Analysis of User Age Predictions in Public Satisfaction Surveys at Public Service Malls Using Decision Tree C4.5

Authors

  • Andysah Putera Utama Siahaan Universitas Pembangunan Panca Budi, Indonesia
  • Ami Abdul Jabar Universitas Pembangunan Panca Budi, Indonesia
  • Nelviony Parhusip Universitas Pembangunan Panca Budi, Indonesia
  • Maida Indrayani Universitas Pembangunan Panca Budi, Indonesia
  • Sipra Barutu Universitas Pembangunan Panca Budi, Indonesia

DOI:

https://doi.org/10.61306/jitcse.v1i2.58

Keywords:

Decision Tree, C4.5, Age Prediction, Satisfaction Survey, Public Service Mall

Abstract

This research analyzes the prediction of user age in the community satisfaction survey at the Public Service Mall (PSM) in Medan using the C4.5 Decision Tree algorithm. The primary objective of the study is to understand the demographic profile of users so that service managers can tailor their approaches to meet the needs of each age group. The data used includes 14,836 respondents with relevant demographic attributes. The analysis begins with data collection and preprocessing. The modeling results indicate that the Decision Tree model is effective in classifying users into age categories, including Late Senior, Early Senior, Middle Aged Adult, Young Adult, Late Teen, Early Teen, Child, and Toddler. The findings reveal a significant concentration in the Young Adult and Early Senior groups, indicating the need for adjustments in public services. The resulting recommendations aim to enhance service responsiveness to demographic needs and improve user satisfaction as well as the effectiveness of service strategies in the future.

References

Ananda M. Risqi, Maharani Nurul Sandra, Fadhila Eka, Rahma Alvia, N. (2023). Data Mining Dalam Perusahaan PT Indofood Lubuk Pakam. Jurnal Ekonomi Manajemen Dan Bisnis (JEMB), 02(1), 97–102. https://doi.org/10.47233/jemb.v2i1.1009

Arisusanto Aditya, Suarna Nana, D. G. (2023). Analisa Klasifikasi Data Harga Handphone Menggunakan Algoritma Random Forest Dengan Optimize Parameter Grid. Jurnal Teknologi Ilmu Komputer, 1(2), 43–47. https://doi.org/10.56854/jtik.v1i2.51

A’yuniyah Qurotul, R. M. (2023). Penerapan Algoritma K-Nearest Neighbor Untuk Klasifikasi Jurusan Siswa Di Sma Negeri 15 Pekanbaru. Indonesian Journal of Informatic Research and Software Engineering (IJIRSE), 3(1), 39–45. https://doi.org/10.57152/ijirse.v3i1.484

Iftitah Amalia, S. R. (2023). Penerapan Algoritma C.45 Untuk Analisis Pengadaan Peralatan dan Mesin Kantor. Journal of Information System Research (JOSH), 4(2), 434–442. https://doi.org/10.47065/josh.v4i2.2673

Lubis, S. S., & Hendrik, B. (2023). Implementasi Data Mining Pengelompokan Data Penjualan Berdasarkan Pembelian Dengan Menggunakan Algoritma K-Means Pada UD.Martua. Jurnal of Information Sysem and Education Development, 1(3), 36–41.https://journal.widyakarya.ac.id/index.php/jusiik-widyakarya/article/view/1531%0Ahttps://journal.widyakarya.ac.id/index.php/jusiik-widyakarya/article/download/1531/1563

Nababan, W., & Situmorang, E. G. V. (2023). STRATEGI PENINGKATAN KEEFEKTIFAN MAL PELAYANAN PUBLIK PADA PENYELENGGARAAN PELAYANAN ADMINISTRASI KEPENDUDUKAN DI KABUPATEN SUMEDANG MENGGUNAKAN ANALISIS SOAR DAN MATRIKS QSPM. Jurnal Registratie, 5(1), 1–19. https://doi.org/10.33701/jurnalregistratie.v5i1.3343

Nazar Yuniar, M. (2023). Klasifikasi Kualitas Air Bersih Menggunakan Metode Naïve baiyes. Jurnal Sains Dan Teknologi, 5(1), 243–246. https://doi.org/10.55338/saintek.v5i1.1383

Nul, H. L. (2020). Urgensi Revisi Undang-Undang tentang Kesejahteraan Lanjut Usia. Aspirasi: Jurnal Masalah-Masalah Sosial, 11(1), 43–55. https://doi.org/10.22212/aspirasi.v11i1.1589

Permataning Tyas, S. M., Rintyarna, B. S., & Suharso, W. (2022). The Impact of Feature Extraction to Naïve Bayes Based Sentiment Analysis on Review Dataset of Indihome Services. Digital Zone: Jurnal Teknologi Informasi Dan Komunikasi, 13(1), 1–10. https://doi.org/10.31849/digitalzone.v13i1.9158

Purbolaksono, M. D., Irvan Tantowi, M., Imam Hidayat, A., & Adiwijaya, A. (2021). Perbandingan Support Vector Machine dan Modified Balanced Random Forest dalam Deteksi Pasien Penyakit Diabetes. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(2), 393–399. https://doi.org/10.29207/resti.v5i2.3008

Rahmansyah Nur, Ningsih Sari, Lantana Dhieka Avrilia, Suryaningtyas Adisti, Wirawan Putri, Wijaya Sifonne Adi, P. D. N. (2023). Komparasi Metode Knn, Naive Bayes, Decision Tree, Ensemble, Linear Regression Terhadap Analisis Performa Pelajar Sma. INNOVATIVE: Journal Of Social Science Research Volume, 3(2), 13880–13892.

Rahmawati Wahyu Eka, S. (2023). Analisis Inflasi-Kurs dan BI Rate Terhadap Indeks Harga Saham Gabungan di Bursa Efek Indonesia Tahun 2015-2019. Jurnal Akuntansi Dan Teknologi Keuangan, 1(2), 64–82. https://doi.org/10.56854/atk.v1i2.164

Septhya Dhini, Rahayu Khairisma, Rabbani Salsabila, Fitria Vindi, Rahmaddeni, Irawan Yuda, H. R. (2022). Implementasi Algoritma Decision Tree dan Support Vector Machine untuk Klasifikasi Penyakit Kanker Paru. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 3(1), 15–19.

Sri Rahayu, D., Afifah, J., & Intan, S. (2023). Klasifikasi Penyakit Diabetes Melitus Menggunakan Algoritma C4.5, Support Vector Machine (SVM) dan Regresi Linear. Institut Riset Dan Publikasi Indonesia (IRPI) SENTIMAS: Seminar Nasional Penelitian Dan Pengabdian Masyarakat, 56–63. https://journal.irpi.or.id/index.php/sentimas

Yuanti, A. H. (2024). Analisis Pengaruh Covid-19 Terhadap Kesehatan Mental dengan Visualisasi Data Rapidminer. Gudang Jurnal Multidisiplin Ilmu, 2(1), 183–187. https://doi.org/10.59435/gjmi.v2i1.225

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Published

2024-07-31

How to Cite

Andysah Putera Utama Siahaan, Ami Abdul Jabar, Nelviony Parhusip, Maida Indrayani, & Sipra Barutu. (2024). Analysis of User Age Predictions in Public Satisfaction Surveys at Public Service Malls Using Decision Tree C4.5. Journal of Information Technology, Computer Science and Electrical Engineering, 1(2), 192–133. https://doi.org/10.61306/jitcse.v1i2.58