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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer EMITTER International Journal of Engineering Technology Indonesia Symposium on Computing Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research JURNAL MEDIA INFORMATIKA BUDIDARMA JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) Indonesian Journal of Applied Informatics Charity : Jurnal Pengabdian Masyarakat Jurnal Abdimas Kartika Wijayakusuma Indonesian Community Journal Jurnal Pengabdian Masyarakat Bhinneka eProceedings of Engineering Community Service Seminar and Community Engagement (COSECANT) Telkatika: Jurnal Telekomunikasi Elektro Komputasi & Informatika LOGIC: Jurnal Penelitian Informatika
Febryanti Sthevanie
Telkom University
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Mechanical Engineering Public Health Social Sciences Other

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Journal : EMITTER International Journal of Engineering Technology

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Nuclei Detection and Classification System Based On Speeded Up Robust Feature (SURF) Amalina, Neneng Nur; Ramadhani, Kurniawan Nur; Sthevanie, Febryanti
EMITTER International Journal of Engineering Technology Vol 7, No 1 (2019)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (842.261 KB) | DOI: 10.24003/emitter.v7i1.288

Abstract

Tumors contain a high degree of cellular heterogeneity. Various type of cells infiltrate the organs rapidly due to uncontrollable cell division and the evolution of those cells. The heterogeneous cell type and its quantity in infiltrated organs determine the level maglinancy of the tumor. Therefore, the analysis of those cells through their nuclei is needed for better understanding of tumor and also specify its proper treatment. In this paper, Speeded Up Robust Feature (SURF) is implemented to build a system that can detect the centroid position of nuclei on histopathology image of colon cancer. Feature extraction of each nuclei is also generated by system to classify the nuclei into two types, inflammatory nuclei and non-inflammatory nuclei. There are three classifiers that are used to classify the nuclei as performance comparison, those are k-Nearest Neighbor (k-NN), Random Forest (RF), and State Vector Machine (SVM). Based on the experimental result, the highest F1 score for nuclei detection is 0.722 with Determinant of Hessian (DoH) thresholding = 50 as parameter. For classification of nuclei, Random Forest classifier produces F1 score of 0.527, it is the highest score as compared to the other classifier.
Nuclei Detection and Classification System Based On Speeded Up Robust Feature (SURF) Neneng Nur Amalina; Kurniawan Nur Ramadhani; Febryanti Sthevanie
EMITTER International Journal of Engineering Technology Vol 7 No 1 (2019)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (842.261 KB) | DOI: 10.24003/emitter.v7i1.288

Abstract

Tumors contain a high degree of cellular heterogeneity. Various type of cells infiltrate the organs rapidly due to uncontrollable cell division and the evolution of those cells. The heterogeneous cell type and its quantity in infiltrated organs determine the level maglinancy of the tumor. Therefore, the analysis of those cells through their nuclei is needed for better understanding of tumor and also specify its proper treatment. In this paper, Speeded Up Robust Feature (SURF) is implemented to build a system that can detect the centroid position of nuclei on histopathology image of colon cancer. Feature extraction of each nuclei is also generated by system to classify the nuclei into two types, inflammatory nuclei and non-inflammatory nuclei. There are three classifiers that are used to classify the nuclei as performance comparison, those are k-Nearest Neighbor (k-NN), Random Forest (RF), and State Vector Machine (SVM). Based on the experimental result, the highest F1 score for nuclei detection is 0.722 with Determinant of Hessian (DoH) thresholding = 50 as parameter. For classification of nuclei, Random Forest classifier produces F1 score of 0.527, it is the highest score as compared to the other classifier.
Co-Authors 1Faizal Bima Prayudha Abdul Rahim Adam Geraldy Katab Adhika Widya Prastomo Agustina, Nur Azizah Al Faraby, Said Alda Putri Utami Amalina, Neneng Nur Anang Kurniawan Anditya Arifanto Anditya Arifianto Anditya Arifiyanto Andri Arindiah Arida Kartika Atria Salim, Muhammad Rizki Bedy Purnama Brilian Aringga Prabowo Danu Hary Prakoso Diah Ajeng Dwi Yuniasih Dian Masmawati Dindin Dhino Alamsyah Dwi Prasetya Sujoko Dwiki Lazzaro Ema Rachmawati Ema Rachmawati Enki Probo Sidhi Farid Hidayat Fazmah Arif Y, Fazmah Fernanda Januar Pratama Fikri Firdaus Gamma Kosala Ghali Marzan Gia Septiana Wulandari Grandhys Setyo Utomo Gugy Lucky Khamdani Hafidh Fikri Rasyid Hizas Sabilal Rasyad Hutomo, Ardityo Cahyo Putro Hutomo I Putu Indra Aristya Imamul Akhyar Indra Bayu Kusuma Jonas de Deus Guterres Ketut Sudyatmika Putra Kurniawan Nur R Kurniawan Nur Ramadhani Kurniawan Nur Ramdhani Laksitowening, Kusuma Ayu Lukmana Sardi, Indra Mahmud Dwi Sulistiyo Maula Ilma Ahgnia Dwi Anjani Mochamad Rakha Luthfi Fahsya Muhammad Afif Amanullah Fawwaz Muhammad Jendro Yuwono Muhammad Jendro Yuwono Muhammad Salman Farhan Muhammad Zaki, Ghilman Muhammad Zulfiqar Shafar Neneng Nur Amalina Nur Hidayah, Maulana Nur Indah Puspa Idham Nur Ramadhani, Kurniawan Nurul Halimatul Azizah Prasti Eko Yunanto Rachmi Azanisa Putri Rahman, Rahadian Yusuf Abdul Retno Novi Retno Novi Reza Dwi Ansari Rimba Whidiana Ciptasari Rita Rismala Rivan Ardyanto Sutoyo Sakinah Indriyani Saputra, Naufal Luthfi Shabran Fauzan Ahmad Sulistiyo, Mahmud Tito Prihambodo Tjokorda Agung Budi W Tjokorda Agung Budi Wiharja Tjokorda Agung Budi Wirayuda Untari Novia Wisesty Wikky Fawwaz Al Maki Zeyhan Aliyah