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All Journal Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Jurnal Nasional Pengabdian Masyarakat
Licantik Licantik
universitas Palangka Raya
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Library & Information Science Other

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Membangun Literasi Digital Bagi Warga Desa Hurung, Kecamatan Banama Tingang, Kabupaten Pulang Pisau Enny Dwi Oktaviyani; Ariesta Lestari; Licantik Licantik
Jurnal Nasional Pengabdian Masyarakat Vol. 2 No. 2 (2021): Jurnal Nasional Pengabdian Masyarakat
Publisher : Training & Research Institute - Jeramba Ilmu Sukses

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47747/jnpm.v2i2.386

Abstract

Digital literacy is defined as the ability to understand and use information in various forms from various sources accessed through computer devices. Digital literacy will create a society with a critical-creative mindset and views. Provocative issues will not easily consume it, become victims of hoax information, or victims of digital-based fraud. Residents of Hurung Village, Banama Tingang District, Pulang Pisau Regency are partners in this community service activity who are active users of digital technology via mobile phones to access the internet and interact with other people. However, in digital technology, partners have not been able to distinguish true or false information and forward information obtained from social media or unclear sources. Likewise, in the search for information, still using sources or references that are not valid. So that the information obtained cannot be justified. In this service activity, the service team created digital media using a village profile website to support digital literacy for Hurung village. The activity was carried out in three stages, namely material presentation, mentoring, and training on the use of village profile websites. With this activity, it is hoped that it can be helpful to add insight, knowledge and build Hurung village to become a pioneer village of digital literacy
SISTEM PENDUKUNG KEPUTUSAN UNTUK PENYELEKSIAN CALON PENERIMA BANTUAN PROGRAM INDONESIA PINTAR MENGGUNAKAN SIMPLE ADDITIVE WEIGHTING Sherly Christina; Enny Dwi Oktaviyani; Licantik Licantik; Jen Paolo Encun; Deddy Ronaldo
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 16 No. 2 (2022): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v16i2.4010

Abstract

The "Program Indonesia Pintar" (PIP) is a program from the Indonesian government to help children from poor families to get the proper education. Schools often deal with obstacles when selecting the students who are eligible for the PIP grant, due to the process of collecting and managing many registration files, thus requiring high accuracy to prevent errors in the selection results. Therefore, this research aims to develop a website of Decision Support System to support the tasks of the PIP recipient selection committee. The Decision Support System applies the Simple Additive Weighting (SAW) method to obtain the selection results from the ranking of the requirements data handed over by the applicants for the selection of PIP beneficiaries. Then this research applies the Waterfall software development method to analyze, design and develop the Decision Support System website. Furthermore, the results of the black box test indicate that the functions of this Decision Support System work according to their objectives. The system can manage the selection registration data and generate the rankings of prospective PIP beneficiaries.
Co-Authors Ariesta Lestari Dilla Ayu Dwipitaloka Enny Dwi Oktaviyani Felicia Sylviana Gunawan, Vincentius Abdi Jadiaman Parhusip, Jadiaman Jen Paolo Encun Karolita, Devi Loure Florentina Marhayu Marhayu Nor Aliya Suganda Nova Noor Kamala Sari Pebriana Lorenza Ronaldo, Deddy Sherly Christina Veni Laola Widiatry, Widiatry Winda Yulistria Yuni Pransika