No abstract

Saat ini perkembangan dunia digitalisasi semakin berkembang. Pada pengukuran panjang umumnya hanya bisa diukur melalui pengukuran manual yaitu mengukur perangkat yang ingin diketahui panjangnya. Namun, sekarang dunia digitalisasi mampu melakukan pengukuran tanpa menyentuh perangkat yang akan diukur. Salah satunya adalah dengan memanfaatkan sumber gelombang suara atau biasa disebut sebagai gelombang ultrasonik. Tujuan dari penelitian ini adalah untuk membuat prototype alat ukur jarak digital berbasis mikrokontroler Arduino Due menggunakan sensor HCSR04. Metode penelitian yang digunakan adalah metode uji perbandingan langsung dan pengukuran secara telemetri. Hasil pengukuran ditampilkan dalam perangkat komputer untuk memudahkan pembacaan. Perancangan ini dikendalikan melalui Arduino Due. Hasil pengujian prototype alat dapat berjalan dengan baik dan bisa diakses secara real time.

Developing a prediction model from risk factors can provide an efficient method to recognize breast cancer. Machine learning (ML) algorithms have been applied to increase the efficiency of diagnosis at the early stage. This paper studies a support vector machine (SVM) combined with an extremely randomized trees classifier (extra-trees) to provide a diagnosis of breast cancer at the early stage based on risk factors. The extra-trees classifier was used to remove irrelevant features, while SVM was utilized to diagnose the breast cancer status. A breast cancer dataset consisting of 116 subjects was utilized by machine learning models to predict breast cancer, while the stratified 10-fold cross-validation was employed for the model evaluation. Our proposed combined SVM and extra-trees model reached the highest accuracy up to 80.23%, which was significantly better than the other ML model. The experimental results demonstrated that by applying extra-trees-based feature selection, the average ML prediction accuracy was improved by up to 7.29% as contrasted to ML without the feature selection method. Our proposed model is expected to increase the efficiency of breast cancer diagnosis based on risk factors. In addition, we presented the proposed prediction model that could be employed for web-based breast cancer prediction. The proposed model is expected to improve diagnostic decision-support systems by predicting breast cancer disease accurately.

Telah dilakukan penelitian tentang uji perbandingan suhu dan kelembaban udara menggunakan alat sensor DHT22 berbasis arduino dengan thermohygrometer. Penelitian ini meninjau dari perkembangan teknologi yang semakin berkembang untuk memudahkan pengukuran suhu dan kelembaban menggunakan sensor DHT22 berbasis Arduino. Tujuan dari penelitian ini untuk mengetahui kinerja sensor dan membandingkan hasil pengukuran antara sensor DHT22 dan thermohygrometer. Metode penelitian yang digunakan yaitu metode perbandingan langsung ntara sensor DHT22 dan thermohygrometer standar. Percobaan ini dilakukan dengan metode repeatability sebanyak 5 kali pada masing-masing variasi suhu ruangan. Perbandingan hasil nilai kesalahan rata-rata pada pengukuran suhu dan kelembaban antara sensor DHT22 dengan Thermohygrometer standar menghasilkan nilai 2,99% untuk kelembaban dan-2,31% untuk suhu. Berdasarkan hasil tersebut dapat disimpulkan akurasi dikatakan baik dan dapat diterima karena sesuai dengan data sheet sensor DHT22 yaitu kelembaban yang terukur harus memiliki range antara 2-5% dan ±5 • C untuk nilai suhu.

Predicting future blood glucose (BG) level for diabetic patients will help them to avoid critical conditions in the future. This study proposed Extreme Gradient Boosting (XGBoost), an ensemble learning model to predict the future blood glucose value of diabetic patients. The clinical dataset of Type 1 Diabetes (T1D) patients was utilized and the prediction models were generated to predict future BG of 30 and 60 minutes ahead of time. The prediction models have been tested tofive children who develop T1D and showed that BG prediction model based on XGBoost outperformed other models, with average of Root Mean Square Error (RMSE) are 23.219 mg/dL and 35.800 mg/dL for prediction horizon (PH) 30 and 60 minutes respectively. In addition, the result showed that by utilizing statistical-based features as additional attributes, most of the performance of predictions model were increased.