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

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

Federated Mining of Interesting Association Rules Over EHRs

Federated learning has a great potential to create solutions working over different sources without data transfer. However current federated methods are not explainable nor auditable. In this paper we propose a Federated data mining method to discover association rules. More accurately, we define what we consider as interesting itemsets and propose an algorithm to obtain them. This approach facilitates the interoperability and reusability, and it is based on the accessibility to data. These properties are quite aligned with the FAIR principles.

Klasifikasi Kebutuhan Jumlah Produk Makanan Customer Menggunakan K-Means Clustering dengan Optimasi Pusat Awal Cluster Algoritma Genetika

<p class="Abstrak">PT. Harum Bakery adalah salah satu perusahaan di Yogyakarta yang bergerak pada bidang produksi dan distribusi produk makanan roti. Setiap konsumen memiliki jumlah kebutuhan roti yang tidak teratur, sedangkan roti hanya dapat bertahan dalam waktu dua hari. Roti yang sudah berusia lebih dari dua hari akan diganti dengan yang baru oleh distributor, sehingga dapat menimbulkan kerugian bagi perusahaan. Penelitian ini mencoba untuk melakukan data mining dengan tujuan mengklasifikasikan jumlah produk makanan kepada <em>customer</em> menggunakan <em>k-</em><em>means clustering</em> dengan optimasi pusat awal <em>cluster</em> algoritma genetika. Pada penelitian ini digunakan 210 data dari penjualan produk selama tiga minggu. Data tersebut akan diproses dengan menerapkan metode data mining melalui tahap <em>preprocessing</em> kemudian tahap klasifikasi. <em>Preprocessing</em> yang dilakukan antara lain, data <em>transformation</em> dan <em>k-</em><em>means</em> <em>clustering</em>. Hasil dari <em>clustering</em> yang membutuhkan aturan tertentu lebih efektif dengan optimasi karena dari 210 data terdapat 200 data yang layak masuk tahap klasifikasi. Hasil dari pengujian mendapatkan akurasi terbaik sebesar 58.50 % dan <em>crossvalidation</em> untuk lima <em>fold</em> berhasil mendapatkan rata-rata akurasi sebesar 50.58% lebih besar 2.51 % dari KNN tanpa <em>preprocessing</em>.</p><p class="Judul2"><strong><em>Abstract</em></strong><em></em></p><p class="Judul2"><em>PT. Harum Bakery is one of the companies in Yogyakarta engaged in the production and distribution of bakery food products. Every consumer has an irregular amount of bread needs while bread can only last for two days. Bread that is more than two days old will be replaced by a new one by the distributor which causes losses for the company. This study tries to apply data mining to classify the number of customer needs for food products using k-means clustering with optimization initial cluster center genetic algorithm. In this study used 210 data from product sales for three weeks. Data will be processed by applying data mining method with preprocessing before going through classification. Preprocessing includes data transformation and k-means clustering. The results of clustering that require certain rules are more effective with optimization because 210 data have 200 data that are worth entering the classification stage. The results of the test get the best accuracy of 58.50% and crossvalidation for five fold managed to get an average accuracy of 50.58% greater than 2.51% of KNN without preprocessing.</em></p>

A Classification Algorithm: Data Mining and Mathematical Model

In this paper, we propose a classification algorithm based on Recency-Frequency-Monetary (RFM) model and K-means data mining method. In addition, the designed algorithm is verified by the experiments on the member data in a large shopping mall. The experiments results show that the proposed algorithm can provide an accurate classification of the members. Finally, some marketing strategies for different classes of members are given according to the classification results.

Data Mining Method for Identifying Biased or Misleading Future Outlook

In this study, we introduce a data mining method to identify biased and/or misleading outlooks for future performance of various factors, such as income, corporate profits, production, countries’ GDP, etc. The method consists of several components. One very important component involves building a general model, where the dependent variable is a factor suspected of projecting an over-optimistic impression in some records. Explanatory variables in the model are viewed as representing the potential for the satisfactory performance of the dependent variable. The second component involves evaluating the potential for the individual records of interest (specific countries, corporations, production facilities, etc.), and allows us to identify possible gaps between the upbeat/optimistic projections into the future (of the dependent variable) versus low and/or declining potential. In other words, low and/or declining potential basically tells us that the optimistic future performance of the dependent variable is unattainable, and could also represent misleading or deceitful information. The important novelty of this study is the capability to identify a highly exaggerated outlook of future performance, by utilizing a soft regression tool and the concept of “performance potential”. The process is explained in detail, including the conditions for successful evaluations. Case studies to evaluate expected economic success are presented.