PENERAPAN ALGORITMA C4.5 DAN METODE FORWARD CHAININGUNTUK ANALISIS KINERJA DOSEN

Analisis kinerja dosen pada instansi pendidikan tinggi memiliki peran penting untuk mengembangkan sumber daya manusia bagi dosen di perguruan tinggi. Salah satu metode yang dapat digunakan dalam proses analisis kinerja adalah algoritma C4.5 dan metode Forward Chaining. Penelitian ini bertujuan untuk menerapkan algoritma C4.5 dan metode Forward Chaining untuk analisis kinerja dosen.Algoritma C4.5 dan metode Forward Chaining dapat digunakan untuk mendesain pohon keputusan, pohon keputusan tersebut dapat digunakan untuk melihat besarnya pengaruh dari setiap variabel meliputi pendidikan dan pengajaran, penelitian, pengabdian masyarakat dan kegiatan penunjang terhadap kinerja dosen sesuai kewajiban yang disyaratkan dalam sistem sasaran kinerja pegawai (SKP) dosen. Penelitian ini menghasilkan pohon keputusan yang dapat digunakan untuk analisis kinerja dosen, dari hasil analisis menunjukan bahwabidang penelitian memiliki pengaruh paling besar atas faktor yang menjadi alasan kenapa dosen tidak dapat memenuhi target kinerja yang diwajibkan. Kata kunci : Penambangan data, Pohon keputusan, Algoritma C4.5, Forward Chaining Method, Analisis kinerja dosen. Performance analysis of lecturers in higher education institutions has an important role to develop human resources for lecturers in universities. One method that can be used in the performance analysis process is the algorithm C 4.5 and the Forward Chaining method. The research aims to implement the C 4.5 algorithm and the Forward Chaining method for lecturer performance analysis. C 4.5 algorithm and Forward Chaining method can be used to design the decision tree, the decision tree can be used to see the magnitude of influence of each variable including education and teaching, research, community service and supporting activities to the performance of lecturers in accordance with the obligation required in the employee performance target system (SKP) lecturer. This research resulted in a decision tree that can be used for lecturer performance analysis, from the analysis showed that the field of research has the most influence on the reasons why the lecturer could not meet the target the required performance. Keywords :Data mining, Decision tree, C4.5 algorithm,Forward Chaining method, Lecturer performace analysis

IoT in healthcare: A scientometric analysis

This paper reviews scientific articles and patents about Internet of Things (IoT) in healthcare. The aim is to explore both the domain of research and the one of practice simultaneously. We compare the annual growth, the country production, and the trend topics of publications and patents, by focusing on the most relevant themes concerning the IoT in the healthcare industry. The analysis started with the selection of the publications and patents for the period 2015–2020. Since this comparative analysis between scientometric data in healthcare is new, the findings of this study can represent the basis for future studies to determine novel research opportunities on IoT. The study provides scholars with a better understanding of IoT research in healthcare and simultaneously extends knowledge of entrepreneurship in this field. Practitioners may benefit from this review to understand new and underexplored opportunities

Measuring Confidence in Classification Decisions for Clinical Decision Support Systems: A Gaussian Bayes Optimization Approach

This thesis generally investigated various aspects of designing and developing Clinical Decision Support Systems (CDSSs), but in particular exploited machine learning techniques in supporting medical diagnosis decisions. Having reviewed the fundamental functional components of existing modern CDSSs, it shows that most such systems were lacking a trusted decision evaluation module that provides reliable information about decision strengths. Therefore a refined CDSS system framework was first proposed, which centralises the concept of confidence-based classification by coupling eventual decision outcomes with a level of decision reliability. Based on measure theory, a unified Decision Score measure of the decision reliability was introduced, which combines the decision outcomes in terms of positive or negative signs together with the decision strength in percentage values. Furthermore, the behaviour of the proposed decision score measure was investigated in more complex and diverse feature spaces of high dimensionality, where the challenges of the “curse of dimensionality” are encountered. Such challenge was handled by revisiting the problem under orthogonal projections of the feature space, and have developed a new measure in performing quantified evaluations on the decision score measure, known as the Decision Sensitivity measure. The key influencing factors for the sensitivity of decisions were found to include not only the dimensionality of the selected features, but also the standard deviation of each feature used in the transformed orthogonal space. After the basic concept of the decision score measure is established, this thesis further extended the uses of the decision score measure in a multiple classifiers setting. This thesis first reviewed the principles and rationales behind various well-established information fusion schemes and tested their strengths and limitations in adapting the proposed decision score measure. Moreover, a correlation-based decision fusion scheme was proposed in maximising the potentials of the decision score measure in complex scenarios. Based on the evaluation results across different datasets, it proves that fusion schemes improve the robustness of the decision models while maintaining a good level of diagnostic accuracy in general. As clinical decision making normally faces new unseen cases and unpredictable challenges, it is essential to maintain a degree of adaptivity in a CDSS for post-deployment robustness of the system. Therefore, the last piece of the research reported in this thesis focused on investigating possible ways to refine the CDSS decision scores model in a time-efficient manner, spontaneously. In particular, this thesis reviewed several commonly used metrics and methods for monitoring and refining prediction models, and further adapted these methods to the proposed decision score measure