Article Per Year (5 Year)
p-Index From 2020 - 2025
0.92
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) JOIV : International Journal on Informatics Visualization Jurnal Pengabdian kepada Masyarakat Nusantara Komposit : Jurnal Pengabdian Dan Pemberdayaan Masyarakat
Yusoff, Marina
Unknown Affiliation
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Social Sciences Other

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
Hybrid filtering methods for feature selection in high-dimensional cancer data Md Noh, Siti Sarah; Ibrahim, Nurain; Mansor, Mahayaudin M.; Yusoff, Marina
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6862-6871

Abstract

Statisticians in both academia and industry have encountered problems with high-dimensional data. The rapid feature increase has caused the feature count to outstrip the instance count. There are several established methods when selecting features from massive amounts of breast cancer data. Even so, overfitting continues to be a problem. The challenge of choosing important features with minimum loss in a different sample size is another area with room for development. As a result, the feature selection technique is crucial for dealing with high-dimensional data classification issues. This paper proposed a new architecture for high-dimensional breast cancer data using filtering techniques and a logistic regression model. Essential features are filtered out using a combination of hybrid chi–square and hybrid information gain (hybrid IG) with logistic regression as classifier. The results showed that hybrid IG performed the best for high-dimensional breast and prostate cancer data. The top 50 and 22 features outperformed the other configurations, with the highest classification accuracies of 86.96% and 82.61%, respectively, after integrating the hybrid information gain and logistic function (hybrid IG+LR) with a sample size of 75. In the future, multiclass classification of multidimensional medical data to be evaluated using data from a different domain.
Hybrid embedded and filter feature selection methods in big-dimension mammary cancer and prostatic cancer data Md Noh, Siti Sarah; Ibrahim, Nurain; M. Mansor, Mahayaudin; Md Ghani, Nor Azura; Yusoff, Marina
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3101-3110

Abstract

The feature selection method enhances machine learning performance by enhancing learning precision. Determining the optimal feature selection method for a given machine learning task involving big-dimension data is crucial. Therefore, the purpose of this study is to make a comparison of feature selection methods highlighting several filters (information gain, chi-square, ReliefF) and embedded (Lasso, Ridge) hybrid with logistic regression (LR). A sample size of n=100, 75 is chosen randomly, and the reduction features d=50, 22, and 10 are applied. The procedure for feature reduction makes use of the entire sample sizes. Each sample size's results are compared, including tests with no feature selection process. The results indicate that LR+ReliefF is the best method for mammary cancer data, whereas LR+IG is the best for prostatic cancer data, making the filter more suitable than embedded for big-dimension data. This study revealed that the sample's features and size influence the most effective method for selecting features from big-dimension data. Therefore, it provides insight into the most effective methods for particular features and sample sizes in high-dimensional data.
TCSC Implementasi Sistem Informasi Menuju SMART Desa di Desa Banyumas Kecamatan Stabat Kabupaten Langkat Roslina, Roslina; Hasan Putra, Purwa; Amelia, Afritha; Dalimunthe, Yulia Agustina; Mohamad Zain, Jasni Bt; Yusoff, Marina
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 4 No. 4 (2023): Jurnal Pengabdian kepada Masyarakat Nusantara (JPkMN)
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Sistem informasi pemerintahan desa berbasis web dapat mendorong masyarakat untuk berpartisipasi aktif dalam pembangunan desa. Di sisi lain, transparansi informasi berbasis web dapat meningkatkan tertib administrasi pemerintahan desa. Masalah utama aparatur pemerintah desa di Desa Banyumas adalah masih rendahnya pengetahuan dan keterampilan aparatur desa tentang pengelolaan dan pemanfaatan sistem informasi desa. Kegiatan pengabdian ini bertujuan untuk meningkatkan aplikasi pelayanan desa kepada masyarakat dan meningkatkan promosi desa serta memberdayakan UMKM dan karang taruna di era digitalisasi agar dapat mencapai hasil yang maksimal.Dibutuhkan pelayanan terhadap masyarakat desa yang maksimal dan kreatifitas serta inovasi yang dapat menciptakan peluang bagi para pelaku usaha atau UMKM untuk mempromosikan terhadap pembeli. Untuk itu dilakukan kegiatan pengabdian kepada masyarakat terkait pemanfaatan SMART Desa, kegiatan tersebut tersebut bertujuan untuk meningkatkan pemahaman UMKM terhadap pemasaran di aplikasi digital dan dengan aplikasi ini diharapkan dapat meningkatkan layanan kepada masyarakat dengan lebih cepat, efektif dan efisien. Metode plaksanaanya menggunakan metode FGD (Forum Group Discussion) dengan pelaku UMKM dan pemerintah desa. Hasil capaian dari kegiatan ini adalah meningkatkan layanan aplikasi desa terhadap pelayanan dan promosi hasil UMKM dan karang taruna terhadap masyarakat luas sehingga berpengaruh dalam peningkatan penjualan produknya.
SCOV-CNN: A Simple CNN Architecture for COVID-19 Identification Based on the CT Images Haryanto, Toto; Suhartanto, Heru; Murni, Aniati; Kusmardi, Kusmardi; Yusoff, Marina; Zain, Jasni Mohammad
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.1750

Abstract

Since the coronavirus was first discovered in Wuhan, it has widely spread and was finally declared a global pandemic by the WHO. Image processing plays an essential role in examining the lungs of affected patients. Computed Tomography (CT) and X-ray images have been popularly used to examine the lungs of COVID-19 patients. This research aims to design a simple Convolution Neural Network (CNN) architecture called SCOV-CNN for the classification of the virus based on CT images and implementation on the web-based application. The data used in this work were CT images of 120 patients from hospitals in Brazil. SCOV-CNN was inspired by the LeNet architecture, but it has a deeper convolution and pooling layer structure. Combining seven and five kernel sizes for convolution and padding schemes can preserve the feature information from the images.  Furthermore, it has three fully connected (FC) layers with a dropout of 0.3 on each. In addition, the model was evaluated using the sensitivity, specificity, precision, F1 score, and ROC curve values. The results showed that the architecture we proposed was comparable to some prominent deep learning techniques in terms of accuracy (0.96), precision (0.98), and F1 score (0.95). The best model was integrated into a website-based system to help and facilitate the users' activities. We use Python Flask Pam tools as a web server on the server side and JavaScript for the User Interface (UI) Design
Peningkatan Prestasi Nasional Tim Hoki SMK T Amir Hamzah Indrapura Melalui Program Digitalisasi Perangkat dan Lapangan Pertandingan Amelia, Afritha; Sundawa, Bakti Viyata; Nasution, Anriza Witi; Roslina, Roslina; Yusoff, Marina; Bt Mohamad Zain, Jasni
Jurnal Pengabdian dan Pemberdayaan Masyarakat Vol. 1 No. 2 (2023): Edisi Desember
Publisher : Jurusan Teknik Sipil, Politeknik Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51510/komposit.v1i2.1451

Abstract

Sekolah adalah tempat dimana seseorang belajar. Mata pelajaran di sekolah diajarkan oleh guru kepada murid-muridnya. Salah satu mata pelajaran yang diajarkan adalah pendidikan jasmani. Pendidikan jasmani ini sangat penting, karena ini mencakup aspek fisik, emosi, sosial dan moral. SMK T Amir Hamzah Indrapura merupakan sekolah mengajarkan pendidikan jasmani. Karena pendidikan jasmani ini penting, pihak sekolah telah menjalankan sistem pembelajaran di luar waktu mata pelajaran atau kegiatan ekstrakurikuler olahraga. Akibat dari kebijakan ini, telah banyak prestasi olahraga yang telah dicapai oleh SMK T Amir Hamzah Indrapura terutama di cabang olahraga Hoki. Prestasi mereka telah mencapai regional dan nasional. Namun masih terdapat kendala yaitu belum lengkapnya fasilitas di lapangan latihan dan pembentukan karakter dan mental pemain belum ditangani secara maksimal. Diharapkan dengan kegiatan TCSC ini dapat membantu tim hoki SMK T Amir Hamzah Indrapura untuk bisa meningkatkan prestasi di level nasional dan pihak sekolah memiliki fasilitas latihan yang memadai berstandar nasional.
Co-Authors Aniati Murni Anriza Witi Nasution Bakti Viyata Sundawa Bt Mohamad Zain, Jasni Dalimunthe, Yulia Agustina Heru Suhartanto Ibrahim, Nurain Jasni Mohammad Zain Kusmardi Kusmardi M. Mansor, Mahayaudin Mansor, Mahayaudin M. Md Ghani, Nor Azura Md Noh, Siti Sarah Mohamad Zain, Jasni Bt Muhammad Rusdi dan Afritha Amelia - Putra, Purwa Hasan Roslina Roslina, Roslina Toto Haryanto