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Al-Khowarizmi
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Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE)
Published by Universitas Muhammadiyah Sumatera Utara
ISSN : -     EISSN : 27213838     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering
ournal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) is being published in the months of March and September. It is academic, online, open access (abstract), peer reviewed international journal. The aim of the journal is to: Disseminate original, scientific, theoretical or applied research in the field of Engineering and allied fields. Dispense a platform for publishing results and research with a strong empirical component. Aqueduct the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research. Seek original and unpublished research papers based on theoretical or experimental works for the publication globally. Publish original, theoretical and practical advances in Computer Science & Engineering, Information Technology, Electrical and Electronics Engineering, Electronics, Communication and Telecommunication, Education Science and all interdisciplinary streams of Social Sciences. Impart a platform for publishing results and research with a strong empirical component. Create a bridge for significant gap between research and practice by promoting the publication of original, novel, industry-relevant research. Solicit original and unpublished research papers, based on theoretical or experimental works. Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) covers all topics of all engineering branches. Some of them are Computer Science & Engineering, Information Technology, Electronics & Communication, Electrical and Electronics, Electronics and Telecommunication, Education Science and all interdisciplinary streams of Social Sciences.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 12 Documents
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Search results for , issue "Vol 4, No 2 (2023)" : 12 Documents clear
Utilization of the Multi Attribute Utility Theory (MAUT) Method in Determining Wedding Halls in Medan City Kadrayani Kadrayani; Hasdiana Hasdiana; Dedy Irwan; Eka Rahayu; Yulia Agustina Dalimunthe
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 4, No 2 (2023)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v4i2.16019

Abstract

The building is an important part of the wedding ceremony. Especially if the event owner does not have sufficient land. So far, it's been difficult for the community to decide which wedding hall they want. Both in terms of location, building design, rental prices etc. The purpose of this research is to apply the Multi Attribute Utility Theory (MAUT) method to recommend building rental services. The criteria used to select a building are location, price, facilities, parking space capacity, and number of guests. The alternatives used are Adi Mulia Hotel Medan, Caffe Bel Mondo Medan, Andaliman Hall, Aceh Sepakat, Al-amjad Convention Hall, Wisma Mahina Center, Mutiara Suara Nafiti Convention Hall, Namaken Hall, Al-Maruf Multipurpose Building, and the Dharma Wanita Petisah Building. The results of applying the MAUT method show that the Al-Amjad Convention Hall is most recommended as the building that best fits the given criteria.
A Hybrid RBF Neural Network and FCM Clustering for Diabetes Prediction Dataset Muhammad Khalil Gibran; Amir Saleh
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 4, No 2 (2023)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v4i2.15573

Abstract

This study aims to predict diabetes by combining the Radial Basis Function Neural Network (RBFNN) and Fuzzy C-Means (FCM) clustering methods. Diabetes prediction is an important part of research in an effort to prevent, manage, and reduce this type of disease. The FCM clustering method is used to group diabetes data into groups that have similar characteristics and obtain the final centroid. Then, the RBFNN method is used to build a predictive model using the center of each group as a reference point in the RBF function based on the centroid generated from the FCM clustering method. This step allows for modeling the non-linear relationship between health attributes and diabetes risk in more detail. In this study, the dataset obtained used input parameters regarding health data and risk factors for the disease. The goal of combining these methods is to develop a predictive model that can help identify individuals at high risk of developing diabetes. This hybrid approach has the potential to improve the effectiveness and accuracy of diabetes prediction. From the tests carried out, the proposed method obtained an accuracy of 92%, a precision of 90%, a recall of 92%, and an F1-score of 91%. By combining the clustering power of FCM clustering with RBF's ability to model non-linear relationships, this hybrid approach can make a good contribution to diabetes prediction and assist in efforts to prevent and control this disease.

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