Journal of Mathematics Theory and Application最新文献

Penerapan Algoritma Branch and Bound dalam Optimalisasi Produk Tenun Sa’be (Studi Kasus: Toko Sutera Mandar Kec. Campalagian) 将分支与限制算法应用于最佳萨贝织布机。Campalagian)

Journal of Mathematics Theory and Application Pub Date : 2022-08-28 DOI: 10.31605/jomta.v4i1.1858

Nurjanna Nurjanna, Darma Ekawati, F. Fardinah

{"title":"Penerapan Algoritma Branch and Bound dalam Optimalisasi Produk Tenun Sa’be (Studi Kasus: Toko Sutera Mandar Kec. Campalagian)","authors":"Nurjanna Nurjanna, Darma Ekawati, F. Fardinah","doi":"10.31605/jomta.v4i1.1858","DOIUrl":"https://doi.org/10.31605/jomta.v4i1.1858","url":null,"abstract":"Setiap pelaku usaha atau pelaku ekonomi pasti melakukan prinsip ekonomi yaitu dengan modal yang sedikit mampu menghasilkan keuntungan maksimal, mengakibatkan munculnya masalah optimasi meliputi meminimumkan biaya atau memaksimumkan keuntungan dengan kapasitas sumber daya yang ada. Penelitian ini bertujuan untuk mengetahui optimalisasi produk tenun sa’be Toko Mandar Sutera menggunakan algoritma Branch and Bound. Metode yang digunakan dalam penelitian ini adalah algoritma Branch and Bound. Dalam penelitian ini diambil 11 jenis sarung, yakni sapeq, sapeq bocoq, pucuk, kotak-kotak, burberry, bunga kaiyang, arjuna, kucing garong, sandeq, lontara, dan malolo. Upaya optimalisasi keuntungan produk tenun sa’be memiliki beberapa kendala, yaitu persedian, bahan baku, waktu pembutan, jumlah karyawan, dan kapasitas gudang. Solusi awal diperoleh dengan menggunakan metode simpleks. Apabila hasilnya bernilai non integer maka dilanjutkan dengan algoritma Branch and Bound untuk mendapatkan solusi yang integer. Berdasarkan hasil penelitian diperoleh bahwa untuk mengoptimalkan produk tenun sa’be dengan keuntungan maksimal, maka Toko Mandar Sutera memproduksi produk jenis sapeq sebanyak 2 buah, burberry sebanyak 2 buah dan lontara sebanyak 1 buah dengan keuntungan produksi sebesar Rp460.000,00 per bulan","PeriodicalId":360082,"journal":{"name":"Journal of Mathematics Theory and Application","volume":"7 5-6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120890523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Penerapan Metode Extreme Gradient Boosting (XGBOOST) pada Klasifikasi Nasabah Kartu Kredit 信用卡客户分类的极端梯度助推器方法的应用

Journal of Mathematics Theory and Application Pub Date : 2022-08-28 DOI: 10.31605/jomta.v4i1.1792

Sri Elina Herni Yulianti, Oni Soesanto, Yuana Sukmawaty

{"title":"Penerapan Metode Extreme Gradient Boosting (XGBOOST) pada Klasifikasi Nasabah Kartu Kredit","authors":"Sri Elina Herni Yulianti, Oni Soesanto, Yuana Sukmawaty","doi":"10.31605/jomta.v4i1.1792","DOIUrl":"https://doi.org/10.31605/jomta.v4i1.1792","url":null,"abstract":"Bad credit card is a problem of inability of credit card users to pay credit card bills that can cause losses to both parties concerned. In order to avoid losses caused by bad credit cards, the provider must conduct a careful analysis of prospective or old customers using credit cards. This study aims to classify bad credit card customers using machine learning techniques, namely classification techniques. One of the classification techniques used is the XGBoost method which is useful for regression analysis and classification based on the Gradient Boosting Decision Tree (GBDT), the XGBoost method has several hyperparameters that can be configured to improve the performance of the model. Hyperparameter tuning method used is grid search cross validation which is then validated using 10-Fold Cross Validation. XGBoost hyperparameters configured include n_estimators, max_depth, subsample, gamma, colsample_bylevel, min_child_weight and learning_rate. Based on the results of this study proves that the use of algorithms with hyperparameter tuning can improve the performance of eXtreme Gradient Boosting algorithm in the process of classification of credit card customers with an accuracy of 80.039%, precision of 81.338% and a recall value of 96.854%. \u0000 \u0000Keywords: XGBoost, classification, Accuracy, Precision, Recall","PeriodicalId":360082,"journal":{"name":"Journal of Mathematics Theory and Application","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129124121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Some Fixed Point Results on Multiplicative Metric Spaces 乘法度量空间上的不动点结果

Journal of Mathematics Theory and Application Pub Date : 2022-08-28 DOI: 10.31605/jomta.v4i1.1845

A. Ansar, Muh. Akbar Idris

引用次数: 0

Hubungan Kemampuan Numerik Dan Kemampuan Verbal Siswa dalam Pembelajaran Matematika 学生在数学学习中的数字能力和语言能力的关系

Journal of Mathematics Theory and Application Pub Date : 2022-08-28 DOI: 10.31605/jomta.v4i1.1878

Andi Quraisy

引用次数: 0

K-Means Clustering dan Principal Component Analysis (PCA) Dalam Radial Basis Function Neural Network (RBFNN) Untuk Klasifikasi Data Multivariat K-Means聚类和主成分分析(PCA) Dalam径向基函数神经网络(RBFNN) Untuk Klasifikasi数据多元

Journal of Mathematics Theory and Application Pub Date : 2022-08-28 DOI: 10.31605/jomta.v4i1.1757

Hayqal Hazmi Qastari Hayqal, Oni Soesanto, Yuana Sukmawaty

{"title":"K-Means Clustering dan Principal Component Analysis (PCA) Dalam Radial Basis Function Neural Network (RBFNN) Untuk Klasifikasi Data Multivariat","authors":"Hayqal Hazmi Qastari Hayqal, Oni Soesanto, Yuana Sukmawaty","doi":"10.31605/jomta.v4i1.1757","DOIUrl":"https://doi.org/10.31605/jomta.v4i1.1757","url":null,"abstract":"Pada penelitian ini dilakukan uji simulasi data berskala besar sehingga diperlukan metode yang handal untuk permasalahan klasifikasi salah satunya adalah Radial Basis Function Neural Network (RBFNN). Untuk training data RBFNN menggunakan struktur khusus yang melibatkan dimensi tinggi pada hidden layer. Dengan struktur RBFNN yang khusus tersebut maka seringkali menimbulkan permasalahan karena hidden layernya terlalu besar, sehingga diperlukan penambahan metode penyederhaan jaringan seperti PCA dan K-Means Clustering. Metode PCA digunakan untuk mereduksi dimensi input pada RBFNN sedangkan metode K-Means Clustering digunakan untuk penentuan inisialisasi center awal pada RBFNN. Pada hasil percobaan metode PCA dihasilkan komponen utama ke-1 dan ke-2 dengan masing-masing mewakili 55.2288% dan 27.3108% dari seluruh variabilitas, secara kumulatif kedua komponen utama menyatakan sebesar 82.5396% dan hasil percobaan perulangan iterasi di metode penelitian ini didapatkan hasil rata-rata proses akurasi PC dan Klas terbaik berada pada PC-2 Klas-3 dengan akurasi di atas 90% untuk proses training dan testing dengan akurasi kesalahan klasifikasi di bawah 10%.","PeriodicalId":360082,"journal":{"name":"Journal of Mathematics Theory and Application","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128396548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0