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All Journal JOIV : International Journal on Informatics Visualization Indonesian Journal of Electrical Engineering and Computer Science International Journal of Economic, Business, Accounting, Agriculture Management and Sharia Administration (IJEBAS) International Journal of Advanced Science Computing and Engineering International Journal of Advances in Artificial Intelligence and Machine Learning
Shahreen Kasim, Shahreen
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering

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A Microarray Data Pre-processing Method for Cancer Classification Hui, Tay Xin; Kasim, Shahreen; Md Fudzee, Mohd Farhan; Abdullah, Zubaile; Hassan, Rohayanti; Erianda, Aldo
JOIV : International Journal on Informatics Visualization Vol 6, No 4 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.4.1523

Abstract

The development of microarray technology has led to significant improvements and research in various fields. With the help of machine learning techniques and statistical methods, it is now possible to organize, analyze, and interpret large amounts of biological data to uncover significant patterns of interest. The exploitation of microarray data is of great challenge for many researchers. Raw gene expression data are usually vulnerable to missing values, noisy data, incomplete data, and inconsistent data. Hence, processing data before being applied for cancer classification is important. In order to extract the biological significance of microarray gene expression data, data pre-processing is a necessary step to obtain valuable information for further analysis and address important hypotheses. This study presents a detailed description of pre-processing data method for cancer classification. The proposed method consists of three phases: data cleaning, transformation, and filtering. The combination of GenePattern software tool and Rstudio was utilized to implement the proposed data pre-processing method. The proposed method was applied to six gene expression datasets: lung cancer dataset, stomach cancer dataset, liver cancer dataset, kidney cancer dataset, thyroid cancer dataset, and breast cancer dataset to demonstrate the feasibility of the proposed method for cancer classification. A comparison has been made to illustrate the differences between the dataset before and after data pre-processing.
Karonese Sentiment Analysis: A New Dataset and Preliminary Result Karo Karo, Ichwanul Muslim; Md Fudzee, Mohd Farhan; Kasim, Shahreen; Ramli, Azizul Azhar
JOIV : International Journal on Informatics Visualization Vol 6, No 2-2 (2022): A New Frontier in Informatics
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.2-2.1119

Abstract

Amount social media active users are always increasing and come from various backgrounds. An active user habit in social media is to use their local or national language to express their thoughts, social conditions, socialize, ideas, perspectives, and publish their opinions. Karonese is a non-English language prevalent mostly in North Sumatra, Indonesia, with unique morphology and phonology. Sentiment analysis has been frequently used in the study of local or national languages to obtain an overview of the broader public opinion behind a particular topic. Good quality Karonese resources are needed to provide good Karonese sentiment analysis (KSA). Limitation resources become an obstacle in KSA research. This work provides Karonese Dataset from multi-domain social media. To complete the dataset for sentiment analysis, sentiment label annotated by Karonese transcribers, three kinds of experiments were applied: KSA using machine learning, KSA using machine learning with two variants of feature extraction methods. Machine learning algorithms include Logistic Regression, Naïve Bayes, Support Vector Machine and K-Nearest Neighbor. Feature extraction improves model performance in the range of 0.1 – 7.4 percent. Overall, TF-IDF as feature extraction on machine learning has a better contribution than BoW. The combination of the SVM algorithm with TF-IDF is the combination with the highest performance. The value of accuracy is 58.1 percent, precision is 58.5 percent, recall is 57.2, and F1 score is 57.84 percent
Co-Authors A Hamid, Isredza Rahmi Ab Razak, Mohd Faizal Abbas, Sara Tarek ElSayed Abbood, Maan Nawaf Abdullah, Noryusliza Abdullah, Nuraini Abdullah, Zubaile Ahmad Firdaus, Ahmad Ahmad, Johanna Alde Alanda, Alde Aldo Erianda, Aldo Ali Shah, Zuraini Aljanabi, Mohammad Alwaisi, Shaimaa Safaa Ahmed Anuar, Anies Nurfazlin Arrova Dewi, Deshinta Arshad, Mohamad Safwan Asmuni, Hishammuddin Bin Jubeir, Mohammed BinJubier, Mohammed Defni, - Dickenn, Haezel Ann Dwiny Meidelfi Efrizoni, Lusiana Eg Su, Goh Fadly Fadly Ferda Ernawan Gusman, Taufik Halim, Shahliza Abd Hanif Jofri, Muhamad Hassan, Norhasniza Hendrick, - Hidra Amnur Hui, Tay Xin Ichwanul Muslim Karo Karo Indrarini Dyah Irawati Ismail, Mohd Arfian Jalil, Luma Fayeq Jaya, M. Izham Kai Yuen, Simon Chong Kamarudin, Nur Fatihah Kamarudin, Nur Khairani Khairuddin, Alif Ridzuan Ling, Teng Mee Mat Isa, Mohd Anuar Mizan, Muhammad Thaqiyuddin Mohamad Sukri, Khairul Amin Mohamad, Radziah Mohd Farhan MD Fudzee, Mohd Farhan Mohd Fauzi, Abdullah Munzir Mohd Fuaad, Nur Atiqah Mohd Fudzee, Mohd Farhan Mohd Noh, Noraziah Moi, Sim Hiew Muhaini Othman, Muhaini Nazirah, Nurul Ain Noorhizama, Nur Khairunnisa Norung, Muhammad Hazim Muhamad Osman, Mohd Zamri Puspita, Kartika Qasim, Adeeb Mansoor Ramlan, Rohaizan Ramli, Azizul Azhar Rassem, Taha H. Rasyidah, - Rd. Rohmat Saedudin Rohayanti Hassan, Rohayanti Rohman, Muhammad Ghofar Ronal Hadi Ruslaan, Mohd Asyraf Saifannur, Andri Saifunnizam, Syamir Thaqif Salamat, Mohamad Aizi Saringat, Mohd Zainuri Selamat, Norhanim Sujon, Khaled Mahmud Sumatrani Saragih, Majied Thevaraju, Devi Priya Tole Sutikno Weng, Fong Cheng Yit, Tan Wen Yong, Pang Yee Yulherniwati, - Yuris Alkhalifi Zainodin, Muhammad Edzuan Zakaria, Mohd Zaki Zakaria, Noor Hidayah Zakaria, Noor Hidayah Binti Zakaria, Zalmiyah Zamri, Nurul Aqilah