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Richki Hardi
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Multica Science and Technology
Published by Universitas Mulia
ISSN : -     EISSN : 27762386     DOI : https://doi.org/10.47002/mst.v1i1
Core Subject : Science,
Computer Science & IT Energy Library & Information Science Physics
Focus and Scope The journal covers all aspects of science and technology, that is: Science: Bioscience & Biotechnology; Chemistry; Food Technology; Applied Biosciences and Bioengineering; Environmental; Health Science; Mathematics; Statistics; Applied Physics; Biology; Pharmaceutical Science; etc. Technology: Artificial Intelligence; Computer Science; Computer Network; Data Mining; Web; Language Programming; E-Learning & Multimedia; Information System; Internet & Mobile Computing; Database; Data Warehouse; Big Data; Machine Learning; Operating System; Algorithm; Computer Architecture; Computer Security; Embedded system; Cloud Computing; Internet of Thing; Robotics; Computer Hardware; Geographical Information System; Virtual Reality; Augmented Reality; Multimedia; Computer Vision; Computer Graphics; Pattern & Speech Recognition; Image processing; ICT interaction with society; ICT application in social science; ICT as a social research tool; ICT in education
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 3 Documents
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Search results for , issue "Vol. 4 No. 1 (2024): Multica Science and Technology" : 3 Documents clear
Determining the Quality of Earthquake Resistant House Buildings Using Simple Additive Weighting (Saw) and Technique For Order Of Preference By Similarity To Ideal Solution (Topsis) Burhanuddin Burhanuddin; Bakhtiar; Emi Maulani; Edi Yusuf
Multica Science and Technology (ACCREDITED-SINTA 5) Vol. 4 No. 1 (2024): Multica Science and Technology
Publisher : Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/mst.v4i1.850

Abstract

This research aims to evaluate the quality of house buildings using the Simple Additive Weighting (SAW) model and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). This research uses six main criteria to assess building quality: Material strength, structural design, foundation, construction technology, construction quality, construction costs. With these six variables in determining the evaluation of house building materials. Determining the final ranking of these alternatives is based on their proximity to the ideal solution. Type A House, Type B House, Type C House, Type D House, Type E House and the weight value for each house C1 = 0.2; C2 = 0.1 ; C3 = 0.15 ; C = 0.20 ; C = 0.15; C = 0.2%. The ranking results for Type A Houses were 0.871, Type B Houses 0.874, Type C Houses 0.813, Type D Houses 0.976 and Type E Houses 0.959. The largest value is in Type D House 0.976 so the alternative Type D House 0.976 is the alternative chosen as the best alternative. Meanwhile, the ranking results for the topsis model for Type A Houses are 0.5423, Type B Houses are 0.5302, Type C Houses are 0.2709, Type D Houses are 0.8515 and Type E Houses are 0.959. The largest value is for Type D House 0.976 so that the Type E House alternative 0.8227 is the alternative chosen as the best alternative for Type E house. The research results show that the combination of the SAW and TOPSIS methods is effective in providing a comprehensive and objective evaluation of the quality of earthquake resistant house buildings. . The results of this research can be applied practically in the construction industry to improve the quality of earthquake-resistant house buildings, helping make more accurate and objective decisions.
Naïve Bayes Classification Algorithm Application on Nutritional Status of Pregnant Women in Lhokseumawe City Ilham Sahputra; Difa Angelina; Mutammimul Ula
Multica Science and Technology (ACCREDITED-SINTA 5) Vol. 4 No. 1 (2024): Multica Science and Technology
Publisher : Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/mst.v4i1.851

Abstract

The nutritional status of pregnant women is a measure of success in fulfilling nutrition for pregnant women. Poor nutritional status of pregnant women will cause an imbalance of nutrients which can cause nutritional problems in pregnant women. Therefore, we need a system that can predict the nutritional status of pregnant women. This can be implemented by utilizing the naïve Bayes classification algorithm. This research was carried out with the aim of further studying how to apply the Naïve Bayes algorithm to predict the nutritional status of pregnant women, and how the success of this application is based on the accuracy value of the resulting calculations. Based on data on the prevalence and condition of pregnant women in Lhokseumawe and calculations using a series of formulas for mean, standard deviation, probability, and gaussian values, it was found that 50 pregnant women were predicted to have normal nutritional status, while 19 others had nutritional status. not enough. From the results of the accuracy carried out, it was found that the error value (error) of the application used was 48% while the accuracy value of the application was 53% or low. That way, the calculation formula developed in this study needs to be further developed to encourage the accuracy of the application made so that the application results are reliable in real life.
Analysis of Measuring Student Satisfaction with Teacher Performance Assessment Using the Naive Bayes Model Irma Yurni; Arief Rahman; Zahratul Fitri
Multica Science and Technology (ACCREDITED-SINTA 5) Vol. 4 No. 1 (2024): Multica Science and Technology
Publisher : Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/mst.v4i1.852

Abstract

Naive Bayes model analysis to evaluate teacher quality based on the grades given by students with the variables of communication, mastery of material, teacher involvement, and teaching methods have an important role in determining student satisfaction assessments. The analysis results from the naive Bayes model show that student satisfaction tends to be higher for teachers who have good communication skills, strong mastery of the material, active involvement in the teaching and learning process, and the use of effective and innovative teaching methods. Therefore, to improve the quality of education, it is necessary to increase teacher competence in these four variables. In addition, the application of Naive Bayes model analysis can be an effective tool for identifying students who need improvement and development in analyzing student satisfaction. Naïve Bayes model analysis is used to predict the probability of student satisfaction based on the attributes involved. The results of the research show that analyzing and classifying student satisfaction assessments with good accuracy using the Naive Bayes model makes it easy to estimate the probability of satisfaction based on the attributes given. The results of research using the naive Bayes model with a probability of yes 0.0576 with a likelihood of yes and no getting a value of 0.6 while the probability of no is 0.0384 with a value of 0.4. for Normalization results 1 and Probability Value YES > Probability Value NO, Then Student Satisfaction with Teacher Performance is Satisfied with Teacher Performance.

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