Keterhubungan global melalui internet menyediakan akses ke dunia bisnis, salah satunya adalah sektor retail fashion. Agar dapat menjangkau konsumen dari berbagai lokasi, dapat bersaing dengan kompetitor dan konsumen merasa puas terhadap pelayanan yang diberikan. Studi ini dilakukan untuk mengukur kualitas layanan Website Retail Urban Icon dengan menggunakan metode Webqual 4.0 dan E-S-Qual. Metode Webqual mengukur kualitas dari segi kenyamanan pengguna terhadap sistem sedangkan E-S-Qual tidak hanya mengukur dari segi kenyamanan pengguna terhadap sistem tetapi juga mengukur dari segi proses pembelian dan keamanan data pengguna. Yang menjadi variabel yang diteliti adalah dimensi-dimensi dari metode Webqual dan E-S-Qual. Teknik analisis yang digunakan adalah teknik analisis deskriptif melalui Webqual Index (WQI). Hasil penelitian menunjukkan nilai kualitas Website Retail Urban Icon Urban Icon sebesar 0,761 berada pada kualitas baik dimana nilai WQI mendekati 1. Dalam meningkatkan nilai kualitas Website, pihak retail Urban Icon dapat fokus pada ddimensi Privacy atau yang berkaitan dengan keamanan data konsumen memiliki pengaruh besar terhadap kualitas Website dengan nilai WQI 0.81 dan dimensi effeciency atau yang berkaitan dengan kinerja kecepatan akses Website yang kurang berkontribusi terhadap kualitas Website dengan nilai WQI 0.72 dibandingkan dimensi lainnya.
Sepeda motor merupakan salah satu kendaraan yang paling digemari oleh masyarakat Indonesia. Pabrikan terus menawarkan sepeda motor dengan keunggulan yang beragam mulai dari kapasitas penyimpanan, jenis desain dan fitur-fitur unggulan lainnya dikarenakan banyaknya peminat dan permintaan dari masyarakat. Inovasi produk yang diluncurkan oleh pabrikan memberikan kesulitan bagi konsumen dalam menentukan sepeda motor yang akan dibeli. Setiap konsumen memiliki preferensi yang berbeda dalam memilih kendaraan., seperti fitur, model atau desain, harga dan tempat penyimpanan (bagasi/storage). Penelitian ini difokuskan untuk membangun sistem pendukung keputusan pemilihan sepeda motor matik. Metode Promethee adalah metode outranking yang diterapkan untuk memberikan hasil perangkingan dari alternatif yang ada sesuai dengan preferensi - preferensi terbaik. Hasil penelitian menunjukkan bahwa SPK yang dibangun dengan menggunakan metode promethee dapat membantu mempermudah dalam menentukan jenis motor matik yang sesuai dengan preferensi konsumen
The demand for forecasting task is very important to determine the number of stocks efficiently. This process should accommodate the demand for a company’s product or service and control the inventory level. Especially for products such as building materials that needed capitals to buy and wide space to keep it safe. This research has objective to minimize the excessive amount of product in inventory and minimize loss in sales. This study was compared between a method named Back Propagation Neural Network (BPNN) that known as one of the most accurate and widely used forecasting model and ARIMA as a time series model to find the most accurate in forecasting of inventory. In this case, the model of BPNN used 6 input neurons as a monthly period of sale, the price of the product, amount of historical selling, an approximation of project renovation, an approximation of new project building and number of a competitor. And for Arima method we have three trials of tentative models. To compare the accuracy between them, we used the performance criteria such as MAD, MAE, RMSE, RRSE and RAE. In this research, we obtained that forecasting with BPNN is more accurate than ARIMA with error prediction of 19.6, 19.6, 30.4, 0.6, 0.5 for those performance criteria consecutively.
Using computer networks in campus area which is open access will cause some problems at the speed to access the information. The allocation of bandwidth that provided sometimes does not match the needs of the client, so it takes an accurate prediction of bandwidth usage. This research obtained that Neural Network backpropagation modeling can solve the problem. The stages of research conducted the stage of training and testing phase. Data training is traffic data weekly and conducted by feed-forward back method, with max error 0.001, max hidden layer neuron 5000, constant momentum 0.95 and increase ratio 0.1. Before the data train is conducted, the scaling of the input and target values in the range of 0.1-0.9, then resumes the denormalization after the data train to return the data into Kb form. The results obtained from the training process in the form of comparison data, training performance, and regression. Furthermore, data testing, conducted by using a network that has been developed from the previous results. The test results are shown in the form of real data and predictive data using 8 input layers. In the prediction process, the mean square error generated is 0.0031792 which indicates a low error rate, so it can be stated that the resulting modeling has a level of output accuracy in predicting the use of computer network bandwidth is very high.
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