Article Per Year (5 Year)
p-Index From 2021 - 2026
3.109
P-Index
This Author published in this journals
All Journal MEDIA PETERNAKAN - Journal of Animal Science and Technology Agromet FORUM STATISTIKA DAN KOMPUTASI Forum Pasca Sarjana Media Statistika IPTEK The Journal for Technology and Science BAREKENG: Jurnal Ilmu Matematika dan Terapan Zero : Jurnal Sains, Matematika, dan Terapan ComTech: Computer, Mathematics and Engineering Applications Inferensi Jurnal Tanah dan Iklim Jurnal Natural Jurnal Teknik Informatika (JUTIF) Jurnal Natural Xplore: Journal of Statistics STATISTIKA PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS Indonesian Journal of Statistics and Its Applications
Khairil Anwar Notodiputro
Department Of Statistics, IPB University
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Immunology & microbiology Mathematics Mechanical Engineering Neuroscience Physics Social Sciences Transportation Other

Published : 28 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : Inferensi

 1 
Effectiveness of GPCA in Reducing Data Dimensions and its Application to Human Development Dimension Indicators Data Zubedi, Fahrezal; Sumertajaya, I Made; Notodiputro, Khairil Anwar; Syafitri, Utami Dyah
Inferensi Vol 7, No 3 (2024)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v7i3.21506

Abstract

Analysis of human development growth at the regency/city level is challenging because the data is high-dimensional, indicators are correlated, and the regencies/cities are correlated. In this study, we propose a Generalized Principal Component Analysis to analyze human development growth by reducing the dimensions of regency/city and indicator. Thus, human development growth at the regency/city level is analyzed using the GPCA results in Biplot to describe each regency/city and its indicators. This study aims to evaluate GPCA in reducing the dimensionality of data whose observations are correlated, and indicators are correlated through simulation and empirical study; to analyze the growth of human development at the regency/city level based on the results of GPCA-Biplot. This research shows that GPCA works well in reducing data dimensions from correlated observations and correlated variables. Based on the results of the GPCA-Biplot visualization, the growth of human development in the Nduga regency from 2019 to 2022 showed significant fluctuations. Although some indicators show progress, especially in 2021, significant challenges remain. In the same way, the growth of human development in each regency/city can be analyzed. Thus, government policy focuses on real problems in the field.
Comparison of GMERF and GLMM Tree Models on Poverty Household Data with Imbalanced Categories Bukhari, Ari Shobri; Notodiputro, Khairil Anwar; Indahwati, Indahwati; Fitrianto, Anwar
Inferensi Vol 8, No 2 (2025)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v8i2.21901

Abstract

Decision tree and forest methods have become popular approaches in data science and continue to evolve. One of these developments is the combination of decision trees with Generalized Linear Mixed Models (GLMM), resulting in the GLMM Tree, which is applicable to multilevel and longitudinal data. Another model, Generalized Mixed Effect Random Forest (GMERF), extends the concept of decision forests with GLMM, effectively handling complex data structures with non-linear interactions. This study compares the performance of GLMM Tree and GMERF models in classifying poor households in South Sulawesi Province, characterized by imbalanced categories. GLMM Tree provides a simple, interpretable classification through tree diagrams, while GMERF highlights variable importance. Initial tests show all three models (GLMM, GLMM Tree, and GMERF) achieve high accuracy and specificity but exhibit low sensitivity. By applying oversampling, sensitivity and AUC are significantly improved, though this is accompanied by a decline in accuracy and specificity, revealing a trade-off. The study concludes that while GLMM, GLMM Tree and GMERF have their strengths, using them together offers a more comprehensive understanding of poverty classification. Handling imbalanced data with oversampling is effective in increasing sensitivity, but careful consideration is needed due to its impact on overall accuracy.
Comparison of ARIMA, LSTM, and Ensemble Averaging Models for Short-Term and Long- Term Forecasting of Non-Stationary Time Series Data Pratiwi, Windy Ayu; Sumertajaya, I Made; Notodiputro, Khairil Anwar
Inferensi Vol 8, No 3 (2025)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v8i3.22643

Abstract

This study aims to forecast the highest weekly selling rate of the Indonesian Rupiah (IDR) against the US Dollar (USD) and identify the most accurate model among ARIMA, LSTM, and Ensemble Averaging. The evaluation results indicate that ARIMA achieves an accuracy of 97.75%, demonstrating strong performance in short-term forecasting, while LSTM achieves 99.98% accuracy, excelling in capturing complex and dynamic patterns in long-term predictions. The Ensemble Averaging approach attains the highest accuracy of 99.99%, proving to be the optimal solution by combining ARIMA’s stability with LSTM’s adaptability, resulting in more precise and stable predictions. The findings of this study highlight that the ensemble approach is more effective than individual models, as it balances accuracy and prediction stability across various forecasting scenarios. This method serves as a reliable tool for addressing market volatility and contributes significantly to the advancement of financial and economic forecasting techniques that are more adaptive and accurate.
Co-Authors . Barizi . SUTIKNO A. Bey Agus Mohamad Soleh Aji H. Wigena Anang Kurnia ASEP SAEFUDDIN Asfar Aulya Permatasari Ayunda Pratiwi Azlam Nas Bagus Sartono Bayu Suseno Budi Susetyo Bukhari, Ari Shobri Cahyani Oktarina D Diapari Dani Al Mahkya Dwi Jayanti Erfiani, Erfiani Etis Sunandi FAHREZAL ZUBEDI FARDILLA RAHMAWATI Farit Mochamad Afendi Fitrianto, Anwar H. M H Bintoro Hari Wijayanto Hari Wijayanto Hari Wijayanto Herdina Kuswari I Las I Made Sumertajaya I. LAS Indahwati Indahwati, Indahwati Intan Nirmala J Jachja Jajah K. Wagiono Kurnia, Anang Kusman Sadik M S Saeni Muhammad Ilham Abidin Muhammad Nur Aidi Nurfadilah, Khalilah Pardomuan Robinson Sihombing Pratiwi, Windy Ayu Pratiwi, Windy Ayu R Boer R. BOER Rahardiantoro, Septian Rizky Zulkarnain Septian P Palupi Stephan Chen Suseno Bayu Tri Listianingrum Ulfah Sulistyowati Utami Dyah Syafitri Yekti Widyaningsih