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All Journal Jurnal Ilmiah KOMPUTASI Jurnal Teknik dan Science (JTS)
Isram Rasal
Universitas Gunadarma
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Rancangan Sistem Penunjang Keputusan Untuk Promosi Jabatan Menggunakan Metode SAW: Array Eva Utari Cintamurni Lubis; - Karmilasari; Isram Rasal
Jurnal Ilmiah Komputasi Vol. 19 No. 3 (2020): Jurnal Ilmiah Komputasi Volume: 19 No. 3, September 2020
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32409/jikstik.19.3.56

Abstract

Salah satu kunci kesuksesan suatu perusahaan tidak terlepas dari peran sumber daya manusia di dalamnya. Salah satu bentuk kegiatan yang berhubungan dengan SDM perusahaan adalah kegiatan promosi jabatan. Penelitian ini bertujuan untuk membangun sistem penunjang keputusan yang dapat digunakan dalam proses promosi jabatan. Dalam melaksanakan proses promosi jabatan, dibutuhkan informasi yang cepat dan akurat mengenai data pegawai yang akan dicalonkan. Sistem informasi kepegawaian yang telah ada, dimodifikasi sehingga dapat digunakan untuk proses promosi jabatan. Hasil penelitian ini adalah sebuah sistem penunjang keputusan untuk promosi jabatan menggunakan metode Simple Additive Weighting
PENERAPAN ALGORITMA NAIVE BAYES DAN SVM UNTUK ANALISIS SENTIMEN BOY BAND BTS PADA MEDIA SOSIAL TWITTER Rina Noviana; Isram Rasal
Jurnal Teknik dan Science Vol. 2 No. 2 (2023): Juni : Jurnal Teknik dan Science
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/jts.v2i2.791

Abstract

BTS or Bangtan Sonyeondan is one of the vocal groups originating from South Korea, which is currently popular among Indonesian teenagers, resulting in many fans providing positive and negative comments through Twitter social media. The method used to determine whether these comments are positive or negative is by conducting sentiment analysis. The stages to perform data analysis are Preprocessing to clean the data, word weighting, labeling data into positive and negative classes, classification, and data visualization using pie charts. In this study, Naive Bayes and Support Vector Machine, were used for comparison, result of an accuracy score is 79% for Naive Bayes and 81% for Support Vector Machine. Among these two methods, Support Vector Machine achieved a higher accuracy score, and the sentiment analysis revealed that the comments obtained from Twitter users are predominantly positive.
Co-Authors Eva Utari Cintamurni Lubis Karmilasari Rina Noviana