Pengkajian Metode Ordering Points to Identify the Clustering Structure (OPTICS) pada Data dengan Permasalahan Missing Value dan Penerapan pada Data Real (Data Socio-Economic) = Assessment of the Ordering Points to Identify the Clustering Structure (OPTICS) Method on Data with Missing Value Problems and Applications to Real Data (Socio-Economic Data)

Metode Biclustering Terurut Berbasis k-Nearest Neighbour, Mean Square Residual, dan Jarak Euclidean dalam Imputasi Missing Values pada Data Ekspresi Gen = Sequential Biclustering Method Based on k-Nearest Neighbor, Mean Squared Residual, and Euclidean Distance in Missing Values Imputation on Gene Expression Data

Implementasi density based spatial clustering of application with noise dan genetic algorithm pada optimisasi portofolio dengan kendala = Implementation of density based spatial clustering of application with noise and genetic algorithm in portfolio optimization with constraint

Implementasi Metode Imputasi Ganda Dynamically Ordered Attribute Trees (DOAT) untuk Estimasi Missing Values pada Data Kategorik = Implementation of Dynamically-Ordered Attribute Tree (DOAT) Multiple Imputation Method to Estimate Missing Values in Categorical Data

Implementasi metode quadratic programming-biclustering (QP_biclustering) untuk imputasi missing values pada data ekspresi gen pasien covid-19 = Implementation of quadratic programming-biclustering (QP_biclustering) method for missing values imputation in gene expression data of covid-19 patients.