Analisis Perbandingan Algoritma Classification Untuk Authentication Uang Kertas (Studi Kasus: Banknote Authentication)

Sani, Khairul; Winarno, Wing Wahyu; Fauziati, Silmi

doi:10.26555/jifo.v10i1.a3344

Cited by 6 publications

(7 citation statements)

References 4 publications

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“…Penelitian-penelitian dalam 5 tahun terakhir terkait perbandingan kinerja klasifier tersebut diantaranya adalah: [5]…”

Section: Pendahuluanunclassified

Analisis Pemeringkatan Kualitas Klasifier Pada Dataset Tidak Seimbang

Anam¹,

Rusdiana²

2020

JIMP

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Classification algorithms C4.5, CART, k-Nearest Neighbors (k-NN) and Naive Bayes are included in the "Top 10 algorithms in data mining". The author tests and analyzes the four to get a ranking order according to the quality of performance. A common method to compare the quality of classifier performance for classifying two class labels with a balanced the proportion of the number of classes of datasets is to test the performance of classifier accuracy. For unbalanced datasets such as in this study using this method can be biased, and can even lead to misleading conclusions. By calculating the scores that are a combination of performance parameters "accuracy", "precision", "recall" and "AUC" where the highest value of each parameter is the best will result in a more representative rating of the classifier's performance indicating the quality of the classifier. Two test methods are used, namely 10-fold Cross Validation and Discrete Testing to ensure the results of a representative performance evaluation of each classifier. The implementation of the testing of the four classification algorithms above and the comparative analysis of the performance results in the ranking of the best quality performance ratings, namely: 1. k-NN, 2. C4.5, 3. CART, 4. Naive Bayes.

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“…Penelitian-penelitian dalam 5 tahun terakhir terkait perbandingan kinerja klasifier tersebut diantaranya adalah: [5]…”

Section: Pendahuluanunclassified

Analisis Pemeringkatan Kualitas Klasifier Pada Dataset Tidak Seimbang

Anam¹,

Rusdiana²

2020

JIMP

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“…Terdapat beberapa penelitian yang telah dilakukan terkait klasifikasi data menggunakan metode. Salah satu penelitian terkait adalah penelitian dalam menganalisis perbandingan algoritma classification untuk authentication uang kertas, dimana hasil persentase akurasi yang sangat tinggi adalah menggunakan metode Decision Tree C4.5 dengan nilai akurasi sebesar 98,5 %, sedangkan Neural Network sebesar 95%, dan Navies Bayes sebesar 85% [3]. Selanjutnya penelitian yang menerapkan teknik data mining menggunakan metode Support Vector Machine (SVM) untuk memprediksi siswa yang berpeluang drop out menghasilkan tingkat akurasi sebesar 43,33% [4].…”

Section: Pendahuluanunclassified

Penerapan Algoritma K-Nearest Neighbour (K-Nn) Untuk Penentuan Mahasiswa Berpotensi Drop Out

Ratniasih¹

2019

JUTIK

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ABSTRACT Drop out is a situation where students are expelled from college because of several factors, one of which is because the status of lectures is not active beyond 5 semesters for undergraduate students. The high level of success and low failure of students can reflect the quality of education in higher education. The high level of student drop outs can affect the value of Higher Education accreditation so that it will affect the level of public trust. Student data drop out becomes something important to be researched and analyzed, so it can be seen how the characteristics of students who have the potential to drop out as early as possible. The data of ITB STIKOM Bali students is very much so that they can utilize data mining techniques for data classification. In this study the K-NN method was implemented to classify students as potential drop outs and the student data used in this study were students of the 2014 Information Systems study program using 6 attributes, namely gender, age, religion, class status, practical work, and grades GPA. The results showed that the accuracy of the method was 81.50%. Keywords: KNN, Drop Out, ITB STIKOM Bali ABSTRAK Drop out adalah suatu keadaan dimana mahasiswa dikeluarkan dari perguruan tinggi karena beberapa faktor salah satunya karena status perkuliahannya tidak aktif melebihi 5 semester untuk mahasiswa S1. Tingginya tingkat keberhasilan dan rendahnya kegagalan mahasiswa dapat mencerminkan kualitas pendidikan di perguruan tinggi. Tingginya tingkat drop out mahasiswa dapat mempengaruhi nilai akreditasi Perguruan Tinggi sehingga akan mempengaruhi tingkat kepercayaan masyarakat. Data mahasiswa drop out menjadi sesuatu hal yang penting untuk diteliti dan dianalisa, sehingga dapat diketahui bagaimana karakteristik mahasiswa yang berpotensi drop out sedini mungkin. Data mahasiswa ITB STIKOM Bali sangat banyak sehingga dapat memanfaatkan teknik data mining untuk klasifikasi data. Pada penelitian ini diimplementasikan metode K-NN untuk klasifikasi mahasiswa berpotensi drop out dan data mahasiswa yang digunakan dalam penelitian ini adalah mahasiswa program studi Sistem Informasi angkatan 2014 dengan menggunakan 6 atribut yaitu jenis kelamin, umur, agama, status kelas, kerja praktek, dan nilai IPK. Hasil penelitian menunjukkan bahwa tingkat akurasi metode sebesar 81.50%. Kata kunci: KNN, Drop Out, ITB STIKOM Bali

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“…Naive Bayes classification is a statistical classifier can be used to predict probability membership a class. The Bayesian classification has similar classification capabilities to decision tree and neural network [4][5].…”

Section: Naive Bayesmentioning

confidence: 99%

“…In the Bayesian analysis, final classification is generated by combining both prior and posterior information source to produce probabilities using Bayes rule [4,5].…”

Section: Naive Bayesmentioning

confidence: 99%

Students academic performance based on behavior

Maulida¹,

Kariyam²

2017

AIP Conference Proceedings

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Abstract. Utilization of data in an information system that can be used for decision making that utilizes existing data warehouse to help dig useful information to make decisions correctly and accurately. Experience API (xAPI) is one of the enabling technologies for collecting data, so xAPI can be used as a data warehouse that can be used for various needs. One software application whose data is collected in xAPI is LMS. LMS is a software used in an electronic learning process that can handle all aspects of learning, by using LMS can also be known how the learning process and the aspects that can affect learning achievement. One of the aspects that can affect the learning achievement is the background of each student, which is not necessarily the student with a good background is an outstanding student or vice versa. Therefore, an action is needed to anticipate this problem. Prediction of student academic performance using Naive Bayes algorithm obtained accuracy of 67.7983% and error 32.2917%.

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Analisis Perbandingan Algoritma Classification Untuk Authentication Uang Kertas (Studi Kasus: Banknote Authentication)

Cited by 6 publications

References 4 publications

Analisis Pemeringkatan Kualitas Klasifier Pada Dataset Tidak Seimbang

Analisis Pemeringkatan Kualitas Klasifier Pada Dataset Tidak Seimbang

Penerapan Algoritma K-Nearest Neighbour (K-Nn) Untuk Penentuan Mahasiswa Berpotensi Drop Out

Students academic performance based on behavior

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