A Genetic Algorithm to Determine Research Consultation Schedules in Campus Environment

Idroes, R.; Maulana, Arif; Noviandy, Teuku Rizky; Suhendra, Rivansyah; Sasmita, Novi Reandy; Lala, Archana; Irvanizam, Irvanizam

doi:10.1088/1757-899x/796/1/012033

Cited by 12 publications

(9 citation statements)

References 3 publications

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“…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. The analysis is important because many statistical methods assume a normal distribution [31,32], so analysis results are more accurate if this assumption is met. In addition, normality testing helps identify outliers that may affect the analysis results and allows the researcher to choose the most appropriate statistical method [33,34].…”

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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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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“…Validation of the clustering results will be performed using the Silhouette Index (SI). These validation metrics will assess the quality and coherence of the clusters [21][22][23][24]. Finally, the conclusion will be the culminating step of this study.…”

Section: Data Analysis and Softwarementioning

confidence: 99%

Maternal and Child Healthcare Services in Aceh Province, Indonesia: A Correlation and Clustering Analysis in Statistics

Sasmita,

Ramadeska,

Utami

et al. 2023

Infolitika J. Data Sci.

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Infant mortality remains a public health problem in Aceh Province, Indonesia. Health services during pregnancy are an essential factor in reducing infant mortality. Studies examining factors such as maternal and child health services that have implications for infant mortality in Aceh province are still scarce. Therefore, this study aims to examine the correlation between maternal and child health services variables such as Blood-Supplementing Tablets (TTD), Coverage of the First Visit of Pregnant Women (K1), Coverage of the First Visit of Pregnant Women (K4), and management of Obstetric Complications to live births and to map the maternal and child health services obtained during pregnancy. A cross-sectional study was used as the research study. This study used descriptive statistics, such as measures of data centering and data dispersion. In this work, inferential statistical analysis was conducted using the Shapiro-Wilk test, Spearman test, and fuzzy c-means. The result of the Shapiro Wilk test stated that the live birth rate variable and all Maternal and Child Healthcare Services variables were not normally distributed (p-value < 0.05), all Maternal and Child Healthcare Services variables were positively correlated to live birth rate based on the Spearman test (p-value < 0.05). Based on the Silhouette Index with 0.555, the formation of 3 clusters is the optimal cluster. The clustering is based on the Maternal and Child Healthcare Services that have been provided, where the first, second, and third clusters consist of five districts/city, eight districts/city, and ten districts/city, respectively, as a result of Fuzzy C-Means Clustering.

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“…The integration of feature selection methods such as Genetic Algorithm (GA) [43,44], Particle Swarm Optimization (PSO) [45], and Recursive Feature Elimination (RFE) [46] further enhances the effectiveness of QSAR models, allowing the identification of the most relevant molecular descriptors, allowing for a more focused and precise modeling process. This ensures that the QSAR model is built on the most informative features, ultimately improving its predictive accuracy and interpretability.…”

Section: Molecular Descriptorsmentioning

confidence: 99%

Ensemble Machine Learning Approach for Quantitative Structure Activity Relationship Based Drug Discovery: A Review

Noviandy,

Maulana,

Idroes

et al. 2023

Infolitika J. Data Sci.

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This comprehensive review explores the pivotal role of ensemble machine learning techniques in Quantitative Structure-Activity Relationship (QSAR) modeling for drug discovery. It emphasizes the significance of accurate QSAR models in streamlining candidate compound selection and highlights how ensemble methods, including AdaBoost, Gradient Boosting, Random Forest, Extra Trees, XGBoost, LightGBM, and CatBoost, effectively address challenges such as overfitting and noisy data. The review presents recent applications of ensemble learning in both classification and regression tasks within QSAR, showcasing the exceptional predictive accuracy of these techniques across diverse datasets and target properties. It also discusses the key challenges and considerations in ensemble QSAR modeling, including data quality, model selection, computational resources, and overfitting. The review outlines future directions in ensemble QSAR modeling, including the integration of multi-modal data, explainability, handling imbalanced data, automation, and personalized medicine applications while emphasizing the need for ethical and regulatory guidelines in this evolving field.

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A Genetic Algorithm to Determine Research Consultation Schedules in Campus Environment

Cited by 12 publications

References 3 publications

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

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

Maternal and Child Healthcare Services in Aceh Province, Indonesia: A Correlation and Clustering Analysis in Statistics

Ensemble Machine Learning Approach for Quantitative Structure Activity Relationship Based Drug Discovery: A Review

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