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All Journal Journal of Information Systems Engineering and Business Intelligence IJISTECH (International Journal Of Information System & Technology) IJISTECH Science in Information Technology Letters
Hardiyanti, Cicin
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Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Social Sciences

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Journal : Science in Information Technology Letters

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Gender inequality in HDI and per capita expenditure: A probabilistic distribution and spatial data analysis Fadilah, Zainal; Purwaningsih, Tuti; Inderanata, Rochmad Novian; Konate, Siaka; P, Cicin Hardiyanti
Science in Information Technology Letters Vol 3, No 2 (2022): November 2022
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v3i2.1214

Abstract

Men and women have different habits or lifestyles, which inevitably leads to variances in other areas. As a result, gender statistics emerged. In this example, researchers seek to discover if there are discrepancies in HDI and per capita expenditure in Indonesia between men and women. To determine this, data from reliable sources is required; thus, researchers use data from the official BPS website, bps.go.id. The data comes from many tables, so the researcher will join them so that they may be studied. The data used in this scenario are HDI data by gender in 2020 and Per Capita Expenditure data by gender in 2020. Researchers employed graphical tools, such as boxplots and thematic charts, to examine whether there are differences in HDI and per capita expenditure between men and women in Indonesia. Aside from that, researchers used the two-sample t-test approach to see if there were variations in HDI and per capita expenditure between men and women. Researchers will utilize Python software to run this hypothesis test. According to the findings of the investigation, there is still gender imbalance in Indonesia in terms of HDI and per capita expenditure. As a result, it is intended that this research can be utilized as a reference in analyzing existing policies to ensure that there is no gender discrepancy in terms of HDI and per capita expenditure between men and women. It is also envisaged that this research would be beneficial to many people.
Optimizing breast cancer classification using SMOTE, Boruta, and XGBoost Hardiyanti P, Cicin
Science in Information Technology Letters Vol 6, No 1 (2025): May 2025
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v6i1.2109

Abstract

Breast cancer remains one of the leading causes of death among women worldwide. This study aims to develop a clinical data-based breast cancer classification framework by integrating the Synthetic Minority Oversampling Technique (SMOTE), the Boruta feature selection algorithm, and the XGBoost classifier. The proposed approach is tested using the Wisconsin Breast Cancer Diagnostic (WBCD) dataset, consisting of 569 samples and 30 numerical features. SMOTE addresses class imbalance, Boruta selects the most relevant diagnostic features, and XGBoost is the main classification algorithm due to its tabular and imbalanced data robustness. Model validation is conducted through Repeated Stratified K-Fold Cross Validation with 30 repetitions to ensure statistical stability. The resulting model achieves excellent classification performance, with an average accuracy of 0.9608 ± 0.0274, precision of 0.9465 ± 0.0481, Recall of 0.9512 ± 0.0524, and F1-score of 0.9475 ± 0.0374. The ROC-AUC value reaches 0.9926 ± 0.0094, the PR-AUC is 0.9906 ± 0.0113, and the Matthews Correlation Coefficient (MCC) is 0.9179 ± 0.0575, indicating a well-balanced model. Clinically, this model can aid early diagnosis by effectively reducing irrelevant diagnostic attributes, retaining only 10 key features without compromising accuracy, thereby offering a lightweight yet reliable diagnostic tool. However, limitations include the relatively small dataset and the absence of hyperparameter tuning. Future research should explore larger datasets, advanced ensemble methods, and interpretability techniques such as SHAP or LIME to improve clinical transparency and adoption.
Co-Authors Anna Hendri Soleliza Jones Fadilah, Zainal Konate, Siaka Rahadian Kurniawan Rochmad Novian Inderanata Sri Kusumadewi Tuti Purwaningsih, Tuti