Penelitian ini dilatar belakangi oleh rendahnya siswa dalam kemampuan pemecahan masalah matematika. Agar kemampuan pemecahan masalah matematika siswa tidak rendah maka dibutuhkan model pembelajaran. Model pembelajaran yang dimaksud adalah model pembelajaran Problem Based Learning. Adapun rumusan masalah dalam penelitian ini yaitu “Apakah terdapat pengaruh model pembelajaran Problem Based Learning terhadap kemampuan pemecahan masalah matematika siswa kelas XI SMA Negeri 15 Surabaya”. Tujuannya adalah untuk mengetahui ada tidaknya pengaruh model Problem Based Learning terhadap kemampuan pemecahan masalah matematika siswa kelas XI SMA Negeri 15 Surabaya. Populasi dalam penelitian ini adalah seluruh siswa di kelas XI SMA Negeri 15 Surabaya tahun pelajaran 2019/2020. Sampel yang digunakan dalam penelitian ini yaitu kelas XI-IPA 3 sebagai kelompok eksperimen dan kelas XI-IPA 2 sebagai kelompok kontrol. Hasil penelitian secara keseluruhan menunjukkan bahwa berdasarkan analisis data nilai thitung adalah 2,15. Dengan menggunakan taraf signifikan α = 5% dan dengan derajat kebebasan (dk) = 63, ttabel = 1,9983, karena thitung > ttabel, maka H0 ditolak dan H1 diterima. Sehingga dapat ditarik kesimpulan bahwa ada pengaruh model pembelajaran Problem Based Learning terhadap kemampuan pemecahan masalah matematika siswa kelas XI SMAN 15 Surabaya.
Sebagai sebuah model, petri net merupakan grafik dua arah yang terdiri dari place, transisi serta tanda panah yang menghubungkan keduanya. Disamping itu, untuk merepresentasikan keadaan sistem, token diletakkan pada place tertentu. Ketika sebuah transisi terpantik, token akan bertransisi sesuai tanda panah. (Place) menggambarkan aktivitas pada suatu sistem, (Transisi) menggambarkan transisi atau perpindahan dari setiap aktifitas, dan Token (benda bulat di dalam ) menggambarkan status dari aktifitas state tersebut. Pada penelitian ini, dibahas mengenai aplikasi petri net pada sistem pengenalan sidik jari menggunakan pendekatan aljabar max plus. Pendekatan aljabar max-plus mampu menentukan dan menganalisa sifat sistem pengenalan sidik jari dengan sinkronisasi. Dalam proses identifikasi sidik jari, satuan waktu yang digunakan adalah detik, dimana merupakan awal waktu proses, dan merupakan waktu selesai proses, sehingga ketika seluruh proses identifikasi telah dilalui, diperoleh nilai optimum untuk setiap aktifitas state.
Machine Learning is increasingly popular among the public because of the many devices that are made using the capabilities of Machine Learning algorithms. Machine Learning's ability to process and analyze data quickly and accurately, as well as generate useful and relevant information for users, is the main reason for its popularity. Many machine learning algorithms have been used to solve various problems in society, including Deep Learning (DL). Deep Learning is an algorithm that works by representing data in layers of learning layers so that the representation becomes more meaningful. "Deep" in Deep Learning means that Deep Learning begins layers of sequential representation. This study aims to provide a reference on how to create a system and analyze the results of identifying face masks using deep learning algorithms. From the results of research conducted, it is known that this model can recognize faces well, both those who wear masks and those who do not use masks. This is evident from the average specificity and precision of 96.00%, and the average sensitivity or recall value of 93.47%. In addition, this model has also proven to be quite accurate in conducting overall classification with an average accuracy of 94.73%.
Handwritten recognition is how computer can identify a handwritten character or letter from a document, an image or another source. Recently, many devices provide a feature using handwritten as an input such as laptops, smartphones, and others, affecting handwritten recognition abilities become important thing. As the mother tongue of Muslims, and the only language used in holy book Al Qur’an, therefore recognizing in arabic character is a challenging task. The outcome of that recognizing system has to be quite accurate, the results of the process will impact on the entire process of understanding the Qur’an lesson. Basically handwritten recognition problem is part of classification problem and one of the best algorithm to solve it is Support Vector Machine (SVM). By finding a best separate line and two other support lines between input space data in process of training, SVM can provide the better result than other classify algorithm. Although SVM can solve the classify problem well, SVM must be modified with kernel learning method to be able to classify nonlinear data. However, determining the best kernel for every classification problem is quite difficult. Therefore, some technique have been developed, one of them is Multi Kernel Learning (MKL). This technique works by combining some kernel function to be one kernel with an equation. This framework built an application to recognize handwritten arabic numeral character using SVM algorithm that modified with Kernel Learning Method. The result shows that the application can recognize data well with average value of Accuracy is 84,37%
A drawdown is a tool for defining trading strategies for commodities, stocks, and investments. This analysis is one way of monitoring the decline in asset value over a certain period of time. This journal will discuss PT.MayoraTbk stock trading strategy. By analyzing the observed drawdown in the specified time period. The drawdown analysis here uses the feedback control on PT.MayoraTbk stock trading is assumed to follow the geometric Brownian motion. The data obtained is tested whether the data meets Brown's motion assumptions. Then the maximum drawdown expectation is determined at the selected time interval. An estimate is carried out for the maximum expected drawdown percentage of the share value. To test the validity of the estimation results, a Monte Carlo simulation is carried out. Monte Carlo simulation with the term Sampling Simulation or Monte Carlo Sampling Technique. This simulation sampling illustrates the possible use of sample data using the Monte Carlo method and also the distribution can be known or estimated. This simulation uses existing data (historical data) that is actually used in a simulation that includes inventory or sampling with a known and determined probability distribution, so this Monte Carlo simulation can be used. The basic idea of this Monte Carlo simulation is to generate or generate a value to form a model of the variables and study it.
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