Article Per Year (5 Year)
p-Index From 2020 - 2025
0
P-Index
This Author published in this journals
All Journal Jurnal INKOM Journal of Information Technology and Computer Science
Burhan, M.Shochibul
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Newton’s Method for Distance Optimization in Firefly Algorithm in Determining Optimum Nutrition for Laying Hens Burhan, M.Shochibul; Utaminingrum, Fitri
INKOM Journal Vol 11, No 1 (2017)
Publisher : Pusat Penelitian Informatika - LIPI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (711.793 KB) | DOI: 10.14203/j.inkom.509

Abstract

An accurate calculation of feed nutrition and more affordable price is an extremely complex. Firefly algorithm is an algorithm designed for optimization calculation whose output is highly dependent on light intensity (β), which is influenced by distance (r). Therefore, in order to produce maximum output values, an optimization of firefly distance should be done. The most appropriate method is Newton’s Method as it has the capability of solving roots of equations accurately. From the testing of distance optimization in firefly algorithm, a fairly good increase in the fitness value was obtained.Keywords: Newton Method, Firefly Algorithm
Sugeno-Type Fuzzy Inference Optimization With Firefly and K-Means Clustering Algorithms For Rainfall Forecasting Burhan, M.Shochibul; Mahmudy, Wayan Firdaus; Dermawi, Rizdania
Journal of Information Technology and Computer Science Vol. 3 No. 1: June 2018
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3521.752 KB) | DOI: 10.25126/jitecs.20183134

Abstract

Rainfall is very influential in our daily lives, ranging from agriculture, aviation, to flood-prone areas. The intensity of rainfall is used as an early detection for preventing harmful effects of rainfall. This research used Sugeno-Method Fuzzy Logic, in which the prediction is accomplished by mapping rules from the data obtained using the K-Means Clustering Algorithm as the classification to form the membership function and mapping rules and Firefly Alghorithm for optimization output. The test result from the 30 examined data found is 2.93 RMSE. This shows that the data support from K-Means Clustering and Firefly Algorithms of the fuzzy logic can predict precipitation accurately.
Co-Authors Rizdania, Rizdania Utaminingrum, Fitri Wayan Firdaus Mahmudy