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Yogi Saputra
UIN Sunan Gunung Djati Bandung
Computer Science & IT

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Natural Language Processing and Random Forest for Mental Health Symptom Identification Using Social Media Data Sigit Sugara; Popon Dauni; Novianti Indah Putri; Yogi Saputra; Nana Suryana
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.
Optimizing Subscription Package Selection Using a Business Intelligence-Based Decision Support System on Customer Payment Data Moch Mukhsin Nauval; Kamaludin Kamaludin; Yogi Saputra
CoreID Journal Vol. 4 No. 2 (2026): July 2026
Publisher : CV. Generasi Intelektual Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60005/coreid.v4i2.164

Abstract

Subscription-based business models generate large amounts of customer payment data that has the potential to be used to support strategic decision-making. However, in many companies, the use of this data is still limited to presenting descriptive information through Business Intelligence dashboards, thus failing to produce objective decision recommendations. This condition often results in the selection of superior subscription packages being based on subjective assessments. This study aims to design a Decision Support System (DSS) integrated with Business Intelligence to determine the optimal subscription package. The Analytic Hierarchy Process method is used to determine the criteria weights, Simple Additive Weighting is used to rank alternatives, and the Technique for Order Preference by Similarity to Ideal Solution is used to validate the ranking results. The research data comes from customer payment data including the number of customers, number of transactions, total revenue, and average transaction value. The results show that the New Hit package consistently ranks highest in the SAW and TOPSIS methods. These findings demonstrate that the integration of Business Intelligence and Decision Support System (DSS) can produce objective, stable and reliable data-driven recommendations to support a company's strategic decisions.
Co-Authors Kamaludin Kamaludin Moch Mukhsin Nauval Nana Suryana Novianti Indah Putri Popon Dauni Sigit Sugara