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All Journal Jurnal Edukasi Universitas Jember Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Advances in Intelligent Informatics Scientific Journal of Informatics Journal of Information Systems Engineering and Business Intelligence Register: Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Ilmiah Universitas Batanghari Jambi Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Bisma: Jurnal Bisnis dan Manajemen JURNAL AKUNTANSI KEUANGAN DAN MANAJEMEN Jurnal Tekinkom (Teknik Informasi dan Komputer) Journal of Soft Computing Exploration Jurnal Bisnis dan Manajemen (JBM) Jurnal Abdimas Ekonomi dan Bisnis Transekonomika : Akuntansi, Bisnis dan Keuangan Perwira Journal of Science and Engineering (PJSE) Reviu Akuntansi, Manajemen, dan Bisnis PENA ABDIMAS : Jurnal Pengabdian Masyarakat Journal of Advances in Information Systems and Technology Indonesian Journal of Informatic Research and Software Engineering Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Pemberdayaan Ekonomi eProceedings of Management Journal of Student Research Exploration Journal of Information System Exploration and Research Recursive Journal of Informatics IJEB JPM Jurnal Akuntansi dan Governance Andalas Media Penelitian dan Pengembangan Kesehatan Jurnal Ekonomi, Manajemen, Akuntansi INOVTEK Polbeng - Seri Informatika Jurnal Abdi Negeri
Budi Prasetiyo, Budi
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Journal : journal of soft computing exploration

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Restricted boltzmann machine and softmax regression for acute respiratory infections disease identification Pranata, Afrizal Rizqi; Alamsyah, Alamsyah; Prasetiyo, Budi; Vember, Hilda
Journal of Soft Computing Exploration Vol. 3 No. 2 (2022): September 2022
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v3i2.90

Abstract

Restricted boltzmann machines (RBM) have attracted much attention lately after being proposed as building blocks of deep learning blocks. RBM is an algorithm that belongs to the artificial neural network (ANN) algorithm. Deep learning models can be used in the health field to identify diseases using medical data records. Acute Respiratory Infection (ARI) is a disease that infects the respiratory tract. A patient infected by ARI diseases is high. To identify ARI can use the symptoms that the patient had experienced. Based on this background, this study aims to help identify ARI disease using its symptoms. The method used for identification is the deep learning model, which was built using the RBM and softmax regression. Three steps were used in this research, which are training, testing, and implementation. The trained deep learning model will be implemented to identify ARI disease. This research will use ARI data from Puskemas Warungasem, Indonesia. From the research result, the deep learning model can get an accuracy of 96%. The deep learning configuration used in this research has 4 RBM layers, 1 Softmax layer as the output layer, and a learning rate value of 0.01 and 1000 iterations. This research can be used as a reference so that the next researcher can add other algorithms to Deep learning to improve accuracy.
News text classification using Long-Term Short Memory (LSTM) algorithm Triyadi, Indra; Prasetiyo, Budi; Nikmah, Tiara Lailatul
Journal of Soft Computing Exploration Vol. 4 No. 2 (2023): June 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v4i2.136

Abstract

Over the past few years, the classification of texts has become increasingly important. Because knowledge is now available to users through various sources namely electronic media, digital media, print media, and many more. One of them is the development of so much news every day. LSTM is one of the algorithms of deep learning methods that can classify a text. This research proves for the LSTM algorithm on the classification of news text sentences. The data used is the news text from the Kaggle data center set i.e. aggregator news data. The results of the LSTM experiment from 10 epochs obtained with an accuracy value of 93,15% on the classification of texts into four categories, namely entertainment, bussines, science, and health.
Optimizing the implementation of the BFS and DFS algorithms using the web crawler method on the kumparan site Mustaqim, Amirul; Dinova, Dony Benaya; Fadhilah, Muhammad Syafiq; Seivany, Ravenia; Prasetiyo, Budi; Muslim, Much Aziz
Journal of Soft Computing Exploration Vol. 5 No. 2 (2024): June 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i2.309

Abstract

Efficient access to timely information is critical in today's digital era. Web crawlers, automated programs that navigate the Internet, play an important role in collecting data from websites such as Kumparan, a leading news site in Indonesia. This research shows the effectiveness of the Breadth-First Search (BFS) and Depth-First Search (DFS) algorithms in indexing Kumparan content. The results of the research show that BFS consistently indexes more files comprehensively but with longer execution times compared to DFS, which provides faster initial results but with fewer files. For example, at depth 4 BFS indexed 949 files in 886.94 seconds, while DFS indexed 470 files in 233.02 seconds. These findings highlight the balance between precision and speed when selecting a crawling algorithm tailored to the needs of a particular website. This research provides insights into optimizing web crawler technology for complex websites such as Coil and suggests avenues for further research to improve permission efficiency and adaptability across a variety of crawling scenarios.
Co-Authors Aditya Wardhana Afrizal Rizqi Pranata, Afrizal Rizqi Ahmad Roziqin, Ahmad Aisy, Salsabila Rahadatul Aji Purwinarko, Aji Alamsyah - Amidi Amidi, Amidi Anggraini, Tasya Fitria Anggyi Trisnawan Putra Aziz, Alif Abdul Azura, Amberia Narfi Bachtiar, Muhammad Irgi Bambang Widjajanta, Bambang Bayuaji, Hibatullah Zamzam Tegar Beta Noranita Biyantoro, Arell Saverro Choirunnisa, Rizkiyanti D.W, Made Bagus Paramartha Darfayanti, Aulia Diyan Fahirah Didimus Tanah Boleng Dinova, Dony Benaya Endang Sugiharti, Endang Fachrezi, Farhan Rifa Fadhilah, Muhammad Syafiq Fadlil, Affan Fajriati, Nafa Fata, Muhamad Nasrul Fata, Muhamad Nasrul Ferninda, Varin Fikri Mohamad Rizaldi Fitria, Yunita Fitriana, Jevita Dwi Hakim, Ade Anggian Hakim, M Faris Al Hakim, M. Faris Al Ilham Maulana Jhonatan, Edward Julianto, Richy Jumanto Jumanto , Jumanto Jumanto Jumanto, Jumanto Jumanto Unjung KA, Cecep Bagus Suryadinata Korina, Nanda Putri Leo nardo Lestari , Apri Dwi Lestari, Apri Dwi Lestari, Fitri Duwi Lintang, Irendra M. Faris Al Hakim Makrina Tindangen Maulidia Rahmah Hidayah, Maulidia Rahmah Much Aziz Muslim Mukhlisin, Ahmad Munahefi, Detalia Noriza Mustaqim, Amirul Muzayanah, Rini Naufal Zuhdi, Hamzah Ndruru, Toni Krisman Nelly, Fredy Kusuma Nendya, Bima Nicko, Robertus Nikmah, Tiara Lailatul Nina Fitriani, Nina Ningsih, Maylinna Rahayu Nisa, Intan Khairun Niswah Baroroh Partini, Emilia Paundra, Fajar Pertiwi, Dwika Ananda Agustina Pradana, Fadli Dony PRASETYO, ERWIN Pratama, Muhammad Hasbi Puspo Dewi Dirgantari Rachmawati, Eka Yuni Rachmawati, Eka Yuni Rahman, Raihan Muhammad Rizki Rahmat Gernowo Rajagukguk, Ricky Mangihut Ramadhian, M. Arief Rahman Ratih Hurriyati Riesnandar, Edi Ristiawati, Monika Riza Arifudin Robianty, Nenden Sondari Rofik Rofik, Rofik S.Pd. M Kes I Ketut Sudiana . Sadid, Moh Naufal Salsabila, Malika Putri Saparina, Iska Ayu Saputra, Angga Riski Dwi Satriawan, Grace Yudha Satrio Ardiansyah, Adi Seivany, Ravenia Septian, M Rivaldi Ali Subhan Subhan Sulastri, Ai Syaharani, Reisya Triyadi, Indra Ulhaq, Moch Daffa Dhiya Vember, Hilda Wahyu, Aufa Azfa Yahya Nur Ifriza Yosza Dasril Yulia Nur Hasanah