<p class="Abstrak">Klasifikasi merupakan teknik dalam <em>data mining</em> untuk mengelompokkan data berdasarkan keterikatan data terhadap data sampel. Pada penelitian ini, kami melakukan perbandingan 9 teknik klasifikasi untuk mengklasifikasi respon pelanggan pada <em>dataset Bank Direct Marketing</em>. Perbandingan teknik klasifikasi ini dilakukan untuk mengetahui model dalam teknik klasfikasi yang paling efektif untuk mengklasifikasi target pada <em>dataset Bank Direct Marketing</em>. Teknik klasifikasi yang digunakan yaitu <em>Support Vector Machine</em>, <em>AdaBoost</em>, <em>Naïve Bayes</em>, <em>Constant, KNN, Tree, Random Forest, Stochastic Gradient Descent</em>, dan <em>CN2 Rule</em>. Proses klasifikasi diawali dengan <em>preprocessing</em> data untuk melakukan penghilangan <em>missing value</em> dan pemilihan fitur pada <em>dataset</em>. Pada tahap evaluasi digunakan teknik <em>10 fold cross validation</em>. Setelah dilakukan pengujian, didapatkan bahwa hasil klasifikasi menunjukkan akurasi terbaik diperoleh oleh model <em>Tree, Constant</em>, <em>Naive Bayes</em>, dan <em>Stochastic Gardient Descent</em>. Kemudian diikuti oleh model <em>Random Forest</em>, <em>K-Nearest Neighbor</em>, <em>CN-2 Rule</em>, <em>AdaBoost</em> dan <em>Support Vector Machine</em>. Dari keempat model yang menunjukkan hasil akurasi terbaik, untuk kasus ini <em>Stochastic Gradient Descent</em> terpilih sebagai model yang memiliki akurasi terbaik dengan nilai akurasi sebesar 0,972 dan hasil visualisasi yang dihasilkan lebih jelas untuk mengklasifikasi target pada <em>dataset Bank Direct Marketing</em>.</p><p class="Abstrak"><em><strong><br /></strong></em></p><p class="Abstrak"><em><strong>Abstract</strong></em></p>Classification is a technique in data mining to classify data based on the attachment of data to the sample data.. In this paper, we present the comparison of 9 classification techniques performed to classify customer response on the dataset of Bank Direct Marketing. The techniques performed to find out the effectiveness model in the classification technique used to classify targets on the dataset of Bank Direct Marketing. The techniques used are Support Vector Machine, AdaBoost, Naïve Bayes, Constant, KNN, Tree, Random Forest, Stochastic Gradient Descent, and CN2 Rule. The classification process begins with preprocessing data to perform missing value omissions and feature selection on the dataset. Cross validation technique, with k value is 10, used in the evaluation stage. After testing, it was found that the classification results showed the best accuracy obtained when using the Tree model, Constant, Naive Bayes and Stochastic Gradient Descent. Afterwards the Random Forest model, K-Nearest Neighbor, CN-2 Rule, AdaBoost, and Support Vector Machine are followed. Of the four models with the high accuracy results, in this case Stochastic Gradient Descent was selected as the best accuracy model with an accuracy value of 0.972 and resulting visualization more clearly to classify targets on the dataset of Bank Direct Marketing.
Inflation is an indicator that illustrated the economic condition of a country. This moneter phenomenon is signed with the increase of price in the entire case. It can cause an effect on the political sector which impacts economic stability in a nation. The importance of inflation control is very important due to the high and unstable inflation that will harm economic and social in society. One of the solutions to control the inflation rate is determining an appropriate monetary policy based on future prediction of the inflation rate. This research using SVR as machine learning that is being optimized by GA as an evolutionary algorithm as a predicting method. SVR can solve nonlinear regression problems to linear regression using the Kernel function that easies to implement. But, in SVR there is no general rule to set the parameters of SVR. Therefore, this research proposed to use GA to optimize the parameters of SVR. GA can solve the optimization problems in various research on economics prediction problem. Based on the testing that has been conducted, GA-SVR generates the MSE value is 0.03767, lower than SVR basic method is 0.053158. It proves that the GA-SVR method can be utilized for predicting.
Penurunan produksi tanaman buah apel disebabkan oleh semakin berkurangnya lahan untuk melakukan budidaya buah apel di kota Batu. Untuk memaksimalkan produksi, maka petani perlu memilih lahan yang tepat. Untuk memilih lahan yang tepat bukanlah hal yang mudah sehingga penggunaan Fuzzy Inference System (FIS) menggunakan metode Tsukamoto dapat mempermudah petani menentukan lahan yang layak untuk membudidayakan buah apel. Pada penelitian ini digunakan empat kriteria utama yang dibutuhkan untuk menentukan kesesuaian lahan yaitu curah hujan, kedalaman efektif perakaran, kelerengan, dan erosi. Output yang dihasilkan pada penelitian ini merupakan hasil penentuan lahan tanam dengan kategori sangat sesuai, sesuai marginal, cukup sesuai, dan tidak sesuai dengan kondisi masukan. Berdasarkan perhitungan akurasi sistem yang diukur menggunakan aturan yang didapatkan dari literatur pada penelitian ini menghasilkan tingkat akurasi sebesar 100%.
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