<p><em>Penerapan metode Naïve Bayes dan C4.5 dibuat untuk digunakan terhadap seleksi dan klasifikasi calon pegawai yang berpotensi untuk masuk ke dalam kampus dengan cara membuat perhitungan dari persamaan pada setiap kriteria. Permasalahan yang sering ditemukan adalah tidak efektifnya penggunaan metode yang digunakan untuk menghasilkan penerimaan pegawai yang di perlukan sehingga belum sesuai dengan bidang keahlian bagi pelamar. Metode Naïve Bayes dan C4.5 tersebut merupakan metode klasifikasi yang diterapkan pada data mining. Tujuan dibuatnya penelitian ini untuk menentukan tingkat akurasi antara kedua metode tersebut berdasarkan ketepatan perhitungan Correctly Classified Instance dan Incorrectly Classified Instance. Pengujian metode pada penelitian ini dilakukan dengan menggunakan tools Weka 3.8. Hasil yang didapat Pada metode Naïve Bayes tingkat akurasi yang didapat 77,7778% dan C4.5 memiliki tingkat akurasi 94,444% dari 36 data latih berhasil diuji. Sehingga hasil yang didapat C4.5 merupakan metode yang lebih tepat di gunakan dari pada Naïve Bayes. </em></p>
Students are one of the important pillars in the life cycle of a university. In the process of developing, a university can be influenced by how many bachelor degree (S1) graduates from the university are. The number of graduations of a college sometimes has a low ratio when compared to the number of students admitted in the same school year. This low passing rate of students can be caused by several factors, such as the number of student activities that are participated in, economic factors, and several other unexpected factors. This makes a university must have a scheme or a formula that can predict whether the student can graduate on time. Normally, a bachelor (S1) student takes 8 semesters of education. But the existence of several factors that have been mentioned can make the time to take S1 education to be more, or even fail to graduate. This study will try to compare the results of the analysis of the two methods in the classification algorithm to predict student graduation. The algorithm used is the K-Nearest Neighbor and Naïve Bayes Algorithm. This study also aims to identify the best algorithm among the two classification algorithm choices. This research concluded that the Naïve Bayes algorithm has the same level of accuracy as the KNN algorithm in predicting the graduation of students in the Medical Education study program, which is 90%
The Gifted Young Scientists (GYS) in Indonesia in this decade are getting stronger. However, the minimum level of education makes equity in Indonesia not running optimally.
This Research aims to develop a decision support system that can facilitate the proposal selection process and provide an alternative ranking for the selection results of student creativity program proposal selection. This decision support system uses a combination calculation of the Simple Additive Weighting and Weigthted Product methods, hereinafter referred to as Modified SAW. The criteria used in this assessment refer to the 2020 Student Creativity Program Guidebook. The data used in this decision support system uses proposal selection data in the Student Creativity Development Unit of Muhammadiyah University of North Sumatra in 2019 for 2020. This system was developed by determining criteria and weight determination using the Simple Additive Weighting method and then make improvements to the weight and determine the preference value using the Weighted Product method. Each of the SAW and WP methods certainly has advantages and disadvantages. The advantages of SAW with a simple and simple ranking process, can be applied to decision-making cases such as in the recommendation of selecting proposals with various attributes. While the use of Weighted Product (WP) is often used because the weight is calculated based on the level of importance and can evaluate the set of attributes by multiplying all criteria with alternative results as well as the power between weights and alternative multiplication results. This WP method can also be used in assisting in recommendation of proposal selection based on what is needed by the University. By utilizing the advantages and disadvantages of each method, this combination is able to produce an accuracy of 91% for the SAW method, 97% for the accuracy using the WP and 99% for the accuracy value for the combination of the SAW and WP methods. This decision support system using MOD SAW can help facilitate the proposal selection process and provide alternative ranking results. Further research is suggested for the development of a decision support system for proposal selection using a combination of different methods between SAW and other methods.
Analisis cacat coran pada produk roda gila hasil proses pengecoran menggunakan cetakan pasir. Untuk memperbaiki cacat cor dari material besi tuang kelabu, maka dilakukan beberapa pengujian, yaitu: uji visual, pengecekan dimensi, uji metalografi dan uji mekanik. Metode pembuatan cetakan pasir menggunakan mesin double squeeze dengan variasi komposisi kadar air dan bentonit tertentu selanjutnya diteliti pengaruhnya terhadap hasil akhir produk cor. Hasil percobaan yang memberikan komposisi pasir yang optimum adalah: (a) kadar air = 4%, (b) kadar bentonit = 8,36%, (c) kuat geser (gr/cm2) = 370, (d) kuat tekan (gr/cm2) = 1.370, (e) kekerasan cetakan (N/mm2) = 19,85 – 21,4, (f) permeabilitas = 110. Berdasarkan hasil uji visual ada 7 jenis cacat coran, yaitu: (a) rongga gas, (b) pasir rontok, (c) inklusi terak, (d) penyinteran, (e) sirip, (f) kekasaran permukaan, (g) perluasan koreng. Dari pengecekan dimensi diketahui terdapat dua jenis cacat coran, yaitu: (a) pergeseran, (b) penyusutan dalam. Sedangkan hasil uji komposisi kimia dan harga kekerasan telah memenuhi standard JIS G4303 dan JIS 5501.
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