Article Per Year (5 Year)
p-Index From 2021 - 2026
2.339
P-Index
This Author published in this journals
All Journal Jurnal JSIKA JOIN (Jurnal Online Informatika) INTEGER: Journal of Information Technology DUNAMIS: Jurnal Teologi dan Pendidikan Kristiani Jurnal Sisfokom (Sistem Informasi dan Komputer) Kurios JKIE (Journal Knowledge Industrial Engineering) Jurnal Telematika BIA': Jurnal Teologi dan Pendidikan Kristen Kontekstual PASCA: Jurnal Teologi dan Pendidikan Agama Kristen SPEKTA (Jurnal Pengabdian Kepada Masyarakat : Teknologi dan Aplikasi) Unilib: Jurnal Perpustakaan SOPHIA: Jurnal Teologi dan Pendidikan Kristen Sola Gratia: Jurnal Teologi Biblika dan Praktika Jurnal Sistem Informasi Universitas Dinamika Danum Pambelum: Jurnal Teologi dan Musik Gereja eProceedings of Engineering Jurnal Informatika: Jurnal Pengembangan IT Basilius Eirene: Jurnal Agama dan Pendidikan
Yohanes Setiawan
Prodi Matematika Jurusan Matematika FMIPA Universitas Brawijaya
Religion Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Social Sciences Other

Published : 21 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 21 Documents
Search

 1   2   3 
Data Mining berbasis Nearest Neighbor dan Seleksi Fitur untuk Deteksi Kanker Payudara Setiawan, Yohanes
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 2 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i2.4994

Abstract

Detecting breast cancer in early stage is not straightforward. This happens because biopsy test requires time to determine whether the type is benign or malignant. Data mining algorithm has been widely used to automate diagnosis of a disease. One of popular algorithms is nearest neighbor based because of its simplicity and low computation. However, too many features can cause low accuracy in nearest neighbor based models. In this research, nearest neighbor based with feature selection is developed to detect breast cancer.  Conventional k-Nearest Neighbor (KNN) and Multi Local Means k-Harmonic Nearest Neighbor have been chosen as nearest neighbor based models to experiment. The feature selection method used in this study is filter based, namely Correlation based, Information Gain, and ReliefF. The experimental result shows that the highest recall metric of MLM-KHNN and Information Gain is 94% with 5 features. In brief, MLM-KHNN algorithm with Information Gain can increase the recall of the prediction of breast cancer compared with the conventional K-NN algorithm and have been deployed into website using Streamlit such that the model can be used to detect breast cancer from chosen Wisconsin dataset features.
Co-Authors Anggraini, Sukma Dewi Anjas Ardiansyah, Muhammad Anjik Sukmaaji Ari Setiawan Bernadus Anggo Seno AjiAji Dawan, Anil Dwi Ardhani, Yuniar Edi Purwanto Ichsan Khoirudin, Machfud Lase, Iman Karyani Mastuty Ayu Ningtyas Mochamad Nizar Palefi Ma’ady Mohammad Hamim Zajuli Al Faroby Oktavia Ayu Permata Prasetyo, Waluyo Ramadhani, Cisa Valentino Cahya Rashid Syaputra, Muhammad Riza Akhsani Setyo Prayoga Sanjaya, I Made Wisnu Adi Sari, Liliana Chandra Sari SIN, SIA KOK Vicky M Taufik Wachda Yuniar Rochmah Wahyu Listyanto, Ferdhyan Wardani, Ken Ratri Willianto, Henoch Yuda, M. Pradata