Digital technology is now very sophisticated. Its use is widely applied in all areas of human life. Starting from waking up, human activities and others always use technology. In carrying out their activities, modern humans now almost all use vehicles as a mode of transportation. Today's vehicles use a variety of sensors as a sixth sense. The results of detection using sensors on the vehicle are usually displayed on the dashboard of the vehicle. Modern humans currently use sensors to complete their needs. Besides that, the internet of things technology is growing rapidly in its role and development to support the needs of modern humans. Micro-controller technology is also experiencing rapid and massive development. One of the most common and most popularly used microcontrollers is Arduino. In many streets in Indonesia, people still use vehicles not equipped with many sensors. One of them is a simple parking sensor that many old vehicles don't have. Parking sensor problems are needed at the time of parking so that the vehicle that will be parked does not hit other objects or vehicles. There are many types of ultrasonic sensors. The purpose of this research is to make a prototype ultrasonic sensor that is applied to vehicles and compare some of the most accurate ultrasonic sensors in measuring the distance between the vehicle and the object being measured.

Classification of ordinal data is part of categorical data. Ordinal data consists of features with values based on order or ranking. The use of machine learning methods in Human Resources Management is intended to support decision-making based on objective data analysis, and not on subjective aspects. The purpose of this study is to analyze the relationship between features, and whether the features used as objective factors can classify, and predict certain talented employees or not. This study uses a public dataset provided by IBM analytics. Analysis of the dataset using statistical tests, and confirmatory factor analysis validity tests, intended to determine the relationship or correlation between features in formulating hypothesis testing before building a model by using a comparison of four algorithms, namely Support Vector Machine, K-Nearest Neighbor, Decision Tree, and Artificial Neural Networks. The test results are expressed in the Confusion Matrix, and report classification of each model. The best evaluation is produced by the SVM algorithm with the same Accuracy, Precision, and Recall values, which are 94.00%, Sensitivity 93.28%, False Positive rate 4.62%, False Negative rate 6.72%,  and AUC-ROC curve value 0.97 with an excellent category in performing classification of the employee talent prediction model.

<p><strong>Abstrak.</strong> Kepuasan kerja pekerja sangat berhubungan dengan pekerjaan maupun kondisi dirinya ditempat kerja. Tingkat kepuasan kerja pekerja dapat di analisis dan menjadi bahan evaluasi perusahaan dalam menjalankan bisnis untuk mencapai target yang diinginkan. Kombinasi teknik clustering dan classification merupakan algoritma machine learning yang dapat membantu bagian Sumber Daya Manusia dalam menganalisis dan prediksi tingkat kepuasan kerja pekerja di perusahaan. Teknik clustering yang digunakan dalam penelitian ini adalah KMeans dan teknik classification menggunakan algoritma classificafier dari library Pycaret. Hasil analisis dari penggunaan teknik clustering dan classification dari ke-5 model classifier yang dipilih, 3 model yaitu LightGBM, Catboost dan XGBoost menunjukkan performa yang konsiten dan menghasilkan tingkat accuracy prediksi diatas 98% dengan jumlah cluster ideal 2, ncomponent 27, waktu proses rata-rata setiap model kurang dari 2 menit setiap tahapan proses dan menggunakan K-means clustering.</p><p>Kata kunci: <em>Kepuasan pekerja; Klaster; Klasifikasi; Pembelajaran mesin</em> <br /> <br /><em><strong>Abstract</strong>. Job satisfaction of workers is closely related to their work and conditions at work. The level of job satisfaction of workers can be analyzed and become an evaluation material for companies in running a business to achieve the desired target. The combination of clustering and classification techniques is a machine learning algorithm that can assist the Human Resources department with analyzing and predicting the level of job satisfaction of workers in the company. The clustering technique used in this research is K-Means in the classification technique using a binary classification algorithm from the Pycaret library. The results analysis of the clustering and classification techniques from the five selected classifier models, three models namely LightGBM, Catboost, and XGBoost shown consistent performance and the prediction accuracy levels above 98% with the ideal number of clusters 2, n-components 27, the average of processing time each model is less than 2 minutes each stage process and using K-means clustering.</em></p><p><em>Keywords: Employee Satisfaction; Clustering; Classification; Machine Learning</em></p>