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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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Clustering Kualitas Udara DKI Jakarta dengan Algoritma K-Medoids Berdasarkan Indeks Standar Pencemar Udara Rahma Elia Nurazizah; Nusar Hajarisman
Bandung Conference Series: Statistics 301-310
Publisher : UNISBA Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/bcss.v6i2.25833

Abstract

Abstract. Air quality in DKI Jakarta exhibits considerable variation in pollutant concentrations, making clustering methods necessary to identify air quality patterns based on pollutant characteristics. This study aims to cluster air quality conditions in DKI Jakarta using the K-Medoids algorithm based on Air Pollution Standard Index. Daily ISPU data from January 2024 to November 2025 were obtained from the Satu Data Jakarta platform, covering five air quality monitoring stations and six pollutant parameters: PM₁₀, PM₂.₅, SO₂, CO, O₃, and NO₂. After data preprocessing, 3,090 observations were retained for analysis. Data preprocessing included missing value inspection, duplicate removal, outlier detection, and Z-score standardization. The optimal number of clusters was determined using the Silhouette Coefficient, followed by clustering with the K-Medoids algorithm. The results indicate that the optimal configuration consists of three clusters with a Silhouette Coefficient of 0.1944. The clusters exhibit distinct pollutant characteristics and different air quality patterns across monitoring stations. These findings provide an overview of air quality patterns and may support air quality monitoring and pollution control strategies in DKI Jakarta. Keywords: K-Medoids, Air Pollution Standard Index, air quality. Abstrak. Kualitas udara di DKI Jakarta menunjukkan variasi konsentrasi parameter pencemar yang tinggi, sehingga diperlukan metode pengelompokan untuk mengidentifikasi pola kualitas udara berdasarkan karakteristik pencemar. Penelitian ini bertujuan mengelompokkan kondisi kualitas udara di DKI Jakarta berdasarkan Indeks Standar Pencemar Udara (ISPU) menggunakan algoritma K-Medoids. Data yang digunakan berupa data harian ISPU periode Januari 2024 hingga November 2025 yang diperoleh dari platform Satu Data Jakarta, mencakup lima stasiun pemantauan kualitas udara dengan enam parameter pencemar, yaitu PM₁₀, PM₂.₅, SO₂, CO, O₃, dan NO₂. Setelah melalui tahap data preprocessing, diperoleh 3.090 data yang dianalisis. Tahapan prapemrosesan meliputi pemeriksaan missing value, data duplikat, outlier, serta standarisasi data menggunakan metode Z-score. Jumlah cluster optimal ditentukan menggunakan Silhouette Coefficient, kemudian dilakukan pengelompokan menggunakan algoritma K-Medoids. Hasil penelitian menunjukkan bahwa konfigurasi optimal terdiri atas tiga cluster dengan nilai Silhouette Coefficient sebesar 0,1944. Setiap cluster memiliki karakteristik yang berbeda berdasarkan dominasi parameter pencemar dan menunjukkan pola kualitas udara yang berbeda pada stasiun pemantauan di DKI Jakarta. Hasil penelitian ini memberikan gambaran pola kualitas udara serta dapat menjadi dasar dalam mendukung kegiatan pemantauan dan penyusunan strategi pengendalian pencemaran udara di DKI Jakarta. Kata Kunci: K-Medoids, Indeks Standar Pencemar Udara, kualitas udara.

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