Simple combination method of FTIR spectroscopy and chemometrics for qualitative identification of cattle bones

Nadia, Yola; Ramli, Muliadi; Muslem,; Japnur, A. F.; Rusyana, Asep; Idroes, Ghazi Mauer; Suhendra, Rivansyah; Muhammad, .; Sasmita, Novi Reandy; Tallei, Trina Ekawati; Idroes, Rinaldi

doi:10.1088/1755-1315/364/1/012040

Cited by 14 publications

(10 citation statements)

References 19 publications

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“…Data analysis used Python programming language software version 3.10.0 with Jupyter Notebook editor version 6.5.2. Descriptive analysis was used to calculate data concentration and data distribution to summarize and describe the characteristics of a dataset [14,20,21]. The statistical method used in this study consists of frequency as part of descriptive statistical analysis [18,20,22].…”

Section: Discussionmentioning

confidence: 99%

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Decision Tree versus k-NN: A Performance Comparison for Air Quality Classification in Indonesia

Sasmita,

Ramadeska,

Kesuma

et al. 2024

Infolitika J. Data Sci.

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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.

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Section: Discussionmentioning

confidence: 99%

“…Model evaluation in this study uses the confusion matrix method which can represent information on the comparison of prediction results with actual conditions [13,14]. Performance is measured based on a combination of accuracy, precision, recall, and F1-score [15].…”

Section: Classification Algorithms Data Sharing and Performance Evalu...mentioning

confidence: 99%

Decision Tree versus k-NN: A Performance Comparison for Air Quality Classification in Indonesia

Sasmita,

Ramadeska,

Kesuma

et al. 2024

Infolitika J. Data Sci.

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“…This makes it a good choice when you are concerned about detecting departures from normality, even with a relatively small sample size [26]. The purpose of testing for data normality in statistics is to check whether the data follows a normal distribution [27][28][29][30]. In the Shapiro-Wilk normality test, the null hypothesis (H0) is that the data follows a normal distribution, and the alternative hypothesis (H1) is that the data does not follow a normal distribution.…”

Section: Methodsmentioning

confidence: 99%

Statistical Assessment of Human Development Index Variations and Their Correlates: A Case Study of Aceh Province, Indonesia

Sasmita,

Phonna,

Fikri

et al. 2023

Grimsa J. Bus. Econ. Stud.

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The Human Development Index (HDI) provides a holistic measure of human development in a country or locality. This study aims to identify factors correlated with changes in the Human Development Index and analyze changes in the distribution of the Human Development Index in Aceh Province from 2012 to 2022. Apart from the Human Development Index as the variable used in this study, five variables are used in this study as indicators: Life Expectancy, Gross Regional Domestic Product (GRDP), Per Capita Expenditure, Average Years of Schooling, and Expected Years of Schooling as socioeconomic factors. This research uses an ecological study design. Data was sourced from the "Aceh in Figures" report by the Central Bureau of Statistics of Aceh Province. The statistical methods used were descriptive statistics, the Shapiro-Wilk test for normality, the Spearman test for correlation analysis, the Wilcoxon one-sample test for data distribution, and the Kruskal-Wallis test to compare distributions. Based on the correlation analysis, the study revealed that the five socioeconomic variables tested showed a significant positive correlation with changes in the HDI in Aceh Province (p-value < 0.05). In addition, the difference analysis showed a significantly different distribution of HDI across the years studied (p-value < 0.05), with a pattern of increasing HDI observed from the beginning to the end of the study period. The recommended based on finding of the study is policymakers and stakeholders focus on strategies that enhance the positive correlates identified Finally, these results provide important and structured insights into the role of factors in HDI change.

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“…One key algorithm within the MCMC method is the Gibbs Sampling technique, as explained by [24] [25]. Gibbs Sampling simplifies complex calculations by generating random variables from the marginal distribution without the need for density calculations [26] [27]. It focuses on identifying the univariate conditional distribution, involving only one variable to be determined [28][29].…”

Section: The Markov Chain Monte Carlomentioning

confidence: 99%

Spatial Estimation of Relative Risk for Dengue Fever in Aceh Province using Conditional Autoregressive Method

Rahayu

2023

J. Appl. Data Sci.

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The study aims to comprehensively analyze the spatial distribution and varying risk levels of Dengue Hemorrhagic Fever (DHF) within Aceh Province. The primary objective is to identify and delineate regions within Aceh Province that demonstrate diverse probabilities of DHF occurrences. By investigating the discrepancies in DHF cases and population susceptibility across districts and cities, the research intends to facilitate strategic planning and targeted interventions for disease mitigation and control. Utilizing secondary data sourced from the Aceh Provincial Health Profile spanning 2016 to 2022, this study employs the Bayesian Conditional Autoregressive (CAR) prior model Besag-York-Mollie (BYM). This statistiThe research significantly contributes to enhancing our understanding of DHF distribution patterns and associated risks within Aceh Province. By employing a robust statistical model and analyzing secondary health profile data, the study offers valuable insights into identifying areas with varying levels of DHF risk. The findings are pivotal in guiding evidence-based decision-making for targeted interventions, resource allocation, and strategic planning aimed at mitigating the impact of DHF in high-risk regions. The study's outcomes highlight notable fluctuations in mortality due to dengue cases within Aceh Province, particularly evident in the peaks observed during 2016 and 2022. Furthermore, through the Bayesian CAR (BYM) model, the research identifies districts and cities with varying relative risk values for DHF occurrences. Notably, Sabang city emerges with the highest relative risk value of 3.54, signifying elevated susceptibility, while Bener Meriah district demonstrates the lowest relative risk at 0.2, indicating lower vulnerability to DHF. These findings provide critical insights into the heterogeneous DHF risk landscape across Aceh Province, informing targeted interventions and planning strategies to effectively address the disease burden.

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Simple combination method of FTIR spectroscopy and chemometrics for qualitative identification of cattle bones

Cited by 14 publications

References 19 publications

Decision Tree versus k-NN: A Performance Comparison for Air Quality Classification in Indonesia

Decision Tree versus k-NN: A Performance Comparison for Air Quality Classification in Indonesia

Statistical Assessment of Human Development Index Variations and Their Correlates: A Case Study of Aceh Province, Indonesia

Spatial Estimation of Relative Risk for Dengue Fever in Aceh Province using Conditional Autoregressive Method

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