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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Teknik Komputer AMIK BSI Informatics for Educators and Professional : Journal of Informatics INOVTEK Polbeng - Seri Informatika JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jusikom : Jurnal Sistem Komputer Musirawas J I M P - Jurnal Informatika Merdeka Pasuruan Jurnal Manajemen Informatika JISICOM (Journal of Information System, Infomatics and Computing) Journal of Information System, Applied, Management, Accounting and Research JSAI (Journal Scientific and Applied Informatics) Jurnal Mantik JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Jurnal Sistem Informasi dan Informatika (SIMIKA) Abdi Laksana : Jurnal Pengabdian Kepada Masyarakat Jurnal Teknik Informatika (JUTIF) Teknika Jurnal AbdiMas Nusa Mandiri Indonesian Journal of Engineering Research JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Multidisiplin Madani (MUDIMA) Paradigma Journal of Innovation and Sustainable Empowerment jabdimasunipem
Hafis Nurdin
Universitas Bina Sarana Informatika
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Physics Social Sciences Other

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Evaluating the Sensitivity–Specificity Trade-off in Risk Factor-Based Cervical Cancer Classification Using SMOTE and ACO-Optimized XGBoost Titin Prihatin; Suharjanti Suharjanti; Resti Lia Andharsaputri; Hafis Nurdin
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 4 (2026): Research Articles October 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i4.16797

Abstract

Class imbalance is a major challenge in risk factor-based cervical cancer classification because positive cases are substantially less frequent than negative cases. This study aims to evaluate the effects of the Synthetic Minority Over-sampling Technique (SMOTE) and Ant Colony Optimization (ACO) on XGBoost performance and to investigate the trade-off between sensitivity, specificity, and overall discriminative ability. The Cervical Cancer (Risk Factors) dataset from the UCI Machine Learning Repository, containing 858 observations, was used with Biopsy as the binary classification target, comprising 803 negative and 55 positive cases. A total of 28 risk-factor features were retained after excluding diagnostic attributes with potential data leakage. Three classification scenarios were evaluated: XGBoost, SMOTE-XGBoost, and SMOTE-ACO-XGBoost. Model performance was assessed using Nested Repeated Stratified Cross-Validation, consisting of an outer 5-fold cross-validation repeated five times and an inner 3-fold cross-validation for hyperparameter and decision-threshold optimization. The results showed that SMOTE-XGBoost achieved the highest recall of 0.4182 ± 0.3340, whereas SMOTE-ACO-XGBoost obtained the highest accuracy of 0.7667 ± 0.1698 and specificity of 0.8042 ± 0.1950. Baseline XGBoost achieved the highest ROC-AUC and PR-AUC values of 0.6227 ± 0.0663 and 0.1290 ± 0.0739, respectively. The Friedman test indicated significant differences among the models across all evaluation metrics. Pairwise Wilcoxon signed-rank tests with Holm correction showed that ACO significantly improved accuracy and specificity compared with SMOTE-XGBoost but significantly reduced recall from 0.4182 to 0.2182. These findings demonstrate that class-imbalance handling and hyperparameter optimization do not produce a single model that consistently dominates all performance measures. SMOTE tends to improve sensitivity, whereas ACO optimization shifts the classifier toward higher accuracy and specificity at the cost of reduced sensitivity.
Prediction Of Infant Mortality Using The Decission Tree And Genetic Algorithm Methods Suhardjono, Suhardjono; Sudradjat, Adjat; Wahid, Bilal Abdul; Sugiarto, Hari; Nurdin, Hafis
Paradigma - Jurnal Komputer dan Informatika Vol. 25 No. 1 (2023): March 2023 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v25i1.1819

Abstract

One of the things that plays a role in reducing infant mortality is the government. Based on infant mortality data in Jakarta in 2018 that has been previously tested with the decision tree algorithm, the update in this study is to use the genetic algorithm. The purpose of the update is to increase the accuracy of the results to be maximized. From the test results with the DT algorithm optimized by GA, the maximum accuracy value is 100%, and each attribute has a weight value of 1 where the value is the maximum value. After obtaining maximum results, the data will be used to reduce infant mortality, especially in Jakarta
Comparative Analysis of Multi-Classifier Models with Resampling Techniques for Imbalanced Student Graduation Prediction Carolina, Irmawati; Lia Andharsaputri, Resti; Suharjanti, Suharjanti; Prihatin, Titin; Nurdin, Hafis
Paradigma - Jurnal Komputer dan Informatika Vol. 28 No. 1 (2026): March 2026 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v28i1.11976

Abstract

Student graduation prediction supports early academic intervention but commonly suffers from class imbalance, where on-time graduates dominate the dataset. This study evaluates five classifiers—Random Forest (RF), XGBoost, Logistic Regression (LR), k-Nearest Neighbors (k-NN), and Gaussian Naïve Bayes (GNB)—under five class-imbalance handling scenarios: Baseline (no resampling), Random Undersampling (RUS), SMOTE, ADASYN, and Borderline-SMOTE. Experiments were conducted on 796 student records (10 attributes) with an imbalanced distribution (634 on-time vs. 162 not on-time; ratio 1:3.9) using Stratified 5-Fold Cross-Validation. Performance was assessed using confusion-matrix metrics and AUC-ROC to reflect minority-class detection. Under baseline, RF achieved the highest accuracy (0.873) but limited minority recall (0.573), confirming majority-class bias. Resampling consistently improved minority recall across models; for example, LR recall increased to 0.802 with RUS, while GNB reached 0.833 with ADASYN, although accuracy decreased due to the sensitivity–specificity trade-off. Overall, RF and XGBoost showed the most stable discrimination across resampling scenarios based on AUC (RF: 0.870–0.883; XGBoost: 0.847–0.866). The main contribution is a systematic, reproducible comparative evaluation of classifier–resampling combinations for imbalanced graduation prediction, providing practical guidance for selecting robust models to identify students at risk of delayed graduation.  
Co-Authors Ade Christian Adjat Sudradjat Adjat Sudradjat Ahmad Hafidzul Kahfi Ahmad Yani Alfan Khudori Ananda Bagus Pramastya Anus Wuryanto Astriana Mulyani Bambang Wijonarko Berlian Sigit Sugiarto Buya Ibrahim Dewi Yuliandari Eri Riana Fattya Ariani Felix Wuryo Handono Fernando B Siahaan Ganda Wijaya Giovanni Maria Vianney Tobia Mariatmojo Hari Sugiarto - UBSI Hary Sugiarto Helen Nahumury Herman Kuswanto Imam Nawawi Imam Nawawi, Imam Indra Chaidir, Indra Indriyanti Irmawati Carolina Jajang Jaya Purnama Karlena Indriani Mohammad Roffi Suhendry muhammad qomaruddin Muhammad Qomaruddin Panca Saputra, Elin Pramastya, Ananda Bagus Resti Lia Andharsaputri Ridwansyah Ridwansyah Riki Supriadi Riki Supriyadi Sartini Sartini Sobari, Irwan Agus Sri Rahayu Sudibyo, Aji Sugiono Suhar Janti Suhardjono S Suhardjono Suhardjono Sumanto Sumanto Sumarna Sumarna Sumarna - Sumarna . Sumarna Sumarna Sumarna Sumarna Sumarna Sumarna Sumarna Sumarna Sumarna Sumarna Sumarna Sumarna Supriadi, Riki Supriatiningsih Taufik Asra Taufik Rahman Titin Prihatin Verry Riyanto Wahid, Bilal Abdul Yana Iqbal Maulana