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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Media Statistika ECSOFiM (Economic and Social of Fisheries and Marine Journal) JFMR (Journal of Fisheries and Marine Research) Jurnal Kelautan dan Perikanan Terapan (JKPT) Jurnal Litbang Polri Inferensi Enthusiastic : International Journal of Applied Statistics and Data Science Xplore: Journal of Statistics Jurnal Sistem Informasi Indonesian Journal of Statistics and Its Applications
Siswantining, Titin
Universitas Indonesia
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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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.
Indeks Pembangunan Kesehatan Polri: Indeks Pembangunan Kesehatan Polri Frans Tjahyono; Dwi Purwoko; Titin Siswantining
Jurnal Litbang Polri Vol 27 No 3 (2024): JURNAL LITBANG POLRI
Publisher : Pusat Penelitian dan Pengembangan Polri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46976/litbangpolri.v27i3.252

Abstract

National Police health development is a long-term investment in the development of National Police personnel with the hope that it can play a role in increasing life expectancy and maintaining and improving their level of health so that they can carry out their main duties optimally in safeguarding and maintaining security and public order optimally. This research was conducted in 11 Polda and 96 Polres. Data collection was carried out by distributing questionnaires to all Civil Servants at the National Police, FGDs with key officials at the Regional Police and Regional Police, as well as FGDs with health workers at the National Police Polyclinic/FKTP. The results showed that the national percentage of PNPP health conditions was 29% (84,102 PNPP) obesity, 20.40% (58,997 PNPP) central obesity, the highest prevalence rate of PTM (Gastric, Heart Diabetes Mellitus and Stroke), the highest prevalence rate of PM (Diarrhea, Typus and Malaria). Nationally, around 0.7% (1,902 PNPP) SRQ-20 screening results indicated mild GME. A total of 0.15% (432 PNPP) indicated moderate GME, and 0.1% (292 PNPP) indicated heavy GME. Apart from that, there were also 0.07% (206 PNPP) who thought about committing suicide.
Implementation of Ensemble Self-Organizing Maps for Missing Values Imputation Siswantining, Titin; Vivaldi, Kathan Gerry; Sarwinda, Devvi; Soemartojo, Saskya Mary; Mattasari, Ika; Al-Ash, Herley
Indonesian Journal of Statistics and Applications Vol 6 No 1 (2022)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v6i1p1-12

Abstract

The purpose of this study is to implement the ensemble self-organizing maps (E-SOM) method to impute missing values at the preprocessing data stage, which is an important stage when making predictions or classifications. The Ensemble Self-Organizing Maps (E-SOM) is the development of the SOM imputation method, in which the E-SOM method is implemented by applying an ensemble framework using several SOMs to improve generalization capabilities. In this study, the E-SOM imputation method is implemented in South African heart disease data using random forest as a classification model. The results of the model evaluation showed that for accuracy in testing data, the Random Forest model formed from E-SOM imputed data yields better accuracy values than the Random Forest model formed from SOM-imputed data for variations of 36, 49, 64, and 81 neurons, while for variation of 25 neurons both models produce the same accuracy value. From the variation of the number of ensembles applied, the E-SOM imputation method with a combination of 81 neurons and 15 ensemble numbers produced a Random Forest model with the most optimal value of accuracy.
Co-Authors Al-Ash, Herley Bustamam, Alhadi Chairunnisa, Shasa Darayani Putri Devvi Sarwinda Dewi Susiloningtyas Dewi Susiloningtyas Diana Nur Vitasari Dwi Purwoko Frans Tjahyono Herley Al-Ash Herley Shaori Al-Ash Ika Marta Sari Ika Mattasari Kasiyah Junus Kathan Gerry Vivaldi Lia Sadita Mariam Rahmania Mastika, Mastika Mattasari, Ika Mohamad Irlin Sunggawa Muhammad Ihsan Muhammad Taufik Nurulludin Nurulludin Pramana, Setia Rudy Masuswo Purwoko Sarwinda, Devvi Saskya Mary Soemartojo Saskya Mary Soemartojo Shasa Chairunnisa Shasa Chairunnisa Silalahi, Agatha Soemarso, Ditoprasetyo Rusharsono Soemartojo, Saskya Mary Stanley Pratama Sunandi, Etis Tesdiq Prigel Kaloka Tuty Handayani Tuty Handayani Vivaldi, Kathan Gerry