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All Journal Bulletin of Electrical Engineering and Informatics JOIV : International Journal on Informatics Visualization JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknoinfo Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Building of Informatics, Technology and Science Jurnal Teknik Informatika (JUTIF) KLIK: Kajian Ilmiah Informatika dan Komputer J-Intech (Journal of Information and Technology) Jurnal Telematics and Information Technology (TELEFORTECH) Jurnal Media Borneo Journal of Information Technology, Software Engineering and Computer Science
Megawaty, Dyah Ayu
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Library & Information Science

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Journal : JOIV : International Journal on Informatics Visualization

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A New Feature Extraction Approach in Classification for Improving the Accuracy of Proteins Damayanti, -; Lumbanraja, Favorisen Rosyking; Junaidi, Akmal; Sutyarso, -; Susanto, Gregorius Nugroho; Megawaty, Dyah Ayu
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2589

Abstract

Proteins play a vital role in life as essential macromolecules, consisting of linear heteromeric biopolymers formed by amino acids covalently bonded through peptide bonds. They contribute to cell development and bolster the body's defense mechanisms. Post-translational modification processes, such as glycosylation, are necessary for proteins to function optimally. Glycosylation involves adding sugar groups to proteins, playing a critical role in various protein folding processes. Dysregulation of protein glycosylation can lead to diseases like Alzheimer's and cancer. Manual classification of glycosylated proteins is time-consuming, necessitating a faster approach. This study aims to expedite glycosylated protein classification using novel methods like AAindex, CTD, SABLE, hydrophobicity, and PseAAC for increased accuracy, comparing them with existing approaches. The dataset comprises protein sequences sourced from the openly accessible UniProt database. Results demonstrate that glycosylated protein prediction achieved 100% accuracy, surpassing previous approaches. Several features contributed to this improvement, with Hydrophobicity making a significant contribution at 24%, and PseAAC making the most significant contribution at 40% among the five extraction methods developed.
Development of a Decision Support System Based on New Approach Respond to Criteria Weighting Method and Grey Relational Analysis: Case Study of Employee Recruitment Selection Megawaty, Dyah Ayu; Damayanti, Damayanti; Sumanto, Sumanto; Permata, Permata; Setiawan, Dandi; Setiawansyah, Setiawansyah
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2744

Abstract

The purpose of this research is to propose a new approach in the criteria weighting method using the RECA method, the RECA method can help provide a systematic and structured framework for determining criteria weights in multi-criteria decision making. The determination of weights using the RECA method is to increase objectivity and accuracy in the candidate assessment and selection process by determining the appropriate weight for each criterion based on responses and assessments from experts or stakeholders. Testing the RECA Method with Multi Attribute Decision Making (MADM) techniques is an important step in measuring the effectiveness of the RECA Method in the context of multi-criteria decision making. Ranking tests using Spearman correlation between the RECA method and other methods such as SAW with a correlation value of 1, MOORA with a correlation value of 0.9636, MAUT with a correlation value of 0.9515, WP with a correlation value of 0.891, SMART with a correlation value of 0.9636, and TOPSIS with a correlation value of 0.8788 show a high level of rank consistency between the RECA method and these methods. This indicates that the RECA Method has a strong ability to generate similar candidate rankings with other methods, validating its reliability and consistency in the context of multi-criteria decision making. Implications for further research include exploring the application of the RECA method in different decision-making contexts other than recruitment, such as performance evaluation, project selection, or supplier selection. Further research could investigate the integration of the RECA method with other decision-making methods or algorithms to improve its performance and applicability in complex decision environments. Comparative studies with larger sample sizes and diverse datasets can provide deeper insights into the effectiveness and reliability of the RECA method compared to other methods.
Co-Authors - Damayanti Aditia Yudhistira Agustina, Intan Ahmad Ari Aldino Akmal Junaidi Andi Nurkholis Apriyani, Risa Fitria Ari Sulistiyawati Chandra, Iryanto Damayanti Damayanti Dedi Darwis Erwanto Favorisen R. Lumbanraja Gibtha Fitri Laxmi Heni Sulistiani Irsan, Aqilla Hattami Kurniawan, M. Fadli Palupiningsih, Pritasari Parjito, Parjito Permata, Permata Riska Aryanti Ryan Randy Suryono Saputra, Williyandi Setiawan, Dandi Setiawansyah Setiawansyah Sitna Hajar Hadad Sumanto, Sumanto Susanto, Gregorius Nugroho Sutyarso, - Ulum, Faruk Wahyuda, Hana Wang, Junhai Waqas Arshad, Muhammad Wardana, Suwarman Adi Widiyanti, Adella Wirahudha, Muhammad Arif Yadin, Yuli Yuri Rahmanto