cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Rani Santika
Contact Email
rsantika851@gmail.com
Phone
+6285793538817
Journal Mail Official
journaliss01@gmail.com
Editorial Address
Jl. Pangeran Cakrabuana Blok E6, Sendang, Kec. Sumber, Cirebon, Jawa Barat 45611
Location
Unknown,
Unknown
INDONESIA
Interdisciplinary Social Studies
Published by CV. International Journal Labs
ISSN : 28080467     EISSN : 28085051     DOI : 10.55324
Core Subject : Health, Education, Social,
Environmental Science Languange, Linguistic, Communication & Media Public Health Social Sciences
nterdisciplinary Social Studies (ISS) is an interdisciplinary publication of social studies and writing which publishes papers to international audiences of social researchers. ISS aims to provide a forum for scholarly understanding of social studies and plays an important role in promoting the process that accumulated knowledge, values, and skills are transmitted from one generation to another; and making methods and contents of evaluation and research in social, available to socialist and research workers. The journal encompasses a variety of topics, including education, management, cultural studies, law, social health, psychology, and geography, to economics belonging to the social context. Papers accepted: 1) Report evaluation and original research; 2) Literature review; and 3) An extensive book reviews section on social materials and equipment.
Arjuna Subject : Ilmu Sosial - Ilmu Sosial (Lain-Lain)
Articles 1 Documents
 1 
Search results for , issue "Vol. 2 No. 2 (2022): Interdisciplinary Social Studies" : 1 Documents clear
Prediction of Lung Disease Using Machine Learning Based on Hyperparameter Tuning Dita Yuliana
Interdisciplinary Social Studies Vol. 2 No. 2 (2022): Interdisciplinary Social Studies
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Based on data from the 2020 Global Cancer Statistics, lung cancer is the deadliest malignancy in the world with the most incidents occurring in Southeast Asia. Deaths caused by lung disease are still very high, so it is necessary to increase prevention efforts, for example by increasing the results of the prediction model. The application of machine learning methods to lung cancer survey datasets that are generally used by researchers for lung disease prediction, including the development of assistive devices, still does not handle missing values, noisy data, unbalanced classes, and even data validation efficiently. Therefore, a mean/mode imputation approach is proposed to handle missing value replacement, Min-Max Normalization to handle smoothing noisy data, K-Fold Cross Validation to handle data validation, and a hyperparameter tuning approach that can unify the performance of each machine learning method. to make classification decisions as well as to reduce unbalanced classes. The results of this study indicate that the proposed method provides an accuracy of 0.9%, so as to improve the accuracy performance of machine learning methods, the difference is 0.95% with Logistic Regression, 0.9% with KNeighborsClassifier, 0.34% with Gaussian NB.

Page 1 of 1 | Total Record : 1

 1 

Filter by Year

2022 2022


Reset
Filter By Issues
All Issue Vol. 5 No. 1 (2025): Regular Issue: October-December 2025 Vol. 4 No. 4 (2025): Regular Issue: July-September 2025 Vol. 4 No. 3 (2025): Regular Issue: April-June 2025 Vol. 4 No. 2 (2025): Regular Issue: January-March 2025 Vol. 4 No. 1 (2024): Regular Issue: October-December 2024 Vol. 3 No. 4 (2024): Regular Issue: July-September 2024 Vol. 3 No. 3 (2024): April-June 2024 (Regular Issue) Vol. 3 No. 2 (2024): January-March 2024 (Regular Issue) Vol. 3 No. 1 (2023): October-December 2023 (Regular Issue) Vol. 2 No. 12 (2023): Special Issue Vol. 2 No. 11 (2023): Special Issue Vol. 2 No. 10 (2023): Special Issue Vol. 2 No. 9 (2023): Special Issue Vol. 2 No. 8 (2023): Special Issue Vol. 2 No. 7 (2023): Special Issue Vol. 2 No. 6 (2023): Special Issue Vol. 2 No. 5 (2023): Special Issue Vol. 2 No. 4 (2023): Regular Issue Vol. 1 No. 12 (2022): Special Issue Vol. 1 No. 11 (2022): Special Issue Vol. 1 No. 11 (2022): Interdisciplinary Social Studies Vol. 1 No. 10 (2022): Special Issue Vol. 1 No. 10 (2022): Interdisciplinary Social Studies Vol. 2 No. 3 (2022): Regular Issue Vol. 2 No. 2 (2022): Interdisciplinary Social Studies Vol. 2 No. 2 (2022): Regular Issue Vol. 2 No. 1 (2022): Regular Issue Vol. 1 No. 9 (2022): Special Issue Vol. 1 No. 8 (2022): Special Issue Vol. 1 No. 7 (2022): Special Issue Vol. 1 No. 6 (2022): Special Issue Vol. 1 No. 5 (2022): Special Issue Vol. 1 No. 4 (2022): Reguler Issue Vol. 1 No. 3 (2021): Reguler Issue Vol. 1 No. 2 (2021): Reguler Issue Vol. 1 No. 1 (2021): Interdisciplinary Social Studies Vol. 1 No. 1 (2021): Reguler Issue More Issue