The public needs information about the predicted inflation rate for education costs to manage family finances and prepare education funds. This information is also beneficial for the government to create policies in education. Malang is one of the educational cities in Indonesia, but research on the prediction of the inflation rate of education costs in the city still needs to be made available. Besides, the researchers have yet to find previous studies on forecasting that used the specific inflation rate for education costs in Indonesia by applying the Deep Learning method, especially those using the Consumer Price Index (CPI) data for the Education Expenditure Group. This research aims to develop a model to forecast the inflation of education costs in Malang using the Deep Learning Method. This research was conducted using Consumer Price Index (CPI) data for the Education Expenditure Group in Malang during 1996-2021s taken from the Central Bureau of Statistics (BPS) Malang. The forecasting method used is the Long and Short-Term Memory (LSTM) method, which is a development of the Recurrent Neural Network (RNN). The results showed that it achieved the best accuracy by a model with one hidden layer and four hidden nodes, namely MAPE=2.8765% and RMSE=8.37.
KBPR Amanah adalah sebuah BPR (Bank Perkreditan Rakyat) yang terletak di Kecamatan Kepanjen, Kabupaten Malang yang menyediakan layanan kredit, tabungan, dan deposito. Selama ini penilaian kelayakan pengajuan kredit masih dilakukan dengan cara manual.KBPR Amanah Kepanjen merekrut Account Officer sebagai surveyor lapangan untuk melihat kondisi riil dari calon debitur. Kemudian data hasil survei diberikan kepada bagian kredit untuk diperiksa.Kepala Bagian Kredit memiliki kewenangan untuk memutuskan diterima atau tidaknya pengajuan kredit.Banyaknya calon debitur yang mengajukan permohonan kredit dengan kondisi yang berbeda-beda menyebabkan pengambilan keputusan penilaian kelayakan pengajuan kredit menjadi lebih sulit dan membutuhkan kejelian yang tinggi dari pengambil keputusan.Kekurangan pada metode penilaian kelayakan kredit konvensional ini adalah adanya human error yang membuat pengambilan keputusan menjadi kurang akurat.Hal inidapat meningkatkan resiko kredit macet akibat pengambilan keputusan yang kurang tepat mengenai penilaian kelayakan kredit.Maka, dibuat sebuah Aplikasi Sistem Pendukung Keputusan untuk menyelesaikan permasalahan tersebut. Aplikasi ini menggunakan metode TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) karena konsepnya mudah dimengerti, komputasinya efisien, dan memiliki kemampuan untuk mengukur kinerja relatif dari alternatif-alternatif keputusan dalam bentuk matematis yang sederhana. Sistem ini tidak menggantikan peran Bagian Kredit sebagai pengambil keputusan, melainkan dapat menjadi pendamping pengambilan keputusan mengenai kelayakan pengajuan kredit di KBPR Amanah.Dengan adanya aplikasi ini, pengambilan keputusan menjadi lebih efektif, serta menghasilkan keputusan yang terbaik bagi pihak debitur maupun pihak KBPR Amanah.Aplikasi Sistem Pendukung Keputusan ini telah diuji dengan membandingkan hasil keputusan manual dengan kaputusan yang dihasilkan sistem. Berdasarkan hasil pengujian tersebut, tingkat keakuratan SPK dalam penelitian ini mencapai 97,78%
The number of Virtual Machines in the Cloud Data Center may affect the value of Cloud Data Center's utilization. When there are too many Virtual Machines dedicated in the Physical Machines, the utilization is tend to be low. As the result, some idle Virtual Machines will consume much electricity [12]. However, the adequate number of Virtual Machines should be used to maintain the data center's performance, which is indicated by some QoS parameters, such as queue length, system response time, and drop rate. The system model which is observed this research insists of a Load Balancing and some Physical Machines which contain a number of Virtual Machines in each. However, the commonly used Markovian Model of Queuing Theory will be replaced by The General Model, which is more suitable to the characteristics of the Cloud Data Center. This research is aimed to get the optimum number of Virtual Machines in a Cloud Data Center to improve its performance. The simulation results show that 25 Virtual Machines in each Physical Machine are needed to obtain the optimum level of utilization and other indicator parameters.
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