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Discrete Wavelet Transform (DWT) dan Random Forest untuk Deteksi Kanker Berdasarkan Klasifikasi Data Microarray Triyani, Monica; Adiwijaya, Adiwijaya; Aditsania, Annisa
JURNAL INFOTEL Vol 12 No 3 (2020): August 2020
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v12i3.484

Abstract

Cancer is one of the leading causes of death worldwide. According to the World Health Organization (WHO), in 2018, about 9.6 million deaths caused by cancer. DNA microarray technology has played an important role in analyzing and diagnosing cancer. The accuracy resulting from the classification of Random Forests is not optimal because microarrays have large dimensional data. Therefore, it is necessary to reduce the dimensions of the Discrete Wavelet Transform (DWT) as a feature to reduce dimensions and increase accuracy in microarray data. Based on the simulation, the dimension can be reduced and improve the accuracy of classification up to 8% - 20%. DWT approximation coefficient can improve accuracy better than detailed coefficients for data on colon cancer 100%, lung cancer 100%, ovarian 100%, prostate tumor 80%, and central nervous system 83.33%.
Fitur Seleksi pada Data Microarray untuk Deteksi Kanker Berdasarkan Klasifikasi Random Forest Nuklianggraita, Tita Nurul; Adiwijaya, Adiwijaya; Aditsania, Annisa
JURNAL INFOTEL Vol 12 No 3 (2020): August 2020
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v12i3.485

Abstract

Cancer is a disease that can affect all organs of humans. Based on data from the World Health Organization (WHO) fact sheet in 2018, cancer deaths have reached 9.6 million. One known way to detect cancer that is with Microarray Technique, but the microarray data have large dimensions due to the number of features that are very much compared to the number of samples. Therefore, dimension reduction should be made to produce optimum accuracy. In this paper, we compare Minimum Redundancy Maximum Relevance (MRMR) and Least Absolute Shrinkage and Selection Operator (LASSO) to reduce the dimension of microarray data. Moreover, by using Random Forest (RF) Classifier, the performance of classification (cancer detection) is compared. Based on the simulation, it can be concluded that LASSO is better than MRMR because it can produce an evaluation of 100% in lung and ovarian cancer, 92% colon cancer, 93% prostate tumor, and 83% central nervous system.
Perancangan Signage Lapangan Gasmin Kota Bandung Soedewi, Sri; Murdowo, Djoko; Wulandari, Ratri; Yuniati, Arnanti Primiana; Gunawan, Putu Harry; Aditsania, Annisa; Adrin, Athaya Fatharani; Prabasworo, Bhanu
Visualita Jurnal Online Desain Komunikasi Visual Vol 9 No 1 (2020)
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/visualita.v9i1.3335

Abstract

The Gasmin Field is a public facility area located in Antapani, Bandung city. This field often uses for general community activities such as sports, gatherings, bazaars, ceremonies, and other activities. The condition of the environment around the Gasmin Field area is not well maintained. The absence of signage makes comfortless and makes it difficult for visitors when they want to find the target area. Therefore, signage and wayfinding designs are needed to make it easier to access locations around the Gasmin Field and increase visitor convenience. Design-based research use as a design method for Environmental Graphic Design (EGD) includes predesign, design, and post-design and data collection. Observations, interviews, and literature studies were carried out to obtain data and analyzed. The results of data analysis used to design signage and wayfinding in the Gasmin Field, Bandung City, West Java.