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Rian Aditia
Universitas Islam Al Azhar Mataram
Agriculture, Biological Sciences & Forestry Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Languange, Linguistic, Communication & Media Social Sciences

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Analisis Pengaruh Jumlah Penduduk terhadap Insidensi Demam Berdarah Dengue di Provinsi Nusa Tenggara Barat: Pendekatan Regresi Linear Sederhana dan Implikasinya terhadap Kebijakan Pembangunan Daerah Rian Aditia; Hisbullah; Hanif Al Jauziah
Journal of Science and Technology: Alpha Vol. 2 No. 2 (2026): Journal of Science and Technology: Alpha, April 2026
Publisher : Lembaga Publikasi Ilmiah Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/alpha.v2i2.487

Abstract

Dengue Hemorrhagic Fever (DHF) remains a major vector-borne public health problem in Indonesia, including West Nusa Tenggara Province (NTB). Beyond its epidemiological consequences, dengue also affects household economic burden, community productivity, and regional health expenditure. This study aims to analyze the effect of population size on dengue incidence in NTB and to formulate its implications for regional development policy. A quantitative approach was applied using simple linear regression. The study used secondary data on population size and dengue cases from ten districts/cities in NTB during the 2018-2023 period, resulting in 60 observations. Population size was treated as the independent variable, while the number of dengue cases was used as the dependent variable. The analysis showed a positive and statistically significant linear relationship between population size and dengue incidence, with the regression equation Y = -66.384 + 0.0037X. The ANOVA test produced an F-value of 199.284 with p < 0.001, while the coefficient of determination calculated from the model sum of squares yielded an R² of 0.775. These findings indicate that population size explains approximately 77.5% of the variation in dengue cases within the simple model. The results highlight the importance of integrating demographic information into health planning, environmental control, sanitation improvement, and evidence-based regional development policy. Nevertheless, further studies should incorporate additional variables such as population density, rainfall, temperature, humidity, sanitation quality, and socioeconomic factors to obtain a more comprehensive epidemiological understanding.
Prediksi Kunjungan Wisatawan Nusantara dan Mancanegara di Provinsi Nusa Tenggara Barat Menggunakan Long Short-Term Memory Berbasis Adam Optimizer dan Gradient Clipping Muhammad Habibi; Muhammad Zaenul Hari; Rian Aditia
Journal of Science and Technology: Alpha Vol. 2 No. 2 (2026): Journal of Science and Technology: Alpha, April 2026
Publisher : Lembaga Publikasi Ilmiah Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/alpha.v2i2.488

Abstract

Tourism is one of the strategic sectors that significantly contributes to regional economic growth, particularly in West Nusa Tenggara (NTB), Indonesia. Accurate forecasting of tourist arrivals is essential to support tourism planning, destination management, and evidence-based policy making. However, conventional forecasting methods often experience limitations in capturing nonlinear and long-term temporal patterns in tourism time-series data. This study proposes a Long Short-Term Memory (LSTM)-based forecasting model optimized using the Adam Optimizer and Gradient Clipping techniques to improve prediction accuracy and training stability. Monthly tourist arrival data consisting of domestic and international visitors during the period of 2014-2023 were obtained from the Tourism Office of West Nusa Tenggara Province. Data preprocessing was performed using Min-Max Scaling before dividing the dataset into training and testing sets with ratios of 70:30 and 80:20. The proposed model was evaluated using the Root Mean Squared Error (RMSE) metric under two training scenarios of 100 and 200 epochs. Experimental results demonstrate that the best forecasting performance was achieved using a 70:30 training-testing ratio with 200 epochs, resulting in the lowest RMSE value of 66.70. The integration of Adam Optimizer and Gradient Clipping improves model convergence stability while reducing prediction errors. Furthermore, the proposed model effectively captures seasonal patterns and long-term trends in tourist arrivals, making it suitable for supporting smart tourism development and intelligent decision-support systems for tourism management in West Nusa Tenggara.
Co-Authors Hanif Al Jauziah Hisbullah Muhammad Habibi Muhammad Zaenul Hari