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All Journal Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Jurnal Teknik Informatika (JUTIF)
Arita Fitri, Triyani
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Computer Science & IT Engineering

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Implementation of The Open Group Architecture Framework to See the Readiness of Smart Schools in Pekanbaru Anam, M. Khairul; Hendrawan, Riki; Arita Fitri, Triyani; Agustin, Wirta; Zamsuri, Ahmad
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 14 No. 2 (2023): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/digitalzone.v14i2.14916

Abstract

Smart Schools are a derivative of smart people in the 6 pillars of Smart City. Smart Schools is also a school concept utilizing information technology used in the teaching and learning process in the classroom and school administration. One of the schools in Pekanbaru City that will implement smart schools is Junior High School 17 Pekanbaru. Currently, the school already has several infrastructures including servers, laboratories, and administrative rooms, but it is necessary to analyze the technology architecture aimed at implementing Smart Schools. The technological architecture would be analyzed using TOGAF (The Open Group Architecture Framework) Framework version 9. The TOGAF framework is a framework for enterprise architecture which is able to develop an enterprise architecture in an organization. Enterprise architecture is an explicit explanation and current documentation of the relationship between management, business processes, and information technology. This research describes the current architectural conditions and target architectures to include the rules, standards, and lifecycle of information systems to optimize and maintain the environment of organizations that want to create and maintain by managing the IT portfolios. The results of this study are to produce an IT blueprint that is used as a school guide in implementing technology architecture to support the implementation of Smart Schools in Pekanbaru City.
Predicting Mental Health Status using a Fine-Tuned CNN-LSTM Hybrid Model Agustin, Agustin; Junadhi, Junadhi; Erlinda, Susi; Arita Fitri, Triyani; Efrizoni, Lusiana
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5882

Abstract

Mental health has become a critical global concern in the digital era, particularly as social media platforms increasingly serve as spaces where users express psychological conditions, emotions, and personal struggles. This study aims to predict mental health status from Twitter text using a fine-tuned hybrid CNN–LSTM deep learning model. A total of 12,214 tweets were collected, cleaned, and labeled into five categories: Normal, Stress, Anxiety, Depression, and High-Risk Condition. The dataset was split using stratified sampling into 70% training, 15% validation, and 15% testing portions. Text was transformed into numerical representations through tokenization, padding, and 100-dimensional word embeddings. The hybrid CNN–LSTM architecture combines the CNN’s ability to extract local linguistic features with the LSTM’s strength in capturing long-term contextual dependencies, supported by dropout, early stopping, and hyperparameter fine-tuning. Experimental results show that the hybrid model achieves superior performance compared to standalone CNN and LSTM architectures, obtaining an overall accuracy of 0.892, macro precision of 0.874, macro recall of 0.861, and a macro F1-score of 0.865. Class-wise evaluation indicates that the Normal category achieves the highest accuracy (0.960), followed by Anxiety (0.884) and High-Risk Condition (0.808). Meanwhile, Stress (0.751) and Depression (0.745) show lower accuracies due to semantic overlap in linguistic expressions commonly found on social media. The training process demonstrates stable convergence without significant overfitting, confirming the effectiveness of the selected architecture and training strategy. Overall, this study highlights the effectiveness of the hybrid CNN–LSTM model for early mental health detection based on text data. The findings provide a strong foundation for developing scalable and data-driven mental health monitoring systems in digital environments and contribute to advancing natural language processing approaches for mental health analysis.
Co-Authors Agustin Agustin Ahmad Zamsuri, Ahmad Anam, M Khairul Efrizoni, Lusiana Erlinda, Susi Hendrawan, Riki Junadhi, Junadhi Wirta Agustin