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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Ilmu Komputer dan Informasi Jurnal Teknik Elektro Tadris: Jurnal keguruan dan Ilmu Tarbiyah Sinergi Jurnal Penelitian dan Pembelajaran IPA Economic Education Analysis Journal IJIE (Indonesian Journal of Informatics Education) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Diglosia: Jurnal Kajian Bahasa, Sastra, dan Pengajarannya Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Teknik Informatika (JUTIF) Community Empowerment Journal of Soft Computing Exploration TIERS Information Technology Journal MATRIX : JURNAL MANAJEMEN TEKNOLOGI DAN INFORMATIKA Jurnal Pendidikan Indonesia (Japendi) Information Technology Education Journal Journal of Mandalika Literature Jurnal Bina Desa Jurnal Abdi Negeri
Yahya Nur Ifriza
Information System Study Program, Universitas Negeri Semarang, Indonesia
Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Public Health Social Sciences Other

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Journal : journal of soft computing exploration

 1 
A combination of TDM and KSAM to determine initial feasible solution of transportation problems Muhammad Sam'an; Ifriza, Yahya Nur
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.v2i1.16

Abstract

In case of the Transportation Problem (TP), it was found that TP had equal the smallest so that the existing methods will be generated two or more IFS values. The newly developed algorithm is generated through a combination of Total Difference Method (TDM) and Karagul-Sahin Approximation Method (KSAM) algorithm, is capable to determine the initial feasible solution of TP. Based on the numerical illustration of TP example to evaluate the performance of the new proposed algorithm. The computational performances have been compared to the existing methods (TDM1 and KSAM) and the results shown this algorithm achieved better performance than the existing methods for TP example.
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.
Analysis of earthquake forecasting using random forest Budiman, Kholiq; 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.v2i2.51

Abstract

The subject of forecasting earthquakes is an intriguing one to investigate. As a natural calamity, earthquakes continue to be devastating, not just to the economy but also to the lives of individuals. This gave rise to the concept of creating an early warning system against seismic catastrophes to minimize deaths. Researchers have been making earthquake forecasts and seismic hazard ratings of a location for a few years now. In this work, we attempt to forecast earthquakes before they occur using p-arrival data, which includes information on disaster arrival time and amplitude height from the arrival station. Several studies on earthquake prediction have been carried out so far and have developed and used the Random Forest method and one of the Machine Learning. According to [1], the process of predicting earthquakes has been studied for a long time, but there is still uncertainty due to the diversity and complexity of the earthquake phenomenon itself. According to [2], conducting a random forest prediction model to identify the structural safety status of buildings damaged by the earthquake is probabilistic. An earthquake's latitude, longitude, magnitude, and depth may be predicted using the random forest algorithm. A random forest with multioutput technique is employed, with variables being each station's recorded value and geographic position. This study's predictions were accurate to within 63 percent.
Rainfall prediction in Blora regency using mamdani's fuzzy inference system Damayanti, Dela Rista; Wicaksono, Suntoro; Hakim, M. Faris Al; Jumanto, Jumanto; Subhan, Subhan; Ifriza, Yahya Nur
Journal of Soft Computing Exploration Vol. 3 No. 1 (2022): March 2022
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v3i1.69

Abstract

In the case study of weather prediction, there are several tests that have been carried out by several figures using the fuzzy method, such as the Tsukamoto fuzzy, Adaptive Neuro Fuzzy Inference System (ANFIS), Time Series, and Sugeno. And each method has its own advantages and disadvantages. For example, the Tsukamoto fuzzy has a weakness, this method does not follow the rules strictly, the composition of the rules where the output is always crisp even though the input is fuzzy, ANFIS has the disadvantage of requiring a large amount of data. which is used as a reference for calculating data patterns and the number of intervals when calculating data patterns and Sugeno has the disadvantage of having less stable accuracy results even though some tests have been able to get fairly accurate results. In research on the implementation of the Mamdani fuzzy inference system method using the climatological dataset of Blora Regency to predict rainfall, it can be concluded as follows: (1) The fuzzy logic of the Mamdani method can be used to predict the level of rainfall in the city of Blora by taking into account the factors that affect the weather, including temperature, wind speed, humidity, duration of irradiation and rainfall. (2) Fuzzy logic for prediction with uncertain input values is able to produce crisp output because fuzzy logic has tolerance for inaccurate data. (3) The results of the accuracy of calculations using the Mamdani fuzzy inference system method to predict rainfall in Blora Regency are 66%.
Co-Authors Abdul Jabbar Agung Kuswantoro Alamsyah - Amalina Shabrina Antonius Supriyadi Azkiya, M. Akiyasul Budi Prasetiyo, Budi Budiman, Kholiq Damayanti, Dela Rista Decky Avrilianda Dhela Septian Anggaretta Dimas Ramadhani Djuniadi Djuniadi Efrilianda, Devi Ajeng Erna Noor Savitri, Erna Noor Fateah, Nur Febriyanto, Hendra Hakim, M Faris Al Hakim, M. Faris Al Istijabah Ifti Mufsiroh Jumanto Jumanto Jumanto, Jumanto Kamaruddin, Azlina Masfufati Azizah Mu'arifuddin, Mu'arifuddin Muarifuddin Muarifuddin, Muarifuddin Much Aziz Muslim Muhammad Sam'an Muhammad Sam’an Niam Wahzudik Nina Oktarina Nur Fateah Nurul Faizah Parmin - Parmin Parmin Parmin, Parmin Prabowo Yudo Jayanto Purasani, Hana Netti Raditya Ahmad Rifandi Rahayu, Endah Fitriani Rakainsa, Senda Kartika Riza Arifudin Sam'an, Muhammad Sam’an, Muhammad Sanusi, Ratna Nur Mustika Satrio Ardiansyah, Adi Siminto Siminto Siminto Siminto, Siminto Sri Murti Retnoningrum Subhan Subhan Subhan Sudarno Sudarno Supriyadi, Antonius Syarif, Deny Lukman Trisni Suryarini Trisni Wulandari Veronika Veronika, Trisni Wulandari Wicaksono, Suntoro Widhihastuti, Endah Widhiya Ninsiana, Widhiya Wisudani Rahmaningtyas Yosza Dasril Yusuf Wisnu Mandaya