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Rancangan Aplikasi Marketplace UMKM D’Camilan Di kota Padang Menggunakan Aplikasi Blogger Yolanda Irma Julia Putri; Muhammad Rakha; Dhea vega Anandha; Sularno; Rafidola Mareta Riesa
Jurnal Imiah Pengabdian Pada Masyarakat (JIPM) Vol 2 No 2 (2024): Oktober - Desember
Publisher : CV. ITTC INDONESIA

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Abstract

The aim of this research is to design a marketplace application for D'Camilan Micro, Small and Medium Enterprises (MSMEs) in Padang City using the Blogger platform. With increasing public interest in local products, the use of digital technology has become important to increase the visibility and accessibility of MSME products. This research uses a design method consisting of needs analysis, appearance design, and implementation of basic features such as product catalogs, ordering systems, and promotional media. The results of this design show that using Blogger as a platform allows MSMEs to utilize existing tools at a lower cost, while still providing a good user experience. It is hoped that this application can increase sales and expand the market reach of D'Camilan MSMEs in Padang City and encourage local economic development.
Analisis Sentimen Opini Publik Mengenai Wajib BPJS Pada Media Sosial Twitter Menggunakan Metode Naïve Bayes Muhammad Rakha
OKTAL : Jurnal Ilmu Komputer dan Sains Vol 3 No 09 (2024): OKTAL : Jurnal Ilmu Komputer Dan Sains
Publisher : CV. Multi Kreasi Media

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Abstract

On January 6, 2022, President Joko Widodo signed the Presidential Instruction (Inpres) to the public. One of the points of Presidential Instruction Number 1 of 2022 concerning BPJS as a public service obligation, is a public discussion, concerning applicants for SIMs, STNK marks, and Police Certificates (SKCK) that must accompany BPJS Health membership. Due to the many public complaints about presidential education guidelines, this study was conducted to assess public perceptions using the Naive Bayes algorithm using TF-IDF feature selection. This study uses 362 data which is divided into two classes, positive and negative. The classification process gives the best accuracy results with a percentage of 88.89% using the selection of the TF-IDF function with 90% training data and 10% test data.