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All Journal TEKNIK INFORMATIKA Linguistika JSI: Jurnal Sistem Informasi (E-Journal) Science and Technology Indonesia Pendas : Jurnah Ilmiah Pendidikan Dasar Jurnal Performa: Jurnal Manajemen dan Start-up Bisnis Jurnal Ilmiah Spectral Akrab Juara : Jurnal Ilmu-ilmu Sosial Yayasan Cita Cendikiawan Al Khwarizmi Jurnal Manajemen Bisnis dan Kewirausahaan Golden Ratio of Mapping Idea and Literature Format Kajian Moral dan Kewarganegaraan SWARNA Ihsanika : Jurnal Pendidikan Agama Islam Indonesian Journal of Science, Technology, and Humanities Jurnal Pengabdian Masyarakat dan Riset Pendidikan JMAEKA: Jurnal Manajemen Ekonomi Akuntansi Integra: Journal of Integrated Mathematics and Computer Science Journal Tafkirul Iqtishodiyyah
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Journal : science and technology indonesia

 1 
LSTM-CNN Hybrid Model Performance Improvement with BioWordVec for Biomedical Report Big Data Classification Kurniasari, Dian; Warsono; Usman, Mustofa; Lumbanraja, Favorisen Rosyking; Wamiliana
Science and Technology Indonesia Vol. 9 No. 2 (2024): April
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/sti.2024.9.2.273-283

Abstract

The rise in mortality rates due to leukemia has fueled the swift expansion of publications concerning the disease. The increase in publications has dramatically affected the enhancement of biomedical literature, further complicating the manual extraction of pertinent material on leukemia. Text classification is an approach used to retrieve pertinent and top-notch information from the biomedical literature. This research suggests employing an LSTM-CNN hybrid model to tackle imbalanced data classification in a dataset of PubMed abstracts centred on leukemia. Random Undersampling and Random Oversampling techniques are merged to tackle the data imbalance problem. The classification model’s performance is improved by utilizing a pre trained word embedding created explicitly for the biomedical domain, BioWordVec. Model evaluation indicates that hybrid resampling techniques with domain-specific pre-trained word embeddings can enhance model performance in classification tasks, achieving accuracy, precision, recall, and f1-score of 99.55%, 99%, 100%, and 99%, respectively. The results suggest that this research could be an alternative technique to help obtain information about leukemia.
The Kernel Function of Reproducing Kernel Hilbert Space and Its Application on Support Vector Machine Utami, Bernadhita Herindri Samodera; Warsono; Usman, Mustofa; Fitriani
Science and Technology Indonesia Vol. 10 No. 4 (2025): October
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/sti.2025.10.4.1096-1108

Abstract

Reproducing Kernel Hilbert Space (RKHS) is a Hilbert space consisting of functions that can be represented or reproduced by a kernel function. The development of data science has made RKHS a method that refers to an approach or technique using the concept of reproducing kernels in certain applications, especially machine learning. Support Vector Machine (SVM) is one of the machine learning methods included in the supervised learning category for classification and regression tasks. This research aims to determine the form of linear kernel functions, polynomial kernel functions, and Gaussian kernel functions in Support Vector Machine analysis and analyze their performance in Support Vector Machine classification and regression. Application of the RKHS method in SVM classification analysis using World Disaster Risk Dataset data published by Institute for International Law of Peace and Armed Conflict (IFHV) from Ruhr-University Bochum in 2022 obtained results that are based on the results by comparing the predictions of training data and testing data using linear kernel functions, polynomial kernels and Gaussian kernels, it is recommended that classification using linear kernels provides the best prediction performance.
Co-Authors ACHMAD HERIYANTO ACHMAD ZULFIKAR WIDIYANTO Aisyah, Synthia Nur Angestika Wilandari Anggi Yusniar Arif Rahman Arista, Roynaldi Asmiati Budi Santosa Chalimatuz Sa'diyah Dani Chandra Utama Davied Wahyu Wismanindra Dede Muhammad Hidayat Desyafitri, Naia DEWI PUTRIANA Dian Berliansyah Putra Dian Kurniasari Dian Kurniasari DIANTINA, RIA Djoko Sutopo Erna Retna Rahadjeng Fajri Ariandi FARID NURDIANSYAH Fitriani Gamar Al Haddar Hendro Kuncoro, Hendro Humairosi, Lutfia Iman Pasu Marganda Hardianto Purba Intan Permata Desi Iqbal Iwan Setiawan Jihadi, Muhammad Krisnanda, Mikhael Ekklesia Kusmawan, Novit Ari Lestari, Novi Puji Lukman Rosyidi Lumbanraja, Favorisen R M.Syekh Ikhsan Syaipudin Maharani, Maria Marsella, Dhea MAYA MUSTIKA KARTIKA SARI, MAYA MIFTAHQUL JAYA ROSITA Muhammad Syaifullah Mustofa Usman Natal Indra Nauval Adi C Notiragayu Oksiana Jatiningsih Okzarima, Wenty Osman, Isnawati Putri Alfia Frisca Hidayat Putri, Amelia Fallizia R, Lukman Rahmanu Wijaya Rais, Rinovian Rif'an, Muhamad Riyanti, Apriani Rizqi Anisa Salman Fathy Shiroth Sarmini Shella, Tiara Pramay Siti Khusnul Siti Utami Su'admaji, Arif Suciati, Indah Syabrinildi Syafiq Mughni syafira, qorina aulya Tri Utami Utami, Bernadhita Herindri Samodera Wamiliana Widiarti Wulandari, Diah Ayu Zahro, Fatimataz ZAKI BASTOMI