Article Per Year (5 Year)
p-Index From 2019 - 2024
0.444
P-Index
This Author published in this journals
All Journal Prosiding Seminar Nasional Teknologi Informasi dan Bisnis
Dwiki Rasya Rahadian
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering Physics

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Sistem Pendukung Keputusan Pemilihan Mahasiswa Terbaik Menggunakan Metode Multi- Objective Optimization on The Basis of Ratio Analysis (MOORA) Dwiki Rasya Rahadian; Rico Dwi Yulianto; Tiara Putri Maharani
Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Prosiding Seminar Nasional Teknologi Informasi dan Bisnis (SENATIB) 2022
Publisher : Fakultas Ilmu Komputer Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (356.783 KB)

Abstract

Sistem pendukung keputusan adalah suatu system yangdapat menyelesaikan masalah yang terjadi di dalam penentuanperingkat dengan cepat serta dapat mengetahui nilai tertinggisamapai terendah di dalam sebuah seleksi. Pada penulisan iniadalah salah satu merupakan studi kasus yang dapat diselesaikandengan menggunakan sistem pendukung keputusan dimana yangmenjadi persoalan adalah pemilihan mahasiswa/I terbaik padaperguruan tinggi. Pemilihan tersebut membutuhkan beberapakriteria diantaranya yakni nilai UAS, absensi, nilai tugas, danprestasi. Metode yang digunakan dalam membangun systempendukung keputusan pemilihan mahasiswa/I terbaik adalah metodeMulti-Objective Optimization on The Basic of Ratio Analysis(MOORA). Hasil akhir yang diperoleh dari penelitian ini adalahdapat menyeleksi alternatif dan melakukan perangkingan dalammenentukan mahasiswa/I terbaik berdasarkan kriteria-kriteria yangsudah ditentukan.
Analisis Kemiskinan Menggunakan Metode Algoritma Clustering K-Means Dwiki Rasya Rahadian; Nurmalitasari
Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Prosiding Seminar Nasional Teknologi Informasi dan Bisnis (SENATIB) 2023
Publisher : Fakultas Ilmu Komputer Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Poverty has a broad and serious impact on the lives of individuals and society. When people live in poverty, they may face difficulties meeting basic needs, such as adequate food, adequate housing, and proper education. These limitations can negatively impact physical and mental health, education, employment opportunities, and overall quality of life. The purpose of this study is to find out the grouping of districts/cities that have similar characteristics based on the 2019 poverty indicators. This research uses data obtained from the BPS (Central Bureau of Statistics). The method used is the k-means clustering method which is a clustering partition method for grouping objects into k clusters. Based on the research results, the characteristics of each cluster were grouped based on the poverty indicator values in several districts/cities in 2019 as many as 2 clusters. Formed from 20 districts/cities in cluster 1 and 29 districts/cities in cluster 2. Cluster 1 has the characteristics of Low Work Challenges, with Low Per Capita Expenditure Rates and Low Unemployment Rates while Cluster 2 has the characteristics of High Job Challenges, with Per Capita Expenditure Levels High and High Non Working Rate.
Co-Authors Nurmalitasari Rico Dwi Yulianto Tiara Putri Maharani