Article Per Year (5 Year)
p-Index From 2020 - 2025
3.979
P-Index
This Author published in this journals
All Journal TEKNIK INFORMATIKA Linguistika JSI: Jurnal Sistem Informasi (E-Journal) Science and Technology Indonesia Kuttab: Jurnal Ilmu Pendidikan Islam Jurnal Performa : Jurnal Manajemen dan Start-up Bisnis Jurnal Ilmiah Spectral 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 Sciencestatistics: Journal of Statistics, Probability, and Its Application JMAEKA: Jurnal Manajemen Ekonomi Akuntansi Integra: Journal of Integrated Mathematics and Computer Science
Warsono
Unknown Affiliation
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering 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 Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

Published : 34 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search
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.
On the Characteristic Function of the Four-Parameter Generalized Beta of the Second Kind (GB2) Distribution and Its Approximation to the Singh-Maddala, Dagum, and Fisk Distributions Warsono; Kurniasari, Dian
Science and Technology Indonesia Vol. 10 No. 1 (2025): January
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.2025.10.1.201-211

Abstract

Researchers have thoroughly investigated generalized distributions due to their inherent flexibility, which allows them to include several well-known distributions as special cases. Among these, the four parameter Generalized Beta of the Second Kind (GB2) distribution stands out as one of the most versatile frameworks in probability theory. Despite its broad applications, the GB2 distribution’s characteristic function, a critical tool in probability and statistical analysis, lacks a closed-form solution in the existing literature. This study pursues two primary objectives: first, to derive the characteristic function and the kth moment of the GB2 distribution, and second, to demonstrate how the GB2 distribution can serve as a close approximation to the Singh-Maddala, Dagum, and Fisk distributions using its characteristic function and kth moment. These derivations and approximations rely on gamma and beta functions, supplemented by the Maclaurin series expansion.
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 Budi Susetyo Chalimatuz Sa'diyah Dani Chandra Utama Davied Wahyu Wismanindra Dede Muhammad Hidayat Desyafitri, Naia DEWI PUTRIANA Dewi, Ria Rahmawati Diah Ayu Wulandari Dian Kurniasari DIANTINA, RIA Djoko Sutopo Erna Retna Rahadjeng Fajri Ariandi FARID NURDIANSYAH Favorisen R. Lumbanraja Fitriani Hendro Kuncoro, Hendro Humairosi, Lutfia Iman Pasu Marganda Hardianto Purba Iwan Setiawan Jihadi, Muhammad Krisnanda, Mikhael Ekklesia Kusmawan, Novit Ari Lestari, Novi Puji Lukman Rosyidi Maharani, Maria Marsella, Dhea MAYA MUSTIKA KARTIKA SARI, MAYA MIFTAHQUL JAYA ROSITA Muhammad Syaifullah Mursidi Mustofa Usman Nauval Adi C Notiragayu Oksiana Jatiningsih Okzarima, Wenty Putri, Amelia Fallizia R, Lukman Rahmanu Wijaya Rizqi Anisa Sarmini Shella, Tiara Pramay Shiroth, Salman Fathy Siti Khusnul Sriati Su'admaji, Arif Suciati, Indah Sulandra, Ardelia Maharani Syabrinildi Syafiq Mughni syafira, qorina aulya Tri Utami Utami, Bernadhita Herindri Samodera Wamiliana Wardhani Utami Dewi Widiarti Zahro, Fatimataz ZAKI BASTOMI