Pub Date : 2020-02-07DOI: 10.30998/faktorexacta.v12i4.5252
L. Harsono
{"title":"Content Analysis Untuk Menetapkan Konsep Penting Financial Technology (FINTECH)","authors":"L. Harsono","doi":"10.30998/faktorexacta.v12i4.5252","DOIUrl":"https://doi.org/10.30998/faktorexacta.v12i4.5252","url":null,"abstract":"","PeriodicalId":53004,"journal":{"name":"Faktor Exacta","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48643604","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}
Pub Date : 2020-02-07DOI: 10.30998/faktorexacta.v12i4.4981
Nanang Ruhyana, Didi Rosiyadi
{"title":"Klasifikasi Komentar Instagram untuk Identifikasi Keluhan Pelanggan Jasa Pengiriman Barang dengan Metode SVM dan Naïve Bayes Berbasis Teknik Smote","authors":"Nanang Ruhyana, Didi Rosiyadi","doi":"10.30998/faktorexacta.v12i4.4981","DOIUrl":"https://doi.org/10.30998/faktorexacta.v12i4.4981","url":null,"abstract":"","PeriodicalId":53004,"journal":{"name":"Faktor Exacta","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47884733","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}
{"title":"Analisis Sentimen Publik terhadap Sistem Zonasi Sekolah Menggunakan Data Twitter dengan Metode Naïve Bayes Classification","authors":"Rani Nooraeni, Amirah Balqis Safiruddin, A. Afifah, Krisna Dwi Agung, Nada Nabila Rosyad","doi":"10.30998/faktorexacta.v12i4.5205","DOIUrl":"https://doi.org/10.30998/faktorexacta.v12i4.5205","url":null,"abstract":"","PeriodicalId":53004,"journal":{"name":"Faktor Exacta","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46119064","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}
Pub Date : 2020-02-07DOI: 10.30998/faktorexacta.v12i4.5224
Prima Dina Atika, S. Suhadi
{"title":"Implementasi Algoritma Naïve Bayes Classifier untuk Analisis Sentimen Customer pada Toko Online","authors":"Prima Dina Atika, S. Suhadi","doi":"10.30998/faktorexacta.v12i4.5224","DOIUrl":"https://doi.org/10.30998/faktorexacta.v12i4.5224","url":null,"abstract":"","PeriodicalId":53004,"journal":{"name":"Faktor Exacta","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46367194","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}
Pub Date : 2020-02-07DOI: 10.30998/faktorexacta.v12i4.4623
Harry Dhika, Robby Awaldi
{"title":"SISTEM PEMESANAN MAKANAN DAN MINUMAN DI OSAKA RAMEN DEPOK BERBASIS JAVA","authors":"Harry Dhika, Robby Awaldi","doi":"10.30998/faktorexacta.v12i4.4623","DOIUrl":"https://doi.org/10.30998/faktorexacta.v12i4.4623","url":null,"abstract":"","PeriodicalId":53004,"journal":{"name":"Faktor Exacta","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46800838","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}
Pub Date : 2020-01-31DOI: 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.
{"title":"Analisis Pemilihan Supplier Menggunakan Metode Fuzzy Analytical Hierarchy Process (FAHP) pada PT XYZ","authors":"Endang Suhendar Mochamad Miftah Farid","doi":"10.30998/faktorexacta.v12i4.5025","DOIUrl":"https://doi.org/10.30998/faktorexacta.v12i4.5025","url":null,"abstract":"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.","PeriodicalId":53004,"journal":{"name":"Faktor Exacta","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43857835","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}
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.
{"title":"Penerapan Fuzzy C-Means Cluster dalam Pengelompokkan Provinsi Indonesia Menurut Indikator Kesejahteraan Rakyat","authors":"Nurfidah Dwitiyanti, Noni Selvia, Finata Rastic Andrari","doi":"10.30998/faktorexacta.v12i3.4526","DOIUrl":"https://doi.org/10.30998/faktorexacta.v12i3.4526","url":null,"abstract":"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.","PeriodicalId":53004,"journal":{"name":"Faktor Exacta","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47535378","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}
Pub Date : 2019-11-22DOI: 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%。
{"title":"SENTIMEN ANALISIS OPERASI TANGKAP TANGAN KPK MENURUT MASYARAKAT MENGGUNAKAN ALGORITMA SUPPORT VECHTOR MACHINE, NAÏVE BAYES, BERBASIS PARTICLE SWARM OPTIMIZITION","authors":"Hernawati Hernawati, Windu Gata Kedua","doi":"10.30998/faktorexacta.v12i3.4992","DOIUrl":"https://doi.org/10.30998/faktorexacta.v12i3.4992","url":null,"abstract":"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%.","PeriodicalId":53004,"journal":{"name":"Faktor Exacta","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47148124","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}
Pub Date : 2019-11-22DOI: 10.30998/faktorexacta.v12i3.4678
Achmad Noeman, D. Handayani
{"title":"PERANCANGAN SISTEM INFORMASI DOCUMENT MONITORING SAMPLING PRODUCT PADA PT. XY DENGAN METODE PROTOTYPE","authors":"Achmad Noeman, D. Handayani","doi":"10.30998/faktorexacta.v12i3.4678","DOIUrl":"https://doi.org/10.30998/faktorexacta.v12i3.4678","url":null,"abstract":"","PeriodicalId":53004,"journal":{"name":"Faktor Exacta","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43985509","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：