Article Per Year (5 Year)
p-Index From 2020 - 2025
0.61
P-Index
This Author published in this journals
All Journal Zonasi: Jurnal Sistem Informasi TIERS Information Technology Journal Formosa Journal of Sustainable Research (FJSR)
Rezki Fauzi
Universitas Nahdlatul Ulama Sumatera Barat
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Artificial Intelligence Implementation Of Rough Set In Decision System Unemployments Rezki Fauzi; M. Arif Rahman
ZONAsi: Jurnal Sistem Informasi Vol. 2 No. 2 (2020): Publikasi Artikel ZONAsi : Jurnal Sistem Informasi, September 2020
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/zn.v2i2.6183

Abstract

Penelitian ini bertujuan bagaimana pengambilan keputusan bagi pencari kerja yang menyebabkan mereka berada pada antrian pengangguran. Semoga dengan diterapkan ini kepada pencari kerja diharapkan ada perbaikan bagi nasib mereka di dunia kerja. Akan tetapi banyak pencari kerja tidak menyadari pentingnya kompetensi dan mental yang baik sebagai syarat utama sukses berkarier. Hasil analisis diharapkan dapat dijadikan pedoman pengambilan keputusan bagi pencari kerja dan perusahaan. Calon pencari kerja yang telah diuji berasal dari pendidikan Sarjana (S1), Ahli Madya (D3) dan SMA/SMK kota Padang berjumlah 100 orang . Penelitian ini menggunakan kuisoner baik online maupun offline dan wawancara/interview sebagai alat pengumpul data dan metode Rough Set sebagai metode analisis pengujian kriteria. Data analisis menggunakan aplikasi Rosseta 1.4.4.1. Penelitian ini menunjukkan keputusan yang menghasilkan pengetahuan baru (1) uji kompetensi (2) uji diagnosa mental yang mudah- mudahan membantu pihak terkait penelitian ini. Hasil penelitian baik metode Rough Set maupun tools rosseta 1.4.41 didapatkan rule rule yang dihasilkan 7 reduce diekstraksi menjadi 21 general rules metode Rough Set dan tools rosseta 1.4.4.1 knowledge 31 general rules sehingga sudah optimal dalam menghasilkan suatu pengetahuan baru.
Community Behavior in Artificial Intelligence-Based Waste Management Sanny Edinov; Rezki Fauzi
Formosa Journal of Sustainable Research Vol. 2 No. 2 (2023): February 2023
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/fjsr.v2i2.2993

Abstract

The community is not yet aware that proper and correct waste management is the main capital in waste management. It is hoped that the community will improve their behavior and be used as a guide by the local government regarding what steps to take in the future to be more synergized with the community in managing waste. The people who have been tested are seen from recent education, discipline, and environmental knowledge, with a total sample of 100 people. The study used questionnaires and interviews as data collection tools. The data that has been obtained is analyzed by testing the criteria used is the Rough set. The results of the extracted criteria produce 9 varied general rules, which can produce solutions and steps forward as supporting success in creating a clean environment from waste
Data Mining Rough Set Method In Analyzing Communities Disposing of Garbage in Rivers Sanny Edinov; Rezki Fauzi
TIERS Information Technology Journal Vol. 4 No. 2 (2023)
Publisher : Universitas Pendidikan Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38043/tiers.v4i2.4478

Abstract

The waste management approach is a worldwide problem and is often neglected. The situation in urban areas is worse than in rural areas. It is a big challenge to deal with this dilemma, therefore, the management of a better management system is inevitable. The input data used in this study included the community's last educational data and social stratification, both through questionnaires and interviews with the community. Furthermore, the data that has been presented in the decision system is collected or eliminated for each object that has the same attributes, then summed and collected into the same class so that the cleaning technique process becomes simple (equivalent class). The Advanced Discernibility Matrix and the Discernibility Modulo D Matrix will compare a collection of attributes based on the equivalence class that will be modeled with the last Education modeled "A", the social stratification of society is modeled "B". The reduction results obtained reveal that there are several reasons why people throw garbage in the river. This provides information, the latest differences in social stratification and education can influence people's mindset and habits of disposing of garbage. The social stratification of people who are not from the wealthy class and have the last elementary-junior high school education contributes more negatively to the fullness of the river flow with garbage.
Co-Authors M. Arif Rahman Sanny Edinov