Arrie Kurniawardhani

Research interest:
Computer Vision, Data Science, Machine Learning , Remote Sensing

Email: arrie.kurniawardhani[at]uii.ac.id

Education:

M.Kom. | Magister Komputer (Master of Computer) – Institut Teknologi Sepuluh Nopember, Indonesia

S.Si.  | Sarjana Sains (Bachelor of Science) – Universitas Gadjah Mada, Indonesia

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Description

Arrie Kurniawardhani is a lecturer and researcher at the Department of Informatics, Universitas Islam Indonesia, Yogyakarta, Indonesia. She accomplished her Bachelor’s degree in Electronics and Instrumentation at Universitas Gadjah Mada in 2011 and her Master’s degree in Informatics at Institut Teknologi Sepuluh Nopember in 2014. She actively joins Center of Data Science, Universitas Islam Indonesia, and has been assigned as the Research Coordinator since 2023. Machine learning, data science, and computer vision are among her research interests. Her research experience includes developing computer-assisted diagnosis applications for Mycobacterium Tuberculosis using digital microscopic images of sputum samples. This work was supported by research grants from the Indonesian Ministry of Research, Technology, and Higher Education from 2016 to 2018. Subsequently, she received further grants from the ministry to develop a decision support system for analyzing facial skin conditions using computer vision techniques (2021-2022) and to develop a deep learning-based mobile model for On-Shelf Availability Monitoring in Retail (2023-2024). Currently, she is engaged in a research project focused on semantic segmentation in satellite imagery using computer vision techniques.

Selected publications

  • Respati, R. G., Kurniawardhani, A., & Paputungan, I. V. (2024, November). Enhancing Emotion Recognition in Voice: Leveraging Support Vector Machines. In 2024 9th International Conference on Information Technology and Digital Applications (ICITDA) (pp. 1-7). IEEE.
  • Kurniawardhani, A., Rahman, H. A., & Paputungan, I. V. (2024, June). Non-Invasive Automatic Drowsiness Detection using Independently Recurrent Neural Network. In 2024 21st International Joint Conference on Computer Science and Software Engineering (JCSSE) (pp. 534-539). IEEE.
  • Fudholi, D. H., Kurniawardhani, A., Andaru, G. I., Alhanafi, A. A., & Najmudin, N. (2024). YOLO-based Small-scaled Model for On-Shelf Availability in Retail. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 8(2), 265-271.
  • Muhimmah, I., Prasetyo, D., Kurniawardhani, A., & Rani, S. (2023, March). Prototype for automatically detecting acne in face images using digital image processing techniques. In AIP Conference Proceedings (Vol. 2508, No. 1). AIP Publishing.
  • Azizi, F. N., Kurniawardhani, A., & Paputungan, I. V. (2022, November). Facial Expression Image based Emotion Detection using Convolutional Neural Network. In 2022 IEEE 20th Student Conference on Research and Development (SCOReD) (pp. 157-162). IEEE
  • Muhimmah, I., Muchlis, N. F., & Kurniawardhani, A. (2021). AUTOMATIC FACIAL REDNESS DETECTION ON FACE SKIN IMAGE. IIUM Engineering Journal, 22(1), 68-77.
  •  Kurniawan, R., Muhimmah, I., Kurniawardhani, A., & Kusumadewi, S. (2019). Segmentation of Tuberculosis Bacilli Using Watershed Transformation and Fuzzy C-Means. CommIT (Communication and Information Technology) Journal, 13(1), 9-16.
  • Kurniawan, R., Kurniawardhani, A., & Muhimmah, I. (2018). Inflammatory Cell Extraction in Pap smear Images: A Combination of Distance Criterion and Image Transformation Approach. TELKOMNIKA (Telecommunication Computing Electronics and Control), 16(5), 2048-2056.
  • Kurniawardani, A., Nanik, S., & Arieshanti, I. (2016). Texture Feature Extraction Using Improved Completed Robust Local Binary Pattern for Batik Image Retrieval.
  • Minarno, A. E., Munarko, Y., Kurniawardhani, A., Bimantoro, F., & Suciati, N. (2014, May). Texture feature extraction using co-occurrence matrices of sub-band image for batik image classification. In 2014 2nd international conference on information and communication technology (ICoICT) (pp. 249-254). IEEE.