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All Journal Marine Kreatif Journal of Applied Data Sciences Media Publikasi Promosi Kesehatan Indonesia (MPPKI) Infolitika Journal of Data Science South East Asian Management Concern Journal
Mardalena, Selvi
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Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Environmental Science Public Health

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Pemanfaatan Buah Mangrove Sonneratia alba Sebagai Sirup Alami di Desa Lhok Bubon, Aceh Barat Salsabila, Farah; Gazali, Mohamad; Anggraini, Deri; Ropita, Ropita; Mardalena, Selvi; Alfarisi, Irnu; Syafitri, Rina; Zuriat, Zuriat
Marine Kreatif Vol 8, No 1 (2024): Marine Kreatif
Publisher : Universitas Teuku Umar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35308/mk.v8i1.9790

Abstract

Mangrove memiliki banyak manfaat dan dapat dijadikan sebagai sumber pangan fungsional. Mangrove merupakan jenis tanaman dikotil yang hidup di habitat air laut dan air payau. Tujuan dari pelaksanaan pengabdian masyarakat adalah untuk mentransferkan keterampilan dan pengetahuan dalam memproduksi sirup buah pedada (Sonneratia alba) kepada masyarakat pesisir Lhok Bubon Aceh Barat. Metode pelaksanaan kegiatan pengabdian masyarakat meliputi sosialisasi, persiapan bahan baku, dan pelatihan kepada masyarakat pesisir di Lhok Bubon Aceh Barat. Pelaksanaan kegiatan pengabdian kepada masyarakat dilaksanakan dengan melibatkan Wanita pesisir Lhok Bubon yang menghasilkan produk sirup buah pedada yang berasal dari mangrove S. alba. Outcomes yang diharapkan dalam kegiatan ini adalah pemanfaatan secara berkelanjutan mangrove S. alba sebagai sirup alami yang dapat meningkatkan pendapatan masyarakat pesisir.
A Statistical Clustering Approach: Mapping Population Indicators Through Probabilistic Analysis in Aceh Province, Indonesia Sasmita, Novi Reandy; Khairul, Moh; Sofyan, Hizir; Kruba, Rumaisa; Mardalena, Selvi; Dahlawy, Arriz; Apriliansyah, Feby; Muliadi, Muliadi; Saputra, Dimas Chaerul Ekty; Noviandy, Teuku Rizky; Watsiq Maula, Ahmad
Infolitika Journal of Data Science Vol. 1 No. 2 (2023): December 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v1i2.130

Abstract

The clustering, one of statistical analysis, can be used for understanding population patterns and as a basis for more targeted policy making. In this ecological study, we explored the population dynamics across 23 districts/cities in Aceh Province. The study used the Aceh Population Development Profile Year 2022 data, focusing on the total population, in-migrants, out-migrants, fertility, and maternal mortality as variables. The study employed descriptive statistics to ascertain the data distribution, followed by the Shapiro-Wilk test to evaluate normality, which is crucial for selecting the appropriate statistical methods. The Spearman test was used to determine correlations between the total population and the variable as indicators. Probabilistic Fuzzy C-Means (PFCM) method is used for clustering. To optimize clustering, the silhouette coefficient was calculated using the Euclidean Distance and the elbow method, with the results analyzed using R-4.3.2 software. This study's design and methods aim to provide a nuanced understanding of demographic patterns for targeted policy-making and regional development in Aceh, Indonesia. Based on the data normality test results, only fertility (p-value = 0.45), while the other variables are not normally distributed. Spearman test was used, and the results showed that only in-migrants (p-value = 1.78 x 10-6) and out-migrants (p-value = 2.30 x 10-6) correlated to the Aceh Province population. Using the population variable and the two variables associated with it, it was found that 4 is the best optimum number of clusters, where clusters 1, 2, 3, and 4 consist of three districts/city, nine districts/city, four districts/city and seven districts/city respectively.
Unraveling Geospatial Determinants: Robust Geographically Weighted Regression Analysis of Maternal Mortality in Indonesia Rahayu, Latifah; Ulfa, Elvitra Mutia; Sasmita, Novi Reandy; Sofyan, Hizir; Kruba, Rumaisa; Mardalena, Selvi; Saputra, Arif
Infolitika Journal of Data Science Vol. 1 No. 2 (2023): December 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v1i2.133

Abstract

Maternal Mortality Rate (MMR) in Indonesia has experienced a concerning annual increase, reaching 4,627 deaths in 2020 compared to 4,221 in 2019. This upward trajectory underscores the urgency of investigating the factors contributing to MMR. Recognizing the spatial heterogeneity and outliers in the data, our study employs the Robust Geographically Weighted Regression (RGWR) method with the Least Absolute Deviation approach. Using secondary data from the 2020 Indonesian Health Profile publication, the research seeks to establish province-specific models for MMR in 2020 and identify the key influencing factors in each region. Standard regression analyses fall short in addressing the complexities present in the data, making the RGWR approach crucial for understanding the nuanced relationships. The chosen RGWR model utilizes the Least Absolute Deviation method and a fixed kernel exponential weighting function. Notably, this model maintains a consistent bandwidth value across all locations, showcasing its robustness. In evaluating the model variations, the exponential fixed kernel weighting function emerges as the most optimal, boasting the smallest Akaike Information Criterion (AIC) value of 23.990 and the highest coefficient of determination  value of 93.66%. The outcomes of this research yield 24 distinct models, each tailored to the unique characteristics of every province in Indonesia. This nuanced, location-specific approach is vital for developing effective interventions and policies to address the persistently high MMR. By providing insights into the complex interplay of factors influencing maternal mortality in different regions, the study contributes to the groundwork for targeted and impactful public health initiatives across Indonesia.
Spatial Estimation for Tuberculosis Relative Risk in Aceh Province, Indonesia: A Bayesian Conditional Autoregressive Approach with the Besag-York-Mollie (BYM) Model Sasmita, Novi Reandy; Arifin, Mauzatul; Kesuma, Zurnila Marli; Rahayu, Latifah; Mardalena, Selvi; Kruba, Rumaisa
Journal of Applied Data Sciences Vol 5, No 2: MAY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i2.185

Abstract

Tuberculosis (TB) remains a significant public health challenge globally, with Indonesia being the second-highest country in TB cases worldwide. Aceh Province has one of the highest TB incidence rates in Indonesia. This study aims to estimate and map the spatial distribution patterns of TB relative risk across districts in Aceh Province, Indonesia, to reveal significant variations. The study employed an ecological time-series study design, utilizing the Bayesian Conditional Autoregressive (CAR) approach with the Besag-York-Mollie (BYM) model for spatial estimation and mapping of TB relative risk. TB case data and population data for 23 districts/cities in Aceh Province from 2016 to 2022 were analyzed. Spatial analysis was used to estimate and map TB's relative risk, aiding in identifying areas with higher transmission risks. The results showed that the relative risk of TB varied across districts/cities in Aceh Province over the study period. However, Lhokseumawe and Banda Aceh consistently exhibited high to very high relative risks over the years. In 2022, Lhokseumawe City and Banda Aceh City had the highest relative risks by 2.26 and 2.17, respectively, while Sabang City and Bener Meriah District had the lowest by 0.43 and 0.32, respectively. This study provides valuable insights into the heterogeneous landscape of TB risk in Aceh Province, which can inform targeted interventions and planning strategies for effective TB control. Using the Bayesian CAR BYM model proved effective in estimating and mapping TB's relative risk, highlighting areas requiring prioritized attention in TB prevention and control efforts.
Relative Risk and Distribution Assessment of Tuberculosis Cases: A Time-Series Ecological Study in Aceh, Indonesia Sasmita, Novi Reandy; Khairul, Mhd; Fikri, Mumtaz Kemal; Rahayu, Latifa; Kesuma, Zurnila Marli; Mardalena, Selvi; Kruba, Rumaisa; Chongsuvivatwong, Virasakdi; Asshiddiqi, M. Ischaq Nabil
Media Publikasi Promosi Kesehatan Indonesia (MPPKI) Vol. 8 No. 6: JUNE 2025 - Media Publikasi Promosi Kesehatan Indonesia (MPPKI)
Publisher : Fakultas Kesehatan Masyarakat, Universitas Muhammadiyah Palu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56338/mppki.v8i6.7264

Abstract

Introduction: Tuberculosis (TB) remains a critical public health issue, particularly in high-incidence regions like Aceh Province, Indonesia. This study aimed to estimate the Relative Risk (RR) and analyze significant differences in the temporal distribution of TB cases across Aceh Province. Methods: A time-series ecological study was conducted using TB case and population data from 23 districts/cities in Aceh Province between 2016 and 2022. Data were analyzed using R software, applying descriptive and inferential statistics. The Standardized Morbidity Ratio (SMR) method estimates RR and is categorized into five risk levels. The Kolmogorov-Smirnov test assessed data normality, guiding the selection of statistical tests. The Friedman and Wilcoxon Signed-Rank tests examined differences in TB case distribution trends. Results: Significant spatial and temporal variations in TB risk were identified. Districts such as Banda Aceh (RR = 2.29–2.13) and Lhokseumawe (RR = 1.89–2.21) consistently demonstrated high RR from 2016 to 2022, reflecting persistent TB transmission. A general upward trend in TB cases was observed across districts, with significant spatial variation (p < 0.001), highlighting a worsening TB burden. Conclusions: The study emphasizes the urgent need for targeted public health interventions tailored to TB's unique spatial and temporal dynamics in Aceh Province, Indonesia. Applying SMR and robust statistical analyses provides valuable insights to inform localized TB control policies and strengthen management strategies in high-burden areas.
Klasifikasi Status Pembangunan Literasi Masyarakat di Provinsi Aceh menggunakan Analisis Diskriminan Linear Rahmadani, Novi; Mulyani, Riska; Fitri , Fathimah Atika; Yulia , Elfi; Ramadhani, Evi; Mardalena, Selvi
South East Asian Management Concern Vol. 2 No. 2 (2025): May
Publisher : Science, Technology, and Education Care

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61761/seamac.2.2.56-63

Abstract

Indeks pembangunan literasi masyarakat (IPLM) merupakan indikator yang digunakan untuk menilai upaya pemerintah daerah dalam membina dan mengembangkan literasi masyarakat melalui layanan perpustakaan. Penelitian ini bertujuan untuk mengklasifikasikan kabupaten/kota di Provinsi Aceh berdasarkan indeks pembangunan literasi masyarakat (IPLM) tahun 2024 menggunakan analisis diskriminan. Tiga variabel digunakan sebagai prediktor dalam klasifikasi, yaitu pemerataan layanan perpustakaan, ketercukupan koleksi perpustakaan, dan tingkat kunjungan masyarakat per hari, yang diukur terhadap 23 kabupaten/kota. Hasil penelitian menunjukkan bahwa data memenuhi syarat kenormalan multivariat, kesamaan matriks varians-kovarians, dan perbedaan signifikan antar kelompok. Model diskriminan yang terbentuk menghasilkan fungsi klasifikasi dengan akurasi prediksi sebesar 78,26% pada data pelatihan dan 73,91% pada validasi silang. Nilai canonical correlation sebesar 0,981 dan eigenvalue sebesar 24,13 menunjukkan kemampuan diskriminatif yang sangat kuat dalam membedakan kelompok IPLM tinggi dan rendah
A Stochastic Projection for Tuberculosis Elimination in Indonesia by 2030 Sasmita, Novi Reandy; Ramadani, Maya; Ikhwan, Muhammad; Munawwarah, Munawwarah; Rahayu, Latifah; Mardalena, Selvi; Ischaq Nabil Asshiddiqi, M.; Suyanto, Suyanto; Safira, Nanda
Media Publikasi Promosi Kesehatan Indonesia (MPPKI) Vol. 8 No. 11: NOVEMBER 2025 - Media Publikasi Promosi Kesehatan Indonesia (MPPKI)
Publisher : Fakultas Kesehatan Masyarakat, Universitas Muhammadiyah Palu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56338/mppki.v8i11.8548

Abstract

Introduction: Indonesia, with the world's second-highest tuberculosis (TB) burden, has targeted TB elimination (65 cases per 100,000) by 2030. This study aimed to evaluate the feasibility of achieving this goal by projecting TB incidence trends using a stochastic epidemic model that accounts for the uncertainties inherent in TB transmission dynamics in latent TB infections. Methods: The initial values for state variables and parameters were derived from a comprehensive literature review and calibrated against publicly available epidemiological data from the Indonesian Ministry of Health reports from 2018-2022. A Susceptible, Vaccinated, Three Exposed, Three Infectious, Recovered (SVE3I3R) model was developed, incorporating Gaussian noise into the exposed compartments to simulate real-world unpredictability in latent infection dynamics. The model was solved numerically using the fourth-order Runge-Kutta (RK4) method in R software. Key outcomes measured were the projected incidence of drug-susceptible TB (DS-TB), multidrug-resistant TB (MDR-TB), and extensively drug-resistant TB (XDR-TB). Results: Model projections suggest that the overall TB incidence rate will fall from 387 cases per 100,000 people in 2023 to a projected 320 cases per 100,000 by 2030. However, this remains far above the national target. While DS-TB cases decreased to 730,283, MDR-TB and XDR-TB cases were projected to surge dramatically to 120,939 cases and 104,651 individuals, respectively. The estimation signals a critical shift in the epidemic's profile. Conclusions: Indonesia is not on track to achieve its 2030 TB elimination target under current interventions. The alarming rise of drug-resistant TB necessitates an urgent, aggressive, and multifaceted policy response. This study underscores the critical value of incorporating stochasticity into epidemiological models for more realistic forecasting and public health planning in high-burden settings.
Co-Authors Ahmad Watsiq Maula Alfarisi, Irnu Apriliansyah, Feby Arif Saputra, Arif Arifin, Mauzatul Asshiddiqi, M. Ischaq Nabil Chongsuvivatwong, Virasakdi Dahlawy, Arriz Deri Anggraini Dimas Chaerul Ekty Saputra Farah Salsabila, Farah Fikri, Mumtaz Kemal Fitri , Fathimah Atika Hizir Sofyan Ischaq Nabil Asshiddiqi, M. Khairul, Mhd Khairul, Moh Kruba, Rumaisa Mohamad Gazali Muhammad Ikhwan Muliadi Mulyani, Riska nanda safira, nanda Raden Mohamad Herdian Bhakti Rahayu, Latifa RAHAYU, LATIFAH Rahmadani, Novi Ramadani, Maya Ramadhani, Evi Rina Syafitri, Rina Ropita, Ropita Sasmita, Novi Reandy Suyanto Suyanto Teuku Rizky Noviandy Ulfa, Elvitra Mutia Yulia , Elfi Zuriat, Zuriat Zurnila Marli Kesuma