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Menampilkan 1–3 dari 3 artikel
Integrating Hybrid Statistical and Unsupervised LSTM-Guided Feature Extraction for Breast Cancer Detection
Setiadi, De Rosal Ignatius Moses
; Ojugo, Arnold Adimabua
; Pribadi, Octara
; Kartikadarma , Etika
; Setyoko, Bimo Haryo
; Widiono, Suyud
; Robet, Robet
; Aghaunor, Tabitha Chukwudi
; Ugbotu, Eferhire Valentine
Journal of Computing Theories and Applications
Vol 2
, No 4
(2025)
Breast cancer is the most prevalent cancer among women worldwide, requiring early and accurate diagnosis to reduce mortality. This study proposes a hybrid classification pipeline that integrates Hybrid Statistical Feature Selection (HSFS) with unsupervised LSTM-guided feature extraction for breast cancer detection using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. Initially, 20 features were selected using HSFS based on Mutual Information, Chi-square, and Pearson Correlation. To addres...
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PENGOLAHAN LIMBAH MINYAK JELANTAH MENJADI LILIN AROMATERAPI BERNILAI JUAL DALAM GERAKAN 3R
Kumoro, Imanuel Dimas Cahyo
; Trisnapradika, Gustina Alfa
; Rahma, Khalida Nur
; Kartikadarma, Etika
Abdi Masya
Vol 5
, No 1
(2024)
Indonesia is one of the largest waste-contributing countries in the world. This is no exception in Kelurahan Bandarharjo which has a large population so that household waste production is directly proportional to the population. The processing of household waste needs special attention, both from the household scale to the national scale. So far, household waste has only been collected at a TPS and ends up piled up in a TPA. This cannot continue because these piles of rubbish can become a source...
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Enhancing Lung Cancer Classification Effectiveness Through Hyperparameter-Tuned Support Vector Machine
Gomiasti, Fita Sheila
; Warto, Warto
; Kartikadarma, Etika
; Gondohanindijo, Jutono
; Setiadi, De Rosal Ignatius Moses
Journal of Computing Theories and Applications
Vol 1
, No 4
(2024)
This research aims to improve the effectiveness of lung cancer classification performance using Support Vector Machines (SVM) with hyperparameter tuning. Using Radial Basis Function (RBF) kernels in SVM helps deal with non-linear problems. At the same time, hyperparameter tuning is done through Random Grid Search to find the best combination of parameters. Where the best parameter settings are C = 10, Gamma = 10, Probability = True. Test results show that the tuned SVM improves accuracy, precisi...
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