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All Journal Jurnal Informatika Polinema (JIP) Journal of Elektronik Sistem InformasI Scientific Journal of Informatics
Priyo Haryoko
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Library & Information Science

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Development of a Mental Health Classifier Using LSTM and Text Preprocessing Techniques Haryoko, Priyo; Syukur, Abdul; Rijati, Nova
Scientific Journal of Informatics Vol. 12 No. 1: February 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i1.21216

Abstract

Purpose: This study aims to address undiagnosed mental health conditions using social media for early detection. By applying advanced preprocessing techniques and LSTM models, the research improves classification accuracy for depression and PTSD. It highlights deep learning’s potential to process unstructured data and provides a scalable solution for real-world mental health monitoring. Methods: Data was collected from Twitter using keywords like "depression" and "anxiety." Preprocessing included normalization, tokenization, stemming, and stopword removal. An LSTM-based model with GloVe embeddings, LSTM layers, and dropout was developed. The model’s performance was evaluated using metrics like accuracy, precision, recall, and F1-score to ensure robust and applicable results. Result: The LSTM model achieved 90% accuracy, outperforming Random Forest (89%) and SVM (89%). Preprocessing steps like tokenization and stemming boosted performance by 15%. The model effectively captured temporal dependencies in text, showcasing its ability to analyze unstructured social media content for mental health detection. Novelty: This study integrates advanced text preprocessing with LSTM to enhance mental health detection. Unlike traditional methods, it captures temporal nuances using GloVe embeddings. The scalable framework provides a reliable solution for real-world applications, paving the way for multilingual and cross-platform research in mental health analytics.
Sistem Informasi Berbasis Web untuk Pendaftaran Kompetisi Internasional Didiek Trisatya; Priyo Haryoko; Gunawan Gunawan; Nur Tulus Ujianto
JESII: Journal of Elektronik Sistem InformasI Vol 3 No 2 (2025): Journal of Elektronik Sistem InformasI - JESII (DECEMBER)
Publisher : Departement Information Systems Universitas Kebangsaan Republik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31848/jesii.v3i2.4409

Abstract

The management of international student competitions in higher education institutions often faces challenges related to fragmented registration processes, manual data handling, and inefficient submission management. These issues may lead to data inconsistency, administrative errors, and limited transparency for participants. This research aims to design and implement a web-based information system that integrates competition registration and work submission into a single platform. The study adopts a system development–oriented approach, including requirement analysis, system design, implementation, and functional testing. The proposed system supports an integrated registration and submission process for participants, role-based access for administrators, administrative verification, and real-time status monitoring. The implementation results indicate that the system operates according to functional requirements and improves administrative efficiency, data accuracy, and accessibility for users. By centralizing registration and submission processes, the system reduces redundancy and simplifies competition management workflows. This research provides a practical solution for managing international student competitions, with a case implementation at Universitas Pancasakti Tegal.
Improving Customer Churn Detection Through Balanced Ensemble Learning Didiek Trisatya; Priyo Haryoko
Jurnal Informatika Polinema Vol. 12 No. 3 (2026): Vol. 12 No. 3 (2026)
Publisher : UPT P2M State Polytechnic of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jip.v12i3.9500

Abstract

Predicting customer churn represents a major challenge for telecommunication providers, driven by fierce market competition and frequent customer switching that can significantly threaten long-term revenue stability. Failure to accurately identify customers with high churn potential often leads to ineffective retention strategies. This study examines the effectiveness of integrating data balancing techniques with ensemble learning models to enhance churn prediction performance on imbalanced datasets. A quantitative experimental method is applied using a publicly available telecommunications dataset. The preprocessing phase focuses on handling incomplete records, transforming categorical attributes into numeric representations, and scaling feature values to improve data quality. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is applied exclusively to the training data. The study evaluates three classifiers, including Logistic Regression as a baseline and two ensemble methods, Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM). Model performance is examined using several evaluation metrics such as accuracy, precision, recall, F1-score, and the Area Under the ROC Curve (AUC). The results reveal that ensemble learning approaches outperform Logistic Regression, particularly with respect to recall and AUC performance. LightGBM achieves the best overall performance and demonstrates stable predictive capability across all evaluation measures. Feature importance analysis reveals that customer tenure and billing-related attributes, including monthly charges and total charges, are dominant factors influencing churn behavior. These results demonstrate that integrating data balancing techniques with ensemble learning methods offers a robust and effective solution for supporting proactive customer retention initiatives in the telecommunications sector.
Co-Authors Abdul Syukur Didiek Trisatya Gunawan Nova Rijati Nur Tulus Ujianto