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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi BAREKENG: Jurnal Ilmu Matematika dan Terapan Journal of Mathematics Education and Science
Siagian, Kimberly Maserati
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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Hubungan Kondisi Udara Terhadap Produktivitas dan Produksi Kacang Tanah Indonesia Berdasarkan Korelasi Kanonik Ramadhan, Achmad Wahyu; Ghasani, Anisah Nabilah; Siagian, Kimberly Maserati; Pambudi, Daffa Satrio; Mardianto, M. Fariz Fadillah; Amelia, Dita; Ana, Elly
Journal of Mathematics Education and Science Vol. 7 No. 2 (2024): Journal of Mathematics Education and Science
Publisher : Universitas Nahdlatul Ulama Sunan Giri Bojonegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32665/james.v7i2.2650

Abstract

Sektor pertanian di Indonesia, yang didominasi oleh tanaman kacang tanah sebagai sumber pangan kaya protein dan lemat, menghadapo tantangan produktivitas akibat ketidaksesuaian lahan dan juga kondisi udara, seperti suhu dan kelembaban yang tidak optimal. Tujuan dari penelitian ini adalah untuk mengetahui hubungan antara suhu dan kelembaban udara dengan produktivitas dan produksi kacang tanah di 34 provinsi di Indonesia untuk memahami dampaknya terhadap hasil pertanian dan ketahanan pangan di Indonesia. Pada penelitian ini, metode penelitian yang digunakan adalah analisis korelasi kanonik. Penelitian ini menggunakan data sekunder, yang merujuk pada informasi yang diperoleh dari sumber data Badan Pusat Statistik (BPS) melalui e-book statistika lingkungan hidup di Indonesia tahun 2022. Berdasarkan analisis korelasi kanonik disimpulkan bahwa hubungan antara suhu udara terhadap produktivitas kacang tanah mengakomodasi 99,26487% hubungan kanonikal, lalu 0,73513% diakomodasi dalam hubungan antara kelembaban udara terhadap produksi kacang tanah. Kesimpulan dari penafsiran koefisien variabel kanonikal, termasuk bobot, muatan, dan muatan-silang, menunjukkan bahwa terdapat interaksi antara suhu dan kelembaban udara dengan produktivitas dan produksi kacang tanah di Indonesia.
MODELING FACTORS CAUSING ALZHEIMER’S DISEASE USING LOGIT, PROBIT, AND GOMPIT LINK FUNCTIONS IN GENERALIZED LINEAR MODEL Kurniawan, Ardi; Budijono, Gabriella Agnes; Siagian, Kimberly Maserati; Abdillah, Adrian Wahyu
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 4 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss4pp2877-2890

Abstract

This study addresses the ongoing challenge of clarifying the risk factors contributing to Alzheimer's disease, a neurodegenerative condition marked by progressive cognitive decline and memory dysfunction, with cases rising globally. To provide a more accurate and comprehensive understanding of the predictors associated with the disease, this research models the contributing factors using logit, probit, and gompit link functions within the Generalized Linear Model (GLM). Utilizing secondary data from 2024, which includes predictor variables such as age, family history, head injury, hypertension, memory complaints, and behavioral disturbances, this research models the relationship between these variables and Alzheimer's diagnosis. The analysis finds that the logit, probit, and gompit link functions yield significant results in identifying risk factors associated with Alzheimer's diagnosis, particularly memory complaints and behavioral disturbances. The gompit link is selected as the best model due to its highest deviance R-squared value of 30.01%, indicating better reliability in predicting Alzheimer's diagnosis than other models. This GLM approach provides insights to support early prevention and intervention efforts for Alzheimer's disease and contribute to achieving Sustainable Development Goals (SDGs) number 3 on good health and well-being.
Nasdaq Inc. Stock Price (NDAQ) Prediction Due to Trump's Tariff Policy Using Pulse Function Intervention Analysis Sediono, Sediono; Siagian, Kimberly Maserati; Aditya, Josephin Viona
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 1 (2026): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v11i1.37168

Abstract

Nasdaq Inc. (NDAQ) is one of the leading stock exchanges in the United States, ranking second globally after the New York Stock Exchange (NYSE) based on market capitalization. As a highly dynamic and information-sensitive market, Nasdaq Inc. stock prices respond quickly to internal corporate conditions and external macroeconomic or policy changes. One notable event affecting market stability was President Donald Trump's import tariff policy, aimed at protecting U.S. industries from foreign competition, particularly Chinese imports. The implementation of this policy triggered significant volatility, including a sharp decline in Nasdaq Inc. stock prices on March 2, 2025. This study examines the impact of this policy on Nasdaq Inc. stock movements using the ARIMA(0,2,1) model with an intervention of order b=0, r=1, and s=0. The results show that all model parameters are statistically significant and produce accurate forecasts, with a MAPE of 2.19%, an RMSE of 5.98766, and an MAE of 2.05232. These findings indicate that intervention analysis effectively captures the impact of import tariff policies on stock market dynamics and provides valuable insights for investors and policymakers in anticipating market fluctuations driven by global economic policy changes.
Co-Authors Abdillah, Adrian Wahyu Aditya, Josephin Viona Ardi Kurniawan Budijono, Gabriella Agnes Dita Amelia Elly Ana Ghasani, Anisah Nabilah M. Fariz Fadillah Mardianto Pambudi, Daffa Satrio Ramadhan, Achmad Wahyu Sediono, Sediono