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All Journal Jurnal Riset Rumpun Matematika dan Ilmu Pengetahuan Alam (JURRIMIPA) Algoritma: Jurnal Matematika, Ilmu Pengetahuan Alam, Kebumian dan Angkasa
Asni Al Amini
Unknown Affiliation
Earth & Planetary Sciences Mathematics

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Analisis Probabilitas Hujan Menggunakan Data Historis Dari BMKG Wilayah I Tahun 2013-2015 Arnah Ritonga; Asni Al Amini; Livia Mutianda; Riamonda Singarimbun; Aiman Hidayat Baeha; Glensius Rayhane Pasaribu; Juanda Arief Darmawan Damanik
JURNAL RISET RUMPUN MATEMATIKA DAN ILMU PENGETAHUAN ALAM Vol. 4 No. 1 (2025): April : Jurnal Riset Rumpun Matematika dan Ilmu Pengetahuan Alam
Publisher : Pusat riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jurrimipa.v4i1.4367

Abstract

Rainfall potential analysis plays a critical role in the management of air resources, mitigation of hydrometeorological disasters, and agricultural activity planning. Accurate estimation of rainfall patterns is essential to ensure effective decision-making in irrigation systems, water resource management, and disaster risk reduction strategies. This study aims to model the probability of rainfall occurrence using a statistical approach based on historical data obtained from the Bureau of Meteorology. The data spans a multi-year period and captures seasonal and regional variability in rainfall events. To characterize rainfall patterns, various probability distributions are tested, including the exponential distribution and the Weibull distribution, which are commonly applied in hydrological studies. Furthermore, the Markov chain method is employed to assess the likelihood of rainfall occurrence on a given day based on the conditions of the preceding day, thereby capturing temporal dependencies. Parameter estimation is conducted using Maximum Likelihood Estimation (MLE), a robust statistical method that enhances the precision of the model. The suitability of each probability distribution in representing the observed rainfall data is evaluated through goodness-of-fit tests such as the Kolmogorov-Smirnov test. The findings reveal that certain distributions align more closely with the local rainfall characteristics, demonstrating the importance of regional analysis in climate modeling. The combination of probabilistic modeling, Markov analysis, and rigorous statistical testing provides a reliable framework for forecasting rainfall. These results are expected to serve as a scientific basis for stakeholders in agriculture, environmental planning, and disaster preparedness, offering insights that support sustainable water resource utilization and risk management.
Aplikasi Integral dalam Menghitung Volume dan Panjang Busur pada Design Cone Es Krim Berbentuk Kelopak Bunga Asni Al Amini; Kenjo Oktaviano Damanik; Monica Triyuni Sinaga; Riby Tamara; Zahra Marsanda Mahisa; Suvriadi Panggabean
Algoritma : Jurnal Matematika, Ilmu pengetahuan Alam, Kebumian dan Angkasa Vol. 3 No. 3 (2025): Algoritma : Jurnal Matematika, Ilmu pengetahuan Alam, Kebumian dan Angkasa
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62383/algoritma.v3i3.480

Abstract

This study aims to apply integral calculus methods to calculate the volume and arc length of an ice cream cone design shaped like a flower petal. The cone design is modeled using a quadratic function derived from three reference points on the petal curve. Using the solid of revolution method around the y-axis, the calculated petal volume is 150.8 cm³, and the arc length is 7.14 cm. The results demonstrate that calculus-based modeling supports efficient material usage while enhancing aesthetic and functional aspects of packaging. This research highlights the connection between mathematical concepts and practical product design in the food industry
Co-Authors Aiman Hidayat Baeha Arnah Ritonga Glensius Rayhane Pasaribu Juanda Arief Darmawan Damanik Kenjo Oktaviano Damanik Livia Mutianda Monica Triyuni Sinaga Riamonda Singarimbun Riby Tamara Suvriadi Panggabean Zahra Marsanda Mahisa