COMPARISON OF CLASSIFICATION ALGORITHM IN CLASSIFYING AIRLINE PASSENGER SATISFACTION

In order to revive the airline industry, which is being hit by the current recession, it is essential to restore passenger confidence in airlines by improving the services provided by airlines. With the influence of technology in all industrial fields, airlines can now use Machine Learning to find the essential points that can make passengers feel satisfied with airline services and classify passenger satisfaction. This study presents the making of Machine Learning models starting from Data Acquisition, Data Cleaning, Exploratory Data Analysis, Preprocessing, and Model Building. It is concluded that Random Forest is the best algorithm used in this case study, with an F1 accuracy score of 89.4, ROC-AUC score of 0.90, and a shorter modeling period than other algorithms used in this study

THE USE OF DATA AUGMENTATION AND EXPLORATORY DATA ANALYSIS IN ENHANCING IMAGE FEATURES ON APPLE LEAF DISEASE DATASET

Apples are an essential commodity produced in Batu City, Malang. In 2017, Batu City, Malang, produced 19.1 tons of apples, while in 2018, Batu City, Malang, produced 15.9 tons of apples. It can be concluded that the decline in the number of apple harvests in Batu City, Malang. With the influence of technology in agriculture, the influence of technology can be used to detect diseases on leaves to overcome the decrease in the number of harvests. With the Image Augmentation method used in this study, the existing dataset can have 6x more features. So that the healthy category, which previously had 516 image features, now has 3096 image features, the scab category, which previously had 592 image features, now has 3552 image features and the rust category, which previously had 622 image features, now has 3732 image features. With a dataset with 3000 image features, the model to be made can have a higher accuracy value. The model can be said to be sturdy/sturdy/good, or the model to be made can carry out the classification process with a good level of accuracy

Laptop Price Prediction with Machine Learning Using Regression Algorithm

Since the COVID-19 pandemic, many activities are now carried out in a Work From Home (WFH) manner. According to data from the Central Statistics Agency (BPS) of East Java, in 2021, large and medium-sized enterprises (UMB) who choose to work WFH partially are 32.37%, and overall WFH is 2.24% (BPS East Java, 2021 ). With this percentage of 32.37%, many people need a work device (in this case, a laptop) that can boost their productivity during WFH. WFH players must have laptops with specifications that match their needs to encourage productivity. To prevent buying laptops at overpriced prices, a way to predict laptop prices is needed based on the specified specifications. This study presents a Machine Learning model from data acquisition (Data Acquisition), Data Cleaning, and Feature Engineering for the Pre-Processing, Exploratory Data Analysis stages to modeling based on regression algorithms. After the model is made, the highest accuracy result is 92.77%, namely the XGBoost algorithm. With this high accuracy value, the model created can predict laptop prices with a minimum accuracy above 80%

Desain Prototype Smart Building Menggunakan Internet of Things dengan Protokol MQTT

Energy saving is the most wanted thing to prevent overspending in carrying out daily activities in the building. One form of energy savings is implementing Smart Building technology to control Air conditioners (AC) dan lamps according to its need. Methods used in this research were started with the Architectural Design of the devices, Managing of the devices, and their Decommission. The result carried in this research is that the prototype made was running well on low-scale implementation. Buttons in the website functioned well, even though there are still many problems when implementing the project on a huge scale because this research still uses a freeware-based MQTT broker

Comparison of Classification Algorithm in Predicting Stroke Disease

ABSTRAK- To prevent stroke, we need a way to predict whether someone has had a stroke through medical parameters. With the influence of technology in the medical world, stroke can be predicted using the Data Science method, which starts with Data Acquisition, Data Cleaning, Exploratory Data Analysis, Preprocessing, and the last stage is Model Building. Based on the model that has been made, it is concluded that the algorithm with the best performance, in this case, is XGBoost with a precision value of 0.9, a recall value of 0.95, an f1 value of 0.92, and a ROC-AUC value of 0.978 after receiving five folds of cross-validation. With these results, the model created can be used to make predictions in real-time. Kata kunci : Machine Learning, Logistic Regression, Random Forest, XGBoost, Strok

Perbandingan Algoritma K-Nearest Neighbors (K-NN) dan Random forest terhadap Penyakit Gagal Jantung

Penelitian ini bertujuan untuk membandingkan akurasi algoritma K-Nearest Neighbor (K-NN) dan Random Forest dalam mengklasifikasikan penyebab penyakit gagal jantung. Penyakit ini menjadi salah satu penyebab utama kematian di seluruh dunia dan kasusnya terus meningkat di Indonesia. Oleh karena itu, penanganan dan klasifikasi dini terhadap penyebab gagal jantung sangat diperlukan untuk mencegah penyakit tersebut. Penelitian ini diharapkan dapat memberikan informasi tentang metode terbaik untuk mengklasifikasikan penyebab penyakit gagal jantung serta memberikan manfaat bagi tenaga medis dan masyarakat umum dalam menjaga kesehatan jantung mereka

Analisis Perbandingan Decision Tree, Support Vector Machine, dan Xgboost dalam Mengklasifikasi Review Hotel Trip Advisor

Jaringan media sosial pada saat ini terus berkembang dan berdampak pada industri perhotelan. Pelanggan dan traveler telah memposting hasil review secara online untuk menunjukkan tingkat kepuasan mereka terhadap hotel dan berbagi pengalaman terkait hotel yang dikunjungi dengan pelanggan lain yang ada di seluruh belahan dunia. Situs web yang bergerak dalam pariwisata dan perhotelan berkembang pesat secara online seperti Trip advisor. Trip advisor merupakan platform penyedia layanan perjalanan dan pemesanan hotel. Penelitian menggunakan teknik analisis sentimen untuk mengkategorikan opini pengguna yang bernilai negatif maupun positif dengan bantuan kecerdasan buatan yaitu Machine Learning. Penelitian ini menguji tiga algoritma Machine Learning, yaitu Decision Tree Classifier, Support Vector Machine (SVM) dan Xgboost Classifier, dalam melakukan analisis sentimen terhadap review hotel di platform Trip advisor. Hasilnya menunjukkan bahwa Xgboost memiliki tingkat keakuratan (accuracy) yang paling tinggi, mencapai 99%, dibandingkan dengan Decision Tree (97%) dan Support Vector Machine (98%). Dengan demikian, Xgboost dianggap sebagai algoritma terbaik untuk melakukan analisis sentimen pada review hotel di Trip advisor.Â

Implementation of Greedy Algorithm for Profit and Cost Analysis of Swallow's Nest Processing Dirty to Finished Products

Swallow's nest is made from the saliva of swallows, especially species of swallows of the genus Collocalia. Swallow's nest is used traditionally to improve health so it is widely consumed by the community. Swallow nest products are difficult to produce, causing the product to be expensive. This study aims to analyze the costs and benefits of swallow nest production. The analysis uses the Greedy algorithm, which is looking for solutions to each stage of production. The principle of Greedy's algorithm is "take what you can get now". There are 6 processes in the production of swiftlet nests, namely sorting raw materials, cleaning, drying, printing, in process control (IPC) and packaging. In the sorting and cleaning process, employees in the medium and medium to light nest categories were combined. The total costs incurred in the sorting process are reduced by 14% and the costs incurred in the cleaning process are reduced by 8%. The process of drying dense and medium hair nests takes the same time so that they are carried out simultaneously and the required cost is reduced by 11% to Rp 675,000. The stages of printing the original and super types of nests are combined because they have