Article Per Year (5 Year)
p-Index From 2020 - 2025
0.702
P-Index
This Author published in this journals
All Journal Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI KLIK: Kajian Ilmiah Informatika dan Komputer
Ahmad Rifqi
Universitas Nasional, Jakarta
Computer Science & IT Decision Sciences, Operations Research & Management

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
Sistem Pendukung Keputusan Penilaian Kinerja Karyawan Menerapkan Metode TOPSIS Ahmad Rifqi; Rima Tamara Aldisa
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 1 (2023): Agustus 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i1.1164

Abstract

The development of information technology has the potential to affect the operational level and complexity of human tasks. The success of an organization or company in achieving its set goals is highly dependent on the readiness of its human resources. The role of employees in a company or agency is very vital, because those who meet the needs of organizational skills can increase overall productivity. Therefore, a computer system is needed to meet these information needs, namely a decision support system (DSS). In this study, researchers conducted research on employee performance using the TOPSIS method and used several existing criteria, namely responsibility, discipline, neatness, cooperation and honesty which are expected to help in determining good employee research and can be applied in institutions or companies. Based on the research that has been done, the highest result is obtained by the value of 0.672 On behalf of Erina as the employee with the highest score
Implementasi Sistem Pendukung Keputusan dalam Pemilihan Vendor IT Menerapkan Metode Weighted Product (WP) Ahmad Rifqi; Rima Tamara Aldisa
Resolusi : Rekayasa Teknik Informatika dan Informasi Vol. 4 No. 1 (2023): RESOLUSI September 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/resolusi.v4i1.1240

Abstract

In the era of digital transformation, companies are required to strengthen their information technology (IT) infrastructure in order to compete effectively in an increasingly competitive market. Selection of the right IT vendor is crucial for the success of corporate projects that involve implementing, repairing, or upgrading IT systems. However, the vendor selection process is often complicated and complex, involving multiple criteria and considerations that must be carefully analyzed. To overcome these challenges, this study aims to implement a Decision Support System (SPK) based on the Weight Product (WP) method in selecting IT vendors for corporate projects. The WP method was chosen because of its ability to handle various criteria and provide the right weight for each criterion. The results of this study indicate that DSS based on the WP method can be an effective and efficient tool in helping companies choose the most suitable IT vendor for their projects. By reducing bias and subjective judgments, SPK can provide more objective and data-based results, which can increase the chances of project success and optimize company investment in information technology. This research contributes to companies facing the challenges of selecting IT vendors with a more structured and measurable. It is hoped that the use of DSS based on the WP method can help companies increase their competitiveness and achieve business goals through the application of appropriate and efficient information technology.
Analisa Perbandingan Metode MAUT dan Metode TOPSIS Dengan Menggunakan Pembobotan ROC Dalam Sistem Pendukung Keputusan Pemilihan Calon Kepala Desa Ahmad Rifqi; Rima Tamara Aldisa
Journal of Information System Research (JOSH) Vol 4 No 4 (2023): Juli 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i4.3829

Abstract

This study aims to carry out a comparative analysis process between the MAUT method and the TOPSIS method using ROC weighting. The MAUT method functions to deal with problems in decision making that link several interrelated attributes, in which the method combines the decision maker's preferences for the relevant attributes by giving a weight value to each attribute. Meanwhile, the TOPSIS method is used to select the best alternative from the many choices based on their relative distances from the ideal solution. In this study, weighting using the ROC method is used to objectively determine attribute weight values. The results of the research conducted show that the MAUT Method and the TOPSIS Method with ROC weighting can give good results in determining village heads. The results of the ranking using the MAUT method are A4 with a value of 0.950. while the ranking results using the TOPSIS method are A4 with a value of 0.917.
Penerapan Data Mining dalam Implementasi Algoritma K-Means Clustering untuk Pelanggan Potensial pada Koperasi Simpan Pinjam Ahmad Rifqi; Rima Tamara Aldisa
Building of Informatics, Technology and Science (BITS) Vol 5 No 2 (2023): September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i2.4278

Abstract

Apart from that, there are efforts to provide for the needs of its members as well as financial assistance for education, health and there are also concessions needed by the members. By conducting this customer cluster, it will help the company determine its potential customers so that it can implement the right marketing strategy for each type of existing customer, and will certainly provide benefits for the company in increasing the quality and loyalty of customers towards the company. Data mining has functions, namely prediction, description, classification and clustering functions. Data mining also has many methods for its application, one of these methods is K-Means. The K-Means Clustering algorithm can be implemented in grouping potential customers, especially in savings and loan cooperatives. Based on the data sampling used, the data can be grouped into 2 (two) clusterings.
Co-Authors Rima Tamara Aldisa