Tumor otak menjadi salah satu penyakit yang paling mematikan, salah satu jenis yang paling banyak ditemukan adalah glioma sekitar 6 dari 100.000 pasien adalah penderita glioma. Citra digital melalui Magnetic Resonance Imaging (MRI) merupakan salah satu metode untuk membantu dokter dalam menganalisa dan mengklasifikasikan jenis tumor otak. Namun, klasifikasi secara manual membutuhkan waktu yang lama dan memiliki resiko kesalahan yang tinggi, untuk itu dibutuhkan suatu cara otomatis dan akurat dalam melakukan klasifikasi citra MRI. Convolutional Neural Network (CNN) menjadi salah satu solusi dalam melakukan klasifikasi otomatis dalam citra MRI. CNN merupakan algoritma deep learning yang memiliki kemampuan untuk belajar sendiri dari kasus kasus sebelumnya. Dan dari penelitian yang telah dilakukan, diperoleh hasil bahwa CNN mampu dalam menyelesaikan klasifikasi tumor otak dengan akurasi yang tinggi. Peningkatan akurasi diperoleh dengan mengembangkan algoritma CNN baik melalui menentukan nilai kernel dan/atau fungsi aktivasi.
The economy of a region is affected by the stability of food supplies. If the market price of the food supply is stable, the purchasing power level will increase. The price stability of food supplies can be anticipated by using the Support Vector Regression Method, to predict the Consumer Price Index, known as CPI. In the Consumer Price Index assessment, using data based on recording, measurement and calculation of the goods and services average price which consumed by households in a certain period of time. Goods and services that are deemed to represent household expenses are then averaged. The CPI in this study is a type of food supply issued by the Indonesian Central Statistics, and the input variable is taken from the prices of staple commodities in the city of Surabaya, Malang and Kediri based on data from the Siskaperbapo website. To get the supported vector data, the hyperplane maximized by the SVR concept. This concept is able to overcome the overfitting, in order to obtain more accurate prediction results. In predicting the Consumer Price Index, reference data is divided as training data 2016-2019 and testing data 2017-2020. All four kernels were used in the test, namely Spline kernel, Gaussian-RBF kernel, Linear kernel and Polynomial kernel. All four kernels are compared to see their MAPE, this can be shown by the Mean Absolute Percentage Error (MAPE) of less than three, if by using Gaussian RBF kernel. The smallest MAPE value showed by Malang CPI value, which is 1.8242 with C = 50, followed by Kediri with the MAPE value of 2.251 with C = 50 and MAPE value of Surabaya which is 2.5279 with C = 50.
Monitoring temperature and humidity in cold storage is very important to prevent damage to food raw materials. Checking the temperature in the cold storage is carried out every 4 hours with the results manually written on the log sheet by the working staff and also sometimes there are staff who do not close the cold storage door tightly so that it causes an increase in temperature. Therefore an IoT-based cold storage temperature monitoring system is created using Wemos D1 R2 in real time which is very necessary. The design of this prototype uses hardware, namely the WeMos D1 R2 microcontroller because it is based on ESP8266 which can be connected to WiFi, DHT22 temperature sensor, and HC-SR501 PIR sensor. The software used is blynk and google spreadsheet. The results of this prototype design are being able to read the cold storage temperature and humidity properly which can be seen in the blynk application on a smartphone by displaying the temperature and humidity values. This prototype also has a feature that sends notifications to smartphones when the temperature is read more than 5°C and has temperature data stored on Google spreadsheets. The conclusion of this study has succeeded in designing and making a prototype of an IoT-based cold storage temperature monitoring system. The testing of the prototype can work well. The results of applying the prototype to cold storage Room 21 obtained an average temperature of 2.9°C and an average humidity of 86%.
New and renewable energy has become the green energy trend in the electric power system going forward. The Bayu Power Station (PLTB) is one of the centers of electricity generation with a primary source of energy that is pollution-free and environmentally friendly. Currently in the South Sulawesi system, PLTB has operated with a total power of 70 MW at a base load of around 700 MW. Because PLTB is very dependent on wind conditions, the power generated becomes unstable and this has a significant intermittent effect on the stability of the system. The results of this study indicate that the intermittent composition of nuclear power plants is increasing from the April-July 2018 period. This ever increasing contribution disrupts frequency stability in the South Sulawesi system
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