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"Pada perusahaan penerbangan, gangguan atau disrupsi adalah hal biasa terjadi. Sangat penting bagi industri penerbangan untuk memperkirakan atau memprediksi sumber gangguan/disrupsi untuk mengurangi biaya karena penundaan atau pembatalan jadwal keberangkatan. Ada banyak faktor gangguan pada maskapai yang menyebabkan penundaan atau pembatalan jadwal, seperti masalah mekanis pesawat (maintenance), kondisi cuaca, ketidakhadiran kru karena sakit, keamanan, dll. Dalam penelitian ini, peneliti fokus menyoroti disrupsi yang disesbabkan oleh ketidakhadiran pilot  / kru kokpit karena sakit. Metode yang digunakan untuk memprediksi kru kokpit yang sakit,didasarkan pada data yang diberikan pada periode sebelumnya. Classification and Regression Tree (decision tree) menggunakan fitur dari kru kokpit sebagai variabel untuk memprediksi ketidakhadiran pilot pada periode berikutnya.Data asli / real pada tahun 2017 digunakan sebagai data training dan data uji keakuratan model. Hasil penelitian menunjukkan bahwa data dari administrasi (data HR) dan data riwayat penyakit / riwayat ujian medis dapat menjadi prediktor untuk membangun model prediksi kru kokpit yang akan sakit. Dalam penelitian ini sebagian besar pilot yang memiliki riwayat sakit atau pernah gagal dalam ujian medis dan pilot yang ditugaskan lebih dari 78 jam penerbangan memiliki kemungkinan lebih besar untuk sakit di masa mendatang. Menurut penelitian juga, perusahaan juga dapat melakukan penghematan rata-rata Rp 900.000.000 per bulan jika dapat memprediksi jumlah pilot yang sesuai untuk menutupi pilot yang absen.Penelitian ini juga mengeksplorasi fitur yang membantu manajer maskapai menemukan karakteristik pilot yang akan absen karena sakit pada periode berikutnya dan menentukan jumlah kokpit kru cadangan yang harus disiapkan oleh maskapai untuk menghindari gangguan (delay atau cancel).

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In an airlines company, disruption is a common thing to happen during operations. It’s significant for the airline's industries to forecast or predict the source of disruptions to reduce the cost of schedule recovery due to schedule delay or cancel. There are many factors of disruption in the airlines which causes schedule delay or cancel, such as the mechanical problem of aircraft (maintenance), weather condition, crew sickness, security, etc. In this research, it highlights the absenteeism of the pilot due to sickness. A supervised learning method is proposed to predict the sickness of cockpit crew based on data given on the previous period. The classification and regression tree/decision tree algorithm use the feature of the cockpit crew as predictor variable to predict the future absenteeism of the pilot.

The real data in 2017 is used to train and test the accuracy of the model. The result shows that administrative or human resource and historical sickness data can be the predictor to build model for cockpit crew sickness prediction. In this research most pilot who has sick history or used to fail in medical exam and pilot who assigned more than 78 flight hours has more probability for being sick in the future period. According to the research approximately IDR 900.000.000 per month in average can be saved by company if it can predict the suitable number of reserved pilots to cover the absence pilot.This research also explores the other association rules that help the airline managers find the characteristics of the pilot which are going to be absent due to sickness in next period and determines the number of reserved crews should be prepared by airlines to avoid the disruption."

"This textbook presents a practical approach to predictive analytics for classroom learning. It focuses on using analytics to solve business problems and compares several different modeling techniques, all explained from examples using the SAS Enterprise Miner software. The authors demystify complex algorithms to show how they can be utilized and explained within the context of enhancing business opportunities. Each chapter includes an opening vignette that provides real-life example of how business analytics have been used in various aspects of organizations to solve issue or improve their results. A running case provides an example of a how to build and analyze a complex analytics model and utilize it to predict future outcomes."

"This book explains and explores the principal techniques of Data Mining, the automatic extraction of implicit and potentially useful information from data, which is increasingly used in commercial, scientific and other application areas. It focuses on classification, association rule mining and clustering.Each topic is clearly explained, with a focus on algorithms not mathematical formalism, and is illustrated by detailed worked examples. The book is written for readers without a strong background in mathematics or statistics and any formulae used are explained in detail.Each chapter has practical exercises to enable readers to check their progress. A full glossary of technical terms used is included.This expanded third edition includes detailed descriptions of algorithms for classifying streaming data, both stationary data, where the underlying model is fixed, and data that is time-dependent, where the underlying model changes from time to time - a phenomenon known as concept drift."

"Rumah menjadi tempat tinggal yang memiliki fungsi untuk memberikan rasa aman dan nyaman bagi penghuninya. Oleh sebab itu, pemilihan lokasi tempat tinggal menjadi penting, terutama bagi penduduk Jakarta, dimana Jakarta termasuk daerah rawan terhadap banjir. Banjir di Jakarta berdampak pada keamanan dan keselamatan hingga memberikan kerugian secara materil. Oleh karena itu, penelitian ini mengestimasikan property value harga rumah dengan mempertimbangkan lokasi tempat tinggal. Namun, penelitian ini juga akan menggunakan faktor penentu lokasi dalam pemilihan rumah lainnya, seperti atribut aksesibilitas dan atribut struktutal. Dalam pembuatan model estimasi ini akan menggunakan machine learning (ML) sebagai metodenya, yaitu Gradient Boosting Decision Trees (GBDT) dan Random Forest (RF), dengan optimasi Genetic Algorithm (GA) untuk meningkatkan kinerja model. Hasil penelitian ini menunjukkan GBDT dan RF memiliki performa sama baiknya dalam mengestimasi model property value rumah. Serta, penggunaan GA untuk meningkatkan kinerja model berhasil dengan meningkatnya nilai R², serta menurunnya nilai MAPE dan RMSE. Penelitian ini juga melihat faktor – faktor yang berpengaruh terhadap model, dengan luas tanah dan luas bangunan menjadi faktor paling berpengaruh, yang diikuti oleh MRT, rumah sakit, pusat perbelanjaan, tol, SMP, dan lokasi rawan.

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A home serves as a place of residence that provides a sense of safety and comfort for its occupants. Therefore, the selection of the location for a residence is crucial, especially for residents of Jakarta, as Jakarta is prone to flooding. Flooding in Jakarta impacts security, safety, and even material losses. Hence, this research aims to estimate the property value of houses by considering the location of the residence. Additionally, the research will incorporate other factors that influence housing selection, such as accessibility attributes and structural attributes. The estimation model will utilize machine learning (ML) techniques, specifically Gradient Boosting Decision Trees (GBDT) and Random Forest (RF), with Genetic Algorithm (GA) optimization to enhance the model's performance. The research findings indicate that both GBDT and RF perform equally well in estimating the property value model. Moreover, the use of GA to improve the model's performance is successful, as evidenced by an increase in the R2 value and a decrease in the MAPE and RMSE values. The research also examines the factors that influence the model, with land area and building area being the most influential factors, followed by proximity to the MRT, hospitals, shopping centres, toll roads, junior high schools, and flood-prone areas."

"This book is an introduction to both offensive and defensive techniques of cyberdeception. Unlike most books on cyberdeception, this book focuses on methods rather than detection. It treats cyberdeception techniques that are current, novel, and practical, and that go well beyond traditional honeypots.  It contains features friendly for classroom use: (1) minimal use of programming details and mathematicsm (2) modular chapters that can be covered in many orders, (3) exercises with each chapter, and (4) an extensive reference list. Cyberattacks have grown serious enough that understanding and using deception is essential to safe operation in cyberspace. The deception techniques covered are impersonation, delays, fakes, camouflage, false excuses, and social engineering. Special attention is devoted to cyberdeception in industrial control systems and within operating systems. This material is supported by a detailed discussion of how to plan deceptions and calculate their detectability and effectiveness. Some of the chapters provide further technical details of specific deception techniques and their application.  Cyberdeception can be conducted ethically and efficiently when necessary by following a few basic principles. This book is intended for advanced undergraduate students and graduate students, as well as computer professionals learning on their own. It will be especially useful for anyone who helps run important and essential computer systems such as critical-infrastructure and military systems.  "