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All Journal International Journal of Advances in Intelligent Informatics Science and Technology Indonesia JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal Infomedia MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Serambi Engineering JURSIMA (Jurnal Sistem Informasi dan Manajemen) Indonesian Journal of Electrical Engineering and Computer Science jeti Jurnal Teknologi JURSIMA Journal Of Artificial Intelligence And Software Engineering Bulletin of Engineering Science, Technology and Industry
Arhami, Muhammad
Politeknik Negeri Lhokseumawe
Biochemistry, Genetics & Molecular Biology 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 Engineering Environmental Science Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Physics Social Sciences

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Journal : Science and Technology Indonesia

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Handling Missing Data Using Combination of Deletion Technique, Mean, Mode and Artificial Neural Network Imputation for Heart Disease Dataset Anita Desiani; Novi Rustiana Dewi; Annisa Nur Fauza; Naufal Rachmatullah; Muhammad Arhami; Muhammad Nawawi
Science and Technology Indonesia Vol. 6 No. 4 (2021): October
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/sti.2021.6.4.303-312

Abstract

The University of California Irvine Heart disease dataset had missing data on several attributes. The missing data can loss the important information of the attributes, but it cannot be deleted immediately on dataset. To handle missing data, there are several ways including deletion, imputation by mean, mode, or with prediction methods. In this study, the missing data were handled by deletion technique if the attribute had more than 70% missing data. Otherwise, it were handled by mean and mode method to impute missing data that had missing data less or equal 1%. The artificial neural network was used to handle the attribute that had missing data more than 1%. The results of the techniques and methods used to handle missing data were measured based on the performance results of the classification method on data that has been handled the problem of missing data. In this study the classification method used is Artificial Neural Network, Naïve Bayes, Support Vector Machine, and K-Nearest Neighbor. The performance results of classification methods without handling missing data were compared with the performance results of classification methods after imputation missing data on dataset for accuracy, sensitivity, specificity and ROC. In addition, the comparison of the Mean Squared Error results was also used to see how close the predicted label in the classification was to the original label. The lowest Mean Squared Error wasobtained by Artificial Neural Network, which means that the Artificial Neural Network worked very well on dataset that has been handled missing data compared to other methods. The result of accuracy, specificity, sensitivity in each classification method showed that imputation missing data could increase the performance of classification, especially for the Artificial Neural Network method.
Majority Voting as Ensemble Classifier for Cervical Cancer Classification Anita Desiani; Endang Sri Kresnawati; Muhammad Arhami; Yulia Resti; Ning Eliyati; Sugandi Yahdin; Titania Jeanni Charissa; Muhammad Nawawi
Science and Technology Indonesia Vol. 8 No. 1 (2023): January
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/sti.2023.8.1.84-92

Abstract

Cervical cancer is one of the deadliest female cancers. Early identification of cervical cancer through pap smear cell image evaluation is one of the strategies to reduce cervical cancer cases. The classification methods that are often used are SVM, MLP, and K-NN. The weakness of the SVM method is that it is not efficient on large datasets. Meanwhile, in the MLP method, large amounts of data can increase the complexity of each layer, thereby affecting the duration of the weighting process. Moreover, the K-NN method is not efficient for data with a large number of attributes. The ensemble method is one of the techniques to overcome the limitations of a single classification method. The ensemble classification method combines the performance of several classification methods. This study proposes an ensemble method with the majority voting that can be used in cervical cancer classification based on pap smear images in the Herlev dataset. Majority Voting is used to integrate test results from the SVM, MLP, and KNN methods by looking at the majority results on the test data classification. The results of this study indicate that the accuracy results obtained in the ensemble method increased by 1.72% compared to the average accuracy value in SVM, MLP, and KNN. for sensitivity results, the results of the ensemble method were able to increase the sensitivity increase by 0.74% compared to the average of the three single classification methods. for specificity, the ensemble method can increase the specificity results by 3.4%. From the results of the study, it can be concluded that the ensemble method with the most votes is able to improve the classification performance of the single classification method in classifying cervical cancer abnormalities with pap smear images.
Co-Authors Ajeng Islamia Putri Al Khaidar Amalia, Ismi Anita Desiani Annisa Nur Fauza Anwar Anwar Azhar Azhar Kholiq Affandi Cut Adnin Nalisa Derry Fajirwan Des Alwine Zayanti Dwi Meilvinasvita Endang Sri Kresnawati Endro Setyo Cahyono Guntur Syahputra Hendrawati Hendrawati Hendrawaty Husaini Husaini Husaini Husaini Huzaeni Huzaeni Huzeni Huzeni Indra Mawardi Indrawati Indrawati Irmeilyana Irmeilyana Irvan Andrian Khairul Azman Khairunnisa Atami Mahdi Mahdi Muhammad Irmansyah Muhammad Muhammad Muhammad Nawawi Muhammad Nawawi Muhammad Rizka Muhammad Rizki Mustainul Abdi Nabila Mufida Nadia Ulfa Naufal Rachmatullah Naziruddin Naziruddin Nelly Safitri Ning Eliyati Novi Rustiana Dewi Qatrun Nada Renol Herwinsyah Rifkie Primartha Rizaldo Setiawan Safriadi Safriadi Salahuddin Salahuddin Salma Sheila Maulina Putri Shania Putri Andhini Simbolon, Zulfan Khairil Sugandi Yahdin Sugandi Yahdin Sugandi Yahdin Titania Jeanni Charissa Yuli Andirani Yulia Resti Zamzami Zhenyu Cui