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Content Analysis Untuk Menetapkan Konsep Penting Financial Technology (FINTECH) 内容分析Untuk Menetapkan Konsep Penting Financial Technology (FINTECH)
Pub Date : 2020-02-07 DOI: 10.30998/faktorexacta.v12i4.5252
L. Harsono
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引用次数: 0
Klasifikasi Komentar Instagram untuk Identifikasi Keluhan Pelanggan Jasa Pengiriman Barang dengan Metode SVM dan Naïve Bayes Berbasis Teknik Smote 基于SVM方法和朴素贝叶斯Smote技术的Instagram评论分类用于识别送货服务的客户投诉
Pub Date : 2020-02-07 DOI: 10.30998/faktorexacta.v12i4.4981
Nanang Ruhyana, Didi Rosiyadi
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引用次数: 1
Analisis Sentimen Publik terhadap Sistem Zonasi Sekolah Menggunakan Data Twitter dengan Metode Naïve Bayes Classification 基于推特数据的朴素贝叶斯分类法对学校区划系统的公众情绪分析
Pub Date : 2020-02-07 DOI: 10.30998/faktorexacta.v12i4.5205
Rani Nooraeni, Amirah Balqis Safiruddin, A. Afifah, Krisna Dwi Agung, Nada Nabila Rosyad
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引用次数: 3
Implementasi Algoritma Naïve Bayes Classifier untuk Analisis Sentimen Customer pada Toko Online 实现Naïve Bayes分类器算法在网店顾客分析中的应用
Pub Date : 2020-02-07 DOI: 10.30998/faktorexacta.v12i4.5224
Prima Dina Atika, S. Suhadi
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引用次数: 1
SISTEM PEMESANAN MAKANAN DAN MINUMAN DI OSAKA RAMEN DEPOK BERBASIS JAVA 生产许可证持有人的安全和微小保护系统
Pub Date : 2020-02-07 DOI: 10.30998/faktorexacta.v12i4.4623
Harry Dhika, Robby Awaldi
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引用次数: 0
Analisis Pemilihan Supplier Menggunakan Metode Fuzzy Analytical Hierarchy Process (FAHP) pada PT XYZ 基于模糊层次分析法的供应商选择分析
Pub Date : 2020-01-31 DOI: 10.30998/faktorexacta.v12i4.5025
Endang Suhendar Mochamad Miftah Farid
Supplier selection is a multi-criteria issue where each criterion used has different interests and information about it is not precisely known. In this case the selection of suppliers based on low price offers is no longer efficient. To get maximum supply chain performance, it must combine other criteria that are relevant to the company's objectives. PT XYZ faces problems related to suppliers who are not yet stable. Therefore, it is necessary to evaluate the supplier's performance. AHP is a method used in the decision making process of a complex problem. The use of Fuzzy AHP is to accommodate the uncertainty that occurs when making decisions. Based on the results of calculations that have been done using the AHP fuzzy method and weighting assessment of supplier performance, it is found that PT IJB obtained a weighting value of 0.355 with each value for the quality criteria of 0.331, a price criterion of 0.246, a service criterion of 0.182, an accuracy criteria of an amount of 0.160, and a shipping criterion of 0.080. From the results of these values PT IJB is the best supplier for furniture raw materials at PT XYZ.
供应商选择是一个多标准问题,其中每个标准都有不同的利益,并且有关它的信息并不精确地知道。在这种情况下,基于低价报价的供应商选择不再有效。为了获得最大的供应链绩效,它必须结合与公司目标相关的其他标准。PT XYZ面临着与尚未稳定的供应商相关的问题。因此,有必要对供应商的绩效进行评估。层次分析法是一种用于复杂问题决策过程的方法。模糊层次分析法的使用是为了适应决策时出现的不确定性。基于AHP模糊法和供应商绩效权重评价的计算结果,发现PT IJB获得的权重值为0.355,质量标准为0.331,价格标准为0.246,服务标准为0.182,精度标准为0.160,运输标准为0.080。从这些价值的结果来看,PT IJB是PT XYZ家具原材料的最佳供应商。
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引用次数: 1
Penerapan Fuzzy C-Means Cluster dalam Pengelompokkan Provinsi Indonesia Menurut Indikator Kesejahteraan Rakyat 基于人民和平指标的印尼省分组中的模糊C均值聚类表达
Pub Date : 2019-11-22 DOI: 10.30998/faktorexacta.v12i3.4526
Nurfidah Dwitiyanti, Noni Selvia, Finata Rastic Andrari
Metode fuzzy c means clustering adalah salah satu teknik pengelompokkan data dalam satu klaster ditentukan oleh pusat cluster yang akan menandai lokasi rata-rata untuk tiap klaster. Tujuan dari penelitian ini akan dibahas tentang penerapan metode fuzzy c means cluster dalam pengelompokkan provinsi Indonesia berdasarkan indikator kesejahteraan rakyat. Berdasarkan hasil analisis pengelompokkan fuzzy c means dengan 2 klaster diperoleh fungsi objektif yang konvergen pada iterasi ke-18 adalah sebesar 130,7085. Pada klaster 1 yang dikategorikan sebagai kelompok kurang sejahtera terdiri dari 18 propinsi dan klaster 2 adalah kelompok sejahtera, terdiri dari 16 propinsi.
模糊方法c意味着聚类是将数据聚类在由聚类中心确定的一个聚类中的技术之一,该聚类中心将标记每个聚类的平均位置。本研究的目的是探讨模糊c均值聚类方法在印尼基于人口健康指标的省份分组中的应用。基于对具有2个聚类的模糊分组的分析结果,得到了在第18次迭代中收敛的目标函数为1307085。在第1组中,它被归类为一个由18个省组成的不太和平的群体,而第2组是一个由16个省构成的和平群体。
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引用次数: 8
SENTIMEN ANALISIS OPERASI TANGKAP TANGAN KPK MENURUT MASYARAKAT MENGGUNAKAN ALGORITMA SUPPORT VECHTOR MACHINE, NAÏVE BAYES, BERBASIS PARTICLE SWARM OPTIMIZITION KPK操作分析总结将手工操作引入算法支持向量机、朴素贝叶斯、BERBASIS粒子群优化
Pub Date : 2019-11-22 DOI: 10.30998/faktorexacta.v12i3.4992
Hernawati Hernawati, Windu Gata Kedua
It is known from various public sentiments conveyed through comments on social media twitter against the capture operations carried out by the corruption eradication commission (KPK) that currently it does not meet the expectations of the community, where officials who are only officials have small corruption rates, not corruption As for the classification algorithms that have strong accuracy at this time are Support Vector Machine and Naïve Bayes algorithms, calculation of Support Vector Machine method for tweet data from 78 positive tweet data and 78 negative tweet data, resulting in an accuracy of 80.77% and AUC 0.867. Whereas the results of accuracy with the Naïve Bayes method are 76.92% and AUC 0.729. Having a difference in accuracy of 3.3%, and after optimizing with the Operator Vector Machine (PSO) weight Particle Swarm Optimization the accuracy is 83.79% and AUC 0.910, while for Naïve Bayes (PSO) produces an accuracy of 80.13% and AUC 0.771 Has a difference in accuracy of 3.6%.Diketahui dari berbagai sentimen masyarakat yang disampaikan melalui komentar di media sosial twiter terhadap operasi tangkap tangan yang dilakukan oleh Komisi Pemberantasan Korupsi (KPK) nyatanya saat ini belum memenuhi harapan masyarakat, dimana pejabat yang di ott hanya pejabat yang mempunyai angka korupsi kecil, bukan korupsi yang besar adapun algoritma klasifikasi yang kuat akurasinya saat ini adalah algoritma Support Vector Machine untuk data tweet dari 78 data tweet positif dan 78 data tweet negatif, menghasilkan akurasi sebesar 80.77% dan AUC 0.867. Sedangkan hasil akurasi dengan metode Naïve Bayes adalah 76.92% dan AUC 0.729. Memiliki selisih akurasi sebesar 3.3%, dan setelah di optimalisasi dengan oprator Weight Partical Swarm Optimization untuk Support Vector Machine (PSO) menghasilkan akurasi 83.79% dan AUC 0.910, sedangkan untuk Naïve Bayes (PSO) menghasilkan akurasi sebesar 80.13% dan AUC 0.771 memiliki selisih akurasi sebesar 3.6%.
从社交媒体推特上对肃贪委抓捕行动的各种评论中可以得知,目前肃贪委的抓捕行动并没有达到社会的期望,只做官员的官员腐败率小,不腐败。目前准确率较强的分类算法是支持向量机和Naïve贝叶斯算法。用支持向量机方法对78条正推文数据和78条负推文数据进行推文数据的计算,得到准确率为80.77%,AUC为0.867。而Naïve贝叶斯方法的准确率为76.92%,AUC为0.729。准确率相差3.3%,使用算子向量机(PSO)加权粒子群优化后的准确率为83.79%,AUC为0.910,而对于Naïve,贝叶斯(PSO)产生的准确率为80.13%,AUC为0.771,准确率相差3.6%。这句话的意思是:“我的意思是说,我的意思是说,我的意思是说,我的意思是说,我的意思是说,我的意思是说,我的意思是说,我的意思是说,我的意思是说,我的意思是说,我的意思是说,我的意思是说,我的意思是说,我的意思是说,我的意思是说,我的意思是说,我的意思是说,我的意思是说,我的意思是说,我的意思是说,支持向量机(Support Vector Machine)将数据推文的78个数据推文为正,78个数据推文为负,得到的数据推文的80.77%和AUC为0.867。Sedangkan hasil akurasi dengan方法Naïve Bayes adalah 76.92%, AUC 0.729。权重粒子群优化算法支持向量机(PSO) menghasilkan akurasi 83.79%和AUC 0.910, Bayes (PSO) menghasilkan akurasi 80.13%和AUC 0.771 Memiliki selisih akurasi sebesar 3.6%。
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引用次数: 11
PERANCANGAN SISTEM INFORMASI DOCUMENT MONITORING SAMPLING PRODUCT PADA PT. XY DENGAN METODE PROTOTYPE
Pub Date : 2019-11-22 DOI: 10.30998/faktorexacta.v12i3.4678
Achmad Noeman, D. Handayani
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引用次数: 0
PERBANDINGAN KINERJA ALGORITMA DATA MINING PREDIKSI PERSETUJUAN KARTU KREDIT 数据挖掘算法预测信用卡支持率的比较
Pub Date : 2019-11-22 DOI: 10.30998/faktorexacta.v12i3.4310
Ipin Sugiyarto
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引用次数: 1
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Faktor Exacta
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