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All Journal Lontar Komputer: Jurnal Ilmiah Teknologi Informasi JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI Jurnal Pemberdayaan Masyarakat Universitas Al Azhar Indonesia Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Ade Jamal
Universitas Al Azhar Indonesia
Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Public Health Social Sciences

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Journal : Lontar Komputer: Jurnal Ilmiah Teknologi Informasi

 1 
Dimensionality Reduction using PCA and K-Means Clustering for Breast Cancer Prediction Ade Jamal; Annisa Handayani; Ali Akbar Septiandri; Endang Ripmiatin; Yunus Effendi
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 9, No. 3 December 2018
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (364.208 KB) | DOI: 10.24843/LKJITI.2018.v09.i03.p08

Abstract

Breast cancer is the most important cause of death among women. A prediction of breast cancer in early stage provides a greater possibility of its cure. It needs a breast cancer prediction tool that can classify a breast tumor whether it was a harmful malignant tumor or un-harmful benign tumor. In this paper, two algorithms of machine learning, namely Support Vector Machine and Extreme Gradient Boosting technique will be compared for classification purpose. Prior to the classification, the number of data attribute will be reduced from the raw data by extracting features using Principal Component Analysis. A clustering method, namely K-Means is also used for dimensionality reduction besides the Principal Component Analysis. This paper will present a comparison among four models based on two dimensionality reduction methods combined with two classifiers which applied on Wisconsin Breast Cancer Dataset. The comparison will be measured by using accuracy, sensitivity and specificity metrics evaluated from the confusion matrices. The experimental results have indicated that the K-Means method, which is not usually used for dimensionality reduction can perform well compared to the popular Principal Component Analysis.
Embryo Grading after In Vitro Fertilization using YOLO Dewi Ananta Hakim; Ade Jamal; Anto Satriyo Nugroho; Ali Akbar Septiandri; Budi Wiweko
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 13 No 3 (2022): Vol. 13, No. 3 December 2022
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2022.v13.i03.p01

Abstract

In vitro fertilization is an implementation of Assistive Reproductive Technology. This technology can produce embryos outside the mother's womb by manipulating gametes outside the human body. The success rate of in vitro fertilization is the selection of good-grading embryos. In this study, the authors used Yolo Version 3 to perform object detection objectively by introducing grades for each embryo image. The author uses an embryo image sourced from the Indonesian Medical Education and Research Institute with information on the quality of the embryo. In this study, the author separated the data into two schemes. The first scheme separates data into training data of 70%, 15% validation data, and 15% for testing data. The second scheme uses a Stratified K-Fold Cross-Validation with a fold value =3. In training, the writer configures the values ??of Max Batches=6000, Steps=4800,5400, Batch=64, and Subdivision=16 by doing image augmentation (saturation=1.5, exposure=1.5, hue=0.1, jitter=0.3, random=1). For each of the obtained mAP (Mean Average Precision) values ??for data separation schemes, one is 100.00% in the 6000th iteration, while for the two-data separation scheme, the highest mAP is 97.33%.% in the fold=3 and 5000th iteration. It means that both separation schemes are sufficient in terms of mAP.
Co-Authors Ali Akbar Septiandri Ali Akbar Septiandri Ali Akbar Septiandri Annisa Handayani ANNISA HANDAYANI Annisa Utami Annisa Utami Anto Satriyo Nugroho Arie Wahyu Triansyah Arif Supriyanto Bagus Mauludin Budi Wiweko Denny Hermawan Dewi Ananta Hakim Dody Haryadi Endang Ripmiatin Fatimah Ilona Asa Latifah Ramadhana Murilmiani Effendhi Muhammad Nugraha Riri Safitri Solechoel Arifin Teguh Widodo Winangsari Pradani Yunus Effendi