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All Journal JSI: Jurnal Sistem Informasi (E-Journal) Global Medical and Health Communication SISFOTENIKA JMM (Jurnal Masyarakat Mandiri) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Media Keperawatan Indonesia JURNAL TEKNOLOGI INFORMASI Applied Technology and Computing Science Journal International Journal of Informatics and Computation Prosiding Seminar Nasional Multidisiplin Ilmu Respati Jurnal Teknik Informatika (JUTIF) Journal of Computer, Electronic, and Telecommunication (COMPLETE) Prosiding Seminar Nasional Pengabdian Kepada Masyarakat International Journal of Informatics Engineering and Computing
Sri Hasta Mulyani
Universitas Respati Yogyakarta
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Immunology & microbiology Library & Information Science Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Journal : international journal of informatics and computation

 1 
Implementation of CNN for Plant Leaf Classification Mohammad Diqi; Sri Hasta Mulyani
International Journal of Informatics and Computation Vol 2 No 2 (2020): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v2i2.28

Abstract

Many deep learning-based approaches for plant leaf stress identification have been proposed in the literature, but there are only a few partial efforts to summarize various contributions. This study aims to build a classification model to enable people or traditional medicine experts to detect medicinal plants by using a scanning camera. This Android-based application implements the Java programming language and labels using the Python programming language to build deep learning applications. The study aims to construct a deep learning model for image classification for plant leaves that can help people determine the types of medicinal plants based on android. This research can help the public recognize five types of medicinal plants, including spinach Duri, Javanese ginseng, Dadap Serep, and Moringa. In this study, the accuracy is 0.86, precision 0.22, f-1 score 0.23, while recall is 0.2375.
SIMANTUL: Model of Internal Quality Audit Management System in Higher Education Sri Hasta Mulyani; Ariyanto Nugroho; Maisarah Nurain
International Journal of Informatics and Computation Vol. 4 No. 2 (2022): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v4i2.52

Abstract

Many organizations carry out the quality assurance system through the Internal Quality Assurance System (SPMI) and the External Quality Assurance System (SPME). The SPMI framework uses the stages of a continuous quality assurance cycle with the PPEPP method (Application, Implementation, Evaluation, Control, and Improvement), which is carried out periodically to achieve University's Vision, Mission, Goals, and Targets. This paper discusses the implementation stages of the internal quality audit management system at Universitas Respati Yogyakarta, Indonesia. Using an information system, the university audit body, BPM, regularly and consistently carries out an Internal Quality Audit (AMI) every year to audit the implementation of academic and non-academic activities at the University. In this research, we construct an audit system, namely the E-Audit application, with the Waterfall software development method. This study can produce an efficient system called SIMANTUL, which refers to the Higher Education Accreditation assessment instrument version 3.0 and can store documents digitally.
Enhancing Mental Health Disorders Classification using Convolutional Variational Autoencoder Sri Hasta Mulyani
International Journal of Informatics and Computation Vol. 6 No. 1 (2024): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v6i1.65

Abstract

This research investigates the application of Convolutional Variational Autoencoder (CVAE) for multi-class classification of mental health disorders. The study utilizes a diverse dataset comprising five classes: Normal, Anxiety, Depression, Loneliness, and Stress. The CVAE model effectively captures spatial dependencies and learns latent representations from the mental health disorder data. The classification results demonstrate high precision, recall, and F1 scores for all classes, indicating the model's robustness in distinguishing between different disorders accurately. The research contributes by leveraging the unique capabilities of CVAE, combining convolutional neural networks and variational autoencoders to enhance the accuracy and interpretability of the classification process. The findings highlight the potential of CVAE as a powerful tool for accurate and efficient mental health disorder classification. This research paves the way for further advancements in deep learning techniques, supporting improved diagnosis and personalized healthcare in mental health.
Fake News Detection in Health Domain Using Transformer Models Sri Hasta Mulyani; Suwarto; Hamzah; R.Nurhadi Wijaya; Rodiyah; Wita Adelia
International Journal of Informatics and Computation Vol. 6 No. 2 (2024): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v6i2.89

Abstract

The rise of fake news in the health sector poses a serious threat to public well-being and accurate health communication. This study investigates the effectiveness of transformer models, particularly BERT (Bidirectional Encoder Representations from Transformers), in detecting fake news related to health. By leveraging the advanced contextual understanding of BERT, we aim to enhance the accuracy of fake news detection in this critical domain. Our approach involves training the BERT model on a curated dataset of health news articles, followed by rigorous evaluation on its ability to differentiate between genuine and misleading content. The results reveal that the transformer-based model significantly outperforms traditional methods, achieving high accuracy and robust performance metrics. This research underscores the potential of transformer models in combating health misinformation and provides a foundation for future improvements in automated fake news detection systems.
Co-Authors Ariyanto Nugroho Catur Paryadi Fajar Y Zebua Giyawati Yulilania Okinarum Hamzah Hamzah Hamzah Husna Arwa Salsabil Izzurohman, Moh. Jerry Hardianto Maisarah Nurain Marselina Endah Hiswati Mohammad Diqi Okinarum, Giyawati Yulilania Ordiyasa, I Wayan Putra Wanda, Putra R.Nurhadi Wijaya Rizky Erwanto Rizky Erwanto Rodiyah Suwarto Trinidya, Fike Venny Vidayanti Vidayanti, Venny Wijaya, Nurhadi Wita Adelia