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All Journal ILKOM Jurnal Ilmiah JURIKOM (Jurnal Riset Komputer) JURNAL MAHAJANA INFORMASI
Bahari Putra, Fajar Rahardika
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Library & Information Science

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Comparison of Accuracy Level of Certainty Factor Method and Bayes Theorem on Cattle Disease Setyawan, Muhammad Rizki; Bahari Putra, Fajar Rahardika; Fakhri, La Jupriadi
ILKOM Jurnal Ilmiah Vol 16, No 3 (2024)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v16i3.1943.343-355

Abstract

This study aims to address the challenges of livestock disease diagnosis in Okaba district, Meraoke, Papua. A total of 2 paramedics or veterinarians and 1 assistant is not sufficient because of the long distances that the medics have to travel, traveling from all areas of Okaba District to its interior. Keepers can only utilize their basic skills for temporary care. The researcher's process included interviews with experts covering the disease, its symptoms and prevention, then analyzed with the provision of utilizing certainty factors and Bayes' theorem to increase the accuracy and veracity of the findings. In this scenario, the data is used as a reference point for analysis in the web-based expert system. The results obtained when processing the problem estimation are disease information, symptom information, and treatment. The reference in the application and analysis shows that the Certainty Factor method is superior in providing consistent accuracy, with a percentage reaching 98.79% in the case of worms, while the Bayes Theorem method shows lower accuracy, around 73%. The comparison indicates that Certainty Factor is more suitable in high uncertainty environments, while Bayes' Theorem is more effective when sufficient probabilistic data is available. Future suggestions can expand the scope by testing other methods such as Machine Learning or Artificial Neural Networks to increase the accuracy of the diagnosis percentage. In addition, more extensive trials on different types of livestock and different environmental conditions will help in developing a more flexible and robust system.
RANCANG BANGUN APLIKASI SISTEM PAKAR UNTUK MENDIAGNOSA PENYAKIT PADA BALITA MENGGUNAKAN METODE DEMPSTER SHAFER DENGAN PENELUSURAN FORWARD CHAINING BERBASIS ANDROID Muhammad Surahmanto; Bahari Putra, Fajar Rahardika; Muhammad Rizki Setyawan; Ahmad Ilham
Jurnal Mahajana Informasi Vol 10 No 1 (2025): JURNAL MAHAJANA INFORMASI
Publisher : Universitas Sari Mutiara Indonesia Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51544/jurnalmi.v10i1.6079

Abstract

Penelitian ini bertujuan untuk merancang dan membangun aplikasi sistem pakar berbasis Android guna mendiagnosis penyakit pada balita menggunakan metode Dempster-Shafer dan penelusuran Forward Chaining. Aplikasi ini dikembangkan sebagai solusi terhadap pentingnya deteksi dini penyakit pada balita, mengingat masa usia dini merupakan fase pertumbuhan yang sangat krusial. Dengan memanfaatkan metode Dempster-Shafer, sistem mampu mengolah ketidakpastian data gejala dan menghasilkan diagnosis yang akurat, sementara Forward Chaining berperan dalam menelusuri aturan berbasis fakta yang tersedia. Hasil yang di dapat yakni 98% pilek berdasarkan deteksi penyakit dan pengujian dengan metode black box menunjukkan bahwa aplikasi berjalan sesuai fungsinya dan hasil diagnosis konsisten dengan perhitungan manual. Penelitian ini menunjukkan potensi penerapan kecerdasan buatan dalam mendukung pelayanan kesehatan anak, serta membuka peluang pengembangan sistem pakar berbasis Android yang lebih luas di masa mendatang. Saran kedepan agar lebih di perdalam lagi dalam proses mengidentifikasi penyakit balita dengan metode lainnya.
Klasifikasi Kunyit dan Temulawak dengan VGG16 dan Fuzzy Tsukamoto Berbasis Android Setyawan, Muhammad Rizki; Bahari Putra, Fajar Rahardika; Ilham, Ahmad; Suseno, Dimas Adi
JURIKOM (Jurnal Riset Komputer) Vol 12, No 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8696

Abstract

Indonesia has a very rich biodiversity, including various medicinal plants that are highly financially beneficial and health-promoting. Among these medicinal plants, temulawak and turmeric are the two most popular rhizomes widely used in traditional medicine as well as the herbal industry. However, because the shape and color of these two plants are very similar, it is often difficult to distinguish between them, especially for laypeople and new industry workers. This research developed an Android-based application that can effectively and accurately distinguish between temulawak and turmeric to address this issue. For this application, the Convolutional Neural Network (CNN) architecture of the VGG-16 model is used along with the Tsukamoto fuzzy method as an additional layer. The trials conducted on the developed model using test data showed an accuracy rate of 0.97, a recall value of 0.98, and an F1 score of 0.97. Meanwhile, the blackbox testing shows that this application functions stably without technical issues, making it ready for use. Additionally, blackbox testing shows that the system can function stably without any issues, making it suitable for real-world use
Co-Authors Ahmad Ilham Ilham, Ahmad La Jupriadi Fakhri Muhammad Rizki Setyawan Surahmanto Suseno, Dimas Adi