Optimasi Convolutional Neural Network Untuk Deteksi Covid-19 pada X-ray Thorax Berbasis Dropout

Pandemi COVID-19 yang melanda Indonesia sejak pertengahan tahun 2020 telah memberikan dampak luar biasa pada infrastruktur medis di Indonesia. Angka rata-rata penyebaran virus COVID-19 yang cukup tinggi membuat monitoring bed occupancy rate menjadi sebuah tantangan tersendiri. Dengan adanya penetrasi Artificial Intelligence yang tepat pada sistem medis di Indonesia, diharapkan dapat membantu terjadinya transfer knowledge antar paramedis menjadi lebih efektif. Salah satunya dengan menggunakan Deep learning yaitu Convolutional Neural Network (CNN) yang sudah terbukti merupakan salah satu metode yang dapat digunakan untuk melakukan skrining pasien dan mendeteksi COVID-19. Namun untuk melatih sebuah classifier CNN yang ampuh dan siap digunakan di dunia nyata membutuhkan computing power yang besar dan umumnya training rate yang lama.  Penelitian ini bertujuan untuk membuat arsitektur jaringan syaraf tiruan berbasis deep learning yang lebih cepat dan efisien dengan pembuatan network yang  lebih ramping sehingga lebih mudah dibuat oleh orang lain tanpa harus memiliki computing power yang besar. Metode yang digunakan adalah dengan menyisipkan dropout layer pada sistem jaringan syaraf tiruan. Metode ini akan memaksa sistem untuk belajar memakai rute yang tersingkat dengan cara menghilangkan beberapa node secara acak. Arsitektur ini kemudian diuji pada data ronsen thorax penyintas COVID-19 dan kemudian dibandingkan dengan arsitektur lainnya yang sama-sama memakai pendekatan deep learning. Setelah ditraning menggunakan 500 data COVID-19 thorax X-Ray public database dan diuji dengan jumlah data yang sama, classifier yang menggunakan arsitektur ini mampu menghasilkan akurasi sebesar 95,20%, precision 94,80%, recall 95,58%, specificity 94,88%, NVP sebesar 95,60%, F-Score sebesar 95,18 dan dapat menghemat waktu training sampai 62% dibandingkan dengan arsitektur deep learning lainnya. AbstractThe COVID-19 pandemic that hit Indonesia in mid-2020 had a tremendous impact on medical infrastructure in Indonesia. The virus made monitoring the bed occupancy rate became a challenge in itself. New approach can be taken to fight the crisis. The Convolutional Neural Network (CNN), which has proved to be one of the methods that can use to screen patients and detect COVID-19.also have its own problem because it requires enormous computing power and generally a long training rate. Therefore, this study aimed to tackle that problem by creating a leaner network. Thus, it is easier for others to build without having enormous computing power. The method used was to insert a dropout layer on the artificial network system. This method will force the system to learn using the shortest route by eliminating some nodes at random. Then, this architecture was tested on chest X-ray data of COVID-19 survivors and compared with other architectures that both used a deep learning approach. It proved that when this system was tested with COVID-19 thorax x-ray public database data, the classifier that used this architecture could achieve an accuracy rate of 95.20% followed by precision and recall value reaching 94.80% and 94.80%. respectively and last but not least F-score of 95.18% and Negative Predictive value of 95.60%  It could also save training time up to 62% compared to other deep learning architectures. Using dropout layers proved could produce more efficient layers and more powerful classifiers while keeping training time to a minimum

Analisis ketidakpastian pengukuran pada mistar ingsut rentang 0-30 cm

In science of measurement, an error is defined as difference result of measurement and the real value of the measurand.Actually, this real value is never precisely identified; consequently, the error is unknowns as well. In this case, the error canonly be estimated without knowing its real quantity. If a value will be considered as an estimated error, this value should be taken as an uncertainty. Uncertainty of Vernier Calliper determined with Type A and Type B evaluation, then the combinedstandard uncertainty is determined. Vernier Calliper that calibrated with Gauge Block class 1, the maximum uncertainty is ± 3.27 µm in dimension of 12.7 mm, with 95% confidence level

Penerapan Peraturan Daerah Kota Denpasar Nomor 3 Tahun 2015 Tentang Pengelolaan Sampah

The city of Denpasar has not been able to carry out waste management properly, even though waste management is very important to reduce the volume of waste, and it can even use waste into useful objects or products. Denpasar City tries to manage waste in Denpasar. The purpose of this research is to find out how the implementation of Denpasar City Regulation Number 3 of 2015 concerning Waste Management and to find out the supporting and inhibiting factors of the enactment of Denpasar City Government Regulation Number 3 of 2015 concerning Waste Management. This study was designed using empirical legal research with a statutory approach. The data collection techniques used in the study were interviews and documentation. The results show that the action of the Denpasar city government against violations of the Regional Regulation of the City of Denpasar Number 3 of 2015 concerning waste management is to implement minor crimes against offenders where those who violate are tried in light criminal court (Tipiring). Then, the supporting and inhibiting factors for the enforcement of Regional Regulation Number 3 of 2015 on Waste Management are the legal basis for cleanliness management that has been issued by the Denpasar City Government in the form of Legislation, Regional Regulations, and the Mayor of Denpasar. Inhibiting factors for the enforcement of Sanctions by Regional Regulation No.3 of 2015 on Waste Management, namely factors of law enforcement officers, facilities and infrastructure factors, and community factors

Meningkatkan Pendapatan Masyarakat dengan Mesin Pencacah Sampah Plastik

Bali merupakan pintu gerbang pariwisata Indonesia, menjadi sorotan dunia bagaimanamengelola sampah. Reduce, Reuse dan Recycle disingkat 3R atau Mengurangi, MemakaiUlang dan Mendaur Ulang adalah prinsip utama mengelola sampah mulai dari sumbernya,melalui tiga langkah ini akan mampu mengurangi jumlah sampah yang dibuang ke TPA(Tempat Pengolahan Akhir). Pengelolaan sampah yang kurang baik dapat memberikanpengaruh negatif bagi kesehatan, lingkungan, maupun bagi kehidupan sosial ekonomi danbudaya masyarakat. Produksi sampah di TPA Linggasana Karangasem perhari adalah 120m3/hari atau sekitar 18,26 ton/hari, sampah plastik mencapai 22,6 %, yang bisa didaur ulangsekitar 17%. Jadi sampah plastik yang bisa didaur ulang adalah 3,1 ton/hari, ini merupakanpotensi bisnis yang sangat potensial.Masyarakat malas dalam memilah sampah organik dananorganik karena tidak memberikan nilai tambah kepada masyarakat, dilakukan pelatihan danpengenalan mesin pencacah sampah plastik sehingga masyarakat berminat dalam memilahsampah-sampah itu. Sampah plastik itu dijual ke pada kelompok Asri Linggasana ataukelompok Lestari Buana Giri.Kata kunci: sampah plastik, mesin pencacah Bali is Indonesian tourism gateway and become world attention on how to manage waste.Reduce, Reuse and Recycle abbreviated 3R is a major principle of managing the waste fromthe source. Through these three steps will be able to reduce the amount of waste disposed of tolandfill. Poor waste management can have a negative effect on health, the environment, and forsocial, economic and cultural life of the community. Production of waste in the landfillLinggasana Karangasem per day is 120 m3 / day or approximately 18.26 tons / day, reaching22.6% of plastic waste, which can be recycled approximately 17%. So, the plastic waste thatcan be recycled is 3.1 tons / day, this is a highly potential business potential. The society is notinterest in sorting organic and inorganic waste because it does not add value to the community.Training and introduction of plastic waste thrasher is conducted so that people interested insorting out the rubbish. Plastic waste was sold to the group Linggasana Asri or group BuanaLestari Giri.Keywords: plastic waste, thrashe