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All Journal Xplore: Journal of Statistics Indonesian Journal of Statistics and Its Applications
La Ode Abdul Rahman
Department of Statistics, IPB University, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Mathematics Other

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Perbandingan Metode Hot-deck, Regression dan K-Nearest Neighbor Imputation dalam Pendugaan Data Hilang pada Dapodik Tahun 2020 Inayatul Izzati Diana Yusuf; Budi Susetyo; La Ode Abdul Rahman
Xplore: Journal of Statistics Vol. 12 No. 1 (2023): Vol. 12 No. 1 (2023)
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (379.396 KB) | DOI: 10.29244/xplore.v12i1.1056

Abstract

Data Pokok Pendidikan (Dapodik) is a nation-wide data collection system that contains data on education units. Missing value in Dapodik cause the loss of important information. To solve this problem can use imputation. Imputation is a procedure to predict the missing value with a certain method. This study aims to compare three imputation methods which are Hot-deck imputation, Regression Imputation and K-Nearest Neighbor imputation (KNNI). Simulation for generating missing value was carried out by dividing the percentage of 2%, 3%, 4% and 5%, then imputed with the three methods. The best model is determined based on the lowest value of RMSE and MAPE. The best imputation method based on the lowest RMSE and MAPE values is a regression imputation
Identification of Prospective Subindustries Ahead of the 2024 Simultaneous General Elections with K-Medoids Clustering: Identifikasi Subindustri Prospektif Menjelang Pemilihan Umum Serentak 2024 dengan K-Medoids Clustering Vera Amelia; Pika Silvianti; La Ode Abdul Rahman
Indonesian Journal of Statistics and Applications Vol 7 No 2 (2023)
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.v7i2p64-74

Abstract

Indonesia Stock Exchange (IDX) Composite has grown in each general election year since 1998. This indicates that certain subindustries have benefited positively from the election year momentum. However, analyzing each subindustry was less efficient. This study aimed to identify prospective subindustries leading up to the 2024 Simultaneous Election based on the results of K-Medoids clustering on data from the lead-up to the 2019 Simultaneous Election. Research variables covered long-term price rate of change (indicating trends) and volatility (depicting fluctuations). These were derived from transforming historical stock price data for each issuer on a weekly basis in the two years before the 2019 Simultaneous Election. Four clusters emerged: high positive, low positive, high negative, and low negative. Positivity/negativity signify trends and high/low represent fluctuations. High fluctuations indicate higher risks. Prospective subindustries for the 2024 Simultaneous Election with low risk include household furniture manufacturers, basic chemical producers, construction materials, packaging, tires, household goods retail, life insurance, consumer finance, and financial holding companies. On the other hand, sub-industries with high risks for the 2024 Simultaneous Election include aluminum, paper, and textiles.
Co-Authors Budi Susetyo Inayatul Izzati Diana Yusuf Pika Silvianti Vera Amelia