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All Journal Telematika Jurnal Ilmu Komputer dan Desain Komunikasi Visual Jurnal Elektronika Listrik dan Teknologi Informasi Terapan Joutica : Journal of Informatic Unisla
Nur Nafi’iyah
Universitas Islam Lamongan
Arts Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Languange, Linguistic, Communication & Media

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K-NN Klasifikasi Kematangan Buah Mangga Manalagi Menggunakan L*A*B dan Fitur Statistik Arif Patriot Sri Pamungkas; Nur Nafi’iyah; Nur Qomariyah Nawafilah
Jurnal Ilmu Komputer dan Desain Komunikasi Visual Vol 4 No 1 (2019): Jurnal Ilmu Komputer dan Desain Komunikasi Visual (JIKDISKOMVIS)
Publisher : Fakultas Ilmu Komputer Universitas Nahdlatul Ulama Sidoarjo

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Abstract

Fruits are a food source of vitamins. The fruit is quickly damaged by mechanical, chemical and microbiological influences, making it easy to rot. Classification is carried out on a group of mangoes which differ in type of maturity. The distinguishing feature was used is the L*A*B color feature. The purpose of this researchgave the output of the maturity classification ofManalagi mangoes based on color features using the Matlab application. In this research the GLCM method will be proposed for feature extraction in mangoes. By using K-Nearest Neighboor (KNN) to determine the maturity level of the Mango fruit. The dataset used is 130 data, consisting of 65 data for raw, 15 for half-cooked and 50 for mature. The KNN Classification results using the GLCM and L*A*B methods for Feature Extraction get an accuracy value of 62.5% in the test data. Keywords— Matlab, Manalagi Mango, KNN, Lab, GLCM.
Fuzzy C-Mean untuk Mengcluster Pemain Football FIFA (Studi Kasus: Data Kaggle) Mohammad Afan; Retno Wardhani; Nur Nafi’iyah
Jurnal Ilmu Komputer dan Desain Komunikasi Visual Vol 4 No 2 (2019): Jurnal Ilmu Komputer dan Desain Komunikasi Visual (JIKDISKOMVIS)
Publisher : Fakultas Ilmu Komputer Universitas Nahdlatul Ulama Sidoarjo

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Abstract

From the data set in the internet world, we want to do the management. The purpose of managing existing data on the internet especially Kaggle is to find out how the clustering process is and how to apply the c-mean fuzzy algorithm. Fuzzy c-mean is used to cluster and get the centroid value of each cluster. The results of the centroid value of each cluster are used to group new data or the dataset itself. We use the FIFA Football Kaggle dataset to cluster into 2 and 3 groups. Fuzzy c-mean algorithm is implemented to cluster data with 2499 rows with 46 variables. The tool used to process data is matlab. Keywords— FIFA Fottbal, Clustering, Fuzzy C-mean, Matlab
Klasifikasi Kanker Kulit Berdasarkan Fitur Tekstur, Fitur Warna Citra Menggunakan SVM dan KNN Muhammad Faruk; Nur Nafi’iyah
Telematika Vol 13, No 2: Agustus (2020)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v13i2.987

Abstract

Skin cancer is one type of cancer that is quite serious that can not be controlled completely, so that many still result in death, disability and high medical costs. The diagnosis process carried out by dermatologists generally uses the Biopi process which is expensive, painful and requires a long recovery time for the wound, due to taking body tissue that scratches a small piece of tissue or by using a syringe to get a sample. Therefore we need a tool or system that can speed up helping to find out the type of skin cancer suffered, so that it can find out its treatment early by using digital image processing techniques. The purpose of this study is to classify the types of skin cancer based on texture and color image features using the SVM and KNN algorithm. The benefits are expected to help the skin medicine team in diagnosing skin cancer early. The features used are grayscale imagery taken by the average value, standard deviation, skewness, entropy, variance, contrast, energy, correlation, and homogeneity. Furthermore, the value of these features is trained and classified. The classification results using the SVM algorithm have an accuracy value of 69.85%. And accuracy using the KNN algorithm, with a value of K = 2 67.27%, K = 3 67.88%, K = 4 70.15%, K = 5 70.61%, K = 6 69.55%. Thus the best K on KNN is 5, the accuracy is 70.61%. Where the data used are 2637 training dataset images, and 660 test data images. And classified as a class of malignant, benign skin cancer.
KLASIFIKASI KATEGORI BUKU PADA PERPUSTAKAAN TEKNIK INFORMATIKA UNIVERSITAS ISLAM LAMONGAN Nurul Qomariyah; Nur Nafi’iyah; Ayu Ismi Hanifah
Joutica Vol 3, No 1 (2018)
Publisher : Universitas Islam Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (464.164 KB) | DOI: 10.30736/jti.v3i1.203

Abstract

The large number of library materials in the form of new book available in the library every year cause officer have difficulty in classification process book. Similarly, faced by librarian of Islamic University of Lamongan became the object of this research, determining the type or category of book at first the usual system. The process is made a lot of errors because it is based on the knowledge of officers from reading the title or the appropriate category so that makes it difficult for library users to find the materials needed. Application applied in the research is a data classification application that is done automatically using the application developed by Naive Bayes Classifier method with the application of client software web-based. Web-based server application function to input book by administrator and to classify data book based on the title. Based on the experience, and then in this research built an Classification Of Books Category At The Islamic University Of Lamongan Engineering Library
Klasifikasi Kematangan Buah Mangga Berdasarkan Citra HSV dengan KNN Husnul Khotimah; Nur Nafi’iyah; Masruroh
Jurnal Elektronika Listrik dan Teknologi Informasi Terapan Vol. 1 No. 2 (2019): ELTI Desember
Publisher : Jurnal Elektronika Listrik dan Teknologi Informasi Terapan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37338/elti.v1i2.175

Abstract

identification or classification using image processing and computer vision requires pattern recognition from the training dataset. The process of image processing and pattern recognition becomes a highly developed research study. Starting from the process of recognizing an object, or classification of objects and about detecting the level of fruit maturity. This research will classify the level of maturity of mangoes with HSV images. Where the RGB input image is converted to HSV. Then the average values of HSV intensity, skewness, and kurtosis are taken. The process of classification comes into 4 classes: raw, fairly ripe, ripe and very ripe. With the KNN classification method, and the dataset used 129 training data, and 40 testing data. The highest accuracy value at k = 2 is 80%. The tool used to develop the system is matlab.
IMPLEMENTASI SOM DALAM CLUSTERING HASIL IKAN LAUT KABUPATEN PEKALONGAN Bagus Nur Bakti Aji; Nur Nafi’iyah; Miftahus Sholihin
Jurnal Elektronika Listrik dan Teknologi Informasi Terapan Vol. 2 No. 1 (2020): ELTI Juni
Publisher : Jurnal Elektronika Listrik dan Teknologi Informasi Terapan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37338/elti.v2i1.178

Abstract

Data from sea fish in Pekalongan Regency can be processed, one of which is clustered. Clusters are grouping data based on the same criteria. The purpose of doing clustering is to be able to help in sorting and dividing a situation based on the same criteria. Clustering of marine fish products in Pekalongan Regency will be grouped into three groups, namely: a small group of marine fish products, a medium group of marine fish products, and a large group of marine fish products. The clustering process uses the SOM algorithm, and the data is taken from the website data.go.id/dataset. Data is processed in order to show which fish yields are small, medium and large. The processing process uses variable types of fish, years and results of sea fish that are stored in Excel files and then processed using Matlab. The results show that there are fish species that are classified as low and moderate clusters, namely shrimp, squid, serimping, grouper, turmeric, and ray species. The types of fish that enter the cluster and many are Tigawaja. The types of fish that enter the medium cluster are Beloso, Pihi, Pepetek, and those who enter the low cluster are 18 fish species, while those who enter the low, medium and many clusters are Petek.
Co-Authors Arif Patriot Sri Pamungkas Bagus Nur Bakti Aji Hanifah, Ayu Ismi Husnul Khotimah Masruroh Miftahus Sholihin Mohammad Afan Muhammad Faruk Nur Qomariyah Nawafilah Nurul Qomariyah Retno Wardhani