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All Journal Proximal: Jurnal Penelitian Matematika dan Pendidikan Matematika ARBITRER: Jurnal Pendidikan Bahasa dan Sastra Indonesia Tensor: Pure and Applied Mathematics Journal Parameter: Jurnal Matematika, Statistika dan Terapannya Journal of Mathematics, Computation and Statistics (JMATHCOS)
Noya Van Delsen, Marlon Stivo
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Arts Computer Science & IT Education Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Journal : Tensor: Pure and Applied Mathematics Journal

 1 
The Modeling of Factors that Influence the Number of Death Cases of Infant and Toddler in Maluku Province using the Bivariate Poisson Regression Method Haumahu, Gabriella; Djamalullail, Syarifah Fitria Amalia; Noya Van Delsen, Marlon Stivo
Tensor: Pure and Applied Mathematics Journal Vol 5 No 1 (2024): Tensor: Pure and Applied Mathematics Journal
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Pattimura University, Ambon, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/tensorvol5iss1pp17-26

Abstract

The number of cases of infant mortality and under-five mortality have a significant relationship. Although there are differences in age categories, it can be a measure of quality of life early in life. In this study, a bivariate Poisson regression analysis method is used which uses a pair of count data with Poisson distribution. The number of infant deaths and the number of under-five deaths are the dependent variables, while the percentage of poor people , the percentage of married women under 19 years old , the percentage of low birth weight babies , and the percentage of exclusively breastfed babies are the independent variables. Based on the results of the modeling analysis, model 2 of the bivariate Poisson regression proved to be the best model with the lowest AIC value of 123,8951. The results of the analysis at show that variable has an influence on infant mortality cases, shows that variable has a significant effect on under-five mortality cases and at shows that variable has a significant effect simultaneously on infant and under-five mortality cases in Maluku Province in 2022
Application of The Naïve Bayes Algorithm Method for Classification of Families at Risk of Stunting (Case Study: Waeapo District, Buru Regency) Noya Van Delsen, Marlon Stivo; Laamena, Novita Serly; Rumanama, Siti Adnan
Tensor: Pure and Applied Mathematics Journal Vol 5 No 2 (2024): Tensor: Pure and Applied Mathematics Journal
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Pattimura University, Ambon, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/tensorvol5iss2pp111-118

Abstract

Classification is a job of assessing data objects to put them into a certain class from a number of available classes. One algorithm that can be used for classification is the Naïve Bayes Classifier. Naïve Bayes Classifier is a probability concept that can be used to determine class groups of text documents and can process large amounts of data with high accuracy results. The aim of this research is to determine the results of the classification of families at risk of stunting in Waeapo District, Buru Regency and to determine the level of accuracy of three data proportions, namely 70:30, 80:20 and 90:10. The sample in this study was 2290 families. Based on the known level of accuracy, the best accuracy value is a data proportion of 90:10 with an accuracy value of 93.9%.
Clustering of Regencies/Cities in Maluku Province Based on Tuberculosis Cases Using the K-Medoids Algorithm Wattimena, Meyriska; Noya Van Delsen, Marlon Stivo; Salhuteru, Rosalina; Lewaherilla, Norisca
Tensor: Pure and Applied Mathematics Journal Vol 6 No 2 (2025): Vol 6 No 2 (2025): Tensor: Pure and Applied Mathematics Journal
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Pattimura University, Ambon, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/tensorvol6iss2pp67-74

Abstract

Tuberkulosis (TBC) merupakan penyakit menular yang masih menjadi permasalahan kesehatan serius di Indonesia, termasuk di Provinsi Maluku. Penanganan TBC yang efektif memerlukan pemahaman terhadap karakteristik wilayah berdasarkan tingkat kasus TBC. Penelitian ini bertujuan untuk mengelompokkan kabupaten/kota di Provinsi Maluku berdasarkan jumlah kasus TBC periode 2021–2023 menggunakan algoritma K-Medoids, serta menentukan jumlah cluster optimal dengan Metode Elbow dan Silhouette Coefficient. Data yang digunakan diperoleh dari Badan Pusat Statistik Provinsi Maluku. Hasil analisis menunjukkan bahwa jumlah cluster optimal adalah tiga, yaitu: Cluster 0 (kategori rendah) terdiri dari 8 kabupaten/kota, Cluster 1 (kategori sedang) terdiri dari 2 kabupaten/kota, dan Cluster 2 (kategori tinggi) hanya terdiri dari Kota Ambon. Validasi menggunakan Silhouette Coefficient pada k = 3 menunjukkan nilai sebesar 0,641 yang berada pada kategori struktur sedang. Pengelompokan ini diharapkan dapat membantu pemerintah dalam merancang strategi penanggulangan TBC yang lebih terfokus dan sesuai dengan tingkat risiko di masing-masing wilayah.
Co-Authors Djamalullail, Syarifah Fitria Amalia Ferry Kondo Lembang Haumahu, Gabriella Jamco, Juan Charles Laamena, Novita Serly Latuconsina, Fidyah Lewaherilla, Norisca Rosalina Salhuteru Rumanama, Siti Adnan Tomu, Ilham Rizky Maziz Umkeketony, Sarleoki Nancy Wattimena, Meyriska Yudistira