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All Journal Jurnal Sains dan Teknologi Jurnal Teknologi Informasi dan Ilmu Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika JUTIK : Jurnal Teknologi Informasi dan Komputer SINTECH (Science and Information Technology) Journal WIDYABHAKTI Jurnal Ilmiah Populer International Journal of Engineering and Emerging Technology Indonesian Journal of Data and Science International Journal of Engineering, Science and Information Technology JSIKTI (Jurnal Sistem Informasi dan Komputer Terapan Indonesia) Seminar Ilmiah Nasional Teknologi, Sains, dan Sosial Humaniora (SINTESA) Seminar Nasional Aplikasi Iptek (SINAPTEK)
I Gede Totok Suryawan
Institut Bisnis Dan Teknologi Indonesia (INSTIKI), Denpasar
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Optimasi Convolutional Neural Network Untuk Deteksi Covid-19 pada X-ray Thorax Berbasis Dropout Suryawan, I Gede Totok; Darma Udayana, I Putu Agus Eka
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 9 No 3: Juni 2022
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2022935143

Abstract

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.
Sistem Informasi Keuangan Pada Koperasi Karya Utama Mandiri Sugiartawan, Putu; Suryawan, I Gede Totok; Indawan, I Gusti Agung
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 5 No 2 (2022): December
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.181

Abstract

In the advancement and use of technology, information systems are very important in an institution or agency as an easily accessible work tool. An information system is a formal, sociotechnical and organizational system designed and used as a means for storing, accessing and distributing information. Several studies have analyzed the design of information systems for sharia cooperatives outside the island of Bali, by developing a financial system based on customer data in the city of Madiun. This research is devoted to the Karya Utama Mandiri Cooperative located in Sanggulan BTN Housing, Tabanan, Bali, established in 2005. Currently, cooperative operations are still carried out manually using Excel, which is prone to errors and data loss. The research methods used include interviews, observation, literature recall, and documentation. The result of this research is the development of a website-based information system with important features, such as Customer Master Data Input, Credit Application Validation, Deposit Master Data, Loan Master Data, Outgoing and Incoming Cash Transactions, Cash Adjustment Transactions, Marketing Master Data, Employee Master Data , and KAS Report Data. The implementation of this financial information system at the Karya Utama Mandiri Cooperative has succeeded in facilitating transaction processes, data processing, reporting, and reducing data damage caused by software.
Co-Authors Anak Agung Kompiang Oka Sudana Andika, Ary Wira Arsana, I Made Dwi Putra Arslayandi, Fiqhan Handika, I Putu Susila I Gede Dana Paramitha I Gede Iwan Sudipa I Ketut Gede Darma Putra I Putu Agus Eka Darma Udayana, I Putu Agus Eka I Putu Susila Handika Indawan, I Gusti Agung Made Sudarma Meliana, Putu Mita Ni Made Ari Radha Devi Ni Wayan Jeri Kusuma Dewi Pramita, Dewa Ayu Kadek Pramitha, Gede Dana Purnama, I Wayan Adi Putra, I Kadek Nurcahyo Putra, Putu Satria Udyana PUTU SUGIARTAWAN Putu Surya Wedra Lesmana Satyawati, I Gusti Agung Ayu Ari Wayan Eny Mariani Wayan Galang Mahendra Negara Widiartha, Komang Kurniawan Yoga Pramana Putra, I Putu Gede