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All Journal JURNAL SISTEM INFORMASI BISNIS Jurnal Khatulistiwa Informatika Jurnal Penelitian Pendidikan IPA (JPPIPA) Insect (Informatics and Security) : Jurnal Teknik Informatika METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Jurnal Inotera J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Ilmiah Mandala Education (JIME) Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi J-SAKTI (Jurnal Sains Komputer dan Informatika) DECODE: Jurnal Pendidikan Teknologi Informasi Jurnal Pengabdian Masyarakat Jurnal Ilmu Komputer dan Teknologi (IKOMTI) Journal of Advanced Health Informatics Research
Muntiari, Novita Ranti
Politeknik Bisnis Kaltara
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Other

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Journal : Journal of Advanced Health Informatics Research

 1 
Application of the Certainty Factor Method for Diagnosing Osteoarthritis Using the Python Programming Language Muntiari, Novita Ranti; Hanif , Kharis Hudaiby
Journal of Advanced Health Informatics Research Vol. 1 No. 1 (2023)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v1i1.17

Abstract

Osteoarthritis is a disease that causes the joints in the bones to become weaker and less able to function properly. Osteoarthritis can cause pain, stiffness, and even deformity in the joints. The solution to the problem of diagnosing Osteoarthritis is to use a tool, namely a system (expert system) that uses computer technology to make decisions more easily, effectively and efficiently. This study uses the Certainty Factor method in diagnosing osteoarthritis and uses 8 symptoms, namely, pain in the joints, joints feel stiff, clicks or cracks appear when the joints are bent or moved, joints lose their flexibility properties, joints feel softer when pressure is applied, spurs appear bones around the joints, namely hard and sharp bony protrusions, swelling around the joints, and muscles around the joints. The conclusions from the calculations and program implementation, the application of the certainty factor in diagnosing osteoarthritis with existing test data, results in 95.38% that the patient has severe symptoms of osteoarthritis, so he must be taken to an orthopedic specialist.
A Bibliometric Analysis of Knowledge Distillation in Medical Image Segmentation Muntiari, Novita Ranti; Rania Majdoubi; Rajiansyah
Journal of Advanced Health Informatics Research Vol. 2 No. 3 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v2i3.297

Abstract

This study conducts a bibliometric analysis and systematic review to examine research trends in the application of knowledge distillation for medical image segmentation. A total of 806 studies from 343 distinct sources, published between 2019 and 2023, were analyzed using Publish or Perish and VOSviewer, with data retrieved from Scopus and Google Scholar. The findings indicate a rising trend in publications indexed in Scopus, whereas a decline was observed in Google Scholar. Citation analysis revealed that the United States and China emerged as the leading contributors in terms of both publication volume and citation impact. Previous research predominantly focused on optimizing knowledge distillation techniques and their implementation in resource-constrained devices. Keyword analysis demonstrated that medical image segmentation appeared most frequently with 144 occurrences, followed by medical imaging with 110 occurrences. This study highlights emerging research opportunities, particularly in leveraging knowledge distillation for U-Net architectures with large-scale datasets and integrating transformer models to enhance medical image segmentation performance
Co-Authors Abdul Fadlil Ana Sriekaningsih Andri Yogi Adyatma Prasetyo Arif Fadllullah Asma Asma Bustomi, Iqbal E, Ermin Ermin Ermin Hanif, Kharis Hudaiby Haryansyah Herawati, Lily Indah Chairun Nisa Irgi Ahmad Fahrezi Khoirun Nisa Muhammad Yusril Purwono, Purwono Putri Ananda Ramadhani Rajiansyah Rania Majdoubi Rejin, Rosaleni Rusydi Umar Sriyanti, Nur Sunardi, Sunardi Tella, Fitriyani