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Pola Pengembangan Paragraf Pada Majalah Neotech Julpandi, Julpandi; Gunawan, Kristian; Fauzi, Fikri; Kurniawan, Eva Dwi
Jurnal Riset Multidisiplin dan Inovasi Teknologi Том 3 № 01 (2025): Jurnal Riset Multidisiplin dan Inovasi Teknologi
Publisher : PT. Riset Press International

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59653/jimat.v3i01.1397

Abstract

This research examines paragraph development patterns in the text of the July 2001 edition of Neotech Magazine using qualitative descriptive methods. Data was obtained through documentation studies and note-taking techniques, with a focus on analyzing the structure and linguistic elements of paragraphs. The research results identified five dominant paragraph development patterns: chronology, illustration, definition, comparison, and cause and effect. The chronology pattern is used to show the time sequence in technological development, the illustration pattern provides a concrete picture of technological concepts, the definition pattern explains technical terms, the comparison pattern contrasts various technologies, and the cause-and-effect pattern explains causal relationships in technological development. The use of these patterns helps writers convey technology information effectively to readers, with each pattern having a specific function in increasing reader understanding. This research contributes to the development of more effective technology text writing strategies and can be used as a reference in technology journalistic writing.
Implementation of Python-Based Topsis Method for Best Stock Selection Analysis Using Yahoo Finance: English Gunawan, Kristian; Ikrimach, Ikrimach
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 7 No. 2 (2024): Jurnal Teknologi dan Open Source, December 2024
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v7i2.3873

Abstract

This study developed a web-based application implementing the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method for stock investment analysis and recommendations. The application was built using the Flask framework and integrated with the Yahoo Finance API for real-time stock data retrieval. The TOPSIS method evaluated stock alternatives based on criteria such as closing price, P/E ratio, revenue growth, and dividend per share. Testing included functional evaluation, response time analysis, and simulations of three investor scenarios: High Risk-High Return, Low Risk-Low Return, and Balanced. Results indicate that the application effectively delivers stock recommendations aligned with investor preferences, achieving an average response time of 1–4 seconds per feature. Simulations highlight its adaptability in adjusting criteria weights to match different risk profiles. Despite limitations due to external API dependencies, the application demonstrates effectiveness as a decision support tool for stock investment, offering accessibility and flexibility to investors.