Article Per Year (5 Year)
p-Index From 2021 - 2026
4.568
P-Index
This Author published in this journals
All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Teknologi dan Manajemen Informatika PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic SISFOTENIKA Journal of Information Technology and Computer Science (JOINTECS) JURNAL MEDIA INFORMATIKA BUDIDARMA JITK (Jurnal Ilmu Pengetahuan dan Komputer) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) STRING (Satuan Tulisan Riset dan Inovasi Teknologi) INFOMATEK: Jurnal Informatika, Manajemen dan Teknologi Jurnal Mantik Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Teknologi Informatika dan Komputer Journal of Applied Data Sciences Jurnal Ilmiah Wahana Pendidikan Journal of Technology and Informatics (JoTI) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Bulletin of Information Technology (BIT)
Aris Gunaryati
Universitas Nasional
Religion Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Social Sciences Other

Published : 52 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 52 Documents
Search

 2   3   4   5   6 
Memprediksi Kualitas Produk Inspeksi Dalam Meminimalisasi Resiko Produk Ng Meggunakan Algoritma Regresi Linier Dini Rahayu; Aris Gunaryati
Bulletin of Information Technology (BIT) Vol 4 No 3: September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v4i3.894

Abstract

Predicting the product is a form of analyzing data, predicting the product is an important factor that determines the smooth running of the product. Utilization of product and defect data can be used in carrying out the process of data mining and modeling stages to predict the number of product defects at one time. The application of the simple Linear Regression algorithm equation model can be implemented where the results also provide a new insight for the prediction needs of the number of product defects. The simple Linear Regression equation model after comparison with actual calculation results (observations) and also with the Rapid Miner application in general shows similar results. Evaluation and testing of the RMSE value was also obtained when evaluating the applied linear regression model, with an RMSE value of 0.984 with a standard deviation of +- 0.0
Improving University Ranking Robustness Using Rank Geometric Weight Integration with CoCoSo Method for Reducing Ordinal Weighting Instability Septi Andryana; Teddy Mantoro; Achmad Benny Mutiara; Ernastuti Ernastuti; Prihandoko Prihandoko; Aris Gunaryati
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1024

Abstract

This study lies in the field of decision support systems, focusing on the application of Multi-Criteria Decision Making (MCDM) for ranking alternatives based on predefined organizational criteria. A persistent challenge in this domain is the instability and subjectivity of ordinal weighting methods - such as Rank Order Centroid (ROC), Rank Sum (RS), Rank Reciprocal (RR), and Rank Order Distribution (ROD), which derive weights solely from rank positions, often leading to inconsistent and unreliable outcomes. To address this, this study introduces Rank Geometric (RG) weights, a geometric mean aggregation of ROC, RS, RR, and ROD designed to reduce subjectivity, stabilize weight distribution, and enhance robustness. By using the Combined Compromise Solution (CoCoSo) method, the RG against Times Higher Education’s (THE) official weights were evaluated, and the four individual ordinal methods, applied to the top 10 Indonesian universities across five THE 2025 ranking criteria. Empirical results show that RG-CoCoSo produces stronger and more consistent correlations with THE’s rankings than THE-CoCoSo, as validated by Spearman and Pearson correlation tests, with a p-value of 0.0251. This study contributes a practical, data-driven weighting framework that strengthens the reliability of MCDM-based institutional performance evaluation and can be generalized to other ranking contexts.
Co-Authors Abdul Refli Gunawan Achmad Benny Mutiara Adryan Syahputra Agung Sembiring Agung Triayudi Alam Supriyadi Albaar Rubhasy Amalia Khansa Apriliani Wulandari Arif Setyo Pambudi Bagus Tegar Dwi Irianto Bayu Baskoro Ben Rahman Benrahman Bimo Salasa Putra Danang Aji Pangestu Diana Caniago Dicky Pradana Abdulrahman Julianto Dini Rahayu Ernastuti Ernastuti Fakhrul Mu'minin Fauziah Fauziah Gio Fandi H Nainggolan Guon Fernando Tarimakase Haris Amanullah Indah Adinda Marleni Iskandar Fitri Istiarati Ayustina Anjaswari Krisna Ruliyanto Lasriana Lasriana Mikhael Kristian Mikhael Kristian Mohammad Iwan Wahyuddin Mohammad Susilo Muhamad Afriyan Kahfi Muhamad Rayhan Akbar Muhamad Sopiyan Muhammad Ariel Shakaramiro Muhammad Reza Akbar Muhammad Taufiq Ismail MUHAMMAD YUSUF Nadhif Azharandi Nadia Aulia Nadya Hanifah Kamal Nizar Nizar Nur Widya Pratiwi Prihandoko . Rafli Ramadhan Gunawan Rifqi Zulfikar Rima Tamara Aldisa Riyo Efendi Romantika Sirait Sari Ningsih Septi Andriyana Septi Andryana Singgih Yulianto Bastian Teddy Mantoro Teddy Mantoro Topan Yulianto Tri Ichsan Saputra Ucuk Darusalam Wahyu Mahatma Kurniawan Yustika Maulina