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All Journal Marine Kreatif Journal of Applied Data Sciences 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

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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.
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
Co-Authors Ahmad Watsiq Maula Alfarisi, Irnu Apriliansyah, Feby Arif Saputra, Arif Arifin, Mauzatul Dahlawy, Arriz Deri Anggraini Dimas Chaerul Ekty Saputra Elfi Yulia Farah Salsabila, Farah Fitri , Fathimah Atika Hizir Sofyan Khairul, Moh Kruba, Rumaisa Mohamad Gazali Muliadi Mulyani, Riska RAHAYU, LATIFAH Rahmadani, Novi Ramadhani, Evi Rina Syafitri, Rina Ropita, Ropita Sasmita, Novi Reandy Teuku Rizky Noviandy Ulfa, Elvitra Mutia Zuriat, Zuriat Zurnila Marli Kesuma