Phishing has become a serious and concerning problem within the past 10 years, with many reviews describing attack patterns and anticipating different method utilizations. This indicates that the results are still not comprehensive, subsequently leaving a critical gap in phishing reports. Therefore, this study aims to conduct a systematic review, to show a more crucial issue in phishing attacks, namely classification techniques. These issues were categorized into techniques, datasets, performance evaluation, and phishing types. The obtained results are expected to help developers prevent future phishing attacks more effectively, especially in selectively and carefully determining the techniques and evaluations to address specific types of phishing.

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The development of autonomy University drives management innovation to increase the alternative sources of income with the purpose of the efficiency improvement and productivity of the institution. One of a management model that leads to increase productivity through cost reduction is Lean service. The implementation of Lean Higher Education Institution (LHEI) requires total involvement of organization maneuver, including social culture, infrastructure, and leadership support. Therefore, the readiness of the institution in welcoming Lean concepts becomes significant. This article tried to develop a prototype of an intelligent performance measurement tool by analyzing the readiness indicators using the Analytical Hierarchy Process (AHP) method. This tool provided the classification of organizational readiness into five performances level. The measurement performed as a Decision Support System (DSS) to recommend University management level in making a decision and correcting action towards the optimal execution of Lean service. As a case study, this prototype system has been tested with Black Box and User Acceptance Test (UAT) in Indonesia Islamic Higher Education Institution. The finding reveals that the prototype system can be used as a performance measurement tool in measuring the readiness of Lean's service in Islamic Higher Education Institution.

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Klasifikasi merupakan teknik pengelompokkan data sesuai dengan karakteristik data yang telah ditentukan. Hasil performansi akurasi dapat menjadi ukuran keakuratan metode yang digunakan dalam proses klasifikasi. Teknik pengambilan data yang tidak sesuai  dapat mengurangi hasil akurasi. Pada penelitian ini menggunakan metode Learning Vector Quantization (LVQ) 1, 2, dan 3 untuk melihat keakuratan metode klasifikasi dengan menggunakan teknik  pengambilan data sampling. Data yang digunakan merupakan data pengukuran tulang tengkorak laki-laki dan perempuan yang berjumlah 2524 data. Pada LVQ 1 mendapatkan akurasi terbaik yaitu 91.39% dengan learning rate 0.1, 0.4, 0.7, 0.9. LVQ 2 mendapatkan akurasi terbaik 77.05% dengan learning rate 0.9 dan window 0.2. LVQ 3 mendapatkan akurasi terbaik yaitu 80.04% dengan learning rate 0.7, window 0.1, dan epsilon 0.3. Hal ini menunjukkan bahwa LVQ 1 lebih tepat untuk diterapkan terhadap multi-fitur pada dataset William W. Howells Craniometric dibandingkan LVQ 2 dan LVQ 3.