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Pemodelan Prevalensi Angka Kesakitan Malaria Berdasarkan Persentase Sanitasi Layak Dengan Pendekatan Estimator Least Square Spline Kurniawan, Ardi; Widyawati, Ayu Zulva; Pratiwi, Firda Aulia; Faizun, Nurin; Meliana, Relin
Jurnal Kesehatan Lingkungan Indonesia Vol 23, No 3 (2024): Oktober 2024
Publisher : Master Program of Environmental Health, Faculty of Public Health, Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jkli.23.3.287-293

Abstract

Latar belakang: Malaria masih menjadi tantangan kesehatan global yang cukup besar, terutama di daerah tropis seperti Indonesia. Penelitian ini bertujuan untuk mengestimasi dan menentukan model terbaik untuk prevalensi malaria di Indonesia dengan menggunakan data persentase rumah tangga yang memiliki akses terhadap sanitasi yang layak.Metode: Penelitian ini menggunakan metode kuantitatif dengan pendekatan regresi nonparametrik menggunakan estimator Least Square Spline. Penelitian ini menggunakan data sekunder yang diperoleh dari Badan Pusat Statistik (BPS) untuk melihat prevalensi kejadian malaria dan persentase rumah tangga yang memiliki akses terhadap sanitasi yang layak di 34 provinsi di Indonesia.Hasil: Temuan tersebut mengungkapkan bahwa rata-rata 81% rumah tangga di Indonesia memiliki akses terhadap sanitasi yang layak, dengan Daerah Istimewa Yogyakarta memiliki persentase tertinggi yaitu 96,21% dan Papua yang terendah yaitu 40,34%. Selain itu, prevalensi rata-rata morbiditas malaria di Indonesia adalah 3,91 per 1.000 orang, dengan angka tertinggi di Papua sebesar 113,07 dan terendah di beberapa provinsi seperti Sumatera Selatan, Bengkulu, Jawa Barat, Banten, dan Kalimantan Barat sebesar 0,00. Pemodelan menggunakan estimator Least Square Spline menunjukkan bahwa akses sanitasi layak berpengaruh signifikan terhadap prevalensi angka kesakitan malaria. Hasil estimasi model menunjukkan bahwa setiap peningkatan satu persen akses sanitasi layak dapat mengurangi prevalensi angka kesakitan malaria, kecuali di provinsi dengan akses sanitasi layak di atas 80%. Model ini memiliki akurasi tinggi dengan nilai R-Square sebesar 99,11%.Simpulan: Akses sanitasi layak berperan penting dalam menurunkan prevalensi angka kesakitan malaria, namun perlu perhatian khusus di provinsi dengan akses sanitasi layak di bawah 80%. ABSTRACTTitle: Modelling the Prevalence of Malaria Rates based on the Percentage of Adequate Sanitation with the Least Square Spline Estimator ApproachBackground: Malaria remains a significant global health challenge, especially in tropical regions such as Indonesia. This study aims to estimate and determine the best model for malaria prevalence in Indonesia using data on the percentage of households that have access to proper sanitation.Method: This study uses quantitative methods with a nonparametric regression approach using the Least Square Spline estimator. This study uses secondary data obtained from the Central Bureau of Statistics (BPS) to see the prevalence of malaria incidence and the percentage of households that have access to proper sanitation in 34 provinces in Indonesia.Result: The findings revealed that on average 81% of households in Indonesia have access to proper sanitation, with the Special Region of Yogyakarta having the highest percentage at 96.21% and Papua the lowest at 40.34%. In addition, the average prevalence of malaria morbidity in Indonesia is 3.91 per 1,000 people, with the highest rate in Papua at 113.07 and the lowest in several provinces such as South Sumatra, Bengkulu, West Java, Banten, and West Kalimantan at 0.00. Modelling using the Least Square Spline estimator shows that access to proper sanitation has a significant effect on the prevalence of malaria morbidity. The model estimation results show that every one per cent increase in access to proper sanitation can reduce the prevalence of malaria morbidity, except in provinces with access to proper sanitation above 80%. The model has high accuracy with an R-Square value of 99.11%.Conclusion: Adequate access to sanitation is crucial in reducing the prevalence of malaria morbidity, but special emphasis needs to be placed on provinces where sanitation access rates are below 80%.
MODELING LONGITUDINAL FLOOD DATA IN WEST SUMATRA USING THE GENERALIZED ESTIMATING EQUATION (GEE) APPROACH Nitasari, Alfi Nur; Sa'idah, Andini; Faizun, Nurin; Darmawan, Kezia Eunike; Fitri, Marfa Audilla; Chamidah, Nur
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 4 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss4pp2181-2190

Abstract

Flooding is one of the many natural disasters that often hit Indonesia. In July 2023, three areas in West Sumatra experienced floods and landslides which caused damages and even 2 missing victims. Since November 16th, 2023, 8 hamlets in Meranti Village, Landak District, West Sumatra have been inundated by floods which affected families and many public facilities. This research uses data from West Sumatra Province Central Statistics Agency. The data used is 2014, 2018 and 2021. The response variable used is the number of villages/sub-districts experiencing natural disasters according to district/city ( ). The predictor variables used are regional topography , the number of water channels such as rivers, reservoirs, etc. , the number of fields cleared through burning , the number of villages/sub-districts in C excavation area , and the number of dumpsters . This research uses Negative Binomial Regression with the Generalized Estimating Equation (GEE) approach. In the Poisson regression test, the QIC value based on Independent Working Correlation Structure (WCS) is with deviance value of , degree of freedom of , and dispersion score of 4,6144. Because the dispersion value is greater than 1, it can be concluded that there is overdispersion. Because there is more than one overdispersion, it is overcome by using negative binomial. The results of parameter estimation using negative binomial regression based on Independent WCS showed that only one variable was significant, which is the number of fields cleared through burning with deviance value of , degrees of freedom of and a QIC of . Negative Binomial regression model that was formed is ). From the two regression models used, namely Poisson and negative binomial, it was found that the negative binomial regression model was the best model because it had the lowest QIC value of .
FORECASTING THE CLOSING PRICE OF META STOCKS USING A PULSE FUNCTION INTERVENTION ANALYSIS APPROACH Mardianto, M. Fariz Fadillah; Faizun, Nurin; Nauvaldy, Muhammad; Sediono, Sediono
MEDIA STATISTIKA Vol 18, No 1 (2025): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.18.1.83-92

Abstract

Meta Platforms, Inc. (META), the holding company that owns Facebook, Instagram, and WhatsApp, plays a crucial role in advancing artificial intelligence (AI). In early 2024, CEO Mark Zuckerberg announced an ambitious initiative to develop Artificial General Intelligence (AGI), leading to a significant rise in Meta's stock during the first quarter. Consequently, an analysis using the pulse function intervention method was conducted to model and forecast future data. The study utilized weekly data consisting of 124 training and 7 testing observations, spanning from March 13, 2022, to September 15, 2024. The optimal intervention model determined is ARIMA (0,2,1), with parameters (0,0,1) and an intervention point at t = 99. Predictions for a further 8 periods resulted a MAPE of 9.682003% and an MSE of 2411.771. These findings suggest that investors should consider the influence of Zuckerberg's AGI strategy announcement on stock performance. The post-announcement surge indicates a favorable market reaction, and investors should closely follow the AGI project's development to assess META's long-term potential in the technology sector.