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All Journal Statistika International Journal of Advances in Intelligent Informatics Jurnal Statistika dan Matematika (Statmat) Journal of Applied Data Sciences Media Publikasi Promosi Kesehatan Indonesia (MPPKI) Infolitika Journal of Data Science Unnes Journal of Public Health
Zurnila Marli Kesuma
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering Mathematics Public Health Other

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Feature Selection Data Indeks Kesehatan Masyarakat Menggunakan Algoritma Relief Zurnila Marli Kesuma
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 11, No 1 (2011)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v11i1.1041

Abstract

Feature selection adalah suatu metode penganalisaan data yang bertujuan untuk memilih fitur yangberpengaruh (fitur optimal) dan mengesampingkan fitur yang tidak berpengaruh. Ada beberapaalgoritma feature selection yang dapat digunakan, salah satunya adalah Relief. Relief memanfaatkanteknik bobot (weight) untuk mengukur signifikansi fitur dalam konteks klasifikasi dan fitur yangmemiliki nilai bobot di atas ambang batas (threshold) yang digunakan akan dipilih. Penelitian inibertujuan untuk mendapatkan fitur optimal dari data data indeks kesehatan masyarakat.Hasil pengolahan data menunjukkan bahwa untuk setiap data yang diuji hanya menghasilkan satufitur optimal dengan nilai threshold yang berbeda.
Decision Tree versus k-NN: A Performance Comparison for Air Quality Classification in Indonesia Sasmita, Novi Reandy; Ramadeska, Siti; Kesuma, Zurnila Marli; Noviandy, Teuku Rizky; Maulana, Aga; Khairul, Mhd; Suhendra, Rivansyah
Infolitika Journal of Data Science Vol. 2 No. 1 (2024): May 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v2i1.179

Abstract

Air quality can affect human health, the environment, and the sustainability of ecosystems, so efforts are needed to monitor and control air quality. The Plume Air Quality Index (PAQI) is one of the indices to measure and determine the level of air quality. In measuring the accuracy of the air quality level, it is necessary to do the right classification. Some previous studies have conducted classification analysis using the decision tree and K-Nearest Neighbor (k-NN) methods, but only evaluated using accuracy values. Therefore, this study uses both methods to evaluate the results of air quality level classification not only with accuracy but also with precision, recall, and F1-score. Secondary data of pollutant concentration values and PAQI categories based on particulate matter (PM2.5 and PM10), nitrogen dioxide (NO2), and ozone (O3) derived from Plume Labs for 33 provincial capitals in Indonesia in the time period from July 1 to December 31, 2022, were used in this study. From the results of comparing the performance of the two methods, it is found that the decision tree has a greater performance value than the performance value of k-NN. The decision tree performance values for accuracy, precision, recall and F1-score are 90.67%, 90.61%, 90.67%, and 90.63%, respectively. So, it can be concluded that the decision tree performs better than k-NN in classifying PAQI categories with better overall evaluation metric values.
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.
Spatial Estimation of Relative Risk for Dengue Fever in Aceh Province using Conditional Autoregressive Method Rahayu, Latifah; Sasmita, Novi Reandy; Adila, Wulan Farisa; Kesuma, Zurnila Marli; Kruba, Rumaisa
Journal of Applied Data Sciences Vol 4, No 4: DECEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i4.141

Abstract

Dengue Fever (DHF) is a dangerous infectious disease that can cause death in an infected person. DHF is a disease transmitted by the Aedes Aegypti mosquito. Dengue cases have been reported in 449 districts/cities spread across 34 provinces with deaths spread across 162 districts/cities in 31 provinces, one of which is in Aceh Province. However, there are districts and cities in Aceh Province with a large number of cases and population at risk, and there are also districts and cities with fewer cases and population at risk. As a result, the number of cases and population at risk of DHF varies. Therefore, it is important to do planning to see which districts and cities have a high chance of DHF. In this study, the type of data used is secondary data sourced from the Aceh Provincial Health Profile from 2016 to 2022. The approach used is the Bayesian Conditional Autoregressive (CAR) prior model Besag-York-Mollie (BYM). The results of this study showed that mortality in dengue cases in Aceh Province from 2016 to 2022 had the highest mortality values in 2016 and 2022. The results of estimating the relative risk of DHF cases using the Bayesian Conditional Autoregressive (CAR) approach of the Besag-York-Mollie (BYM) Model in Aceh Province fulfill all categories with their relative risk values. Some districts/cities have relative risk values. Some districts/cities have high relative risk values of DHF cases and low relative risk values of DHF cases. Sabang city had the highest relative risk value of 3.54 and Bener Meriah district had the lowest relative risk of 0.2.
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.
Leveraging hybrid ANN–AHP to optimize cement industry average inventory levels Fradinata, Edy; Noor, Muhamad Mat; Kesuma, Zurnila Marli; Suthummanon, Sakesun; Asmadi, Didi
International Journal of Advances in Intelligent Informatics Vol 10, No 1 (2024): February 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v10i1.631

Abstract

In recent years, inventory has been critical due to the production cost and overstock risk related to the expiration date and the fluctuation price risk. This study's minimization of overstock and price fluctuation in the warehouse used a hybridized artificial neural network (ANN) and analytical hierarchy process (AHP) to produce an optimum model. The variables, such as average demand, reorder point, order quantity, factor service level, safety stock, and average inventory level, were used to obtain the optimal condition of the average inventory levels to maximize the profit. Then, the type of inventory system that guarantees the minimum risks in managing the inventory would be selected. The result shows that the data has a mean of 39.2 units, and the standard deviation (SD) was 12.9. This means that the order quantity is 20.2 units, the average inventory level is 57.3, and the average demand is 39. These conditions used the factor z, which is 97% service level. This study concludes that the optimum average inventory level is 91 units, the order quantity is 11 units with the maximum average profit is $1098, and the peak fluctuation condition maximum profit is $1463 when the average inventory level is 7.3, and the inventory policy system used to minimize the risk is the continuous review policy type. The study could be beneficial to reduce production costs and enhance overall profitability and operational efficiency in the sector by mitigating the risks associated with excessive inventory and price volatility while also minimizing the potential for expired inventory.
Spatial-Temporal Epidemiology of COVID-19 in Aceh, Indonesia: A Statistical Perspective Sasmita, Novi Reandy; Phonna, Rahmatil Adha; Kesuma, Zurnila Marli; Kamal, Saiful; Yusya, Nudzran
Unnes Journal of Public Health Vol. 13 No. 2 (2024)
Publisher : Universitas Negeri Semarang (UNNES) in cooperation with the Association of Indonesian Public Health Experts (Ikatan Ahli Kesehatan Masyarakat Indonesia (IAKMI))

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/ujph.v13i2.3428

Abstract

 The development of COVID-19 cases in Aceh for each region based on spatio-temporal is vital information to know. Spatio-temporal mapping is carried out to knowthe distribution of cases in diversity based on regional and time conditions. The timeseries design study was used as the research design in this study. This study aims toobtain factors that influence the incidence of COVID-19 cases in Aceh using paneldata regression analysis and the GTWR model for more accurate results. There arenine variables from 23 districts/cities in Aceh Province in 2020 and 2021. Based onpartial panel data regression analysis, of the eight independent variables that arefactors for analysis, it shows that only the variable number of doctors (p < 0.000),number of Tuberculosis Cases (p < 0.000), Number of Villages with Puskesmas (p< 0.026), and Number of Poor population (p < 0.035) have a significant effect onthe increase in COVID-19 cases in Aceh. The number of Tuberculosis Cases is avery dominant variable. Then, the results of the GTWR analysis using the AdaptiveKernel Exponential weighting function show that regional and time diversity affectthe factors that cause an increase in COVID-19 cases in Aceh. These factors need tobe a concern in controlling COVID-19 cases in Aceh in the future. 
OPTIMALISASI TEMPAT TIDUR MENGGUNAKAN MODEL SISTEM DINAMIK DI RUMAH SAKIT ZAINAL ABIDIN KOTA BANDA ACEH Kesuma, Zurnila Marli; Fradinata, Edy; Fitri, Aida
STATMAT : JURNAL STATISTIKA DAN MATEMATIKA Vol 3 No 1 (2021)
Publisher : Math Program, Math and Science faculty, Pamulang University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/sm.v3i1.7859

Abstract

Sistem dinamik dapat digunakan untuk sistem yang kompleks. Model sistem dinamik menyediakan cara untuk memahami bagaimana penyebab perilaku suatu sistem, mendeteksi perubahan dari waktu ke waktu dan penentuan faktor-faktor yang meramalkan perilaku secara signifikan. Model sistem dinamik juga memungkinkan dalam penentuan skenario yang masuk akal sebagai masukan untuk keputusan dan kebijakan suatu sistem. Pelayanan rawat inap di Rumah Sakit Umum Daerah dr. Zainoel Abidin (RSUDZA) memiliki 710 tempat tidur yang didistribusikan ke beberapa ruangan. Dalam prakteknya, pasien lebih memilih dirawat di rumah sakit swasta daripada RSUDZA, dikarenakan lamanya menunggu sampai mendapat tempat tidur untuk rawat inap. Tujuan penelitian ini adalah untuk mengoptimalisasi pemakaian tempat tidur rawat inap di RSUDZA dengan menggunakan metode sistem dinamik. Berdasarkan perilaku suatu sistem, diramalkan jumlah pasien dengan tempat tidur dan membuat beberapa skenario sebagai alternatif kebijakan untuk rumah sakit.. Data yang digunakan yaitu data bulanan jumlah pasien dengan tempat tidur yang berjumlah 16 bulan dari bulan Januari tahun 2018 sampai bulan April tahun 2019 yang diperoleh dari RSUDZA Banda Aceh. Data diolah dan dianalisis menggunakan software Vensim PLE versi 6.3. Hasil penelitian menunjukkan bahwa terdapat dua ruangan dengan jumlah kapasitas tempat tidur belum optimal, yaitu ruang Aqsha dan Raudhah. Hasil peramalan jumlah pasien dengan tempat tidur rawat inap ruang Aqsha dan Raudhah, diperoleh bahwa jumlah pasien dengan tempat tidur cenderung menurun dari waktu ke waktu. Pada skenario pengoptimalan tempat tidur di ruangan Aqsha, diperoleh bahwa skenario 3 merupakan skenario terbaik dengan menurunkan laju pasien yang mendaftar rawat inap sebesar 10%, dengan nilai MAPE yang diperoleh sebesar 4,3%. Selanjutnya, skenario pengoptimalan tempat tidur di ruangan Raudhah, diperoleh bahwa skenario 4 merupakan skenario terbaik dengan menurunkan laju pasien yang mendaftar rawat inap sebesar 15%, dengan nilai MAPE yang diperoleh sebesar 6,34%
Co-Authors Adila, Wulan Farisa Arifin, Mauzatul Asep Rusyana Asmadi, Didi Asshiddiqi, M. Ischaq Nabil Chongsuvivatwong, Virasakdi Fikri, Mumtaz Kemal Fitri, Aida Fradinata, Edy Kamal, Saiful Khairul, Mhd Kruba, Rumaisa Latifah Rahayu Mardalena, Selvi Maulana, Aga Noor, Muhamad Mat Phonna, Rahmatil Adha Rahayu, Latifa RAHAYU, LATIFAH Ramadeska, Siti Romi Rosadi Sasmita, Novi Reandy Siti Rusdiana Suhendra, Rivansyah Suthummanon, Sakesun Teuku Rizky Noviandy Yusya, Nudzran