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Journal of Soft Computing Exploration
Published by shm publisher
ISSN : 27467686     EISSN : 27460991     DOI : -
Core Subject : Science,
Computer Science & IT
Journal of Soft Computing Exploration is a journal that publishes manuscripts of scientific research papers related to soft computing. The scope of research can be from the theory and scientific applications as well as the novelty of related knowledge insights. Soft Computing: Artificial Intelligence Applied Algebra Neuro Computing Fuzzy Logic Rough Sets Probabilistic Techniques Machine Learning Metaheuristics And Many Other Soft-Computing Approaches Area Of Applications: Data Mining Text Mining Pattern Recognition Image Processing Medical Science Mechanical Engineering Electronic And Electrical Engineering Supply Chain Management, Resource Management, Strategic Planning Scheduling Transportation Operational Research Robotics
Arjuna Subject : Ilmu Komputer (semua kategori) - Perangkat Lunak
Articles 13 Documents
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Search results for , issue "Vol. 2 No. 2 (2021): September 2021" : 13 Documents clear
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.
Implementation of fuzzy tsukamoto in employee performance assessment Dewi, Meilina Taffana; Zaaidatunni'mah, Untsa; Al Hakim, M. Faris; Jumanto, Jumanto
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.v2i2.52

Abstract

Employees are one of the important things for the sustainability of a company, because employees are company assets. In addition, employee performance is also something that cannot be ignored because it determines the achievement of company goals. So it is important to monitor employee performance and conduct performance appraisals. With the addition of performance appraisal, the company can determine the provision of rewards, promotions, and punishments. It can be used as a work evaluation stage to improve the quality of work. Employee performance appraisal is based on several predetermined criteria, including responsibility, discipline, and attitude which in the end results in between two linguistic values, namely good or bad. One method for evaluating employee performance is the Tsukamoto fuzzy method. With the Tsukamoto fuzzy method, it is hoped that the assessment can be carried out fairly and measurably.
Room occupancy classification using multilayer perceptron Wijaya, Dandi Indra; Aulia, Muhammad Kahfi; Jumanto, Jumanto; Hakim, M. Faris Al
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.v2i2.53

Abstract

A room that should be comfortable for humans can create a sense of absence and appear diseases and other health problems. These rooms can be from boarding rooms, hotels, office rooms, even hospital rooms. Room occupancy prediction is expected to help humans in choosing the right room. Occupancy prediction has been evaluted with various statistical classification models such as Linier Discriminat Analysis LDA, Classification And Regresion Trees (CART), and Random Forest (RF). This study proposed learning approach to classification of room occupancy with multi layer perceptron (MLP). The result shows that a proper MLP tuning paramaters was able estimate the occupancy with 88.2% of accuracy

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