Employee attrition can become a serious issue because of the impacts on the organization’s competitive advantage. It can become costly for an organization. The cost of employee attrition would be the cost related to the human resources life cycle, lost knowledge, employee morale, and organizational culture. This study aimed to analyze employee attrition using logistic regression. The result obtained can be used by the management to understand what modifications they should perform to the workplace to get most of their workers to stay. The data for the study were around four thousand employees, covering 261 days (one year working days) during 2015 — the data period between January and December. We use R for data integration, exploratory data analysis, data preparation, logistic regression, model evaluation, and visualization. The study has five steps: (1) data collection and business understanding, (2) data pre-processing, (3) exploratory data analysis, (4) model selection and training, and (5) test and evaluation of the model. The result of the study found eleven variables as key driving factors for employee attrition. It also showed that the model has 75 percent accuracy with 73 percent sensitivity and 75 percent specificity.
The reading performance of an analog thermometer, Liquid in Glass Thermometer (LiGT), can be improved using a digital camera. The aim is to minimize the human error on the reading of LiGT and increase the accuracy of temperature measurement results. In order to achieve an accurate result, a robust image processing method is required in the measurement. In this work, the LiGT image generated using a digital camera is analyzed using the technique in HSV color space which consists of some image processing methods (e.g., thresholding, morphology filter). The type of LiGT used is the glass thermometer with the colored liquid. There are three main parts to this developed technique process, i.e., identifying the scale of LiGT to calculate the pixel per temperature unit value (ppt), segmentation of the liquid column, and calculate the temperature based on the ppt value. Through simulation with a synthetic image, we demonstrate that the developed technique in this work has successfully read (measured) the temperature value of the LiGT (having a scale unit of 1oC) with a measurement error of 0.04oC. In the experimental results, we also report the developed technique performed on a real image of LiGT.
The purpose of this study is to demonstrate how to make air quality forecasting to predict the Nitrogen Dioxide quality index in the future. In this paper, we demonstrate exploratory data analysis and compare the performance of the Autoregressive Integrated Moving Average and Exponential Smoothing Model. We used R Language and R Studio to integrate all the datasets, exploratory data analysis, data preparation, performing Autoregressive Integrated Moving Average and Exponential Smoothing methods, model evaluation, and visualization. This study used data from the automatic remote air quality-monitoring station located in an urban area in Madrid, Spain. The dataset in the period from 1 January 2001 to 31 December 2017. The dataset recorded six pollutants such as Nitrogen Dioxide, Particulate Matter 10 micrometres, Sulphur Dioxide, Carbon Monoxide, Ozone and Particulate Matter 2.5 micrometres. In this study, we focus only on Nitrogen Dioxide pollutants. From our model, we saw that exponential smoothing has better accuracy compared to the Autoregressive Integrated Moving Average. We also exposed that Nitrogen Dioxide pollutant shows unhealthy for sensitive group’s level in November to March and has the lowest level in June and July.
Superhost Airbnb is an experienced host and provides excellent service to its customers. Superhost has features that can increase the number of bookings and revenue. SuperHost profile is one thing that can be used as a reference for other hosts to improve the quality of service. In this study, an analysis of exploration and visualization of Airbnb hosts' data in the city of Amsterdam and the city of Madrid to find out the profile of superhost from the aspect of price and consumer reviews. The city of Amsterdam and the city of Madrid are chosen because the two cities are the leading destinations for tourists in Europe. The study was conducted using a machine learning approach that has four work steps, namely understanding business processes, data retrieval, data processing, and exploratory analysis and data visualization. The tools used in this study are Jupyter Notebook with the Python programming language. The results obtained from this study are superhost in Madrid, mostly offering rental prices in the price range of $60 - $80. They get the highest reviews from customers based on cleanliness, communication, and check-in. As for Amsterdam, the superhost offers the most rental prices in the price range above $ 140. Superhosts in this price range gets the most reviews from customers in all review groups.
<p class="abstrak">Perkembangan Teknologi Infromasi, internet, dan perangkat bergerak telah mengubah perilaku konsumen dalam menjalankan aktivitasnya. Hal ini direspon oleh industri dengan menyediakan berbagai aplikasi berbasis web dan perangkat bergerak dalam interaksinya dengan pelanggan. Salah satu industri yang beradaptasi dengan perubahan teknologi dan perilaku konsumen ini adalah industri pariwisata dan perhotelan. Kebutuhan konsumen yang sebelumnya menggunakan akomodasi wisata tradisional seperti hotel, berubah menjadi lebih memilih rumah-rumah penduduk disekitar tempat wisata sebagai tempat penginapan sementara wisatawan. Perubahan ini berdampak kepada semakin banyaknya properti pribadi yang disewakan sehingga menyebabkan persaingan harga sewa. Harga sewa merupakan salah satu faktor penting yang dipertimbangkan calon penyewa dalam menentukan properti yang akan disewanya. Hal ini tentunya membuat para pemiliki properti harus memikirkan strategi penentuan harga sewa agar propertinya laku dipasaran. Penelitian ini bertujuan untuk mendapatkan fitur apa saja yang dapat mempengaruhi penentuan harga sewa properti berdasarkan data pengguna Airbnb di Berlin. Data penelitian diambil dari dataset yang disediakan oleh InsideAirbnb berupa file dengan format CSV. Penelitian dilakukan menggunakan teknik <em>machine learning</em> dengan pendekatan algoritma XGBoost. Terdapat lima tahapan pengerjaan dalam penelitian ini, yaitu <em>data understanding, data pre-processing, exploratory data analysis</em>, pemodelan, dan <em>insights</em>. Hasil yang didapatkan dari penelitian ini adalah <em>room type private room, room type entire home/apt</em>, dan <em>cancellation policy super strict 60 days</em> merupakan tiga fitur tertinggi yang mempengaruhi penentuan harga sewa. Luas properti menempati urutan keempat berdasarkan rekomendasi algoritma yang diterapkan.</p><p class="abstrak"> </p><p class="abstrak"><em><strong>Abstract</strong></em></p><p class="abstrak"><em>The development of information technology, the internet, and mobile devices has changed the behavior of consumers in carrying out their activities. The industry responded by providing various web-based and mobile applications in their interactions with customers. The tourism and hospitality industry is adapting to changes in technology and consumer behavior. The needs of consumers who previously used traditional tourist accommodations such as hotels have changed to prefer residents' houses around tourist attractions as their temporary lodging. This change has an impact on the increasing number of private properties being leased, causing competition in rental prices. It is undeniable that the rental price is one of the essential factors that prospective tenants consider in making choices. This certainly makes property owners, who will rent out their properties, have to think about rental pricing strategies. This study aims to obtain any features that affect pricing based on Airbnb user data in Berlin. The study was conducted using machine learning techniques with the XGBoost algorithm approach. There are five stages of work in this study, namely understanding data, pre-processing data, exploratory data analysis, modeling, and insights. The results obtained from this study are room type private room, room type entire home / apt, and cancellation policy type super strict 60 are the three highest features that affect price determination. Property size ranks fourth based on algorithmic recommendations.</em></p><p class="abstrak"><em><strong><br /></strong></em></p>
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