Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : telkomnika telecommunication computing electronics and control

Stemming Influence on Similarity Detection of Abstract Written in Indonesia Tari Mardiana; Teguh Bharata Adji; Indriana Hidayah
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 1: March 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v14i1.1926

Abstract

In this paper we would like to discuss about stemming effect by using Nazief and Adriani algorithm against similarity detection result of Indonesian written abstract. The contents of the publication abstract similarity detection can be used as an early indication of whether or not the act of plagiarism in a writing. Mostly in processing the text adding a pre-process, one of it which is called a stemming by changing the word into the root word in order to maximize the searching process. The result of stemming process will be changed as a certain word n-gram set then applied an analysis of similarity using Fingerprint Matching to perform similarity matching between text. Based on the F1-score which used to balance the precision and recall number, the detection that implements stemming and stopword removal has a better result in detecting similarity between the text with an average is 42%. It is higher comparing to the similarity detection by using only stemming process (31%) or the one that was done without involving the text pre-process (34%) while applying the bigram.
Similarity measurement on digital mammogram classification Erna Alimudin; Hanung Adi Nugroho; Teguh Bharata Adji
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 4: August 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i4.10698

Abstract

Breast cancer is one of the dominant causes of death in the world. Mammography is the standard for early detection of breast cancer. In examining mammograms, the overall parenchyma pattern of the left and right breast was placed side by side for symmetry assessed of left and right breast tissue by radiologist. Thus, in building computer-aided diagnosis (CAD) system for screening mammography, it is necessary to adapt the working procedure of the radiologist. In this study, 30 training images and 30 testing images from Kotabaru Oncology Clinic in Yogyakarta were used. The first step was to enhance the image quality with median filter and contrast limited adaptive histogram equalization (CLAHE). Then, feature extraction was processed by histogram-based and by gray level co-occurrence matrix (GLCM) based. Furthermore, the similarity measurement process was used to measure the difference value between selected features, i.e. angular second moment (ASM), inverse difference moment (IDM), contrast, entropy based GLCM, and energy, on the left and right mammograms. This process was intended to assess the symmetry of left and right mammograms as radiologists do in mammography screening. The obtained results of the classification between normal and abnormal images with backpropagation algorithm were accuracy of 0.933, sensitivity of 0.833, and specificity of 1.000.