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All Journal Jurnal Riset Informatika TeknoIS : Jurnal Ilmiah Teknologi Informasi dan Sains
Zulkarnaen Noor Syarif
Universitas Budi Luhur
Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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IMPLEMENTATION OF 360-DEGREE FEEDBACK AND SAW FOR DECISION SUPPORT SYSTEM OF ACHIEVING TEACHER'S RECOMMENDATION Novita BR Ginting; Zulkarnaen Noor Syarif; Mamay Maesaroh; Jejen Jaenudin; Dahlia Widhyaesteoty; Muhamad Alfian Yusup; Leny Tritanto Ningrum
Jurnal Riset Informatika Vol 4 No 4 (2022): Period of September 2022
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1683.511 KB) | DOI: 10.34288/jri.v4i4.447

Abstract

Education requires quality teacher resources in the current era of industry 4.0 and society 5.0. Teachers act as educators, teachers, mentors, directors, trainers, assessors, and evaluators. Students must have critical thinking competencies, creativity and innovation, interpersonal and communication skills, teamwork and collaboration, and self-confidence. At SMK Yasbam, the selection process for outstanding teachers is carried out every year. The problem faced is that the selection process is still assessed, selected, and determined by the school principal only, so there is still a process that is deemed not transparent, accountable, and fair. To make the assessment process fairer, try using the 360-degree feedback method, a multi-source assessment, and then weighting the performance value using the Simple Additive Weighting method to obtain recommendations for outstanding teachers. Respondents consisted of principals, fellow teachers, students, and themselves (the assessed teachers). Furthermore, combining these two methods is applied in a decision support system to make the assessment process and selection of outstanding teachers more objective.
Penerapan Information Gain Dan Algoritma K-Means Untuk Klasterisasi Kedisiplinan Pegawai Menggunakan Rapidminer Zulkarnaen Noor Syarif
TeknoIS : Jurnal Ilmiah Teknologi Informasi dan Sains Vol 13, No 1 (2023)
Publisher : Universitas Binaniaga Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36350/jbs.v13i1.165

Abstract

One aspect of the discipline of an employee in an agency can be seen from the side of attendance. The level of employee attendance is closely related to an employee's disciplinary assessment. The level of employee discipline can be seen by looking at the hours of attendance or check in attendance, so that with these parameters you will get early, on time and late entry. This study explores data on attendance by using the k-means clustering algorithm. Before calculating the k-means clustering algorithm, attribute selection using information gain is expected to reduce attributes with small weights. The calculations are performed using Rapidminer software. The results showed that the attribute that had the greatest influence was the percentage of late entry with a weight of 0.783. Clustering using the k-means algorithm produces three clusters with the performance value of the Davies Bouldin Index (DBI) -0.645. Cluster zero has fifteen members, cluster one has thirty-six members, and cluster two has fifty-two members. Cluster zero is a cluster that has a low level of discipline, cluster one is a cluster that has a high level of discipline, while cluster two is a cluster that has a moderate level of discipline.
Co-Authors Dahlia Widhyaesteoty Jejen Jaenudin Leny Tritanto Ningrum Mamay Maesaroh Muhamad Alfian Yusup Novita Br Ginting