scholarly journals PREDIKSI PENJUALAN PRODUK UNILEVER MENGGUNAKAN METODE K-NEAREST NEIGHBOR

2021 ◽  
Vol 6 (1) ◽  
pp. 155-160
Author(s):  
Aisha Alfani W.P.R. ◽  
Fahrur Rozi ◽  
Farid Sukmana
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
K Nearest Neighbor

Toko Rizky Barokah Nganjuk merupakan sebuah toko retail semi grosir yang terletak di Kelurahan Bogo Kabupaten Nganjuk Jawa Timur. Toko ini menjual berbagai macam produk kebutuhan rumah tangga diantaranya dari Unilever, PG, Nestlé, dan Indofood. Dilihat dari banyaknya permintaan konsumen akan produk Unilever berdasarkan data 3 tahun terakhir, maka dibutuhkan prediksi untuk penjualan produk Unilever terlaris. Hal ini berguna untuk mempermudah pihak pemilik toko dalam perencanaan penyediaan stok. Untuk mengetahui penjualan produk Unilever terlaris digunakan teknik klasifikasi data mining dan algoritma K-Nearest Neighbor. Hasil dari penelitian ini berdasarkan nilai akurasi tertinggi terhadap klasifikasi penjualan produk sebesar 86,66% dan nilai akurasi terendah terhadap klasifikasi penjualan produk sebesar 40%.

Perancangan Aplikasi Prediksi Kelulusan Tepat Waktu Bagi Mahasiswa Baru Dengan Teknik Data Mining (Studi Kasus: Data Akademik Mahasiswa STMIK Dipanegara Makassar)

2015 ◽  
Vol 1 (4) ◽  
pp. 270
Author(s):  
Muhammad Syukri Mustafa ◽  
I. Wayan Simpen
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
Test Results ◽  
K Nearest Neighbor ◽  
Accuracy Rate ◽  
Sample Data ◽  
New Students ◽  
K Nearest Neighbor Algorithm ◽  
Using Data ◽  
Existing Data

Penelitian ini dimaksudkan untuk melakukan prediksi terhadap kemungkian mahasiswa baru dapat menyelesaikan studi tepat waktu dengan menggunakan analisis data mining untuk menggali tumpukan histori data dengan menggunakan algoritma K-Nearest Neighbor (KNN). Aplikasi yang dihasilkan pada penelitian ini akan menggunakan berbagai atribut yang klasifikasikan dalam suatu data mining antara lain nilai ujian nasional (UN), asal sekolah/ daerah, jenis kelamin, pekerjaan dan penghasilan orang tua, jumlah bersaudara, dan lain-lain sehingga dengan menerapkan analysis KNN dapat dilakukan suatu prediksi berdasarkan kedekatan histori data yang ada dengan data yang baru, apakah mahasiswa tersebut berpeluang untuk menyelesaikan studi tepat waktu atau tidak. Dari hasil pengujian dengan menerapkan algoritma KNN dan menggunakan data sampel alumni tahun wisuda 2004 s.d. 2010 untuk kasus lama dan data alumni tahun wisuda 2011 untuk kasus baru diperoleh tingkat akurasi sebesar 83,36%.This research is intended to predict the possibility of new students time to complete studies using data mining analysis to explore the history stack data using K-Nearest Neighbor algorithm (KNN). Applications generated in this study will use a variety of attributes in a data mining classified among other Ujian Nasional scores (UN), the origin of the school / area, gender, occupation and income of parents, number of siblings, and others that by applying the analysis KNN can do a prediction based on historical proximity of existing data with new data, whether the student is likely to complete the study on time or not. From the test results by applying the KNN algorithm and uses sample data alumnus graduation year 2004 s.d 2010 for the case of a long and alumni data graduation year 2011 for new cases obtained accuracy rate of 83.36%.

Data Mining Approach to Analyze COVID-19 Clinical Dataset

2021 ◽  
Vol 15 (6) ◽  
pp. 1812-1819
Author(s):  
Azita Yazdani ◽  
Ramin Ravangard ◽  
Roxana Sharifian
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Clinical Signs ◽  
Study Data ◽  
Mining Machine ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Data Mining Approach

The new coronavirus has been spreading since the beginning of 2020 and many efforts have been made to develop vaccines to help patients recover. It is now clear that the world needs a rapid solution to curb the spread of COVID-19 worldwide with non-clinical approaches such as data mining, enhanced intelligence, and other artificial intelligence techniques. These approaches can be effective in reducing the burden on the health care system to provide the best possible way to diagnose and predict the COVID-19 epidemic. In this study, data mining models for early detection of Covid-19 in patients were developed using the epidemiological dataset of patients and individuals suspected of having Covid-19 in Iran. C4.5, support vector machine, Naive Bayes, logistic regression, Random Forest, and k-nearest neighbor algorithm were used directly on the dataset using Rapid miner to develop the models. By receiving clinical signs, this model diagnosis the risk of contracting the COVID-19 virus. Examination of the models in this study has shown that the support vector machine with 93.41% accuracy is more efficient in the diagnosis of patients with COVID-19 pandemic, which is the best model among other developed models. Keywords: COVID-19, Data mining, Machine Learning, Artificial Intelligence, Classification

scholarly journals Performance of Naïve Bayes, C4.5 and KNN using Breast Cancer, Iris and Hypothyroid Datasets

2020 ◽  
Vol 9 (3) ◽  
pp. 2193-2197
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Specific Pattern ◽  
K Nearest Neighbor ◽  
Data Mining Technique ◽  
Digital Format ◽  
Tree Classifier

Data mining usually specifies the discovery of specific pattern or analysis of data from a large dataset. Classification is one of an efficient data mining technique, in which class the data are classified are already predefined using the existing datasets. The classification of medical records in terms of its symptoms using computerized method and storing the predicted information in the digital format is of great importance in the diagnosis of various diseases in the medical field. In this paper, finding the algorithm with highest accuracy range is concentrated so that a cost-effective algorithm can be found. Here the data mining classification algorithms are compared with their accuracy of finding exact data according to the diagnosis report and their execution rate to identify how fast the records are classified. The classification technique based algorithms used in this study are the Naive Bayes Classifier, the C4.5 tree classifier and the K-Nearest Neighbor (KNN) to predict which algorithm is the best suited for classifying any kind of medical dataset. Here the datasets such as Breast Cancer, Iris and Hypothyroid are used to predict which of the three algorithms is suitable for classifying the datasets with highest accuracy of finding the records of patients with the particular health problems. The experimental results represented in the form of table and graph shows the performance and the importance of Naïve Bayes, C4.5 and K-Nearest Neighbor algorithms. From the performance outcome of the three algorithms the C4.5 algorithm is a lot better than the Naïve Bayes and the K-Nearest Neighbor algorithm.

scholarly journals Analysis and Prediction of CET4 Scores Based on Data Mining Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Hongyan Wang
Keyword(s):  
Data Mining ◽  
Linear Regression ◽  
Test Score ◽  
Nearest Neighbor ◽  
Classification Model ◽  
Data Mining Algorithm ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
K Nearest Neighbor Algorithm ◽  
Classification Efficiency

This paper presents the concept and algorithm of data mining and focuses on the linear regression algorithm. Based on the multiple linear regression algorithm, many factors affecting CET4 are analyzed. Ideas based on data mining, collecting history data and appropriate to transform, using statistical analysis techniques to the many factors influencing the CET-4 test were analyzed, and we have obtained the CET-4 test result and its influencing factors. It was found that the linear regression relationship between the degrees of fit was relatively high. We further improve the algorithm and establish a partition-weighted K-nearest neighbor algorithm. The K-weighted K nearest neighbor algorithm and the partition algorithm are used in the CET-4 test score classification prediction, and the statistical method is used to study the relevant factors that affect the CET-4 test score, and screen classification is performed to predict when the comparison verification will pass. The weight K of the input feature and the adjacent feature are weighted, although the allocation algorithm of the adjacent classification effect has not been significantly improved, but the stability classification is better than K-nearest neighbor algorithm, its classification efficiency is greatly improved, classification time is greatly reduced, and classification efficiency is increased by 119%. In order to detect potential risk graduating students earlier, this paper proposes an appropriate and timely early warning and preschool K-nearest neighbor algorithm classification model. Taking test scores or make-up exams and re-learning as input features, the classification model can effectively predict ordinary students who have not graduated.

Web Graph Clustering for Displays and Navigation of Cyberspace

Web Mining ◽  
2011 ◽  
pp. 253-275
Author(s):  
Xiaodi Huang ◽  
Wei Lai
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
Structural Information ◽  
Graph Clustering ◽  
Data Sets ◽  
K Nearest Neighbor ◽  
New Approach ◽  
Web Graph

This chapter presents a new approach to clustering graphs, and applies it to Web graph display and navigation. The proposed approach takes advantage of the linkage patterns of graphs, and utilizes an affinity function in conjunction with the k-nearest neighbor. This chapter uses Web graph clustering as an illustrative example, and offers a potentially more applicable method to mine structural information from data sets, with the hope of informing readers of another aspect of data mining and its applications.

scholarly journals PENERAPAN METODE K-NEAREST NIGBOAR UNTUK MENENTUKAN KUALITAS BUAH SAWIT LAYAK DITERIMA DI PT. CIPTA CHEMICAL MEDAN OIL

2020 ◽  
Vol 3 (2) ◽  
pp. 108
Author(s):  
Hery H ◽  
Yonata Laia ◽  
Carvirindo Fenaldi ◽  
Devi Susanti Dao ◽  
Willy Wijaya
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
K Nearest Neighbor

Penelitian ini membahas tentang Data Mining Untuk mentukan bagus Buah Sawit yang bagus untuk di olah  Pada PT. Cipta Chemical Medan Oil Menggunakan Metode (K-NN) K-Nearest Neighbor, dimana jumlah produksi kelapa sawit tidak menentu setiap bulannya. Penelitian ini bertujuan untuk menggunakan metode (K - NN) K-Nearest Neighbor dalam pemilihan buah yang yang baik. Objek penelitian ini adalah jumlah produksi kelapa sawit sedangkan data yang digunakan merupakan data historis dari jumlah produksi tanaman kelapa sawit tahun 2017-2019. Dengan menggunakan metode (K - NN) K-Nearest Neighbor, Output dari Rapidminer didapatkan hasil yang baik dan berhasil melakukan penentuan buah yang layak produksi.

scholarly journals PREDIKSI TINGKAT KELULUSAN TEPAT WAKTU DENGAN METODE NAÏVE BAYES DAN K-NEAREST NEIGHBOR

2019 ◽  
Vol 7 (1) ◽  
pp. 7-16
Author(s):  
Sidik Rahmatullah
Keyword(s):  
Data Mining ◽  
Human Capital ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Soft Skills ◽  
Naïve Bayes ◽  
K Nearest Neighbor ◽  
Hard Skills

 Lulusan adalah status yang dicapai mahasiswa setelah menyelesaikan proses pendidikan sesuai dengan persyaratan kelulusan yang ditetapkan oleh program studi. Sebagai salah satu keluaran langsung dari proses pendidikan yang dilakukan oleh program studi, lulusan yang bermutu memiliki ciri penguasaan kompetensi akademik termasuk hard skills dan soft skills sebagaimana dinyatakan dalam sasaran mutu serta dibuktikan dengan kinerja lulusan di masyarakat sesuai dengan profesi dan bidang ilmu. Program studi yang bermutu memiliki sistem pengelolaan lulusan yang baik sehingga mampu menjadikannya sebagai human capital bagi progam studi yang bersangkutan.  Penelitian ini menggunakan metode data mining yang digunakan untuk memprediksi tingkat kelulusan mahasiswa menggunakan dua metode yaitu Naive Bayes dan K-Nearest Neighbor. Hasil dari penelitian ini dapat memprediksi mahasiswa tepat lulus atau terlambat. Uji coba dilakukan dengan menggunakan data lulusan mahasiswa S1 Sistem informasi STMIK Dian Cipta Cendikia Kotabumi  sebanyak 600 data untuk training dan 180 data untuk testing. Hasil uji coba menunjukkan bahwa dengan menggunakan Naive Bayes menghasilkan akurasi  sebesar 85%, sedangkan menggunakan algoritma K-nearest neighbor menghasilkan akurasi sebesar 68.89 %.

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