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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Telematika International Journal of Artificial Intelligence Research Jurnal Pengabdian Masyarakat Bangsa
Anasanti, Mila Desi
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Assessing Performance Across Various Machine Learning Algorithms with Integrated Feature Selection for Fetal Heart Classification Amanda, Laura Rizka; Anasanti, Mila Desi
International Journal of Artificial Intelligence Research Vol 8, No 1 (2024): June 2024
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v8i1.1110

Abstract

The global concern over declining perinatal death rates, particularly in low- and middle-income nations, underscores the importance of adopting Cardiotocography (CTG) as a vital fetal monitoring method. Recent strides in machine learning (ML) present promising opportunities to enhance the accuracy of assessing fetal health, providing a viable alternative to traditional approaches. This study aims to evaluate various ML methodologies and feature selection techniques for predicting fetal health using CTG data. The primary objective is to improve ML algorithms' accuracy, precision, recall, and F1 score while selecting the most critical features. The dataset includes 2,126 expectant mothers in the third trimester, with 35 variables related to fetal heart rate (FHR) and uterine contractions (UC). Preprocessing involves feature scaling, data balancing, and outlier elimination. Additionally, a 10-fold stratified cross-validation approach is employed to ensure robust evaluation and generalizability of the model's performance. Six ML algorithms—Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Naive Bayes (NB), Logistic Regression (LR), and K-Nearest Neighbors (KNN)—are employed, optimized through grid search cross-validation. The RF algorithm outperforms with an impressive 99% accuracy, closely followed by DT at 98.7%. Optimizing 15 features from the original 35 using Simultaneous Perturbation Feature Selection and Ranking (spFSR) yields a remarkable accuracy of 99%, mirroring the full feature set. This underscores the vital role of selected features in improving predictive power and overall model performance. The study emphasizes the efficacy of tree-based classification algorithms, especially RF, in predicting fetal health and highlights the impact of preprocessing on model performance. These findings suggest avenues for future research, including exploring alternative feature engineering methods and assessing algorithm performance in diverse scenarios.
Pelatihan Artificial Intelligence Dalam Membuat Power Point Pada Remaja Masjid Baitul Halim Hasanah, Riyan Latifahul; Kuntoro, Antonius Yadi; Saelan, M. Rangga Ramadhan; Anasanti, Mila Desi
Jurnal Pengabdian Masyarakat Bangsa Vol. 2 No. 8 (2024): Oktober
Publisher : Amirul Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59837/jpmba.v2i8.1475

Abstract

Pengembangan ilmu pengetahuan dan teknologi (IPTEK) memberikan peran dalam meningkatkan kesejahteraan dan perekonomian masyarakat. Salah satu bidang yang dapat merasakan kehadiran teknologi yaitu bidang pendidikan organisasi kemasyarakatan, termasuk pada organisasi Remaja Masjid Baitul Halim Jakarta Selatan. Untuk mengelola data organisasi diperlukan kemampuan administrasi yang baik, sehingga data bisa tertata dengan baik dan keberlanjutan bagi kegiatan organisasi. Dalam rangka menunaikan salah satu Tri Dharma Perguruan Tinggi, maka Universitas Nusa Mandiri melaksanakan Pengabdian Masyarakat berupa Pelatihan Artificial Intelligence Dalam Membuat Power Point untuk memudahkan proses pemaparan program kerja dan juga sebagai media promosi dan publikasi organisasi. Metode pengabdian masyarakat terdiri dari 4 tahapan, yaitu persiapan, pelaksanaan, evaluasi dan pelaporan. Adapun peserta kegiatan ini adalah para anggota Remaja Masjid Baitul Halim yang mengikuti pelatihan komputer secara offline. Dengan pelatihan ini, peserta merasakan manfaat kehadiran teknologi dimana dapat membantu kegiatan sosial, pendidikan dan keagamaan bagi organisasi. Pemanfaatan AI dalam pembuatan Power Point dapat digunakan untuk membuat presentasi yang menarik dalam slide yang bisa dimodifikasi sesuai kebutuhan.
Improving Alzheimer's Disease Prediction Accuracy using Feature Selection, K Fold Cross Validation, and KNN Imputer Techniques Kirso, Kirso; Anasanti, Mila Desi
Telematika Vol 18, No 1: February (2025)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v18i1.3055

Abstract

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and memory loss; it accounts for 60–70% of dementia cases. Early diagnosis remains challenging due to the subtlety of its symptoms. This study explores the effectiveness of ensemble methods, feature selection techniques, and imputation strategies in enhancing the accuracy of AD diagnosis. We applied an ensemble method with Chi-Square feature selection, achieving a high accuracy of 95.733% with 7 optimal features. The combination of classifiers, including Gradient Boosting (GB), Support Vector Machine (SVM), and Logistic Regression (LR), contributed to the high performance. Additionally, the use of KNN Imputer and K-Fold Cross Validation significantly improved accuracy, regardless of whether feature selection was employed. Notably, feature selection slightly reduced model complexity but resulted in a marginal decrease in accuracy. The study highlights the importance of these methods in achieving reliable AD predictions, though dataset dependency and potential biases from methodological choices are acknowledged. Future work may involve exploring alternative classifiers and validating findings across diverse datasets to enhance generalizability and address these limitations.
Classifying Heart Disease through Fusion of Multi-Source Datasets: Integration of Feature Selection and Explainable Machine Learning Techniques Aprianto, Kasiful; Anasanti, Mila Desi
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 3 (2025): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.92395

Abstract

This study delves into heart disease classification through integrated feature selection and machine learning methodologies, utilizing three datasets comprising 4,728 participants and 11 features, with 4.27% missing data. Employing machine learning, we used XGBoost to achieve 0.95 accuracy for one feature, while Random Forest (RF) demonstrated accuracies of 0.92 and 0.99 for the remaining two features. Comparing 11 classification models, RF and XGBoost classified heart disease with 0.97 and 0.99 accuracy, respectively, using all available features. Applying Feature Elimination with Simultaneous Perturbation Feature Selection and Ranking (SpFSR) revealed that RF attained 0.99 accuracy by selecting only four features (cholesterol level, age, resting electrocardiographic measurements, and maximum heart rate), while XGBoost dropped to 0.91. Constructing an RF model with four features enhanced interpretability without compromising accuracy. Explainable Machine Learning (XAI) techniques, including Permutation Importance and SHAP Summary Plot analyses, gauged feature impact on heart disease prediction. The resting electrocardiographic measurements feature held the highest value (0.40 ± 0.01), followed by maximum heart rate (0.32 ± 0.01), cholesterol level (0.28 ± 0.01), and age (0.26 ± 0.005). These results underscore the significance of each feature in diagnosing heart disease via machine learning.
Co-Authors Amanda, Laura Rizka Antonius Yadi Kuntoro Aprianto, Kasiful Hasanah, Riyan Latifahul Kirso, Kirso Saelan, M. Rangga Ramadhan