AN ENHANCED SQL INJECTION DETECTION USING ENSEMBLE METHOD

SQL injection is a cybercrime that attacks websites. This issue is still a challenging issue in the realm of security that must be resolved. These attacks are very costly financially, which count millions of dollars each year. Due to large data leaks, the losses also impact the world economy, which averages nearly $50 per year, and most of them are caused by SQL injection. In a study of 300,000 attacks worldwide in any given month, 24.6% were SQL injection. Therefore, implementing a strategy to protect against web application attacks is essential and not easy because we have to protect user privacy and enterprise data. This study proposes an enhanced SQL injection detection using the voting classifier method based on several machine learning algorithms. The proposed classifier could achieve the highest accuracy from this research in 97.07%

Peningkatan Akurasi Klasifikasi Kemurnian Daging Sapi Berbasis Electronic Nose Dengan Menggunakan Ensemble Method

Daging sapi merupakan salah satu jenis daging yang sering dikonsumsi oleh manusia. Namun, pencampuran jenis daging sapi dengan daging lainnya seperti daging babi dilakukan dalam praktik jual beli dalam rangka mendapatkan keuntungan yang lebih. Hal ini tidak hanya mengurangi kepercayaan publik tentang keaslian daging juga membahayakan kesehatan dan melanggar aturan-aturan agam tertentu. Dalam penelitian ini, kami merancang dan mengusulkan sistem yang lebih akurat dalam melakukan klasifikasi kemurnian daging sapi berdasarkan data sampel aroma yang ditangkap oleh electronic nose. Sistem ini dibangun melalui tujuh tahap: pengambilan sampel data menggunakan electronic nose yang dibuat dari sensor gas dan Arduino; praproses data sensor; ekstraksi fitur statistik; hyperparameter tunning; seleksi fitur menggunakan ANOVA; klasifikasi menggunakan metode SVM, LDA dan MLP; dan peningkatan akurasi menggunakan ensemble method. Hasil penelitian menunjukkan bahwa sistem ini dapat membedakan daging sapi yang dicampur dengan daging babi dengan perbandingan 0%, 10%, 25%, 50%, 75%, 90%, dan 100% dengan akurasi 89,71% menggunakan Bagging MLP. ====================================================================================================== Beef is a type of meat that is often consumed by humans. However, mixing types of beef with other meats such as pork is carried out in buying and selling to get more profit. The adulteration undermines public belief in meat's authenticity and harms health, and violates specific religious rules. In this study, we designed and proposed a more accurate system for classifying beef purity based on the aroma sample data captured by the electronic nose. This system has seven stages: data sampling using an electronic nose made from the gas sensor and Arduino; preprocessing sensor data; statistical feature extraction; hyperparameter tunning; feature selection using ANOVA; classification using the SVM, LDA, and MLP methods; and improved accuracy using the ensemble method. The results showed that this system could distinguish beef mixed with pork with a ratio of 0%, 10%, 25%, 50%, 75%, 90%, and 100% with an accuracy of 89.71% using Bagging MLP

Automatic scheduling using Forward Chaining for Ethics Protocol Review

Research involving humans as the object has to follow ethics protocol. This protocol has to be reviewed by the research ethics committee before the research can be conducted. Though the mechanism for determining the protocol reviewer is simple, there are several obstacles, including the unbalanced workload of each reviewer, the empty schedule because the reviewer is busy or has other tasks, and the length of time it takes to determine the schedule manually. There needs to be a system that can see various obstacles and challenges in scheduling protocol reviewers and resolve them automatically. This paper proposes an automatic scheduling mechanism with a Heuristic Forward Chaining approach that can adjust the rules for determining reviewers from real experts and avoid the constraints that exist in the manual scheduling system. The proposed method is made in the form of a web application and can practically generate accurate schedules automatically