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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.
Implementasi Digital Marketing Pembuatan Website Penjualan Dengan Metode Agile: (Studi Kasus: Danisty Olshop) Rina Noviana; Winarti; Devi Indriani; Dewi Andryani; Kartika Nor Ramadha; Isram Rasal; Rifki Amalia; Aryo Pangestu
Jurnal Ilmiah Matrik Vol. 28 No. 1 (2026): Jurnal Ilmiah Matrik
Publisher : Direktorat Riset dan Pengabdian Pada Masyarakat (DRPM) Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33557/64vjx670

Abstract

Danisty Olshop is a shop that sells vehicle cleaning products through various e-commerce platforms and social media. This research is motivated by the need to increase operational efficiency and increase the competitiveness of digital marketing which is able to reach wider customers through the creation of sales websites. The aim of this research is to create a responsive e-commerce platform that supports effective digital marketing strategies in the form of a website, using Agile methods that allow continuous adjustment according to user needs. The website uses PHP programming and a MySQL database. The website is designed for two users, namely the user as a customer who purchases products and the admin as a seller who has access to manage products and monitor transactions. The results of the implementation and trial of the website that have been created meet expectations and are ready to be accessed via the link https://danistyolshop.my.id/. The application of the Agile method during the manufacturing process provides high flexibility and responsiveness according to user needs, so that implementing an integrated digital marketing strategy can expand market reach and increase sales significantly