scholarly journals PREDIKSI KELULUSAN MAHASISWA MAGISTER TEKNIK INFORMATIKA UNIVERSITAS AMIKOM YOGYAKARTA MENGGUNAKAN METODE K-NEAREST NEIGHBOR

Respati ◽  
2018 ◽  
Vol 13 (2) ◽  
Author(s):  
Eri Sasmita Susanto ◽  
Kusrini Kusrini ◽  
Hanif Al Fatta
Keyword(s):  
Nearest Neighbor ◽  
Nearest Neighbors ◽  
Training Data ◽  
K Nearest Neighbor ◽  
Process Data ◽  
K Nearest Neighbors ◽  
Testing Data ◽  
Estimation Scheme ◽  
Student Graduation ◽  
Feasibility Test

INTISARIPenelitian ini difokuskan untuk mengetahui uji kelayakan prediksi kelulusan mahasiswa Universitas AMIKOM Yogyakarta. Dalam hal ini penulis memilih algoritma K-Nearest Neighbors (K-NN) karena K-Nearest Neighbors (K-NN) merupakan algoritma  yang bisa digunakan untuk mengolah data yang bersifat numerik dan tidak membutuhkan skema estimasi parameter perulangan yang rumit, ini berarti bisa diaplikasikan untuk dataset berukuran besar.Input dari sistem ini adalah Data sampel berupa data mahasiswa tahun 2014-2015. pengujian pada penelitian ini menggunakn dua pengujian yaitu data testing dan data training. Kriteria yang digunakan dalam penelitian ini adalah , IP Semester 1-4, capaian SKS, Status Kelulusan. Output dari sistem ini berupa hasil prediksi kelulusan mahasiswa yang terbagi menjadi dua yaitu tepat waktu dan kelulusan tidak tepat waktu.Hasil pengujian menunjukkan bahwa Berdasarkan penerapan k=14 dan k-fold=5 menghasilkan performa yang terbaik dalam memprediksi kelulusan mahasiswa dengan metode K-Nearest Neighbor menggunakan indeks prestasi 4 semester dengan nilai akurasi= 98,46%, precision= 99.53% dan recall =97.64%.Kata kunci: Algoritma K-Nearest Neighbors, Prediksi Kelulusan, Data Testing, Data Training ABSTRACTThis research is focused on knowing the feasibility test of students' graduation prediction of AMIKOM University Yogyakarta. In this case the authors chose the K-Nearest Neighbors (K-NN) algorithm because K-Nearest Neighbors (K-NN) is an algorithm that can be used to process data that is numerical and does not require complicated repetitive parameter estimation scheme, this means it can be applied for large datasets.The input of this system is the sample data in the form of student data from 2014-2015. test in this research use two test that is data testing and training data. The criteria used in this study are, IP Semester 1-4, achievement of SKS, Graduation Status. The output of this system in the form of predicted results of student graduation which is divided into two that is timely and graduation is not timely.The result of the test shows that based on the application of k = 14 and k-fold = 5, the best performance in predicting the students' graduation using K-Nearest Neighbor method uses 4 semester achievement index with accuracy value = 98,46%, precision = 99.53% and recall = 97.64%.Keywords: K-Nearest Neighbors Algorithm, Graduation Prediction, Testing Data, Training Data

scholarly journals IMPLEMENTASI K-NEAREST NEIGHBORD PADA RAPIDMINER UNTUK PREDIKSI KELULUSAN MAHASISWA

2018 ◽  
Vol 10 (1) ◽  
pp. 35-41
Author(s):  
Sumarlin Sumarlin ◽  
Dewi Anggraini
Keyword(s):  
Cross Validation ◽  
Nearest Neighbor ◽  
Confusion Matrix ◽  
Training Data ◽  
K Nearest Neighbor ◽  
Process Data ◽  
Nearest Neighbor Algorithm ◽  
Student Graduation ◽  
K Nearest Neighbor Algorithm ◽  
Auc Value

Data on graduate students is an important part in determining the quality of a private and public university. Graduate data is included in important assessments in the accreditation process. Data from Uyelindo Kupang STIKOM graduates every year will continue to grow and accumulate like neglected data because it is rarely used. To maximize student data into information that can be used by universities, the data must be processed in this case used as training data in a study using data mining to obtain information in the form of predictions of graduation from Kupang Uyelindo STIKOM students. The method used in this study is K-Nearest Neighbor using rapidminer software to measure K-Nearest Neighbor's accuracy against student graduate data. The criteria used were in the form of student names, gender, cumulative achievement index (GPA) from semester 1 to 6. In applying the K-Nearest Neighbor algorithm can be used to produce predictions of student graduation. To measure the performance of the k-nearest neighbor algorithm, the Cross Validation, Confusion Matrix and ROC Curves methods are used, in this study using a 5-fold cross validation to predict student graduation. From 100 student dataset records Uyelindo Kupang STIKOM graduates obtained accuracy rate reached 82% and included a very good classification because it has an AUC value between 0.90-1.00, which is 0.971, so it can be concluded that the accuracy of testing of student graduation models using K-Nearest Neighbor (K-NN) algorithm is influenced by the number of data clusters. Accuracy and the highest AUC value of 5-fold validation is to cluster data k = 4 with the accuracy value of 90%.

scholarly journals Sistem Pendukung Keputusan Kredit Usaha Rakyat PT. Bank Rakyat Indonesia Unit Kaliangkrik Magelang

2015 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
Agung Nugroho ◽  
Kusrini Kusrini ◽  
M. Rudyanto Arief
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Decision Maker ◽  
Nearest Neighbor ◽  
Nearest Neighbors ◽  
Training Data ◽  
Classification Rule ◽  
K Nearest Neighbor ◽  
K Nearest Neighbors ◽  
Viable Solution

Banyak faktor dan variabel yang mempengaruhi risiko kredit dalam pengambilan keputusan pada permasalahan Kredit Usaha Rakyat (KUR). Faktor-faktor yang digunakan sebagai dasar penilaian Kredit Usaha Rakyat pada PT.Bank Rakyat Indonesia Unit Kaliangkrik menggunakan prinsip dasar yang dikenal dengan prinsip “5 of Credit” yaitu Character, Capacity, Capital, Condition dan Collateral. Dari factor-faktor yang digunakan sebagai dasar penilaian kredit, digunakan metode Mining Classification Rule dalam membuat Sistem Pendukung Keputusan pemberian KUR. Terdapat beberapa algoritma yang dapat digunakan dalam data mining untuk metode klasifikasi salah satunya adalah algoritma k-nearest neightbor. Konsep sistem pendukung keputusan pemberian KUR ini dirancang dapat melakukan klasifikasi terhadap objek berdasarkan data pembelajaran yang jaraknya paling dekat dengan objek tersebut dan memberikan solusi nasabah yang layak menerima KUR berdasarkan masukan dari user dengan menggunakan metode k-nearest neighbors (knn). Data-data transaksi pembayaran nasabah lama akan dijadikan sebagai data training dimana sebelumnya akan ditentukan kelasnya terlebih dahulu. Penentuan kelas dilakukan dengan proses klasifikasi data berdasarkan kategori status nasabah sesuai jumlah tunggakan pembayaran kreditnya. Dari hasil perhitungan kemiripan kasus antara data calon nasabah baru dengan nasabah lama atau data training menggunakan algoritma K-Nearest Neighbor, hasil dengan nilai tertinggi akan dijadikan acuan seorang decision maker dalam mengambil keputusan.Many factors and variables that affect credit risk in decision-making on issues People's Business Credit (KUR). The factors are used as the basis of assessment of the People's Business Credit Unit at PT Bank Rakyat Indonesia Kaliangkrik using basic principle known as the principle of "5 of Credit" ie Character, Capacity, Capital, Collateral Condition and. Of the factors that are used as a basis for credit assessment, Classification Rule Mining method used in making the administration of KUR Decision Support Systems. There are several algorithms that can be used in data mining for classification methods one of which is the k-nearest algorithm neightbor. The concept of the provision of decision support system is designed KUR can perform the classification of objects based on distance learning data that is closest to the object and provide a viable solution customers receive KUR based on input from the user by using the k-nearest neighbors (KNN). Payment transaction data will be used as a customer long training data which will be determined prior to first class. Grading is done with the data classification process based on customer status categories according to the amount of credit outstanding payments. From the calculation of the similarity between the case of data with prospective new customers or old customers training data using the K-Nearest Neighbor algorithm, the results with the highest scores will be used as a reference to a decision maker in making decisions.

scholarly journals Implementation of Zoning and K-Nearest Neighbor in Character Recognition of Wrésastra Script

2019 ◽  
pp. 9 ◽  
Author(s):  
I Wayan Agus Surya Darma
Keyword(s):  
Feature Extraction ◽  
Character Recognition ◽  
Nearest Neighbor ◽  
Nearest Neighbors ◽  
Extraction Process ◽  
Training Data ◽  
K Nearest Neighbor ◽  
K Nearest Neighbors ◽  
Technological Advances ◽  
Different Types

Balinese script is an important aspect that packs the Balinese culture from time to time which continues to experience development along with technological advances. Balinese script consists of three types (1) Wrésastra, (2) Swalalita and (3) Modre which have different types of characters. The Wrésastra and Swalalita script are Balinese scripts which grouped into the script criteria that are used to write in the field of everyday life. In this research, the zoning method will be implemented in the feature extraction process to produce special features owned by Balinese script. The results of the feature extraction process will produce special features owned by Balinese script which will be used in the classification process to recognize the character of Balinese script. Special features are produced using the zoning method, it will divide the image characters area of ??Balinese scripts into several regions, to enrich the features of each Balinese script. The result of feature extractions is stored as training data that will be used in the classification process. K-Nearest Neighbors is implemented in the special feature classification process that is owned by the character of Balinese script. Based on the results of the test, the highest level of accuracy was obtained using the value K=3 and reference=10 with the accuracy of Balinese script recognition 97.5%.

scholarly journals Comparison of Naive Bayes Method, K-NN (K-Nearest Neighbor) and Decision Tree for Predicting the Graduation of ‘Aisyiyah University Students of Yogyakarta

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Tikaridha Hardiani
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Training Data ◽  
K Nearest Neighbor ◽  
Classification Technique ◽  
Testing Data ◽  
Student Graduation ◽  
The University

The students of Universitas ‘Aisyiyah Yogyakarta have been increasing including the number of students in the Faculty of Health Sciences. In 2016 the total number of UNISA students was 1851. The increasing number of students every year leads to great numbers of data stored in the university database. The data provide useful information for the university to predict student graduation or student study period whether they graduate on time with a study period of 4 years or late with a study period of more than 4 years. This can be processed by using a data mining technique that is the classification technique. Data needed in the classification technique are data of students who have graduated as training data and data of students who are still studying in the university as testing data. The training data were 501 records with 10 goals and the testing data were 428 records. Data mining process method used was the Cross-Industry Standard Prosses for Data Mining (CRISPDM). The algorithms used in this study were Naive Bayes, K-Nearest Neighbor (KNN) and Decision Tree. The three algorithms were compared to see the accuracy by using Rapidminer software. Based on the accuracy, it was found that the K-NN algorithm was the best in predicting student graduation with an accuracy of 91.82%. The K-NN algorithm showed that 100% of the students of Nursing study program of Universitas Aisyiyah Yogyakarta are predicted to graduate on time.

scholarly journals Tropical Balls and Its Applications to K Nearest Neighbor over the Space of Phylogenetic Trees

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 779
Author(s):  
Ruriko Yoshida
Keyword(s):  
Supervised Learning ◽  
Phylogenetic Trees ◽  
Nearest Neighbor ◽  
Nearest Neighbors ◽  
High Dimensional ◽  
Learning Method ◽  
Dimensional Vector ◽  
K Nearest Neighbor ◽  
K Nearest Neighbors

A tropical ball is a ball defined by the tropical metric over the tropical projective torus. In this paper we show several properties of tropical balls over the tropical projective torus and also over the space of phylogenetic trees with a given set of leaf labels. Then we discuss its application to the K nearest neighbors (KNN) algorithm, a supervised learning method used to classify a high-dimensional vector into given categories by looking at a ball centered at the vector, which contains K vectors in the space.

Klasifikasi Gerakan Jatuh Berbasis Accelerometer dan Gyroscope Menggunakan K-Nearest Neighbors

2020 ◽  
Vol 4 (2) ◽  
pp. 24-29
Author(s):  
Adlian Jefiza ◽  
Indra Daulay ◽  
Jhon Hericson Purba
Keyword(s):  
Real Time ◽  
Root Mean Square ◽  
Nearest Neighbors ◽  
Training Data ◽  
Mean Square ◽  
K Nearest Neighbors ◽  
Testing Data ◽  
Total Data

Permasalahan utama pada penelitian ini merujuk kepada semakin menurunnya daya tahan tubuh lanjut usia (lansia). Hal ini membutuhkan sistem monitoring aktivitas lansia secara real time. Untuk mendeteksi kegiatan para lansia, dirancang sebuah perangkat monitoring dengan accelerometer 3-sumbu dan gyroscope 3-sumbu. Data sensor diperoleh dari lima partisipan. Setiap partisipan melakukan lima gerakan yaitu terjatuh, duduk, tidur, rukuk dan sujud. Gerakan yang dipilih adalah gerakan yang menyerupai gerakan jatuh. Total data yang diperoleh dari partisipan adalah 75 data yang terbagi menjadi training data dan testing data. Penelitian ini menggunakan metode transformasi Wavelet untuk mengenali fitur dari gerakan. Untuk pengklasifikasian setiap gerakan, digunakan metode K-nearest neighbors (KNN). Hasil klasifikasi gerakan menggunakan lima kelas menghasilkan nilai root mean square sebesar 0.0074 dengan akurasi 100%.

scholarly journals Helmet Monitoring System using Hough Circle and HOG based on KNN

2021 ◽  
Vol 12 (1) ◽  
pp. 13
Author(s):  
Rachmad Jibril Al Kautsar ◽  
Fitri Utaminingrum ◽  
Agung Setia Budi
Keyword(s):  
Police Officers ◽  
Nearest Neighbor ◽  
Training Data ◽  
K Nearest Neighbor ◽  
Accuracy Rate ◽  
Histogram Of Oriented Gradient ◽  
Surveillance Camera ◽  
Testing Data ◽  
Motorized Vehicles ◽  
Traffic Operation

 Indonesian citizens who use motorized vehicles are increasing every year. Every motorcyclist in Indonesia must wear a helmet when riding a motorcycle. Even though there are rules that require motorbike riders to wear helmets, there are still many motorists who disobey the rules. To overcome this, police officers have carried out various operations (such as traffic operation, warning, etc.). This is not effective because of the number of police officers available, and the probability of police officers make a mistake when detecting violations that might be caused due to fatigue. This study asks the system to detect motorcyclists who do not wear helmets through a surveillance camera. Referring to this reason, the Circular Hough Transform (CHT), Histogram of Oriented Gradient (HOG), and K-Nearest Neighbor (KNN) are used. Testing was done by using images taken from surveillance cameras divided into 200 training data and 40 testing data obtained an accuracy rate of 82.5%.

scholarly journals Analysis K-Nearest Neighbor Algorithm for Improving Prediction Student Graduation Time

SinkrOn ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 42
Author(s):  
Rizki Muliono ◽  
Juanda Hakim Lubis ◽  
Nurul Khairina
Keyword(s):  
Higher Education ◽  
Nearest Neighbor ◽  
Training Data ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
Study Program ◽  
Sample Data ◽  
Student Graduation ◽  
K Nearest Neighbor Algorithm

Higher education plays a major role in improving the quality of education in Indonesia. The BAN-PT institution established by the government has a standard of higher education accreditation and study program accreditation. With the 4.0-based accreditation instrument, it encourages university leaders to improve the quality and quality of their education. One indicator that determines the accreditation of study programs is the timely graduation of students. This study uses the K-Nearest Neighbor algorithm to predict student graduation times. Students' GPA at the time of the seventh semester will be used as training data, and data of students who graduate are used as sample data. K-Nearest Neighbor works in accordance with the given sample data. The results of prediction testing on 60 data for students of 2015-2016, obtained the highest level of accuracy of 98.5% can be achieved when k = 3. Prediction results depend on the pattern of data entered, the more samples and training data used, the calculation of the K-Nearest Neighbor algorithm is also more accurate.

scholarly journals Analisis Perbandingan Algoritma Klasifikasi Citra Chest X-ray Untuk Deteksi Covid-19

Teknika ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 96-103
Author(s):  
Mohammad Farid Naufal ◽  
Selvia Ferdiana Kusuma ◽  
Kevin Christian Tanus ◽  
Raynaldy Valentino Sukiwun ◽  
Joseph Kristiano ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Nearest Neighbors ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
K Nearest Neighbors ◽  
X Ray ◽  
Chest X Ray

Kondisi pandemi global Covid-19 yang muncul diakhir tahun 2019 telah menjadi permasalahan utama seluruh negara di dunia. Covid-19 merupakan virus yang menyerang organ paru-paru dan dapat mengakibatkan kematian. Pasien Covid-19 banyak yang telah dirawat di rumah sakit sehingga terdapat data citra chest X-ray paru-paru pasien yang terjangkit Covid-19. Saat ini sudah banyak peneltian yang melakukan klasifikasi citra chest X-ray menggunakan Convolutional Neural Network (CNN) untuk membedakan paru-paru sehat, terinfeksi covid-19, dan penyakit paru-paru lainnya, namun belum ada penelitian yang mencoba membandingkan performa algoritma CNN dan machine learning klasik seperti Support Vector Machine (SVM), dan K-Nearest Neighbor (KNN) untuk mengetahui gap performa dan waktu eksekusi yang dibutuhkan. Penelitian ini bertujuan untuk membandingkan performa dan waktu eksekusi algoritma klasifikasi K-Nearest Neighbors (KNN), Support Vector Machine (SVM), dan CNN  untuk mendeteksi Covid-19 berdasarkan citra chest X-Ray. Berdasarkan hasil pengujian menggunakan 5 Cross Validation, CNN merupakan algoritma yang memiliki rata-rata performa terbaik yaitu akurasi 0,9591, precision 0,9592, recall 0,9591, dan F1 Score 0,959 dengan waktu eksekusi rata-rata sebesar 3102,562 detik.

Methods based on k-nearest neighbor regression in the prediction of basal area diameter distribution

1998 ◽  
Vol 28 (8) ◽  
pp. 1107-1115 ◽  
Author(s):  
Matti Maltamo ◽  
Annika Kangas
Keyword(s):  
Nearest Neighbor ◽  
Basal Area ◽  
Nearest Neighbors ◽  
Volume Estimation ◽  
Diameter Distribution ◽  
K Nearest Neighbor ◽  
K Nearest Neighbors ◽  
Stand Growth ◽  
Weighted Averages ◽  
Growing Stock

In the Finnish compartmentwise inventory systems, growing stock is described with means and sums of tree characteristics, such as mean height and basal area, by tree species. In the calculations, growing stock is described in a treewise manner using a diameter distribution predicted from stand variables. The treewise description is needed for several reasons, e.g., for predicting log volumes or stand growth and for analyzing the forest structure. In this study, methods for predicting the basal area diameter distribution based on the k-nearest neighbor (k-nn) regression are compared with methods based on parametric distributions. In the k-nn method, the predicted values for interesting variables are obtained as weighted averages of the values of neighboring observations. Using k-nn based methods, the basal area diameter distribution of a stand is predicted with a weighted average of the distributions of k-nearest neighbors. The methods tested in this study include weighted averages of (i)Weibull distributions of k-nearest neighbors, (ii)distributions of k-nearest neighbors smoothed with the kernel method, and (iii)empirical distributions of the k-nearest neighbors. These methods are compared for the accuracy of stand volume estimation, stand structure description, and stand growth prediction. Methods based on the k-nn regression proved to give a more accurate description of the stand than the parametric methods.

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