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All Journal JOIN (Jurnal Online Informatika) Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Indonesian Journal of Electrical Engineering and Computer Science Tridharmadimas: Jurnal Pengabdian Kepada Masyarakat Jayakarta Jurnal Pengabdian Ibnu Sina Jurnal Pengabdian Masyarakat Bangsa SWAGATI: Journal of Community Service
Ferda Ernawan
Program Pasca Sarjana Magister Teknik Informatika, Universitas Dian Nuswantoro,Semarang 50131
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Journal : JOIV : International Journal on Informatics Visualization

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Comparative Analysis of Robust Imputation Techniques for Enhancing Cervical Cancer Prediction with Missing Data Mizan, Muhammad Thaqiyuddin; Ernawan, Ferda; Kasim, Shahreen; Erianda, Aldo; Mohd Fauzi, Abdullah Munzir
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.4501

Abstract

Handling missing data is a critical challenge in machine learning applications, as it can significantly affect the accuracy and reliability of predictive models. Addressing this issue is crucial for developing robust systems that can deliver high-performance results. This study provides a comparative analysis of the robust imputation technique for cervical cancer prediction with incomplete information. This study has investigated the importance of robust imputation techniques, particularly Soft Imputer, in addressing missing data challenges and enhancing model performance. This study investigates the impact of various imputations across five distinct approaches: KNN imputer, PCA imputer, MICE imputer, XGBoost imputer, LightGBM imputer, and feature selection methods. These imputation data are tested on several machine learning models such as Random Forest (RF), Extreme Gradient Boosting (XGB), Decision Tree (DT), Support Vector Classifier (SVC), Logistic Regression (LR), Extra Trees Classifier (ETC), CatBoost Classifier, Stochastic Gradient Descent (SGD), and Gradient Boosting (GB) for improving classification accuracy of cervical cancer prediction. The evaluation reveals that the soft imputer method achieves a balanced and effective handling of missing data, significantly improving the reliability of the models. Among the tested methods, LightGBM and XGBoost deliver strong results, each achieving an average accuracy of 96.91%. MICE demonstrated the lowest average accuracy at 95.94%, although it still performs reliably in managing missing data. The findings provide valuable insights for enhancing predictive accuracy in future work by integrating advanced imputation strategies for high-dimensional and complex datasets.
Brain Tumor Classification based on Convolutional Neural Networks with an Ensemble Learning Approach through Soft Voting Puspita, Kartika; Ernawan, Ferda; Alkhalifi, Yuris; Kasim, Shahreen; Erianda, Aldo
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.4609

Abstract

The brain is a vital organ that serves various purposes in the human body. Processing sensory data, generating muscle movements, and performing complex cognitive tasks have all historically relied heavily on the brain. One of the most common conditions affecting the brain is the growth of abnormal tissue in brain cells, leading to the development of brain tumors. The most common forms of brain tumors are pituitary, glioma, and meningioma, which are major global health issues. From these issues, there is a need for appropriate and prompt handling before the brain tumor disease becomes more severe. Quick handling is through an early disease detection approach, and computer vision is one of the trending early disease detection methods that can predict diseases using images. This research proposes a model in computer vision, namely the Convolutional Neural Network (CNN), with a soft voting ensemble learning strategy to classify brain tumors. The dataset consists of 7,023 images without tumors and MRI brain tumors such as glioma, meningioma, and pituitary with a resolution of 512x512 pixels. This experiment investigates classifier models such as VGG16, MobileNet, ResNet50, and DenseNet121, each of which has been optimized to maximize performance. The proposed soft voting ensemble strategy outperformed existing methods, with an accuracy of 97.67% and a Cohen's Kappa value of 0.9688. The proposed soft voting ensemble method approach has proven effective in improving the accuracy.
Co-Authors Aldo Erianda, Aldo Avivah, Siti Nur Bakar, Suraya Abu Dendy K. Pramudito Dwiza Riana Edy Winarno Eko Adi Sarwoko Eni Heni Hermaliani, Eni Heni Fakhreldin, Mohammad Faqih Hamami Gata, Windu Hafidz, Muhammad Afnan Hasanah, Riyan Latifahul Ismail, Mohd Arfian Laela Kurniawati, Laela Liew, Siau-Chuin Lindung Parningotan Manik Manik, Lindung Parningotan Mat Raffei, Anis Farihan Mizan, Muhammad Thaqiyuddin Mohd Fauzi, Abdullah Munzir Na'am, Jufriadif Na’am, Jufriadif Pardede, Hilman F Pardede, Hilman Ferdinandus Prajanto Wahyu Adi, Prajanto Wahyu Puspita, Kartika Shahreen Kasim, Shahreen Sinung Suakanto Yuris Alkhalifi