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PELATIHAN PEMBUATAN BLOG BRANDING USAHA TOKO JIHAN PADANG Nasir, Januardi; Setiawan, Yasha Langitta; Anwar, Hendra
Jurnal Pengabdian Cendikia Nusantara Vol 1 No 2 (2023): Jurnal Pengabdian Cendikia Nusantara
Publisher : Lembaga Riset Cendekia, Yayasan Berkah Putera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Rapid development in the business world in the city of Padang, coupled with an increasing demand from the predominantly working population, requires business actors to adopt creative ideas to thrive in the intensifying competition. In this context, branding emerges as one of the strategic solutions. This study aims to leverage a blog as a tool for branding and promotion for Toko Jihan in the city of Padang. The method involves creating and managing a free blog, along with adjusting the content to meet business needs. Despite utilizing social media platforms such as Facebook and Instagram for marketing, the owner still feels less confident without a means of information delivery like a blog or website. The outcomes of this community service activity encompass three solution steps: blog selection and creation, blog content creation, and blog maintenance. Blog selection focuses on the sustainability of the business by choosing a free platform that can be modified. Content creation involves product information, while blog maintenance discusses actions to ensure continuity and optimize performance. In conclusion, using a blog as a branding tool can boost the owner's confidence and optimize the visibility of Toko Jihan. The applied solution steps are expected to positively contribute to business development amid intense competition.
Sistem Pakar Menentukan Jenis Produk Kecantikan Berdasarkan Ph Pada Kulit Wajah Perempuan Nasir, Januardi; Setiawan, Yasha langitta
Jurnal Komtika (Komputasi dan Informatika) Vol 8 No 1 (2024)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v8i1.11296

Abstract

This research aims to providing alternative solutions to consumers in determining which products are right and suitable for the Ph of their facial skin. Shows that the expert system really helps users in solving problems, in terms of determining the type of cosmetics. Applying a web-based forward chaining method in program creation. Design/methodology/approach: The method used in creating the system to determine which products are suitable for the PH type of facial skin is the Forward Chaining method. Forward chaining is a search technique that starts with known facts, then matches these facts with the IF part of the IF-THEN rules. If there is a fact that matches the IF part, then the rule is executed. Findings/result: To determine the type of cosmetic product that is suitable, it will be analysed using a web-based forward changing method. Using the PHP programming language and MySQL database. Dreamweaver is a software application that is used as an editor. System analysis and design using starUMLOriginality/value/state of the art: From the research results, this expert system has an accuracy of 80% and functions quite well in determining the type of cosmetic product using a web-based forward changing method to make it easier for consumers to find the right cosmetics.
Text Mining of Trade War in Indonesia News (Tempo.co): A Wordcloud, Sentiment Analysis, and Cluster Sari, Dian Fitriarni; Yasha Langitta Setiawan
Journal Of Informatics And Busisnes Vol. 3 No. 2 (2025): Juli - September
Publisher : CV. ITTC INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jibs.v3i2.3422

Abstract

This study focuses on analysing news headlines from Indonesia's leading English daily newspaper, Tempo.co. with total of 4,184 news headlines. We were manually collected it from April to June 2025. The data was processed and analysed using the R Studio package for text mining and sentiment analysis. Various methods such as tokenisation, standardisation, data cleaning, stopword removal, stemming, and lemmatisation were used in the pre-processing stage to extract information. The research methodology included techniques such as wordcloud, sentiment analysis, and clustering to identify the most frequently occurring words, emotional tones, and groups of words that are interrelated in news headlines. Based on the results of the text mining analysis, it was found that the majority of news headlines focused on President Donald Trump's speech on Liberation Day and its impact on trade policy, particularly regarding import tariffs on trading partner countries. This coverage influenced public perception and business decision-making in political and economic aspects.