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All Journal (JELIKU) Jurnal Elektronik Ilmu Komputer Udayana
Gorianto, Frisca Olivia
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Computer Science & IT

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Perbandingan Jenis TF terhadap Hasil Evaluasi Information Retrieval Hendra Suputra, I Putu Gede; Prebiana, Kiki Dwi; Gorianto, Frisca Olivia
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 8 No 2 (2019): Jeliku Volume 8 No 2, November 2019
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JLK.2019.v08.i02.p13

Abstract

Pada sebuah sistem temu kembali,salah satu cara untuk mencari kesamaan antara query dengan dokumen adalah dengan menggunakan Term Frequency – Inverse Document Frequency atau TF-IDF. TF yang umum digunakan adalah langsung menggunakan jumlah term frequency padahal banyak jenis TF lainnya yang dapat dikombinasikan dengan IDF. Penelitian ini akan mengkombinasikan 4 jenis TF, yaitu Natural TF, Normalization/max TF, Logaritma TF, dan Boolean TF dengan tujuan untuk mencari jenis TF mana yang lebih baik setelah dikombinasikan dengan IDF. Hasil penelitian menunjukkan bahwa.Logaritma TF adalah yang terbaik dengan nilai F-measure sebesar 0,00662. Keywords: TF-IDF, Natural TF, Normalization TF, Logaritma TF, Boolean TF
Klasifikasi Penyakit Kanker Payudara Menggunakan Jaringan Syaraf Tiruan dan Seleksi Fitur Gorianto, Frisca Olivia; Santi Astawa, I Gede
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 8 No 2 (2019): Jeliku Volume 8 No 2, November 2019
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JLK.2019.v08.i02.p01

Abstract

Breast cancer is still one of the leading causes of death in the world. Prevention can be done if the cancer can be recognized early on whether the cancer is malignant or benign. In this study, a comparison of malignant and benign cancer classifications was performed using two artificial neural network methods, which are the Feed-Forward Backpropagation method and the Elman Recurrent Neural Network method, before and after the feature selection of the data. The result of the study produced that Feed-Forward Backpropagation method using 2 hidden layers is better after the feature selection was performed on the data with an accuracy value of 99,26%.
Pengaruh Membership Function Pada Fuzzy Dempster-Shafer Gorianto, Frisca Olivia; Santi Astawa, I Gede; Arya Kadyanan, I Gusti Agung Gede
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 9 No 1 (2020): JELIKU Volume 9 No 1, Agustus 2020
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JLK.2020.v09.i01.p08

Abstract

The classification process is the process of labeling a class of data sets that do not have a class label yet. In the classification process there will always be uncertainty. The uncertainty here is that there is a possibility that the label chosen is not right and causing doubts. One method that can be used to overcome uncertainty is to use the Fuzzy and Dempster-Shafer (DS) methods. This research combines the fuzzification step to get the Belief value which will then be used in the DS classification calculation. This research aims to determine the effect of using different types of Membership Function in the classification process and the optimal parameters used in each MF. This research will combine the Fuzzification step from Fuzzy method to obtain the Belief value which will be then used in DS classification calculation. The Fuzzification step uses triangle and bell Membership Function (MF) to produce Belief value for the class label. The MF curve parameter tests are divided into two parts, the first part is where the parameters of the center point of the curve are within the range of the input data and the second part is where the parameters of the center point of the curve are outside the range of the input data. The result show that the optimal parameters for the triangle MF are a1 = a2 = 4, b1 = b2 = 10, c1 = 0 and c2 = 11 and parameters for the bell MF are a1 = -11, b1 = 0, c1 = 11 and a2 = 0, b2 = 1, c2 = 22. 2. The results of the research also show that the shape of bell MF with an accuracy of 88.87% is better than triangle MF.
Co-Authors I Gede Santi Astawa I Gusti Agung Gede Arya Kadyanan I Putu Gede Hendra Suputra Prebiana, Kiki Dwi