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All Journal Jurnal Ilmiah Kursor Jurnal Edukasi dan Penelitian Informatika (JEPIN) Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Sistemasi: Jurnal Sistem Informasi Jurnal Pilar Nusa Mandiri Jurnal Mantik Penusa JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI ILKOM Jurnal Ilmiah MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURTEKSI Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal JOISIE (Journal Of Information Systems And Informatics Engineering) Jurnal Informatika Kaputama (JIK) Jurnal Teknik Informatika (JUTIF) Jurnal Manajemen Informatika Jayakarta Archive: Jurnal Pengabdian Kepada Masyarakat Journal of Artificial Intelligence and Engineering Applications (JAIEA) Journal of Applied Business and Technology Jurnal Informatika: Jurnal Pengembangan IT The Indonesian Journal of Computer Science INOVTEK Polbeng - Seri Informatika
Deny Jollyta
Institut Bisnis Dan Teknologi Pelita Indonesia
Religion Agriculture, Biological Sciences & Forestry Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Public Health Social Sciences

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Journal : jurnal ilmiah kursor

 1 
A TOPSIS AND ELECTRE COMPARISON ANALYSIS ON WEB-BASED SOFTWARE Rivensin Rivensin; Deny Jollyta
Jurnal Ilmiah Kursor Vol 11 No 1 (2021)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v11i1.253

Abstract

Methods in the Decision Support System (DSS) have their own techniques in solving organizational problems. Determining the appropriate DSS method with the problem is a common difficulty experienced by organizations. The performance of a DSS method can be measured in various ways. This research aims to determine the performance of the two DSS methods, specifically Technique for Others Preference by Similarity to Ideal Solution (TOPSIS) and Election at Choix Traduisant La Realite (ELECTRE) which are applied to the best lecturer selection system. The research was carried out on software designed using efficiency as one of the International Organization for Standardization (ISO) 9126. The performance of both methods tested on validity and sensitivity testing. The results showed that the TOPSIS performance was better in terms of efficiency and sensitivity. TOPSIS execution time is 0.0085 seconds faster and has a greater sensitivity value of 2.18% compared to ELECTRE. Validity result gave the best results reaching 100% to ELECTRE. That means, the ELECTRE calculation can be trusted because it has a perfect level of accuracy.
THE INFLUENCE OF DATA CATEGORIZATION AND ATTRIBUTE INSTANCES REDUCTION USING THE GINI INDEX ON THE ACCURACY OF THE CLASSIFICATION ALGORITHM MODEL Willy Fernando; Jollyta, Deny; Dadang Priyanto; Dwi Oktarina
Jurnal Ilmiah Kursor Vol. 12 No. 3 (2024)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v12i3.372

Abstract

Numerical data problems are typically caused by a failure to comprehend the data and the outcomes of its processing. In order to give richer context and a deeper understanding of the facts, numerical data must be transformed into categories. On the other hand, changes in data have a significant impact on the analysis's outcomes. The purpose of this study is to see how transforming numerical data into categories affects the model produced by the classification algorithms. The dataset used in this study is the Maternal Health Risk. Categorization refers to formal arrangements. Categorization is also accomplished by using the Gini Index to limit the number of instances of an attribute. The classification is carried out using the Random Forest (RF), K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) algorithms to produce a model. The influence of data modifications to model can be observed in the confusion matrix with 5 different data splitting. The study results suggested that changing numerical data to categories data significantly improved the performance of the SVM model from 76.92% to 80.77% at a data splitting percentage of 95/5.
Co-Authors Abdul Karim Adiya, M. Hasmil ahmad kamal, ahmad Akwilla, Hiskia Alyauma Hajjah Andreas Halim Tandiano Astri , Renita Ayu Dasriani, Ni Gusti Buaton, Relita Dadang Priyanto Dadang Priyanto Dafwen Toresa Darmanta Sukrianto Darmanta Sukrianto Des Suryani Des Suryani Dewi Nasien Dixsen Dixsen Dwi Oktarina Dwi Oktarina Dwi Oktarina Eri Yanto Erick Santoso Erlin Erlin Fauzi, Achmad Fenly, Fenly Gusrianty Gusrianty Gusrianty Gusrianty Gusrianty Gusrianty, Gusrianty Gustientiedina Hadi Dwi Putra Hairani Hairani Herman Mawengkang Hutri Rizkiyah Alda Iman, Ahmad Robbiul Irvan Firnando Jeffry Kody Jeperson Hutahaean Johan Johan Johan Johan Kadim, Lina Arliana Nur Kody, Jeffry Lasman Toni Loneli Costaner Makaruku, Yoakhina Nicole Marlim, Yulvia Nora Muhammad Siddik Muhammad Siddik Muhammad Zarlis Muhammad Zarlis, Muhammad Mukhsin Mukhsin N Novriyenni Oktariana, Dwi Pratama, Teddy Prihandoko . Prihandoko Prihandoko Rahmadian Yuliendi, Rangga Ramalia Noratama Putri Ramdhan, William Ricky Wijaya Rivensin Rivensin Ryan Christian Sabna, Eka Saputra, Fandri Siddik, Muhammad Sukrianto, Darmanta Surianto Surianto Syahril Effendi Tandiano, Andreas Halim Taslim Taslim Teddy Pratama Tony Tony Toresa, Dafwen Triwijoyo, Bambang Krismono Vincent Wijaya Wahyu Joni Kurniawan Wilda Susanti Willy Fernando