. 1, no. 2, 2022.

[11] D. A. Mukhsinin, M. Rafliansyah, S. A. Ibrahim, R. Rahmaddeni, and D. Wulandari, “Implementasi Algoritma Decision Tree untuk Rekomendasi Film dan Klasifikasi Rating pada Platform Netflix: Implementation of Decision Tree Algorithm for Movie Recommendation and Rating Classification on the Netflix Platform,” MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 4, no. 2, pp. 570–579, Mar. 2024, doi: 10.57152/malcom.v4i2.1255.

[12] M. Bansal, A. Goyal, and A. Choudhary, “A comparative analysis of K-Nearest Neighbor, Genetic, Support Vector Machine, Decision Tree, and Long Short Term Memory algorithms in machine learning,” Decis. Anal. J., vol. 3, p. 100071, Jun. 2022, doi: 10.1016/j.dajour.2022.100071.

[13] I. Arfyanti, M. Fahmi, and P. Adytia, “Penerapan Algoritma Decision Tree Untuk Penentuan Pola Penerima Beasiswa KIP Kuliah,” Build. Inform. Technol. Sci. BITS, vol. 4, no. 3, Dec. 2022, doi: 10.47065/bits.v4i3.2275.

[14] J. Kusuma, A. Jinan, M. Z. Lubis, R. Rubianto, and R. Rosnelly, “Komparasi Algoritma Support Vector Machine Dan Naive Bayes Pada Klasifikasi Ras Kucing,” Generic, vol. 14, no. 1, pp. 8–12, Jan. 2022, doi: 10.18495/generic.v14i1.122.

[15] K. A. Rokhman, B. Berlilana, and P. Arsi, “Perbandingan Metode Support Vector Machine Dan Decision Tree Untuk Analisis Sentimen Review Komentar Pada Aplikasi Transportasi Online,” J. Inf. Syst. Manag. JOISM, vol. 3, no. 1, pp. 1–7, Jan. 2021, doi: 10.24076/JOISM.2021v3i1.341.

[16] A. M. Yolanda and R. T. Mulya, “Implementasi Metode Support Vector Machine untuk Analisis Sentimen pada Ulasan Aplikasi Sayurbox di Google Play Store”.

[17] A. Sudin, M. Salmin, M. Fhadli, and A. M. Mamonto, “Klasifikasi Kelayakan Air Minum Bagi Tubuh Manusia Menggunakan Metode Support Vektor Machine Dengan Backward Elimination”.

[18] M. Rafly and I. Veritawati, “Penggunaan Algoritma Support Vector Machine Untuk Penentuan Rekomendasi Penerima Beasiswa Di SMA Negeri 8 Kota Bogor,” IKRA-ITH Inform. J. Komput. Dan Inform., vol. 9, no. 1, pp. 102–113, Oct. 2024, doi: 10.37817/ikraith-informatika.v9i1.4381.

[19] R. Oktafiani and R. Rianto, “Perbandingan Algoritma Support Vector Machine (SVM) dan Decision Tree untuk Sistem Rekomendasi Tempat Wisata,” J. Nas. Teknol. Dan Sist. Inf., 2023.

[20] A. Z. Praghakusma and N. Charibaldi, “Komparasi Fungsi Kernel Metode Support Vector Machine untuk Analisis Sentimen Instagram dan Twitter,” JSTIE J. Sarj. Tek. Inform. E-J., vol. 9, no. 2, p. 88, Jun. 2021, doi: 10.12928/jstie.v9i2.20181.

[21] R. Hadi, “Optimalisasi Manajemen Pelayanan Gerai Sehat di Layanan Kesehatan Cuma-Cuma (LKC) Dompet Dhuafa Jawa Tengah,” Mabsya J. Manaj. Bisnis Syariah, vol. 5, no. 1, pp. 59–78, May 2023, doi: 10.24090/mabsya.v5i1.8236.

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Published

2025-03-18

How to Cite

[1]
D. Anggraini, I. Gamayanto, and S. Wibowo, “Comparing Decision Tree and Support Vector Machines in Hospital Satisfaction”, JAIC, vol. 9, no. 2, pp. 364–372, Mar. 2025.

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