Klasifikasi Citra Jenis Kapasitor Menggunakan Kombinasi Algoritma K-Nearest Neighbor dan Principal Component Analysis

Nuraini, Rini

doi:10.47065/josyc.v3i3.1694

Cited by 3 publications

(3 citation statements)

References 16 publications

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“…Pada data mining sendiri proses pengolahan data yang dilakukan nantinya akan mendapatkan berupa sebuah informasiinformasi baru yang dapat dipergunakan untuk menyelesaikan berbagai macam masalah. Pada data mining sendiri terdapat berbagai macam bagian untuk proses penyelesaian masalah, salah satunya merupakan Klasifikasi [9]- [11].…”

Section: Pendahuluanunclassified

Penerapan Data Mining Untuk Klasifikasi Penerima Dana Bantuan Sosial Dengan Menggunakan Algoritma K-Nearest Neighbor

Triayudi

2023

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The Social Assistance Fund (Bansos) is a government program carried out to assist in eradicating community poverty in Indonesia and improving the welfare of families in Indonesia. Social Assistance Funds (Bansos) are distributed from the central ministry, then forwarded to local social services and then distributed to the community through each sub-district office. After data collection is carried out, the process of determining and selecting the families who receive Social Assistance Funds (Bansos) is carried out. However, in the implementation process there were several obstacles, one of which was that the provision of Social Assistance Funds (Bansos) was still not on target for families who deserved to receive Social Assistance Funds (Bansos). This problem is an important matter that must be resolved, this is because the main aim of the Social Assistance Fund (Bansos) program is to help eradicate poverty in Indonesia. Reviewing and processing data again based on previous data can be completed using one of the computer techniques. Data mining is a technique used to reprocess data. Data processing returns to data mining based on data previously stored in a data collection or data warehouse. Classification is part of data mining which aims to find out certain models of data so that they can be divided into several classes or groups. The K-Nearest Neighbor (K-NN) algorithm is part of a data mining technique which aims to divide data into certain groups. The results obtained in the research are the K value used in the research, namely K=7, the result of the family data grouping process which has just determined that the family received Social Assistance Funds (Bansos).

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Section: Pendahuluanunclassified

Penerapan Data Mining Untuk Klasifikasi Penerima Dana Bantuan Sosial Dengan Menggunakan Algoritma K-Nearest Neighbor

Triayudi

2023

bits

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“…Komputer dapat memproses gambar daun dan dapat dengan cepat mengubahnya menjadi sistem secara otomatis menghasilkan gambar (Arisanti & Yamasari, 2021). Banyak prosedur yang terlibat dalam proses pengolahan citra, seperti akuisisi gambar, preprocessing, dan pemrosesan gambar hingga produk akhir (Rumandan et al, 2022). Preprocessing dilakukan untuk mempersiapkan citra untuk segmentasi, yaitu dengan mengubahnya menjadi citra grayscale dan selanjutnya menjadi citra hitam putih (Lestari et al, 2019).…”

Section: Pendahuluanunclassified

Klasifikasi Daun Herbal Menggunakan K-Nearest Neighbor dan Support Vector Machine dengan Fitur Fourier Descriptor

Prayoga¹,

Purnawansyah²,

Hasanuddin³

et al. 2023

Edumatic

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Indonesia is a rich country in herbal plants that can be used as traditional medicine. Leaves are one of the main components of herbal plants that are difficult to distinguish in texture and shape. This study aims to classify two types of herbal leaves, namely Sauropus androgynus and Moringa leaves using the K-nearest neighbor (KNN) and Support vector machine (SVM) with fourier descriptor (FD) feature extraction on texture and shape features. The research uses primary data collected through a smartphone camera as much as 480 image data with light and dark scenarios which are then divided into 80:20 training and testing data. Based on the research that has been done, it is found that the KNN for light scenario data and dark scenarios get 92% and 94% accuracy respectively. The test results using SVM with FD feature extraction obtain an accuracy of 96% for light and dark scenarios. Thus, SVM is more recommended in the classification of herbal leaf images.

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“…Berdasarkan Gambar 1 diata, maka penjelasan untuk masing-masing tapan adalah sebagai berikut: 1) Analisis Masalah Untuk melakukan penelitian terlebih dahulu perlu dilakukan analisis masalah untuk mengetahui permasalahan melalui identifikasi masalah. Identifikasi masalah merupakan upaya untuk menggali masalah yang dihadapi untuk diselesaikan [14], [15]. Untuk mendapatkan informasi mengenai permasalahan dalam menentukan Lembaga Bimbingan Belajar maka dilakukan observasi dan wawancara agar dapat didefinisikan permasalahan yang dialami Langkah berikutnya yaitu melakukan analisa kebutuhan.…”

Section: Gambar 1 Tahapan Penelitianunclassified

Implementasi Metode Weighted Product Pada Sistem Pendukung Keputusan Pemilihan Lembaga Bimbingan Belajar

Irfan,

Yanuardi,

Yudaningsih

2022

bulletincsr

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Tutoring provides a process of learning assistance to children to overcome learning difficulties. Currently, there are many tutoring or tutoring institutions that have special programs for preparation for entering state universities. Along with the emergence of Bimbel, it makes parents or students experience difficulties in making choices. To choose the right Bimbel institution, you need to know one by one the profile and services provided by that institution. This will take a long time to make a choice. This study aims to build a Decision Support System (SPK) using the Weighted Product (WP) method to determine tutoring institutions so that parents or prospective students can make the right choices and according to their needs. The WP method can obtain the best alternative based on multiplication between attributes and each attribute is first raised to the power of its weight. Based on the case studies conducted, the highest preference score was obtained for Ganesha Operation (A2) with a value of 0.259, followed by Latis Supercamp (A4) with a value of 0.251, Nurul Fikri (A3) with a value of 0.247 and Brain Academy with a value of 0.244. The results of the DSS which are built with the results of manual calculations get the same value. Besides that, in testing using Black-Box Testing, all functionality can run as it should.

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Klasifikasi Citra Jenis Kapasitor Menggunakan Kombinasi Algoritma K-Nearest Neighbor dan Principal Component Analysis

Cited by 3 publications

References 16 publications

Penerapan Data Mining Untuk Klasifikasi Penerima Dana Bantuan Sosial Dengan Menggunakan Algoritma K-Nearest Neighbor

Penerapan Data Mining Untuk Klasifikasi Penerima Dana Bantuan Sosial Dengan Menggunakan Algoritma K-Nearest Neighbor

Klasifikasi Daun Herbal Menggunakan K-Nearest Neighbor dan Support Vector Machine dengan Fitur Fourier Descriptor

Implementasi Metode Weighted Product Pada Sistem Pendukung Keputusan Pemilihan Lembaga Bimbingan Belajar

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