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Prasetyaningrum, Putri Taqwa
Universitas Mercu Buana Yogyakarta
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Journal : International Journal Of Computer, Network Security and Information System (IJCONSIST)

 1 
Multiclass Classification with Imbalanced Class and Missing Data Pratama, Irfan; Putri Taqwa Prasetyaningrum
IJCONSIST JOURNALS Vol 2 No 1 (2020): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (481.493 KB) | DOI: 10.33005/ijconsist.v2i1.25

Abstract

In any data mining field, the presence of a good shaped data is needed. Yet in the reality, the data condition is far from the expectation as there are possible to have missing values, redundant data, and inconsistent data. There are problems with the dataset to begin with before we overcome the problem of data mining process interpretation. In the raw data level, possible problem such as missing values and data redundancy or inconsistency can be solved by some certain process called preprocessing. On the preprocessing step, the raw dataset is adjusted to the needs of the whole process, one of the adjustments is to handle missing values. Missing values is a certain condition where the expected values of the data are not recorded. The other problems that happen in the real-world dataset especially in categorical data with label or class is the imbalance distribution of the instance for each class. The imbalanced class is a condition where the distribution of the class is skewed or biased. This study emphasizing on the problem solving of missing values and imbalanced class on the dataset. K-NN imputation is a missing value handling method of this study. As for the imbalanced class problem, this study utilizes SMOTE and ADASYN for the comparison. While the dataset will further be tested by various classification methods such as Decision tree, Random Forest, and Stacking. The original dataset produced bad score from the classification process due to the imbalanced data. Then the data undergoing an oversampling process using SMOTE and ADASYN methods in hope that the accuracy will be hugely better. Yet the reality is the accuracy score do not move to the expected number at all with only averaging in 32%-37% of accuracy score in any scheme of process.
COMPARISON OF SUPPORT VECTOR MACHINE RADIAL BASE AND LINEAR KERNEL FUNCTIONS FOR MOBILE BANKING CUSTOMER SATISFACTION ANALYSIS Putri Taqwa Prasetyaningrum; Nurul Tiara Kadir; Albert Yakobus Chandra; Irfan Pratama
IJCONSIST JOURNALS Vol 4 No 1 (2022): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v4i1.75

Abstract

Banking services using mobile banking applications, including Indonesian state bank (called BRI). A study on feedback regarding BRI services based on mobile applications was done. In order to compete with other banks, that is used to enhance and modernize the quality of BRI services provided to clients. Based on phenomena that occur in these situations. This study aims to classify comments from users of the BRI Mobile Banking Application on Google Play services into positive and negative comment sentiments. In this study, the Support Vector Machine (SVM) technique is utilized to determine between positive or negative reviews. The sentiment analysis of BRI google play data was carried out by comparing the Radial Basis Function (RBF) kernel function and the Linear kernel. As well as the experiment of adding feature selection, parameters, and n-grams for a period of two years, from January 1st,, 2017 to December 31st, 2018. The results of the study using the k-fold cross-validation test, the precision value of the SVM kernel linear is 90.80 percent and the SVM kernel RBF is 90.15 percent. In the RBF kernel, there are 1,816 positive classes and 1,455 negative classes. While the Linear kernel obtained a positive class of 1,734 and a negative class of 1,637.
Application of Gray Level Co-Occurrence Matrix (GLCM) for Abdominal Wave Image Classification: A Comparative Study of LVQ, KNN, and SVM Putri Taqwa Prasetyaningrum; Ibnu Rivansyah Subagyo
IJCONSIST JOURNALS Vol 6 No 2 (2025): March
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v6i2.126

Abstract

Medical image classification is a crucial research area in medical imaging analysis to support clinical diagnosis. In this study, we implemented the Gray Level Co-Occurrence Matrix (GLCM) method to extract texture features from abdominal wave images and enhance classification accuracy. Three machine learning classification methods—Learning Vector Quantization (LVQ), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM)—were employed and compared based on their classification performance. The experimental results show that the KNN method achieved the highest accuracy of 96.83%, followed by SVM with 95.24%, and LVQ with 84.13%. These findings indicate that KNN is the most effective classification method for abdominal wave images among those tested. This study highlights the significance of texture feature extraction using GLCM in improving medical image classification accuracy. The results of this study can contribute to the advancement of digital healthcare technologies, particularly in gastrointestinal disorder detection and digestive health monitoring. Future research should explore hybrid deep learning approaches and larger datasets to further enhance classification accuracy and model robustness.
Co-Authors Abdul Hadi Adi Ronggo Wicaksono Affandi Putra Pradana Agung Supoyo Agustin, Isnaini Ahmad Iwan Fadli Ahmad Mukhlasin Ahsan, Moh Ajisari, Lanang Dian Albert Yakobus Chandra Albert Yakobus Chandra Alphi Mukti Anggie Kurniawati Anggo Luthfi Yunanto Ari Wibowo Arita Witanti Aritonang, Roselina Artika Sari Arwa Ulayya Haspriyanti Ati, Gresensia Rosadelima Aziza, Fadilla Maharani Azzahra, Bernica Bagus Nur Solayman Bambang Setio Purnomo Bambang Setio Purnomo Budianto, Alexius Endy Cahyani, Rivana Dwi Cindy Okta Melinda Dapit Virdaus Denny Jean Cross Sihombing Devi Febrianti Dewi, Amelia Kristiana dewi, Ine shinta Dhana Sudana Eka Aryani, Eka Erza, Muhammad Al-Ghifari Fithriatus Shalihah Fransiskus Xaverius Pere GUNARTATIK ESTHININGTYAS Hamam Nurrofiq Hasnidar Hasnidar Heri Agus Prasetyo Herin, Sofia Ibnu Rivansyah Subagyo Ibrahim, Norshahila Imam Riadi Irfan Pratama Irya Wisnubhadra Julius Bata Jumiyati Juwita Juwita Karlina, Leni Khalifah Samiih Sya'bani Sya'bani Khoirut Tamimi Kris Rahayu Kristina Andryani Larasaty, Raditha Latifah, Retno Leni Karlina Lewoema, Scholastica Larissa Zefira luky kurniawan, luky M. Anjas Leonardi M. Irfan Bahri Mita Oktafani Mu'ti, Dewi Lestari Mukti, Alphi Rinaldi Nalendra Mutaqin Akbar Nadeak, Puja Waldi Nanda, Tietan Geovanka Ningsih, Rully Ningsih, Ruly Norshahila Ibrahim Nuning Rusmilawati Nur Sholehah Dian Saputri Nuri Budi Hangesti Nurul Tiara Kadir Okta, Sri Oktafani, Mita Ozzi Suria Ozzi Suria Ozzi Suria Pipin Yuliyanto Pratama, Bagus Wahyu Ari Pratama, Harfin Ibna Pratama, Irfan Puja Waldi Nadeak Puja Putra, Rio Aji Hadyanta Putry Wahyu Setyaningsih Raharjo, Fajar Sujud Rani Dwi Lestari Reny Yuniasanti Resi Dwi Febrianti Rias Ilham Agung Nugroho Robiin, Bambang Rosita, Rani Rustiawan, Muhammad Rizqi Akfani Sabilla, Annisa Calza Sasa saka, Hildegardis Kristina Santoso Pamungkas Sari, Artika Scholastica Lewoema Setiyani, Santi Setyaningsih, Putry Wahyu Simarmata, Penni Wintasari Subagyo, Ibnu Rivansyah Suria, Ozzi Suyoto Suyoto Viony Julianti Sipayung Wahyuningsih Wahyuningsih