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All Journal International Journal of Innovation in Mechanical Engineering and Advanced Materials Jurnal Informatika Polinema (JIP) Jurnal Pengabdian Masyarakat dan Riset Pendidikan Journal of Elektronik Sistem InformasI Scientific Journal of Informatics
Priyo Haryoko
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Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Other

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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.
Benchmarking Nine SMOTE-Balanced Classifiers Including Artificial Neural Network for CNC Predictive Maintenance Didiek Trisatya; Priyo Haryoko
International Journal of Innovation in Mechanical Engineering and Advanced Materials Vol. 8 No. 1 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/ijimeam.v8i1.38645

Abstract

Unplanned equipment failure in CNC manufacturing causes significant economic losses, driving demand for effective predictive maintenance (PdM). A critical research gap persists: existing studies on the AI4I 2020 Predictive Maintenance Dataset apply isolated classifiers under inconsistent preprocessing pipelines, preventing fair algorithmic comparison. No prior study has benchmarked nine diverse classifier families under a unified pipeline integrating SMOTE oversampling with domain-driven feature engineering. This study addresses that gap by systematically evaluating nine ML classifiers—Logistic Regression, K-Nearest Neighbors, Decision Tree, Random Forest, Gradient Boosting, AdaBoost, SVM (RBF kernel), Naive Bayes, and MLP Neural Network—on the AI4I 2020 dataset (10,000 records; 3.4% failure rate; 1:28 class imbalance). Two domain-engineered features were constructed: mechanical power (P = n × T × (π/30)) and thermal gradient (ΔT = T_process - T_air). Features were normalized; SMOTE was applied to training folds only; and 10-fold stratified cross-validation assessed six performance metrics. Three novel contributions are presented: (1) the first nine-classifier benchmark on AI4I 2020 under a unified SMOTE-and-feature-engineering pipeline enabling fair model comparison; (2) empirical demonstration that Average Precision is a more discriminating evaluation metric than AUC-ROC under severe 1:28 class imbalance; and (3) physical interpretation of feature importance linking dominant predictors to CNC failure mechanisms. Gradient Boosting achieved the best-balanced performance (F1-score: 0.6782, Accuracy: 97.20%, AUC-ROC: 0.9723); Random Forest attained the highest AUC-ROC (0.9772). Mechanical power (25.51%) and tool wear (23.91%) were dominant predictors, corresponding to tribological, fatigue loading, and thermal failure mechanisms. These findings support cost-effective condition-based maintenance strategies in industrial CNC environments.
Penerapan Algoritma k-Nearest Neighbor untuk Klasifikasi Kondisi Lingkungan Pertanian Berbasis IoT : Penelitian Panji Pangestu Saputra; Hasbi Firmansyah; Rizki Prasetyo Tulodo; Priyo Haryoko; Wahyu Asriyani
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 3 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 3 (Januari 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i3.4566

Abstract

The development of the Internet of Things (IoT) has encouraged the adoption of smart technologies in agriculture to enable real-time environmental monitoring. This study aims to apply the k-Nearest Neighbor (k-NN) algorithm to classify agricultural environmental conditions into ideal and non-ideal categories based on IoT sensor data. The dataset used in this research was obtained from an open-source repository and consists of several environmental parameters, including temperature, humidity, and soil moisture. The research stages include data preprocessing, attribute and label determination, data normalization using the z-transformation method, and model evaluation through cross validation. The performance of the classification model was assessed using accuracy, precision, recall, and F-measure metrics. The experimental results indicate that the k-NN algorithm is capable of providing good classification performance in identifying agricultural environmental conditions. However, limitations were observed in detecting minority class instances, suggesting the need for further parameter optimization and model enhancement. This research is expected to serve as a foundation for the development of IoT-based smart agriculture systems to support more effective decision-making in agricultural environmental management.
Co-Authors Abdul Syukur Asriyani, Wahyu Didiek Trisatya Gunawan Hasbi Firmansyah Nova Rijati Nur Tulus Ujianto Panji Pangestu Saputra Rizki Prasetyo Tulodo