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UPT Publikasi Ilmiah, Universitas Islam Bandung. Jl. Tamansari No. 20, Bandung 40116, Indonesia, Tlp +62 22 420 3368, +62 22 426 3895 ext. 6891
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Bandung Conference Series: Statistics
Published by Universitas Islam Bandung
ISSN : -     EISSN : 2828206X     DOI : https://doi.org/10.29313/bcss.v2i2
Core Subject : Science, Education,
Decision Sciences, Operations Research & Management Education Mathematics
Bandung Conference Series: Statistics (BCSS) menerbitkan artikel penelitian akademik tentang kajian teoritis dan terapan serta berfokus pada Statistika dengan ruang lingkup sebagai berikut: Alternating Least Square, Analisis Konjoin, Autoregressive, Auxiliary Variabel, Baby Birth, Block Maxima, Churn Distribusi Skellam, Cox Regression, Data spasial, DBD Ordinal Logistic Regression, Diagram kendali, Discrete Choice Experiment Method, Discrete Time Logistic, empirical likelihood, Fisher Scoring, Generalized Structured Component Analysis, Geographically Weighted Regression, GEV, GJR GARCH, Infant Mortality Preferensi, Insurance Claim, Kaplan-Meier, Kernel Bi-Square, Gaussian, Logistic Regression, Maternal Mortality, Mixed Geographically Weighted Regression Model GSTAR, MLE, Model ARIMAX, MSE. Multiple linear regression analysis, Nadaraya Watson, Newton Raphson Method, Nonparametrik Spline Confidence Interval, Optimasi Multi-Objek, orde Spasial, Outlier, Pareto Optimal, Partial Proportional Odds Model, Pemodelan Indeks Pembangunan Manusia. Penduga Rasio dan Produk Tipe Eksponensial, Peramalan, Poisson Bivariate Regression, Poisson Regression, Rata-rata Populasi berhingga, Regresi, Return Period Exogenous Variable, RMSE, Structural Equation Modeling, Survival Analysis, Threshold, Vibrasi Bearing, zero-inflated. Prosiding ini diterbitkan oleh UPT Publikasi Ilmiah Unisba. Artikel yang dikirimkan ke prosiding ini akan diproses secara online dan menggunakan double blind review minimal oleh dua orang mitra bebestari.
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 1 Documents
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Analisis Segmentasi Mahasiswa Program Studi Statistika Universitas Islam Bandung Tahun 2020-2025 Menggunakan Algoritma K-Modes Clustering Safira Nur Latifah; Ilham Faishal Mahdy
Bandung Conference Series: Statistics 161-168
Publisher : UNISBA Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/bcss.v6i2.24868

Abstract

Abstract. Student segmentation is an important approach for understanding student characteristics and supporting higher education institutions in developing more effective and targeted promotional strategies. This study aims to segment students of the Statistics Study Program at Universitas Islam Bandung using the K-Modes Clustering algorithm. The dataset consisted of 376 student records described by four categorical variables, namely city of origin, high school background, admission pathway, and admission wave. The clustering process was performed by evaluating the number of clusters from k= 2 to k = 5. The clustering performance was assessed using the Silhouette Coefficient based on the Simple Matching Dissimilarity measure. The results indicated that the optimal clustering solution was obtained at k = 2, yielding the highest average Silhouette Coefficient value of 0.2301556. Furthermore, each cluster exhibited distinct characteristics in terms of city of origin, high school background, admission pathway, and admission wave. The findings of this study provide useful insights into student characteristics and may assist the Statistics Study Program at Universitas Islam Bandung in developing more effective and targeted promotional strategies. Abstrak. Segmentasi mahasiswa merupakan salah satu pendekatan yang penting untuk memahami karakteristik mahasiswa serta mendukung perguruan tinggi dalam menyusun strategi promosi yang lebih efektif dan tepat sasaran. Penelitian ini bertujuan untuk melakukan segmentasi mahasiswa Program Studi Statistika Universitas Islam Bandung menggunakan algoritma K-Modes Clustering. Data yang digunakan terdiri dari 376 data mahasiswa dengan empat variable kategori, yaitu asal kota, asal sekolah, jalur seleksi, dan gelombang seleksi. Proses clustering dilakukan dengan menguji jumlah clustermulai dari k = 2 hingga k = 5. Kinerja hasil clustering dievaluasi menggunakan Silhouette Coefficient berdasarkan ukuran Simple Matching Dissimilarity. Hasil penelitian menunjukkan bahwa jumlah cluster optimal diperoleh pada k = 2 dengan nilai rata-rata Silhouette Coefficient tertinggi sebesar 0.2301556. Selain itu, masing-masing cluster menunjukkan karakteristik yang berbeda berdasarkan asal kota, asal sekolah, jalur seleksi, dan gelombang seleksi. Hasil penelitian ini memberikan informasi mengenai karakteristik mahasiswa dan diharapkan dapat menjadi bahan pertimbangan bagi Program Studi Statistika Universitas Islam Bandung dalam menyusun strategi promosi yang lebih efektif dan tepat sasaran.

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