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INDONESIA
Jurnal Statistika dan Matematika (Statmat)
Published by Universitas Pamulang
ISSN : 26553724     EISSN : 27209881     DOI : 10.32493
Core Subject : Economy, Science, Education,
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Other
P-ISSN : 2655-3724 E-ISSN : 2720-9881 Jurnal Statmat UNPAM: Jurnal Statistika dan Matematika Universitas Pamulang is a means of publication of scientific articles and research with concentrations of Statistics, Pure Mathematics, Applied Mathematics, Computational Mathematics, Educational Mathematics, and other research articles related to Statistics and Mathematics. Mathematics Department, Faculty of Mathematics and Natural Sciences, University of Pamulang publishes this journal, since 2019, which scheduled periodically every six months (twice a year).
Arjuna Subject : Matematika - Matematika Terapan
Articles 132 Documents
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Analysis of Public Satisfaction Levels Using the Customer Satisfaction Index (CSI) and Importance Performance Analysis (IPA): (Case Study: Public Satisfaction Survey on Services at the Pontianak City Inspectorate) Afifah Diah Afianti; Pitriani
STATMAT : JURNAL STATISTIKA DAN MATEMATIKA Vol 7 No 3 (2025)
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Pamulang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/sm.v7i3.54887

Abstract

Public service delivery serves as a primary indicator of successful governance that emphasizes accountability, transparency, and citizen satisfaction. Poor-quality public services can diminish public trust in governmental performance and hinder the realization of good governance. This study aims to analyze the level of public satisfaction with services provided by Inspectorate of Pontianak City during the second semester of 2024 using the Customer Satisfaction Index (CSI) and Importance Performance Analysis (IPA) methods. The CSI method is employed to measure overall satisfaction levels, whereas the IPA method is utilized to evaluate service attributes more comprehensively by comparing the importance and performance of each attribute and categorizing them into the IPA quadrants. The findings indicate that the overall public satisfaction level falls within the “very satisfied” category, with a CSI score of . This result reflects that the community generally perceives the quality of services delivered by Inspectorate of Pontianak City as highly satisfactory and able to meet user expectations. Furthermore, the IPA results reveal several attributes that require priority improvement, namely requirements, service completion time, service products, and staff competence, all of which fall into Quadrant I (high importance but low performance). These attributes are considered highly important by the public, yet their performance still requires enhancement to achieve optimal service delivery. Meanwhile, the attributes of service fees/tariffs, procedures, staff behavior, and complaint handling fall within Quadrant II (maintain performance), as they demonstrate strong performance and high importance, thus necessitating continued consistency.
Perbandingan Kinerja Model Regresi Logistik, Support Vector Machine, dan Naive Bayes Dalam Memprediksi Kepuasan Mahasiswa Terhadap Proses Pembelajaran Randa, Trigarcia Maleachi; Loria Amisah Lubis
STATMAT : JURNAL STATISTIKA DAN MATEMATIKA Vol 7 No 3 (2025)
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Pamulang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/sm.v7i3.54904

Abstract

Penelitian ini bertujuan untuk menganalisis dan membandingkan kinerja tiga metode klasifikasi Regresi Logistik, Support Vector Machine (SVM), dan Naïve Bayes dalam memprediksi kepuasan mahasiswa terhadap proses pembelajaran pada semester gasal 2025/2026 di Universitas Papua. Data penelitian diperoleh melalui survei yang berisi delapan variabel prediktor kategorik yang berkaitan dengan aspek proses pembelajaran, sementara variabel respon terdiri atas dua kategori, yaitu puas dan tidak puas. Analisis dilakukan dengan membagi data menjadi training dan testing dengan proporsi 80:20. Hasil penelitian menunjukkan bahwa metode Regresi Logistik dan SVM memberikan akurasi tertinggi, masing-masing sebesar 95.24%, sedangkan metode Naïve Bayes menghasilkan akurasi sebesar 87.88% meskipun telah dilakukan penentuan parameter terbaik menggunakan validasi silang 5-fold dan penerapan Laplace smoothing. Temuan ini menunjukkan bahwa Regresi Logistik dan SVM merupakan metode yang paling efektif untuk memprediksi kepuasan mahasiswa pada dataset ini, sementara Naïve Bayes tetap menjadi alternatif yang efisien untuk pemodelan yang sederhana dan cepat. Hasil penelitian ini diharapkan dapat mendukung pengambilan keputusan dalam evaluasi dan peningkatan kualitas pembelajaran di perguruan tinggi.

Page 14 of 14 | Total Record : 132

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