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All Journal Media Statistika JFMR (Journal of Fisheries and Marine Research) Enthusiastic : International Journal of Applied Statistics and Data Science
Siswantining, Titin
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Environmental Science Mathematics

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PERUBAHAN AKTIVITAS NELAYAN DI URK, PROVINSI FLEVOLAND SETELAH PEMBANGUNAN AFSLUITDIJK Chairunnisa, Shasa; Susiloningtyas, Dewi; Handayani, Tuty; Siswantining, Titin
JFMR (Journal of Fisheries and Marine Research) Vol. 5 No. 1 (2021): JFMR
Publisher : Faculty of Fisheries and Marine Science, Brawijaya University, Malang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jfmr.2021.005.01.5

Abstract

Afsluitdijk adalah tanggul laut terbesar yang dibangun oleh pemerintah untuk mengatasi masalah banjir di Belanda. Pembangunan Afsluitdijk menyebabkan beberapa perubahan pada lingkungan dan komunitas pesisir di sekitarnya, salah satunya adalah komunitas nelayan di Urk, Provinsi Flevoland, Belanda. Tujuan dari penelitian ini adalah untuk menganalisis perubahan kegiatan penangkapan ikan setelah pembangunan Afsluitdijk. Penelitian ini dilakukan di Desa Urk, Provinsi Flevoland, Belanda pada Mei - Juni 2019. Penelitian ini menggunakan pendekatan deskriptif kualitatif. Data penelitian diperoleh dari wawancara mendalam dan studi literatur. Hasil penelitian menunjukkan adanya perubahan lingkungan akuatik yang awalnya merupakan air asin menjadi air tawar. Terdapat beberapa nelayan yang masih bertahan hidup untuk menangkap ikan di perairan sekitar Urk dan Harlingen setelah pembangunan, tetapi ada juga yang beralih profesi dan pindah ke daerah lain di Belanda. Kesimpulan dari penelitian ini adalah terdapat perubahan pada kondisi lingkungan perairan dan ekonomi nelayan, serta nelayan harus beradaptasi dengan kondisi baru setelah pembangunan Afsluitdijk.
Genetic Cluster Analysis of Insulin Resistance Using KNN Imputation and FABIA-CCA Biclustering Soemarso, Ditoprasetyo Rusharsono; Siswantining, Titin; Pramana, Setia
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 5 Issue 1, April 2025
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol5.iss1.art10

Abstract

Type 2 diabetes mellitus (T2DM) is a metabolic disorder primarily driven by insulin resistance, involving complex genetic regulation. Understanding the molecular mechanisms underlying insulin resistance is crucial for identifying therapeutic targets. This study compared the performance of two biclustering algorithms, factor analysis for bicluster acquisition (FABIA) and the Cheng and Church algorithm (CCA), in analyzing gene expression data associated with insulin resistance. Using the GSE19420 dataset, simulated missing values were introduced to evaluate the robustness of both methods. Results showed that CCA consistently achieved lower mean squared error (MSE) in reconstructing gene expression patterns, suggesting higher accuracy in capturing co-expression structures. Nevertheless, FABIA effectively detected sparse, biologically relevant clusters. Notably, key genes such as MYO5B, DLG2, AXIN2, and PTK7 were identified within the biclusters, supporting their involvement in insulin signaling and metabolic regulation. These findings underscore the need to select biclustering methods that align with specific analytical goals and offer insights into gene networks involved in insulin resistance.
COMPARISON OF MISSING VALUE IMPUTATION USING MEAN, BAYESIAN KNN, AND NON-BAYESIAN KNN ON TEP GENE EXPRESSION DATA Mastika, Mastika; Siswantining, Titin; Bustamam, Alhadi
MEDIA STATISTIKA Vol 18, No 1 (2025): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.18.1.61-72

Abstract

Analysis of gene expression data, particularly in cancer data, often faces challenges due to the presence of missing values. One approach to overcome this is data imputation. This study evaluates the performance of three imputation methods, namely mean imputation, K-Nearest Neighbors (KNN), and KNN with Bayesian optimization using Gaussian Process modeling, on Tumor Educated Platelets (TEP) gene expression data. Missing values were introduced using Missing Completely at Random (MCAR) gradually at levels of 5%, 10%, 15%, and up to 60%, and performance was evaluated using three metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), and Normalized Root Mean Squared Error (NRMSE). The results show that the three methods produce relatively similar performance, with differences in MAE, MSE, and NRMSE values only at a small decimal scale. Although Bayesian Optimization is expected to improve the accuracy of KNN, the resulting improvement on this dataset is not significant. These findings indicate that simple imputation such as the average and KNN-based methods still provide competitive results on TEP data with data characteristics that have 14,020,496 zeros out of a total of 16,512,496 existing values, which is approximately 84.91% of the total data.
Co-Authors Bustamam, Alhadi Chairunnisa, Shasa Dewi Susiloningtyas Mastika, Mastika Pramana, Setia Soemarso, Ditoprasetyo Rusharsono Tuty Handayani