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All Journal FORUM STATISTIKA DAN KOMPUTASI Indonesian Journal of Geography Statistika JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Statistika dan Aplikasinya Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Parameter: Jurnal Matematika, Statistika dan Terapannya Indonesian Journal of Statistics and Its Applications
Pika Silvianti
Dept. Of Statistics, IPB University
Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Industrial & Manufacturing Engineering Mathematics Social Sciences Other

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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 Amelia, Vera; Silvianti, Pika; Rahman, La Ode Abdul
Indonesian Journal of Statistics and Applications Vol 7 No 2 (2023)
Publisher : Statistics and Data Science Program Study, IPB University, 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.
Comparison Between SARIMA and DeepAR with Optuna Hyperparameter Optimization for Estimating Rice Production Data in Indonesia Zahid, Muhammad Farhan; Fitrianto, Anwar; Silvianti, Pika; Alamudi, Aam
Indonesian Journal of Statistics and Applications Vol 8 No 2 (2024)
Publisher : Statistics and Data Science Program Study, IPB University, 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.v8i2p95-111

Abstract

Forecast is a prediction of future events that had taken a significant role in our society especially when facing time-sensitive issues like food availability. Food is a critical aspect in ensuring people's welfare, especially in a country like Indonesia with a large population. Availability and access to rice are a vital need for the people of Indonesia. Rice is not only the main source of carbohydrates, but also has a central role in the cultural and social aspects of Indonesian society. Forecasting can be a strategy to anticipate fluctuations in food demand and supply. Forecasting can be an important instrument for the government and stakeholders to make the right and effective decisions. The growing period of rice which is heavily influenced by seasonality makes DeepAR and SARIMA techniques a good solution to solve this problem. Both methods offer the ability to address features in rice production such as trends, seasonality, and anomaly effects. This study demonstrates that DeepAR, especially when optimized with Optuna, outperforms SARIMA in forecasting rice production in Indonesia, as evidenced by superior performance in key evaluation metrics such as Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE).
Performance of Multivariate Missing Data Imputation Methods on Climate Data Widyawati, Amalia Safira; Fitrianto, Anwar; Silvianti, Pika
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11316

Abstract

Climate data plays an important role in various aspects of life. However, missing data is often found, which can interfere with data processing and reduce the quality of analysis. Therefore, appropriate handling methods are needed to ensure that the analysis results remain valid. This study aims to compare the performance of several imputation methods for missing multivariate data based on the identification of actual missing data patterns, and to determine the appropriate imputation method based on the mechanism of missing data. This study also aims to apply the best method to data with actual missing data patterns to assess its effect on descriptive statistical changes required for further climatological analysis. The methods used include monthly averages, missRanger, k-Nearest Neighbor (k-NN), and Iterative Robust-Model Imputation (IRMI). The missing data information was obtained from Global Surface Summary of the Day (GSOD) data, namely temperature, precipitation, humidity, pressure, and wind speed variables with a daily frequency for 11 years, with a missing data proportion of 11.4%. The missing data patterns were then applied to relatively complete NASA Power data to evaluate the imputation results. The results show that IRMI is less capable of handling extreme missing data conditions, namely 17 completely missing rows. In contrast, k-NN, missRanger, and monthly averages provided better results in both extreme and non-extreme conditions. Of the four methods, monthly averages were chosen because they were able to overcome missing data while maintaining multivariate structure with 58% on sMAPE and 2.64% on relative difference.
Evaluating the Performance of Ordinal Logistic Regression and XGBoost on Ordinal Classification Datasets Hanifa, Jasmin Nur; Mingka, Rizka Annisa; Indahwati, Indahwati; Silvianti, Pika
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 4 No 3 (2025): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/parameterv4i3pp459-470

Abstract

Choosing the appropriate classification model is crucial, especially when dealing with data featuring an ordinal dependent variable. This study explores and compares the performance of Ordinal Logistic Regression (OLR) and Ordinal XGBoost in classifying ordinal data using ten datasets obtained from the UCI Machine Learning Repository and Kaggle, which vary in the number of observations and features. Each dataset undergoes multicollinearity detection, an 80% training and 20% testing data split, and class balancing using SMOTE. Model performance is evaluated using metrics such as accuracy, F1-score, AUC, MSE, precision, and recall. The results show that ordinal XGBoost outperforms on datasets with complex structures and a higher number of features, achieving a maximum accuracy of 0.953. In contrast, Ordinal Logistic Regression demonstrates more stable performance on datasets with fewer features or balanced class distributions.
Co-Authors Aam Alamudi Ahmad Anshori Mattjik Amon Ra, Az Zahra Anik Djuraidah Aris Yaman Bagus Sartono Bimandra Djaafara Erfiani Eria, Raisa Nida Farit Mochamad Afendi Fitrianto, Anwar Gustiara, Dela Hanifa, Jasmin Nur I Made Sumertajaya Indahwati Indonesian Journal of Statistics and Its Applications IJSA Itasia Dina Sulvianti La Ode Abdul Rahman Melati Mingka, Rizka Annisa Muhadi, Rizqi Annafi Rahardiantoro, Septian Rahma Anisa Rahma Anisa Satria Yudha Herawan Siti Nur Azizah, Siti Nur Siti Nur Laila Vera Amelia Widyawati, Amalia Safira Yuniarsyih R.A, Rizqi Dwi Zahid, Muhammad Farhan