Penelitian ini dilakukan untuk mengumpulkan informasi mengenai biaya pemasaran marketing, promosi, rabat, biaya umr dan biaya dana operasional cabang untuk membuat keputusan yang lebih efektif, efisien, dan lebih mudah bagi manajemen. Metodologi penelitian yang digunakan untuk menulis makalah ini adalah metodologi analisis dengan observasi dan wawancara berdasarkan teori yang berasal dari beberapa buku terkait. Metode desain sistem yang disarankan dilakukan menggunakan metode dan desain berorientasi objek. Menerapkan dan menampilkan biaya sistem informasi pemasaran untuk kebutuhan yang sangat kompleks untuk menghasilkan laporan untuk pengambilan keputusan di masa depan.

In English : The banking world in terms of lending to customers is routine activities that are at high risk. In its execution, the problematic credit or bad credit is often due to the lack of careful credit analysis in the process of granting credit, as well as from poor customers. The purpose of this study is to implement data mining to assist in conducting credit analysis process in order to produce the right information whether the customer who will apply for the credit is worthy or not to be able to see the potential payment by the customer. The attributes used in this study consist of 11 attributes i.e. marital status, number of liabilities, age, last education, occupation, monthly income, home ownership, warranties, loan amount, length of loan and description as a result attribute. The methods of data collection used are observation, interviews, and documentation. The method used in this study is K-Nearest Neighbor (K-NN). From the results of evaluation and validation using the K-5 fold that has been done using the RapidMiner tools obtained the highest accuracy results from the K-Nearest Neighbor (K-NN) method of 93.33% in the 5th test. In Indonesian : Dunia perbankan dalam hal pemberian kredit kepada nasabah adalah kegiatan rutin yang mempunyai resiko tinggi. Dalam pelaksanaannya, kredit yang bermasalah atau kredit macet sering terjadi akibat analisis kredit kurang cermat dalam proses pemberian kredit, maupun dari nasabah yang tidak baik. Tujuan dalam penelitian ini ialah menerapkan data mining untuk dapat membantu melakukan proses analisis kredit agar dapat menghasilkan informasi yang tepat apakah nasabah yang akan mengajukan kreditnya layak atau tidaknya sehingga dapat melihat potensi pembayaran kredit yang dilakukan nasabah. Atribut yang digunakan dalam penelitian ini terdiri dari 11 atribut yaitu status perkawinan, jumlah tanggungan, usia, pendidikan terakhir, pekerjaan, penghasilan perbulan, kepemilikan rumah, jaminan, jumlah pinjaman, lama pinjaman dan keterangan sebagai atribut hasil. Metode pungumpulan data yang digunakan ialah observasi, wawancara, dan dokumentasi. Metode yang digunakan dalam penelitian ini adalah K-Nearest Neighbor (K-NN). Dari hasil evaluasi dan validasi menggunakan k-5 fold yang telah dilakukan menggunakan tools RapidMiner diperoleh hasil akurasi tertinggi dari Metode K-Nearest Neighbor (K-NN) sebesar 93.33% pada pengujian ke 5.

Human resources (HR) plays an important role for the sustainability of higher education therefore xyz universities in conducting the process of selection of employees requires a truly competent employee in their respective fields. The problem in this study is that the selection process of employees is still subjective not based on pure test results and there is no proper method in employee acceptance. The purpose of this research is to get the right staff according to the needs of the college. There are five criteria that will be used in this study are education, work experience, psychotest scores, age, and interview scores. The data collection methods used in this study are interviews, library studies, and observations. The research methods used are the Exponential Comparison Method (MPE) and for the calculation of mpe methods using the python programming language. The result of this study is that mpe method can be applied to determine the admission of employees at xyz college.

Thesis is a scientific work written by undergraduate students that discusses a particular topic or field based on the results of a literature review written by experts, the results of field research, or the results of development (experiments). The problem in this research is that the process of selecting the best thesis is still subjective without regard to other criteria and is also done manually which makes the selection process less efficient. The purpose of this research is to create a system for selecting the best student thesis. The method used in this research is the Composite Performance Index (CPI). Methods of data collection techniques by means of observation, interviews, documentation. The results of this study are the Composite Performance Index (CPI) method can be used for selecting the best student thesis and the results obtained for the first rank are A1 with a value of 218.75, the second rank is A2 with a value of 198.75 and the third rank is A4 with a value of 178.75.

The banking world in terms of providing credit to customers is a regular activity that has a large effect. In its application, non-performing loans or bad loans are often created due to poor credit analysis in the credit granting process, or from bad customers. The purpose of this study is to compare the results of algorithm accuracy between K-Nearest Neighbor (K-NN), Decision Tree, and Naive Bayes which results in the best accuracy will be implemented to determine creditworthiness. The attributes used in this study consisted of 11 attributes, namely marital status, number of dependents, age, last education, occupation, monthly income, home ownership, collateral, loan amount, length of loan and information as result attributes. The methods used in this research are K-Nearest Neighbor, Decision Tree, and Naive Bayes. From the results of evaluation and validation using k-5 fold that has been carried out using RapidMiner tools, the highest accuracy results from a comparison of 3 algorithms is using a decision tree (C4.5) of 98% in the 3rd test.