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All Journal Jurnal Kesehatan Gigi Journal of Health (JoH) Jurnal Surya Masyarakat Jurnal Biomedika Community Development Journal JIDAN (Jurnal Ilmiah Bidan) Medika Alkhairaat : Jurnal Penelitian Kedokteran dan Kesehatan Journal of Intelligent Computing and Health Informatics (JICHI) Jurnal Pengabdian dan Peningkatan Mutu Masyarakat (JANAYU) Jurnal Midwifery PHARMACOLOGY, MEDICAL REPORTS, ORTHOPEDIC, AND ILLNESS DETAILS (COMORBID) Prosiding Seminar Nasional Unimus
Mudyawati Kamaruddin
Program Pascasarjana Ilmu Laboratorium Medik Universitas Muhammadiyah Semarang
Religion Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Neuroscience Nursing Public Health Social Sciences Other

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Identification of Neisseria gonorrhoeae Bacteria Using a Convolutional Neural Network (CNN) Based on Image Classification Maulana, Haris; Kamaruddin, Mudyawati; Suyanto, Agus; Rabban, Auliyaur
PHARMACOLOGY, MEDICAL REPORTS, ORTHOPEDIC, AND ILLNESS DETAILS Vol. 5 No. 1 (2026): JANUARY
Publisher : Transpublika Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55047/comorbid.v5i1.1963

Abstract

Neisseria gonorrhoeae (gonococcus) is the primary bacterium responsible for the sexually transmitted infection gonorrhea, which is transmitted through sexual contact. Traditional identification methods, such as Polymerase Chain Reaction (PCR), are still widely used but have limitations in terms of cost, time, and the need for multiple reagents. This study aims to develop a faster and more efficient identification method using Artificial Intelligence (AI) through a Convolutional Neural Network (CNN) approach based on the Inception V3 architecture. The dataset used consists of 84 JPEG images, comprising 42 images of Neisseria gonorrhoeae and 42 non-Neisseria images. The model was trained using 50 epochs with an early stopping mechanism, which optimally halted at epoch 25, achieving a training accuracy of 94.74% and a validation accuracy of 100%. The resulting model achieved 96% classification accuracy, correctly identifying all 8 positive and 4 negative test images. These findings indicate that CNN based on Inception V3 is effective in classifying Neisseria gonorrhoeae images and has strong potential as a fast, accurate, and efficient diagnostic alternative.
Co-Authors A Maya Rupa Anjeli A. Haryanti Ganie Ganie Agus Suyanto Akhmad Rivandi Akhmad rivandi Andi Asriany Andi Harmawati Novriani HS Andi Yuyun Dian Pertiwi Angle Ananta Ani T Prianti Asriany Ayu Puspaning Tyas, Dyah Budi Santoso Cahyawan, Diawan DaffaPutra Mahardika Defi Nurul Hayati Dewi Purnamasari Dewi Purnamasari Dewi Ratnasari Eka Kurnia, Eka Erniawati Fasilah Basri Haerani Hasrawati Hasriani Saleng Havida Widyastuti Hendra Stevani Idar Karlina Ika handayani Ika Handayani Isnayanti Ivonne Siswanty Jalil, Jumirah Juni Nauli Jusni Jusni Kasmawati Lili Andriani Lisdayanti Malisa Nisaul Alim Marlina Azis Maulana, Haris Mega Dwi Setyaningrum Megawati, Ani Minarti Minarti Misnawaty Misnawaty Misnawaty Misriyani Misriyani Mubarak, Abdul Haris Muhammad Raihan Shane Ady Nugroho Muhammad, Munawir MUKRIMAH MUKRIMAH, MUKRIMAH Muli Sukmawaty Ningsih, Sri Nita Ariani Nur Nita Ariani Nur Nita Ariani Nur Noveppy Cahya Permatasari Nurasia Nurasia Nurfaizah Alza Nurhayani Nurhayani Nurhidayat Triananinsi Nurhidayat Triananinsi Nurhidayat Triananinsi Nurhidayat Triananinsi Nurqalbi Nurqalbi Nurqalbi SR Nursaima Siregar Nurul Amalia Sari Nurul Hidayah Bohari Pia, Pia Prianti, Ani T Rabban, Auliyaur Rafidah Rafidah Ratna Sulistyorini Ratna Sulistyorini, Ratna Ratnasari Dewi Rina Mariana Rinda Aulia Utami Risnayanti Rizka Yolanda Febiaocti Siti Usmia Sri Darmawati Sri Suhartati Sri wahyuni Sukmawati Sumarni Sumarni Sumarni Sumarni Sutrani Syarif Syarif, Sutrani Triananinsi, Nurhidayat Ulfa Dillah Usmia, Sitti Widyastuti, Havida