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Jurnal Ilmu Siber dan Teknologi Digital
Published by Goodwood Publishing
ISSN : -     EISSN : 29867312     DOI : 10.35912/jisted
Core Subject : Science, Education, Engineering,
Computer Science & IT Education Engineering Other
Jurnal Ilmu Siber dan Teknologi Digital (JISTED) is a national, open-access and peer-reviewed journal welcoming high-quality manuscripts of original articles, reports and literature reviews in the field of software engineering and information technology. Jurnal Ilmu Siber dan Teknologi Digital (JISTED) aims to mediate the fresh ideas of researchers and practitioners to accelerate technology and cyber development.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 2 Documents
 1 
Search results for , issue "Vol 4 No 1 (2025): November" : 2 Documents clear
WEB-BASED RESEARCH ARTICLE CLASSIFICATION USING THE RANDOM FOREST ALGORITHM Ahludzikri, Fiqqi; Herwanto, Riko; RZ , Abdul Aziz; Agus, Isnandar; Irianto, Suhendro Yusuf
Jurnal Ilmu Siber dan Teknologi Digital Vol 4 No 1 (2025): November
Publisher : Penerbit Goodwood

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/jisted.v4i1.5547

Abstract

Purpose: This study aims to develop a web-based system that classifies research articles using the Random Forest algorithm to address mismatches between article content and journal scope. Methodology/approach: The research employed the SDLC Waterfall model, with data sourced from 560 articles published by Goodwood Publishing (2019–2024) across four categories. Text preprocessing included case folding, stopword removal, stemming, and tokenization, with TF-IDF applied for feature extraction. Random Forest was trained with 80% training data and 20% testing data. Results/findings: The model achieved 91% accuracy, with high precision and recall across all categories. The system was successfully implemented as a web-based application, providing instant classification and journal recommendations. Limitations: The dataset was limited to one publisher and only Random Forest was applied, which may restrict the generalizability of findings. Contribution: This study contributes to the application of machine learning in scholarly publishing, offering a practical solution for editors to streamline article selection and improve efficiency.
Android Based Rosella Tea Sales Application as Digital Innovation Rahayu, Lusia Septia Eka Esti; Sari, Marlia; Junaidi, Muhammad
Jurnal Ilmu Siber dan Teknologi Digital Vol 4 No 1 (2025): November
Publisher : Penerbit Goodwood

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/jisted.v4i1.5906

Abstract

Purpose: This study aims to develop an Android-based sales application for Rosella Tea as a digital innovation, addressing the need for efficient marketing and sales management among Women Farmers Groups (KWT) in East Ambarawa Village, Lampung. Methodology/approach: The research utilizes the System Development Life Cycle (SDLC) method, with stages including needs analysis, system design using Unified Modeling Language (UML), coding with Java, and data storage via Firebase. Results/findings: The developed application successfully operates on Android devices, presenting products informatively and enabling real-time transactions and sales management. It enhances marketing efficiency and expands the market reach for Rosella Tea, improving transaction processes between sellers and buyers. Conlusion: The Android-based application serves as an innovative solution to promote Rosella Tea digitally, offering better sales management, expanding market access, and increasing business efficiency for women farmers. Limitations: The study did not explore the full range of features needed, such as integration with various payment methods and e-commerce platforms. Contribution: The application provides a digital pathway for Women Farmers Groups to manage and promote local products, contributing to their economic empowerment and aligning with government initiatives to support MSMEs' digitalization.

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