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All Journal Zero : Jurnal Sains, Matematika, dan Terapan JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI
Elis Khatizah
IPB University
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mathematics Physics Public Health

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Modeling Monthly Rainfall Data Using the Alpha Power Transformed X-Lindley Distribution in the Toba Lake Region Mohamad Khoirun Najib; Sri Nurdiati; Elis Khatizah; Aulia Rizki Firdawanti; Hendri Irwandi; Mirza Farhan Azhari; David Vijanarco Martal; Nicholas Abisha
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 3 (2025): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v9i3.25692

Abstract

Modeling rainfall is crucial for hydrological studies and climate adaptation, especially in regions with complex topography such as the Toba Lake area, North Sumatra. Classical probability distributions often struggle to represent skewness, heavy tails, and variability observed in tropical rainfall. This study explores APTXL distribution as a flexible two-parameter model. Through the alpha power transformation, APTXL extends the X-Lindley distribution by introducing an additional shape parameter, allowing better accommodation of asymmetrical and extreme values while maintaining analytical tractability. Statistical properties are derived, and parameters are estimated using maximum likelihood. The model is applied to a long-term dataset from 13 meteorological stations, covering 408 monthly observations per station. Comparative analysis against Gamma, Lognormal, and Generalized Extreme Value distributions using multiple goodness-of-fit criteria indicates that APTXL provides consistently improved performance. These results suggest APTXL as a practical tool for rainfall modeling and water-resource applications in climate-sensitive regions.
Transforming DNA Sequences into Musical Patterns Via A 3-mer Classification Abel Prayoga; Elis Khatizah
JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI Vol 11, No 1 (2026): Januari 2026
Publisher : Universitas Al Azhar Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36722/sst.v11i1.5258

Abstract

DNA can be viewed as a symbolic sequence with patterns that vary across species. This study explores DNA sequences through two complementary approaches: species classification using simple machine learning methods and transformation of DNA into musical note representations. In the first task, DNA sequences from five organisms with different evolutionary distances are represented using 3-mer and 6-mer features. These k-mers form a vocabulary whose frequency counts are converted into feature vectors. Random Forest (RF) and Support Vector Machine (SVM) models are then applied for five-class classification. Using an 80:20 train-test split and 10-fold cross-validation, the SVM model achieved average accuracies above 0.90 for 3-mer features, with low standard deviation, indicating stable performance. In the second approach, 3-mer motifs are mapped to musical notes to generate species-based musical patterns. The resulting musical representations exhibit distinct structural differences across species, reflecting variations in underlying sequence composition. Overall, the results demonstrate that 3-mer features are effective for species discrimination and that musical transformation provides an alternative and intuitive way to visualize DNA sequence patterns.Keywords – DNA Classification, DNA-to-Music, Random Forest, SVM.
Co-Authors Abel Prayoga Aulia Rizki Firdawanti David Vijanarco Martal Hendri Irwandi Linda Leni Fatmawati Mirza Farhan Azhari Najib, Mohamad Khoirun Nicholas Abisha Sri Nurdiati