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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.
Gender-Aware Prediction of Liver Disease Using Machine Learning and Clinical Laboratory Data Umar Zaky; Muhammad Habibi; Adri Priadana; Thomas Edyson Tarigan
International Journal of Artificial Intelligence in Medical Issues Vol. 4 No. 1 (2026): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/wtsdw234

Abstract

Liver disease is a major health problem that may progress silently and lead to severe clinical complications if not detected early. Machine learning offers a promising approach for supporting early screening by identifying predictive patterns from clinical and biochemical patient data. This study developed an explainable gender-aware machine learning framework for liver disease prediction using demographic information and clinical biomarkers. The dataset consisted of 570 patient records after duplicate removal, including age, gender, total bilirubin, direct bilirubin, alkaline phosphatase, SGPT, SGOT, total protein, albumin, albumin/globulin ratio, and liver disease status. Several machine learning algorithms were evaluated under three experimental scenarios: original data, class-weighted learning, and SMOTENC-based oversampling. Model performance was assessed using accuracy, precision, recall, specificity, F1-score, and ROC-AUC. The experimental results showed that Gradient Boosting combined with SMOTENC achieved the best F1-score, with an accuracy of 0.7632, precision of 0.7935, recall of 0.9012, specificity of 0.4242, F1-score of 0.8439, and ROC-AUC of 0.7759. The model correctly identified 73 of 81 liver disease cases in the testing set, indicating strong sensitivity for early screening. Gender-based evaluation showed comparable F1-scores for male and female patients, with values of 0.8430 and 0.8462, respectively. Feature importance analysis identified SGOT, alkaline phosphatase, age, and direct bilirubin as the most influential predictors. These findings suggest that an explainable and gender-aware machine learning approach can support liver disease risk prediction using routinely available clinical biomarkers, although further validation using larger and more balanced datasets is required
Co-Authors Adri Priadana Emy Susanti Muhammad Habibi Umar Zaky