Information security risk assessment (ISRA) and modeling has become a prominent topic in the last decade. ISRA methods have been developed by many researchers, showing that this issue is always on the lookout for review. Business process is a new perspective in ISRA domain. In this perspective, risk assessment is based on business processes rather than organization's assets. This research is aimed to conduct a systematic review of the ISRA model developed in recent years. Research papers from 2010 to 2017 were selected and examined in the context of information security risk assessment, modeling, and its relationship with business process management. In addition to the current taxonomy, new aspects were added to analyze these papers, i.e. risk context, adaptive ability, and model purpose. Based on analysis results, two research gaps in information security risk modeling were found. First, risk model should have comprehensive assessment method that considers vulnerability propagation and resource valuation in different resources level. Second, risk model should also be able to adapt to business process changes. In this paper, research challenges faced with respect to such issues are outlined and a new conceptual model for ISRA is proposed.

Background: Artificial intelligence (AI) has become increasingly prevalent in various industries, including IT governance. By integrating AI into the governance environment, organizations can benefit from the consolidation of frameworks and best practices. However, the adoption of AI across different stages of the governance process is unevenly distributed. Objective: The primary objective of this study is to perform a systematic literature review on applying artificial intelligence (AI) in IT governance processes, explicitly focusing on the Deming cycle. This study overlooks the specific details of the AI methods used in the various stages of IT governance processes. Methods: The search approach acquires relevant papers from Elsevier, Emerald, Google Scholar, Springer, and IEEE Xplore. The obtained results were then filtered using predefined inclusion and exclusion criteria to ensure the selection of relevant studies. Results: The search yielded 359 papers. Following our inclusion and exclusion criteria, we pinpointed 42 primary studies that discuss how AI is implemented in every domain of IT Governance related to the Deming cycle. Conclusion: We found that AI implementation is more dominant in the plan, do, and check stages of the Deming cycle, with a particular emphasis on domains such as risk management, strategy alignment, and performance measurement since most AI applications are not able to perform well in different contexts as well as the other usage driven by its unique capabilities. Keywords: Artificial Intelligence, Deming cycle, Governance, IT Governance domain, Systematic literature review

Effort estimation is pivotal for the triumph of software development endeavors. The appropriate forecasting approach is vital for aligning software project effort estimation outcomes. This process aids in efficiently distributing resources, charting project strategies, and facilitating informed choices in IT Project Management. Machine learning, a facet of artificial intelligence (AI), is dedicated to crafting algorithms and models that empower computers to enhance their performance based on data and facilitate predictions or decision-making. This study discusses the implementation of machine learning in software development effort estimation. We collected 558 relevant papers on software effort estimation and machine learning techniques. After a quality review process, we identified 40 articles for in-depth review. We categorized machine learning techniques into supervised, unsupervised, and reinforcement learning. The results indicate that using ensemble techniques in supervised and unsupervised learning can improve the accuracy of software effort estimation. Artificial Neural Networks, Regression, K-Nearest Neighbors, Decision Trees, Random Forest, and Bootstrap Aggregation are the most commonly used methods. Ensemble techniques also aid in selecting relevant features and preprocessing data to enhance model performance. This study provides insights into implementing machine learning techniques to estimate software effort and highlights the advantages of ensemble technique.

Information Technology Governance is currently widely implemented in companies. One of the domains that can be of concern is risk management. The application of TKTI in this domain can help companies identify, evaluate, reduce, and manage risks related to their business to achieve company goals better. In this case, three frameworks can be considered, including NIST, ISO 27001, and Octave, but implementing these frameworks only sometimes goes as planned. This study aims to identify the factors that cause the ineffectiveness of implementing Information Technology Governance (ITG) in the risk management domain using the NIST, ISO 27001, and Octave frameworks. Through an analysis of existing literature and data processing, this study found that factors such as lack of understanding of the framework, lack of adequate resources, and implementation challenges play an essential role in ineffectiveness. This study concludes by providing valuable insights for organizations seeking to strengthen their risk management capabilities.

A Service Level Agreement (SLA) is an agreement between a service provider and a customer that specifies what the customer can expect from the provider. Our stu

Model Klasifikasi banyak digunakan dalam rangka menganalisis dan menemukan jenis kategori kelas data. Salah satu bentuk pemanfaatan metode klasifikasi adalah mengklasifikasikan hasil penilaian pengukuran kinerja karyawan. Metode klasifikasi yang umum dan dapat digunakan antara lain adalah metode Decision Tree, Naive Bayes, -NN dan Random Forest. Namun tidak semua metode dapat menghasilkan performa yang baik dalam penilaian kinerja Karyawan. Sehingga perlu dilakukan optimasi misalnya melalui penggunaan seleksi fitur. Beberapa penelitian telah dilakukan optimasi metode klasifikasi melalui penggunaan metode seleksi fitur dalam penilaian kinerja karyawan. Namun optimasi ini dipengaruhi oleh karakteristik data yang digunakan. Tidak semua teknik seleksi fitur sesuai untuk meningkatkan hasil klasifikasi dan jumlah penggunaan fitur dapat mempengaruhi performa model klasifikasi. Penelitian ini mengusulkan teknik analisis penggunaan jumlah fitur pada data kinerja dosen melalui metode seleksi fitur ANOVA untuk meningkatkan performa model klasifikasi metode -NN. Tujuannya adalah untuk mendapatkan jumlah fitur yang terbaik dalam peningkatan performa metode klasifikasi -NN. Hasil penelitian menunjukkan bahwa jumlah fitur terbaik dari metode ANOVA adalah sejumlah 5 fitur dengan hasil akurasi klasifikasi -NN sebesar 0.839, precision 0.8323, recall 0.839 dan F1-score 0.833. Teknik analisis ini dapat digunakan oleh sebuah perusahaan dalam mengutamakan fitur terbaik dalam menilai kualitas kinerja karyawannya.