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Journal : Coreid Journal

Strategic Planning of Correspondence Service Information System using TOGAF ADM Framework Putri, Novianti Indah; Yogi Saputra; Zen Munawar; Nurhayati Mahmud
CoreID Journal Vol. 2 No. 3 (2024): November 2024
Publisher : CV. Generasi Intelektual Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60005/coreid.v2i3.68

Abstract

Rapid advancements in information technology have led to businesses and government organizations implementing information systems to improve corporate operations. One of the services provided by the village is correspondence, which includes creating letters of recommendation for application letters. In Leubatang Village, correspondence services are still conducted by hand, and data is gathered using physical books, which makes it difficult to locate data and leaves it susceptible to This study uses the TOGAF ADM framework to suggest strategic planning for Leubatang Village's communication information system. Architectural strategies, gap analyses, business architecture analyses, and technological needs analyses are all prepared using PEST analysis. This strategy planning can be implemented in two to three years, according to the blueprint. Leubatang Village’s correspondence services can be developed using the information system architecture’s design as a foundation for implementation and as a guide.
Natural Language Processing and Random Forest for Mental Health Symptom Identification Using Social Media Data Sugara, Sigit; Dauni, Popon; Putri, Novianti Indah; Saputra, Yogi; Suryana, Nana
CoreID Journal Vol. 3 No. 3 (2025): November 2025
Publisher : CV. Generasi Intelektual Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60005/coreid.v3i3.145

Abstract

This study explores the implementation of machine learning models, specifically Natural Language Processing (NLP) and Random Forest, for detecting mental health symptoms based on text analysis of web-sourced data. The research addresses the challenges of analyzing highly subjective and dynamic text in social media content to identify patterns associated with anxiety, depression, and stress. The methodology involves several preprocessing steps including case folding, cleansing, language normalization, negation conversion, stopword removal, and tokenization, followed by TF-IDF weighting and Random Forest classification. The model evaluation revealed a high accuracy rate of approximately 80%, although achieving a confidence level of 75% proved challenging. This research demonstrates that despite the inherent difficulties in predicting subjectively variable text, the machine learning approaches employed show satisfactory performance in identifying mental health symptoms, offering potential for early detection and intervention systems.