Hepatitis is chronic disease that becomes major problem in developing countries. Health experts estimate that more than 185 billion people have chronic hepatitis worldwide. This paper attempts to detect major disease such as hepatitis in public hospital using ensemble methods. Several ensemble techniques were applied to acquire knowledge from patient medical records. Afterwards, rule extraction from decision tree and neural network are summarized in order to assist experts in detecting hepatitis. Accuracy of those algorithms is also performed and from the experimental result shows that Bagging, with decision tree as base-classifier, denotes best performance among other classifiers.
Seri Teknologi mendukung poin ke-9 SDGs, yakni “membangun infrastruktur yang tangguh, mempromosikan industrialisasi yang inklusif dan berkelanjutan, serta mendorong inovasi”. Bunga rampai ini terbagi menjadi lima subtema, yaitu dinamika dunia penelitian di Indonesia saat ini, analisis teknologi transportasi di Indonesia, industrialisasi rendah emisi, teknologi telekomunikasi, dan pertumbuhan UMKM melalui digitalisasi. Kelima subtema tersebut merupakan bagian dari bidang yang penting untuk diperhatikan dalam penyusunan dan perumusan arah kebijakan di masa mendatang untuk tercapainya tujuan Indonesia Emas 2045. Buku ini diharapkan dapat menjadi bacaan yang bermanfaat bagi masyarakat Indonesia, khususnya para pemangku kepentingan di bidang pembangunan dan inovasi teknologi. Temukan beragam sudut pandang baru terkait upaya pembenahan sektor teknologi di Indonesia. Selamat membaca!
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