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All Journal Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi International Journal of Artificial Intelligence in Medical Issues
Thomas Edyson Tarigan
Universitas Teknologi Digital Indonesia
Computer Science & IT Dentistry Health Professions Medicine & Pharmacology Public Health

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Penilaian Kualitas Sistem Informasi Menggunakan ISO/IEC 25010 Dengan Metode Profile Matching Emy Susanti; Thomas Edyson Tarigan
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 12, No 1: April 2023
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v12i1.1189

Abstract

Information system quality assessment is a benchmark used to determine the extent of success in implementing information systems. From these evaluation activities, further information system development can be carried out either in the form of repairs or adjustments. The standard used is ISO/IEC 25010 which consists of a software product quality model and quality in use model, and the Profile Matching method which is a method for decision support. The number of criteria used is 8 criteria and 31 sub-criteria for assessment, with a case study of the SIAKAD UTDI Academic Information System in Yogyakarta. The results obtained are Functional Suitability=5, Usability=4.6, Compatibility=4.4, Performance Efficiency=4.3, Reliability=4.2, Maintainability=4, Security=3.8, Portability=3.7. The best criterion is Functional Suitability = 5 and what is lacking is Portability = 3.7. In general, SIAKAD UTDI is well received by students and the deficiencies are due to the criteria for functions that are not used directly by students.Keywords: Quality assessment; Information Systems; ISO/IEC 25010; Profile Matching. AbstrakPenilaian kualitas sistem informasi merupakan tolok ukur yang digunakan untuk mengetahui sejauh mana tingkat keberhasilan dalam menerapkan sistem informasi. Dari kegiatan evaluasi tersebut selanjutnya dapat dilakukan pengembangan sistem informasi baik berupa perbaikan, atau penyesuaian. Standar yang digunakan adalah ISO/IEC 25010 yang terdiri dari software product quality model dan quality in use model, dan metode Profile Matching yang merupakan metode untuk dukungan keputusan. Jumlah kriteria yang digunakan ada 8 kriteria dan 31 sub kriteria penilaian, dengan studi kasus Sistem Informasi Akademik SIAKAD UTDI Yogyakarta. Hasil yang diperoleh Functional Suitability=5, Usability=4,6, Compatibility=4,4, Perfomance Efficience=4,3, Reliability=4,2, Maintainability=4, Security=3,8, Portability=3,7. Kriteria yang paling baik adalah Functional Suitability=5 dan yang kurang adalah Portability=3,7. Secara umum SIAKAD UTDI diterima baik oleh mahasiswa dan kekurangan yang ada karena kriteria terhadap fungsi-fungsi yang tidak digunakan secara langsung oleh mahasiswa.
Application of the K-Nearest Neighbors (KNN) Algorithm on the Brain Tumor Dataset Effan Najwaini; Thomas Edyson Tarigan; Fajri Profesio Putra; Sulistyowati
International Journal of Artificial Intelligence in Medical Issues Vol. 1 No. 1 (2023): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijaimi.v1i1.85

Abstract

Brain tumors pose significant challenges in the medical domain, necessitating advanced diagnostic techniques for early and accurate detection. This research paper presents a comprehensive study on the application of the K-Nearest Neighbors (KNN) algorithm to a dataset comprising brain tumor images. The methodology involved segmenting the images using the Canny method, extracting relevant features via Hu Moments, and subsequently employing the KNN algorithm for classification. Using a 5-fold cross-validation, the system consistently achieved an average accuracy of approximately 62%. These findings highlight the potential of traditional machine learning algorithms in medical imaging, providing valuable insights for both researchers and practitioners. While the results are promising, the study also underscores the importance of integrating such algorithms with other diagnostic methods for optimal results
Performance Metrics of AdaBoost and Random Forest in Multi-Class Eye Disease Identification: An Imbalanced Dataset Approach Thomas Edyson Tarigan; Erma Susanti; M. Ikbal Siami; Ika Arfiani; Agus Aan Jiwa Permana; I Made Sunia Raharja
International Journal of Artificial Intelligence in Medical Issues Vol. 1 No. 2 (2023): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijaimi.v1i2.98

Abstract

This study presents a comprehensive evaluation of AdaBoost and Random Forest Classifier algorithms in the classification of eye diseases, focusing on a challenging scenario involving an imbalanced dataset. Eye diseases, particularly Cataract, Diabetic Retinopathy, Glaucoma, and Normal eye conditions, pose significant diagnostic challenges, and the advent of machine learning offers promising avenues for enhancing diagnostic accuracy. Our research utilizes a dataset preprocessed with Canny edge detection for image segmentation and Hu Moments for feature extraction, providing a robust foundation for the comparative analysis. The performance of the algorithms is assessed using a 5-fold cross-validation approach, with accuracy, precision, recall, and F1-score as the key metrics. The results indicate that the Random Forest Classifier outperforms AdaBoost across these metrics, albeit with moderate overall performance. This finding underscores the potential and limitations of using advanced machine learning techniques for medical image analysis, particularly in the context of imbalanced datasets. The study contributes to the field by providing insights into the effectiveness of different machine learning algorithms in handling the complexities of medical image classification. For future research, it recommends exploring a diverse range of image processing techniques, delving into other sophisticated machine learning models, and extending the study to encompass a wider array of eye diseases. These findings have practical implications in guiding the selection of machine learning tools for medical diagnostics and highlight the need for continuous improvement in automated systems for enhanced patient care.
Co-Authors Agus Aan Jiwa Permana Effan Najwaini Emy Susanti Erma Susanti Fajri Profesio Putra I Made Sunia Raharja Ika Arfiani M Ikbal Siami Sulistyowati Sulistyowati