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"Peningkatan valuasi pasar dan grafik perdagangan menimbulkan fluktuasi harga yang tinggi dan berisiko. Oleh karena itu, pembentukan portofolio dan prediksi harga menjadi salah satu alternatif untuk mengurangi risiko investasi. Di sisi lain, metode forecast untuk instrumen investasi sangat banyak, oleh karena itu diperlukan model yang fit terbaik pada kondisi pasar Indonesia. Penelitian ini bertujuan untuk membandingkan akurasi prediksi model Long Short-Term Memory (LSTM) dan Autoregressive Moving-Average (ARMA) dalam membentuk portofolio optimal saham-saham Indeks Kompas100 periode Januari 2019 hingga Mei 2024. Data harga penutupan harian digunakan untuk membentuk portofolio dan memprediksi harga saham di masa mendatang menggunakan kedua model tersebut. Hasil penelitian menunjukkan bahwa model LSTM memiliki akurasi prediksi yang lebih tinggi dibandingkan ARMA dalam menangkap pola non-linear pada data saham, sehingga lebih unggul dalam kondisi pasar yang volatil. Sebaliknya, model ARMA lebih efektif dalam kondisi pasar yang stabil dan data yang stasioner. ARMA memiliki akurasi prediksi yang lebih buruk dari LSTM karena tidak bisa mewakili keseluruhan portofolio akibat keterbatasan stasioneritas data. Disisi lain akurasi LSTM lebih tinggi ARMA karena dapat mewakili keseluruhan portofolio.

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Increased market valuations and trading charts give rise to high and risky price fluctuations. Therefore, portfolio formation and price predictions are an alternative to reduce investment risk. On the other hand, there are many forecast methods for investment instruments, therefore a model that is best suited to Indonesian market conditions is needed. This research aims to compare the prediction accuracy of the Long Short-Term Memory (LSTM) and Autoregressive Moving-Average (ARMA) models in forming an optimal portfolio of Kompas100 Index shares for the period January 2019 to May 2024. Daily closing price data is used to form the portfolio and predict future stock prices using both models. The research results show that the LSTM model has higher prediction accuracy than ARMA in capturing non-linear patterns in stock data, so it is superior in volatile market conditions. In contrast, the ARMA model is more effective in stable market conditions and stationary data. ARMA has worse prediction accuracy than LSTM because it cannot represent the entire portfolio due to limited data stationarity. On the other hand, LSTM accuracy is higher than ARMA because it can represent the entire portfolio."

"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.  "

"Di era global sekarang, persaingan usaha antara perusahaan perusahaan besar semakain ketat. Mereka berlomba lomba merencanakan tujuan serta strategi bisnis mereka untuk menjadi yang terbaik. Pengukuran yang paling banyak digunakan dalam mengukur keberhasilan dari strategi mereka adalah dengan laporan keuangan seperti rasio-rasio penjualan dan ROI. Jika kita kaji lebih dalam lagi, masih banyak cara untuk mengukur performa dari perusahaan itu, seperti balance scorecard sebagai alat ukur dari keberhasilan perusahaan. Dalam metode ini perusahaan juga bisa mengukur performa perusahaan terhadap pembeli, proses bisnis internal, dan pertumbuhan perusahaan dalam mendapatkan ilmu baru dibidangnya. Dalam studi kasus ini penulis akan membahas lebih dalam tentang bagaimana balance scorecard membantu perusahaan dalam mempertajam tujuan dari persusahaan dalam perspektif yang berbeda.

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In these days, companies are more competitive than in the past of planning their strategic objective and measure the performance of the strategy. There is a traditional tool to measure the performance of a company, financial measure, was the only assessment for a company to find out its performance of the year by assessing its financial variables that support financial growth such as ROI, sales turnover and many more. There are a few numbers of measuring companies` performance from different perspective other than financial measures only. Using balance scorecard as a measurement tool for companies can help them to assess other three additional perspectives; customers, internal business processes, and learning and growth. In this report, balance scorecard helped a company to specify objectives of companies in four different perspectives."

"This concise textbook/reference examines the fundamental aspects of intelligent computing for surveillance systems, from camera calibration and data capturing, to secure data transmission. The text covers digital surveillance from the level of an individual object or biometric feature, to the full lifecycle of an event. This is followed by a detailed discussion on how an intelligent system can independently monitor and learn from an event, and invite human input when necessary. The book concludes with a presentation on how the computation speed of the system can be enhanced through the use of supercomputing technology."

"Penelitian ini bertujuan untuk menguji determinan dan dampak keberagaman dewan komisaris/direksi. Keberagaman dewan komisaris/direksi mencakup keberagaman gender, budaya, masa jabatan, tingkat pendidikan, keahlian dan kesibukan. Penelitian dilakukan menggunakan regresi linier berganda pada perusahaan yang terdaftar di Bursa Efek Indonesia pada tahun 2014 – 2018. Hasilnya adalah Indeks Keberagaman Dewan Komisaris (IKDK) terbukti lebih baik dalam meningkatkan kinerja perusahaan, dibandingkan IKDK berdasarkan Bernile et al. (2018) dan Indeks Keberagaman Direksi (IKD) menurunkan manajemen laba. Multinasionalitas perusahaan terbukti sebagai determinan IKD, dan siklus hidup perusahaan sebagai determinan dari IKDK. Tetapi, penelitian ini tidak dapat membuktikan bahwa IKDK dan IKD berpengaruh terhadap risiko perusahaan.

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This study aims to examine the determinant and the impact of the diversity of board of commissioners/directors. The diversity includes diversity in gender, culture, tenure, education level, expertise, and busy ness. The study was conducted using multiple linear regression on companies listed on the Indonesia Stock Exchange during 2014 – 2018. The results prove that Board of Commissioners Diversity Index (IKDK) is proven to be significantly better in increasing company performance, compared to board of commissioner’s diversity index based on Bernile et al. (2018) and Diversity Index (IKD) reduces earnings management. Multinationality of the company is proven as a determinant of the Board of Commissioners Diversity Index (IKDK), and the life cycle of the company as a determinant of the Board of Directors Diversity Index (IKDK). However, this study cannot prove that IKDK and IKD affect company risk."