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All Journal Indonesian Journal of Electrical Engineering and Computer Science Jurnal Teknik Informatika (JUTIF)
Rahmadianti, Fitrah Amalia
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Computer Science & IT

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G2M weighting: a new approach based on multi-objective assessment data (case study of MOORA method in determining supplier performance evaluation) Hendrastuty, Nirwana; Setiawansyah, Setiawansyah; An’ars, M. Ghufroni; Rahmadianti, Fitrah Amalia; Saputra, Very Hendra; Rahman, Miftahur
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp403-416

Abstract

Criteria weighting methods in decision support system (DSS) face various challenges and limitations that can affect their accuracy and reliability. One of the main challenges is subjectivity, this subjective assessment can reduce the objectivity and consistency of results. The main objective of the new weighting method grey geometric mean (G2M) weighting is to provide more objective and robust criteria weights under conditions of uncertainty and incomplete data. The new G2M weighting approach has a significant potential impact on the DSS field, it has the potential to generate more effective and efficient decisions, which can improve organizational performance, reduce risk and optimize outcomes. Pearson correlation test results of two sets of rankings generated by DSS methods namely grey relational analysis (GRA), simple additive weighting (SAW), multi-attributive ideal-real comparative analysis (MAIRCA), weighted product (WP), combined compromise solution (COCOSO), vlsekriterijumska optimizacija i kompromisno resenje (VIKOR), and a new additive ratio assessment (ARAS) that there is a strong positive correlation between the two methods using G2M weighting criteria. The high correlation value indicates that the rankings of the methods used tend to move together, giving confidence in the consistency and validity of the resulting ranking results. This gives confidence that both methods can be used simultaneously or interchangeably with consistent results. The use of G2M weighting in the DSS method used can support better decision-making by providing consistent information and validity of ranking results.
THE INFLUENCE OF FEATURE EXTRACTION ON AUTOMATIC TEXT SUMMARIZATION USING GENETIC ALGORITHM Rahmadianti, Fitrah Amalia; Hendrastuty, Nirwana
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 - SENIKO
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.2064

Abstract

Text summarization using extraction methods is a technique that summarizes by retaining a subset of sentences to create a summary. There are two types of documents commonly used for summarization: single document and multi-document. Multi-document refers to documents originating from one or more sources that contain several main ideas. The data used in this research is obtained from the E-lapor DIY website, consisting of 1000 data entries. E-Lapor DIY is a website provided by the DIY government to accommodate all public aspirations and complaints, such as damaged roads, broken traffic lights, insufficient street lighting, litter in public places, and more. The accumulation of data and the delayed response time has become an issue for the government in addressing these complaints. This research aims to consider the impact of using feature extraction for text summarization using genetic algorithms. The feature extraction compared in this research is the influence of sentence position in feature extraction. The results obtained show that Precision testing using F1 is 0.64, and without using F1, it is 0.66. Recall testing using F1 is 0.65, and without using F1, it is 0.68. F-Measure testing using F1 is 0.65, and without using F1, it is 0.68. This testing using the algorithm can be an interesting alternative for more time-efficient text summarization.
Co-Authors An’ars, M. Ghufroni Nirwana Hendrastuty Rahman, Miftahur Setiawansyah Setiawansyah Very Hendra Saputra