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All Journal FORUM STATISTIKA DAN KOMPUTASI Indonesian Journal of Geography Statistika Jurnal Statistika dan Aplikasinya Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi 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).
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 I Made Sumertajaya Indonesian Journal of Statistics and Its Applications IJSA Itasia Dina Sulvianti La Ode Abdul Rahman Melati Muhadi, Rizqi Annafi Rahardiantoro, Septian Rahma Anisa Rahma Anisa Satria Yudha Herawan Siti Nur Azizah, Siti Nur Siti Nur Laila Vera Amelia Yuniarsyih R.A, Rizqi Dwi Zahid, Muhammad Farhan