scholarly journals DATA MINING ALGORITHM C4.5 CLASSIFICATION DETERMINATION CREDIT ELIGIBILITY FOR JAYA BERSAMA COOPERATIVES (KORJABE)

JURTEKSI ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 59-68
Author(s):  
Christnatalis Christnatalis ◽  
Roni Rayandi Saragih ◽  
Bobby Christianto Tambunan

Abstract: This study uses the C4.5 classification algorithm to determine creditworthness, clasification aims to divide the assigned object intoin a number of categories called classes. In this study, the authorusing data mining and C4.5 algorithm as the selection method. The criteria used are loan installments, prospective customer income, termloan time, status of prospective customers. This study resulted in a classification modeldecision tree using the C4.5 algorithm is included in the Excellent category Classification with an accuracy value of 98.33% and a classification error of 1.67%,so that this study uses 70% training data and 30% test data. From resultthe calculation obtained shows that the C4.5 algorithm can be usedto determine the feasibility of granting credit to Koperasi Jaya customers Together (KORJABE).            Keywords: Analysis, Credit Eligibility, C4 Algorithm, Data Mining, Method  Abstrak: Penelitian ini menggunakan metode Algoritma C4.5 klasifikasi untuk menentukan kelayakan kredit, klasifikasi bertujuan untuk membagi objek yang ditetapkan ke dalam satu  nomor kategori yang disebut kelas. Dalam penelitian ini, penulis menggunankan data mining dan algoritma C4.5 sebagai metode pemilihannya. Kriteria yang digunakan yaitu , angsuran  pinjaman,penghasilan calon nasabah,jangka waktu pinjaman ,status calon nasabah. Penelitian ini menghasillkan model klasifikasi pohon keputusan menggunakan algoritma C4.5 termasuk dalam kategori Excellent Classification dengan nilai akurasi sebesar 98,33% dan klasifikasi eror 1,67%, sehingga penelitian ini kan menggunakan data latih 70% dan data uji 30%. Dari hasil perhitungan yang diperoleh menunjukan bahwa algoritma C4.5 dapat digunakan untuk menen tukan kelayakan pemberian kredit kepada nasabah Koperasi Jaya Bersama (KORJABE). Kata kunci: Algoritma C4.5, Analisis,  Data Mining, Kelayakan Kredit, Metode

2014 ◽  
Vol 543-547 ◽  
pp. 2024-2027
Author(s):  
Chang Jiang Zhu ◽  
Wen Kui Zheng

Network intrusion is shown in more and more concealment, and some intrusion data is potential with inclination property. This paper is aimed to mine the potential inclined intrusion data effectively, and ensure the security of large network. On the basis of the traditional fractional Fourier transform data mining method. An improved potential inclined intrusion accurate data mining algorithm is proposed. New algorithm can separate the time and frequency coupling effectively. The discrete fractional Fourier transform is implemented for the network intrusion data firstly. The data is gathered in the fractional Fourier domain, the inclined intrusion data accumulation is increased. The network signal interference is suppressed effectively. Simulation results show that the proposed data mining algorithm can extract the potential inclined intrusion data in strong concealment. The mining performance is much better than the traditional algorithm, and it can be applied in the network security defense area perfectly.


2020 ◽  
Vol 7 (3) ◽  
pp. 443
Author(s):  
Azahari Azahari ◽  
Yulindawati Yulindawati ◽  
Dewi Rosita ◽  
Syamsuddin Mallala

<p class="Abstrak">Prediksi  kelulusan  dibutuhkan  oleh  manajemen  perguruan  tinggi  dalam  menentukan kebijakan  preventif  terkait  pencegahan  dini  kasus drop  out. Lama masa studi setiap mahasiswa bisa disebabkan dengan berbagai faktor.  Dengan  menggunakan <em>data mining</em> algoritma <em>naive bayes</em> dan <em>neural network</em> dapat  dilakukan  prediksi  kelulusan  mahasiswa di  STMIK  Widya  Cipta  Dharma (WiCiDa) Samarinda . Atribut yang digunakan yaitu, umur saat masuk kuliah, klasifikasi kota asal Sekolah Menengah Atas, pekerjaan ayah, program studi, kelas, jumlah saudara, dan Indeks Prestasi Kumulatif (IPK). Sampel mahasiswa yang lulus dan <em>drop-out</em> pada tahun 2011 sampai 2019 dijadikan sebagai data <em>training</em> dan data <em>testing</em>. Sedangkan angkatan 2015–2018 digunakan sebagai data target yang akan diprediksi masa studinya. Sebanyak 3229 mahasiswa, 1769 sebagai data <em>training</em>, 321 sebagai data <em>testing</em>, dan 1139 sebagai data target. Semua data diambil dari data mahasiswa program strata 1, dan tidak mengikut sertakan data mahasiswa D3 dan alih jenjang/transfer.  Dari data <em>testing </em>diperoleh tingkat akurasi hanya 57,63%. Hasil penelitian menunjukkan banyaknya kelemahan dari hasil prediksi <em>naive bayes</em> dikarenakan tingkat akurasi kevalidannya tergolong tidak terlalu tinggi. Sedangkan akurasi prediksi <em>neural network</em> adalah 72,58%, sehingga metode alternatif inilah yang lebih baik. Proses evaluasi dan analisis dilakukan untuk melihat dimana letak kesalahan dan kebenaran dalam hasil prediksi masa studi.</p><div><div><p><em><strong>Abstract</strong></em></p><p class="Abstract"><em>Graduation predictions are required by the higher education institution preventive policies related to the early prevention of drop-out cases. The duration of study, for each student can be caused by various factors. By using the data mining algorithm Naive bayes and neural network, the student graduation in STMIK Widya Cipta Dharma (WiCiDa) can be predicted. The attributes used are as follows: age at admission, classification of cities from high school, father’s occupation, study program, class, number of siblings, and grade point average (GPA). Samples of students who graduated and dropped out between year 2011 and 2019 were used as training data and testing data. While the year class of 2015to 2018 is used as the target data, which will be predicted during the study period. According to the data mining algorithm Naive bayes, there are 3229 students; 1769 as training data, 321 as testing data, and 1139 as target data. All data is taken from students enrolled in undergraduate program and does not include data on diploma students and transfer student. From the testing data, an accuracy rate only 57.63%. The other side, prediction accuracy of the neural network is 72.58%, so this alternative method is the best chosen. The research results show the many weaknesses of the results of prediction of Naive bayes because the level of accuracy of its validity is not high. The evaluation and analysis process are conducted to see where the errors and truths are in the results of the study period predictions.</em></p><p><em><strong><br /></strong></em></p></div></div>


Author(s):  
Lai Lai Yee ◽  
Myo Ma Ma

Data mining is the task of discovering interesting patterns from large amounts of data where the data can be stored in databases, data warehouses or other information repositories. This can be viewed as a result of the natural evolution of information technology. The key point is that data mining is the application of these and other AI and statistical techniques to common business problems in a fashion that makes these techniques available to the skilled knowledge worker as well as the trained statistics professional. This paper is classification system for Toxicology using C4.5. Firstly, the input data are randomly partitioned into two independent data, a training data and a test data. And then two third of the data are allocated to the training data and the remaining one third is allocated to the test data. Final step is C4.5 Algorithm Process, the training data is used to derive C4.5 algorithm. Classification Process, test data are used to estimate the accuracy of the classification rules. If the accuracy is considered acceptable the rules can be applied to the classification of new data.


2018 ◽  
Author(s):  
Juna Eska

Wallpaper wallpaper or wallpaper wall is a wall decoration with a variety of motifs and colors. Wallpaper isused to change the appearance of a space to be more beautiful and has added value. Plain house walls tend tomake the occupants of the house feel bored because of the monotonous wall appearance. For that, having theinitiative to design the wall of the house with wallpaper into a bright idea that should be tried. Coloring thewalls of the house with wallpaper can add a beautiful impression on a room, so the room looks more expressive.Various motifs, colors, and wallpaper styles can be selected. Therefore, the seller must be more careful toprovide wallpaper which will be a lot of devotees, so it is necessary to recommend the type of wallpaper typeusing Classification method is done using data mining algorithm C4.5. data required is the best wallpaperbrand data, color, motif, material quality, size, and price. Algorithm C4.5 is a data classification algorithm oftype of decision tree. The decision tree The C4.5 algorithm is constructed with several stages including theselection of attributes as roots, creating branches for each value and dividing instances in branches. Thesestages will be repeated for each branch until all the cases on the branch have the same class. From thecompletion of the decision tree there will be some rules.


Author(s):  
Wenika Hidayati ◽  
Paska Marto Hasugian

The hospital is an agency engaged in health services in the which there are a number of special professions that can provide health services to the community items, namely doctors, Midwives and nurses and other professes. In this discussion, Arise and problems that can be raised into case studies to find out the results and information of each process in data mining Carried out with the C4.5 algorithm items, namely nurses. However, there are Several obstacles to Determine the nurses who will be declared passed or failed and accepted to work and can provide health services to the community, especially Patients who come for treatment. Therefore we need a method to identify nurses in a hospital. Data Mining with c4. 5 Algorithm can be used to the make predictions or classifications of nurses who are eligible to perform health services in hospitals by making decision trees based on existing data. This study aims to apply the data mining algorithm C4.5 in Determining nurses based on four attributes of used items, namely Accreditation, GPA, Age, and the value of each criterion has been determined in advance. The results of the study in the form of a decision tree Obtained from the data mining process with the C4.5 algorithm will provide information on the determination of nurses in the Sultan Sulaiman Regional Hospital.


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