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All Journal Sinergi BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Surya Masyarakat Jurnal Surya Masyarakat Journal of Intelligent Computing and Health Informatics (JICHI) Journal of Soft Computing Exploration
Sam'an, Muhammad
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Journal : Journal of Soft Computing Exploration

 1 
Irrigation management of agricultural reservoir with correlation feature selection based binary particle swarm optimization Ifriza, Yahya Nur; Sam'an, Muhammad
Journal of Soft Computing Exploration Vol. 2 No. 1 (2021): March 2021
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v2i1.23

Abstract

The requirement for the applied innovation to farming water system is especially required for supplies, as rural water system focuses. Supplies as one of horticulture water system asset focus that are regularly constraints identified with the conveyance of repository water stream, this brought about lopsided dissemination of rural water system and the term of control of agrarian water system that streams from water system asset focuses. At the point when ranchers need to change the water system way, it will take a long effort to make another water system way. From these troubles to convey rural water systems simpler, it is important to plan a specialist framework to decide rural water system choices. A few researchers focused on improved quality of plant. There have been limited studies concerned with irrigation management Therefore, this research intends to design The objectives of this research are optimization irrigation management of agricultural reservoirs with CFS-BPSO. The consequences of this investigation demonstrate that the exactness of the utilization of the SVM calculation is 62.32%, while after utilizing the CFS calculation precision of 84.12% is acquired and exactness of ten SVM calculations by applying a blend of CFS highlight choice. also, BPSO 91.84%.
New fuzzy transportation algorithm without converting fuzzy numbers Sam'an, Muhammad; Ifriza, Yahya Nur
Journal of Soft Computing Exploration Vol. 2 No. 2 (2021): September 2021
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v2i2.41

Abstract

The ranking function is widely used to convert fuzzy numbers to be crisp on solving fuzzy transportation problems. The converting process can indeed make it easier to play the fuzzy transportation method, but from the convenience, it causes failed in interpreting the results of converting fuzzy numbers. This is because the converting process of fuzzy numbers still has subjectivity values, so it cannot be eliminated, moreover, the ordering can cause incompatible input and output fuzzy numbers resulted. Therefore, the new fuzzy transportation method is proposed by fuzzy Analytical Hierarchy Process to order fuzzy parameters on fuzzy transportation problem without converting fuzzy numbers to crisp numbers, then Algorithm 2 until 6 is used to obtain a fuzzy optimal solution. The advantages of the new proposed method can improve the shortcomings of the existing methods, as well as relevant to solve fuzzy transportation problems in real life
Performance comparison of support vector machine and gaussian naive bayes classifier for youtube spam comment detection Ifriza, Yahya Nur; Sam'an, Muhammad
Journal of Soft Computing Exploration Vol. 2 No. 2 (2021): September 2021
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v2i2.42

Abstract

Youtube is a video sharing site that was begun back in 2005. Youtube produces over 400 hours of substance each moment and more than 1 billion hours of substance are devoured by clients every day. In this work, we present a new approach by comparing the analysis results using a support vector machine and the Gaussian Naive Bayes classificatio. Our proposed methodology We used the dataset from UCI especially Youtube-Shakira for training and testing. The transformed dataset is split into training and testing subsets and fed into Naive Bayes and Support Vector Machin. In all cases, the F1 score was used to evaluate the classifier's performance. The results of the experiment are displayed in Gaussian Naive Bayes with an F1 score of 84.38% and a Support Vector Machine (SVM) with an F1 score of 88.00%. Naive Bayes is consistently the worst performer than SVM.
Co-Authors Fauzi, Fatkhurokhman M. Al Haris Muhammad Munsarif Munsarif, Muhammad Rakainsa, Senda Kartika SAMSUDI RAHARJO Subhan Subhan Syarif, Deny Lukman Widhihastuti, Endah Wulan Sari, Wulan Yahya Nur Ifriza