Penerapan Metode K-Means Dalam Menentukan Kelompok Pendalaman Materi Ujian Nasional

Dewi, Yumi Novita; Rianto, Harsih; Budihartanti, Cahyani; Fibriany, Firstianty Wahyuhening

doi:10.52362/jisamar.v6i1.670

Cited by 2 publications

(2 citation statements)

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“…Data mining merupakan proses menemukan pola dan trend dengan menambang sejumlah repository data dalam jumlah besar dan menggunakan teknologi pengenalan pola statistic dan teknik informatika [4]. Data mining adalah proses melakukan pendataan informasi-informasi penting dari data yang besar [5].…”

Section: Data Miningunclassified

Algoritma K-Means Untuk Mengetahui Minat Siswa Terhadap Jurusan Teknik Informatika

Mardika

2023

FaktorExacta

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There are already many information technology-based companies in the capital city, many job vacancies may be opened because they require experts with an educational background in informatics engineering. The research was conducted on YMIK 2 Jakarta high school students who concentrated on science and social studies. YMIK 2 High School students are familiar with information technology devices because there is a computer lab as a student facility for conducting computer learning activities. And there is an internet network in the form of free Wi-Fi at school. Researchers used data mining techniques with the K-Means algorithm and RapidMiner tools to process data to produce some conclusions about groupings related to whether or not YMIK 2 Jakarta High School students are interested in the Informatics Engineering major. The researcher divided the clusters into 2 groups consisting of cluster_0 which means students who are interested in informatics engineering and cluster_1 which means students who are not interested in informatics engineering. The data set used in this study was 50 data, according to the students participating in SMA YMIK 2 Jakarta. From the results of this study, it is known that students who are interested in majoring in informatics engineering are more numerous than students who are not interested in majoring in informatics engineering based on the k-means clustering algorithm.

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Section: Data Miningunclassified

Algoritma K-Means Untuk Mengetahui Minat Siswa Terhadap Jurusan Teknik Informatika

Mardika

2023

FaktorExacta

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“…Kelompok kedua disebut deteksi tepi kuadratik menggunakan turunan kedua. Contoh yang termasuk dalam kelompok ini adalah Gaussian Laplacian (LoG) [7].…”

Section: Tinjauan Pustaka 21 Deteksi Tepiunclassified

Deteksi Uang Palsu Rupiah dengan Menggunakan Metode Deteksi Tepi Laplacian of Gaussian (LoG) dan Algoritma K-Means Clustering

Saputra¹,

Nangi²,

Ningrum³

et al. 2022

JBI

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Abstract. Detection of Counterfeit Rupiah Using the Laplacian of Gaussian (LoG) Edge Detection Method and the K-Means Clustering Algorithm Counterfeit money is a severe problem that is increasing in every country. The reason is the ease of getting information on making counterfeit money and the development of technology such as color printers. This study used data from 20 images of authentic rupiah banknotes and 20 photos of fake rupiah banknotes. Data analysis in this study consisted of four stages: reading the image, converting the image to grayscale, image segmentation, and grouping image values. The dataset of real money images were taken with a cellphone camera, while counterfeit money images were obtained from the website. After the dataset retrieval process, the image conversion process was carried out into a grayscale image; then, the image segmentation process proceeded. The conclusion obtained from this study is that edge detection with Laplacian of Gaussian combined with the K-Means Clustering algorithm is quite effective in detecting an image to determine the picture as whether real money or counterfeit money.Keywords: Counterfeit Money, Laplacian of Gaussian, K-Means Clustering. Abstrak. Uang palsu adalah masalah serius yang semakin meningkat di setiap negara. Penyebabnya ialah kemudahan mendapatkan informasi cara pembuatan uang palsu serta perkembangan teknologi seperti printer warna. Penelitian ini menggunakan data 20 gambar uang kertas rupiah asli dan 20 gambar uang kertas rupiah palsu. Analisis data pada penelitian ini terdiri dari empat tahap, yaitu membaca gambar, mengubah gambar menjadi skala abu-abu, segmentasi gambar, dan pengelompokan nilai citra. Pengambilan dataset berupa uang asli dilakukan dengan kamera handphone dan gambar uang palsu didapatkan dari website. Setelah proses temu kembali dataset, dilakukan proses konversi citra menjadi citra grayscale, kemudian dilakukan proses segmentasi citra. Kesimpulan yang diperoleh dari penelitian ini adalah deteksi tepi dengan Laplacian of Gaussian yang dikombinasikan dengan algoritma K-Means Clustering cukup efektif mendeteksi suatu citra untuk menentukan gambar tersebut sebagai uang asli atau uang palsu.Kata Kunci: Uang Palsu, Laplacian of Gaussian, K-Means Clustering.

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Penerapan Metode K-Means Dalam Menentukan Kelompok Pendalaman Materi Ujian Nasional

Cited by 2 publications

References 0 publications

Algoritma K-Means Untuk Mengetahui Minat Siswa Terhadap Jurusan Teknik Informatika

Algoritma K-Means Untuk Mengetahui Minat Siswa Terhadap Jurusan Teknik Informatika

Deteksi Uang Palsu Rupiah dengan Menggunakan Metode Deteksi Tepi Laplacian of Gaussian (LoG) dan Algoritma K-Means Clustering

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