Items where Division is "S1-Information System (S1-Sistem Informasi)" and Year is 2018

A

Aida Husaini, Noor and Ghazali, Rozaida and Tri Riyadi Yanto, Iwan (2018) Enhancing Modified Cuckoo Search Algorithm by Using MCMC Random Walk. [Artikel Dosen]

Anwar Lashari, Saima and Ibrahim, Rosziati and Senan, Norhalina and Tri Riyadi Yanto, Iwan and Herawan, Tutut (2018) Application of Wavelet de-noising Filters in Mammogram Images Classification Using Fuzzy Soft Set. [Artikel Dosen]

I

Irsalinda, Nursyifa and Tri Riyadi Yanto, Iwan and Herawan, Tutut (2018) A Framework of Clustering Based on Chicken Swarm Optimization. [Artikel Dosen]

R

Riadi, Imam (2018) Hasil cek similarity "Colorectal Polyp Detection Using Feedforward Neural Network with Image Feature Selection". International Symposium on Advanced Intelligent Informatics (SAIN).

S

Saadi, Younes and Herawan, Tutut and Balakrishnan, Vimala and Risnumawan, Anhar and Tri Riyadi Yanto, Iwan and Chiroma, Haruna (2018) Ringed Seal Search for Global Optimization via a Sensitive Search Model. [Artikel Dosen]

Suhirman, Suhirman and Mohamad Zain, Jasni and Herawan, Tutut and Tri Riyadi Yanto, Iwan and Abdullah, Zailani (2018) A Soft Set Approach for Clustering Student Assesment Datasets. [Artikel Dosen]

T

Thobirin, Aris and Tri Riyadi Yanto, Iwan (2018) Automatic Differentiation Based for Particle Swarm Optimization Steepest Descent Direction. [Artikel Dosen]

Tri Riyadi Yanto, Iwan and Azhari, Imam (2018) Alternative Technique Reducing Complexity of Maximum Attribute Relation. [Artikel Dosen]

Tri Riyadi Yanto, Iwan and Rahman, Arif and Saaadi, Youes (2018) Soft Maximal Association Rule For Web User Mining. [Artikel Dosen]

Tri Riyadi Yanto, Iwan and Saadi, Younes and Hartama, Dedy and Pramudi Ismi, Dewi and Pranolo, Andri (2018) A Framework of Fuzzy Partition Based on Artificial Bee Colony for Categorical Data Clustering. [Artikel Dosen]

Y

Yanto, Iwan Tri Riyadi and Akmar Ismail, Maizatul and Herawan, Tutut (2018) A Modified Fuzzy k-Partition Based on Indiscernibility Relation for Categorical Data Clustering. [Artikel Dosen]