HUBUNGAN POLA MAKAN IBU HAMIL DENGAN KEJADIAN ANEMIA PADA KEHAMILAN DI BPS SITI RAHMAH SAWAH PULO SURABAYA

Anemia pada kehamilan merupakan salah satu penyebab kematian ibu di Indonesia. Ibu hamil yang menderita anemia rata-rata mempunyai masalah pada pola makan misalnya yang tidak suka sayur.Tujuan penelitian untuk menganalisis hubungan pola makan ibu hamil dengan kejadian anemia pada kehamilan di BPS Siti Rahma Surabaya. Desain penelitian adalah analitik observasional dengan pendekatan cross sectional. Populasi penelitian semua ibu hamil yang berkunjung diBPS Siti Rahma Surabayasebesar 44. Menggunakan teknik simple random sampling sebesar 39 sampel. Pengumpulan data dengan kuesioner. Variabel bebas pola makan ibu hamil dan variabel terikat kejadian anemia. Pengumpulan data dengan kuesioner. Pengolahan data dengan teknikediting, coding, prosessing, cleaning, dan tabulating. Dianalisis dengan uji Chi Square, tingkat kemaknaan a = 0,05. Hasil penelitian menunjukkan bahwa sebagian besar 56,4% pola makan kurang pada ibu hamil dan hampir setengahnya 43,6% mengalami anemia. Hasil uji chi square p=0,034 berarti p<a, maka H0 ditolak ada hubungan pola makan ibu hamil dengan kejadian anemia. Simpulan dari penelitian ini sebagian besar ibu hamil yang pola makan kurang sebagian besar mengalami anemia. Oleh karena itu, diharapkan tenaga kesehatan dapat memberikan penyuluhan 2x dalam seminggu dan di adakan kelas hamil

Post-Stroke identification of EEG signals using recurrent neural networks and long short-term memory

Stroke often causes disability, so patients need rehabilitation for recovery. Therefore, it is necessary to measure its effectiveness. An Electroencephalogram (EEG) can capture the improvement of activity in the brain in stroke rehabilitation. Therefore, the focus is on the identification of several post-rehabilitation conditions. This paper proposed identifying post-stroke EEG signals using Recurrent Neural Networks (RNN) to process sequential data. Memory control in the use of RNN adopted Long Short-Term Memory. Identification was provided out on two classes based on patient condition, particularly "No Stroke" and "Stroke". EEG signals are filtered using Wavelet to get the waves that characterize a stroke. The four waves and the average amplitude are features of the identification model. The experiment also varied the weight correction, i.e., Adaptive Moment Optimization (Adam) and Stochastic Gradient Descent (SGD). This research showed the highest accuracy using Wavelet without amplitude features of 94.80% for new data with Adam optimization model. Meanwhile, the feature configuration tested effect shows that the use of the amplitude feature slightly reduces the accuracy to 91.38%. The results also show that the effect of the optimization model, namely Adam has a higher accuracy of 94.8% compared to SGD, only 74.14%. The number of hidden layers showed that three hidden layers could slightly increase the accuracy from 93.10% to 94.8%. Therefore, wavelets as extraction are more significant than other configurations, which slightly differ in performance. Adam's model achieved convergence in earlier times, but the speed of each iteration is slower than the SGD model. Experiments also showed that the optimization model, number of epochs, configuration, and duration of the EEG signal provide the best accuracy settings

Emotion brain-computer interface using wavelet and recurrent neural networks

Brain-Computer Interface (BCI) has an intermediate tool that is usually obtained from EEG signal information. This paper proposed the BCI to control a robot simulator based on three emotions for five seconds by extracting a wavelet function in advance with Recurrent Neural Networks (RNN). Emotion is amongst variables of the brain that can be used to move external devices. BCI's success depends on the ability to recognize one person’s emotions by extracting their EEG signals. One method to appropriately recognize EEG signals as a moving signal is wavelet transformation. Wavelet extracted EEG signal into theta, alpha, and beta wave, and consider them as the input of the RNN technique. Connectivity between sequences is accomplished with Long Short-Term Memory (LSTM). The study also compared frequency extraction methods using Fast Fourier Transform (FFT). The results showed that by extracting EEG signals using Wavelet transformations, we could achieve a confident accuracy of 100% for the training data and 70.54% of new data. While the same RNN configuration without pre-processing provided 39% accuracy, even adding FFT would only increase it to 52%. Furthermore, by using features of the frequency filter, we can increase its accuracy from 70.54% to 79.3%. These results showed the importance of selecting features because of RNNs concern to sequenced its inputs. The use of emotional variables is still relevant for instructions on BCI-based external devices, which provide an average computing time of merely 0.235 seconds

DESAIN PROTOTIPE PADA STARTUP TALENTKU MENGGUNAKAN METODE LEAN UX STARTUP

Talentku is a market validation survey based on the user need to connect the students who have talent and want to hone their talents with companies/organizations that need talent for external and internal activities. The company needs a User Interface (UI) and User Experiences (UX) design as a base building platform. Strategies needed to design the UI/UX to run efective and efficient by prioritizing Just in Time Production. So the problem is how to design the UI/UX of my talent platform effectively and efficiently. The solution used to solve the above problem is designing User Interfaces and User Experiences using Lean UX Startup method. This method is pros "faster, smarter UX, research and learning". Results derived from this research is a prototype final that is a combination of prototype A and B that are validated in terms of criticism and suggestion through the questionnaire. The result of prototype A was selected for 13 featured, and prototype B was selected for 2 featured. Color scheme matching hexa #FE706E, as a branding form in the application Talentku and the font is Segoe UI