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All Journal Teknika Jurnal Pengabdian Masyarakat AbdiMas MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer IJISTECH (International Journal Of Information System & Technology) Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) IJISTECH Journal Automation Computer Information System (JACIS) The Indonesian Journal of Computer Science Research Intechno Journal : Information Technology Journal JURNAL MULTIDISIPLIN BHATARA
Seniwati, Erni
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Religion Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Social Sciences

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Journal : Building of Informatics, Technology and Science

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Clustering Performance Between K-means and Bisecting K-means for Students Interest in Senior High School Seniwati, Erni; Sidauruk, Acihmah; Haryoko, Haryoko; Lukman, Achmad
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3624

Abstract

The interest of high school students is an important thing to do to see the talents of each student based on the academic scores obtained in the first and second semesters. There are two majors of interest in this case study, namely natural and social studies with criteria for natural studies scores including mathematics, chemistry, biology and physics. Meanwhile, the social studies criteria include history, economics, geography and sociology. This research propose comparing of clustering time and accuracy based on manual data from school as a reference of clustering in SMAN 1 Wonosari for 2011/2012 academic year using two clustering methods namely K-means and Bisecting K-Means. The results of this research compare to manual results interest from class teacher, so this work can demonstrate the run time comparison and accuracy of this study. The accuracy result shows 87.5% for both methods but different run times. For bisecting k-means got 0.0229849 seconds to complete the clustering process faster than k-means only got 0.0929448 seconds
Citra Sitentik Untuk Klasifikasi Buah Menggunakan Algoritma SIFT Descriptor, Bag of Features dan Support Vector Machine Lukman, Achmad; Seniwati, Erni; Riswanto, Eko
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6296

Abstract

Recognizing specific objects assigned to a computer using artificial intelligence of course goes through a training and testing process using machine learning methods, the limited number of datasets makes it difficult for deep learning methods to carry out classification, so to overcome this, other methods are needed, including Scale Invariant Features Transform ( SIFT) which is a method of image processing to extract features from a limited amount of data and combined with a method in machine learning. To overcome the inability of deep learning to use limited datasets, this research uses a combination of SIFT and bag of features to extract features and support vector machine (SVM) to carry out classification. In this study, the aim is to observe the effect of synthetic images on the performance of the combination of SIFT descriptor, Bag of Features and Support Vector Machine algorithms in classifying real fruit images. The dataset involved is a synthetic image in the form of a 3D image that is made into a complete object, then taking random views to make an image that represents the object as training data. Furthermore, for testing data, real images taken from the dataset link in previous research will be used. The number of synthetic datasets that can be collected for each fruit is 150 images, so that the total is 450 images, while the real fruit images consist of 148 apple images, 152 banana images, and 166 orange images, so that the total real images are 466 images. The results of this research show that the highest accuracy was 65.45% with an F1-score reaching 58.45%.
Co-Authors Achmad Lukman, Achmad Acihmah Sidauruk Ali Mustopa, Ali Arief Setyanto Eko Riswanto, Eko Gibran, Ibrahim El Hafiyyan, Azka Haryoko, H Haryoko, Haryoko Imran, Muhammad Khamjah Bin Isnaini , Septi Fajar JOKO DWI SANTOSO Mulyatun, Sri Nila Feby Puspitasari, Nila Feby NINIK TRI HARTANTI Noviana , Zakia Rahmadhani Pangemanan, Robertus Aldrin Rahman, Bahtiar Rina Pramitasari Sari , Yulviani Puteri Puspita Sari, Rizka Amelia Setiawanto, Irwan Shahruri, Rifandi Annas TONNY HIDAYAT Wahju Tjahjo Saputro Wulandari, Evita Sekar Yuli Astuti