Feature Selection of Network Intrusion Data using Genetic Algorithm and Particle Swarm Optimization

This paper describes the advantages of using Evolutionary Algorithms (EA) for feature selection on network intrusion dataset. Most current Network Intrusion Detection Systems (NIDS) are unable to detect intrusions in real time because of high dimensional data produced during daily operation. Extracting knowledge from huge data such as intrusion data requires new approach. The more complex the datasets, the higher computation time and the harder they are to be interpreted and analyzed. This paper investigates the performance of feature selection algoritms in network intrusiona data. We used Genetic Algorithms (GA) and Particle Swarm Optimizations (PSO) as feature selection algorithms. When applied to network intrusion datasets, both GA and PSO have significantly reduces the number of features. Our experiments show that GA successfully reduces the number of attributes from 41 to 15 while PSO reduces the number of attributes from 41 to 9. Using k Nearest Neighbour (k-NN) as a classifier,the GA-reduced dataset which consists of 37% of original attributes, has accuracy improvement from 99.28% to 99.70% and its execution time is also 4.8 faster than the execution time of original dataset. Using the same classifier, PSO-reduced dataset which consists of 22% of original attributes, has the fastest execution time (7.2 times faster than the execution time of original datasets). However, its accuracy is slightly reduced 0.02% from 99.28% to 99.26%. Overall, both GA and PSO are good solution as feature selection techniques because theyhave shown very good performance in reducing the number of features significantly while still maintaining and sometimes improving the classification accuracy as well as reducing the computation time

Dimensionality Reduction Algorithms on High Dimensional Datasets

Classification problem especially for high dimensional datasets have attracted many researchers in order to find efficient approaches to address them. However, the classification problem has become very complicatedespecially when the number of possible different combinations of variables is so high. In this research, we evaluate the performance of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) as feature selection algorithms when applied to high dimensional datasets.Our experiments show that in terms of dimensionality reduction, PSO is much better than GA. PSO has successfully reduced the number of attributes of 8 datasets to 13.47% on average while GA is only 31.36% on average. In terms of classification performance, GA is slightly better than PSO. GA†reduced datasets have better performance than their original ones on 5 of 8 datasets while PSO is only 3 of 8 datasets.Keywords: feature selection, dimensionality reduction, Genetic Algorithm (GA), Particle Swarm Optmization (PSO)

Upaya Meningkatkan Keterampilan Guru dalam Menyusun Instrumen Penilaian Melalui in House Training (IHT) pada Guru PAI SMA/SMK

Penelitian Tindakan Kepengawasan tentang Pendidikan Agama Islam yang dilaksanakan oleh guru guru yang ada di wilayah Kabupaten Garut. Penelitian dilakukan di wilayah Kabupaten Garut dengan melibatkan satu variabel bebas dan satu variabel terikat. Jumlah sampel seluruhnya 29 guru. Adapun metode penelitian yang digunakan menggunakan metode korelasional. Dari hasil penelitian sebanyak 29 guru diperoleh kesimpulan bahwa: 1) Terdapat hubungan yang signifikan antara meningkatkan keterampilan guru dengan menyusun instrumen penilaian sebesar 0,426; 2) Terdapat hubungan yang signifikan antara menyususun instrumen penilaian dengan in house training, sebesar 0,464; 3). Terdapat hubungan yang signifikan antara meningkatkan keterampilan guru dengan in house training sebesar 0,515. Hal ini menunjukkan bahwa semakin guru meningkatkan keterampilan dalam menyusun instrumen penilaian melalui in house training maka akan semakin baik pula instrumen penilaian yang dihasilkan

Unsupervised clustering approach for network anomaly detection

This paper describes the advantages of using the anomaly detection approach over the misuse detection technique in detecting unknown network intrusions or attacks. It also investigates the performance of various clustering algorithms when applied to anomaly detection. Five different clustering algorithms: k-Means, improved k-Means, k-Medoids, EM clustering and distance-based outlier detection algorithms are used. Our experiment shows that misuse detection techniques, which implemented four different classifiers (naïve Bayes, rule induction, decision tree and nearest neighbour) failed to detect network traffic, which contained a large number of unknown intrusions; where the highest accuracy was only 63.97% and the lowest false positive rate was 17.90%. On the other hand, the anomaly detection module showed promising results where the distance-based outlier detection algorithm outperformed other algorithms with an accuracy of 80.15%. The accuracy for EM clustering was 78.06%, for k-Medoids it was 76.71%, for improved k-Means it was 65.40% and for k-Means it was 57.81%. Unfortunately, our anomaly detection module produces high false positive rate (more than 20%) for all four clustering algorithms. Therefore, our future work will be more focus in reducing the false positive rate and improving the accuracy using more advance machine learning technique

EKSISTENSI ADR DALAM PENYELESAIAN SENGKETA HARTA WARIS PADA MASYARAKAT SUKU SAMAWA

Berkurangnya angka penyelesaian sengketa harta waris melalui pengadilan yang diajukan oleh masyarakat Suku Samawa, menimbulkan beberapa pertanyaan menyangkut penyelesaian sengketa harta waris melalui mekanisme alternative dispute resolution (ADR).  Apa saja  kasus sengketa harta waris  yang dapat diselesaikan menggunakan ADR? bentuk dan mekanisme kerja alternative dispute resolution (ADR) dalam penyelesaian sengketa harta waris? Dan efektifitas alternative dispute resolution (ADR) dalam penyelesaian sengketa harta waris?. Untuk mengkaji permasalahan tersebut penelitian yang digunakan dalam penelitian ini yakni penelitian hukum empiris dengan menggunakan pedekatan kualitatif. Hasil yang diperoleh dari analisis tersebut ditemukan bahwa sengketa harta waris yang dapat diselesaikan melalui jalur alternative dispute resolution (ADR) yakni sengketa harta waris berupa barang tidak bergerak yakni tanah waris, kemudian bentuk dan mekanisme penyelesaian sengketa harta waris menggunakan jalur mediasi, dimana peran kepala desa sebagai pihak penengah dalam penyelesaian masalah tersebut sehingga efektifitas penyelesaian menggunakan alternative dispute resolution (ADR) sangat efektif

Application of bagging, boosting and stacking to intrusion detection

This paper investigates the possibility of using ensemble algorithms to improve the performance of network intrusion detection systems. We use an ensemble of three different methods, bagging, boosting and stacking, in order to improve the accuracy and reduce the false positive rate. We use four different data mining algorithms, naïve bayes, J48 (decision tree), JRip (rule induction) and iBK( nearest neighbour), as base classifiers for those ensemble methods. Our experiment shows that the prototype which implements four base classifiers and three ensemble algorithms achieves an accuracy of more than 99% in detecting known intrusions, but failed to detect novel intrusions with the accuracy rates of around just 60%. The use of bagging, boosting and stacking is unable to significantly improve the accuracy. Stacking is the only method that was able to reduce the false positive rate by a significantly high amount (46.84%); unfortunately, this method has the longest execution time and so is insufficient to implement in the intrusion detection fiel

THE CORRELATION BETWEEN STUDENTS’ INTEREST IN SPEAKING AND THEIR SPEAKING ACHIEVEMENT

 AbstractThis study was aimed to find out the correlation between students’ interest in speaking and their speaking achievement at MAN 2 Filial Pontianak. There was two problems that need to be discussed in this research, first was how strong the correlation between students’ interest in speaking and their speaking achievement. Second, how much was the contribution of students’ interest through their speaking achievement. The researcher used purposive sampling technique to choose the sample. There were 21 students of the eleventh grade. Class B was chosen as the sample of this research. The instruments of this research were questionnaire and speaking score rubric. The researcher used Pearson product moment to calculate the data of students’ interest and speaking achievement. The result showed that there was significant or positive correlation and indicating a medium correlation. The contribution of students’ interest through their speaking achievement was in the amount of 35.6%. Therefore, this data was answered the research question of this research. Keyword: Correlation, Students’ Interest, Speaking Achievemen

Hospital Length of Stay Prediction based on Patient Examination Using General features

As of the year 2020, Indonesia has the fourth most populous country in the world. With Indonesia’s population expected to continuously grow, the increase in provision of healthcare needs to match its steady population growth. Hospitals are central in providing healthcare to the general masses, especially for patients requiring medical attention for an extended period of time. Length of Stay (LOS), or inpatient treatment, covers various treatments that are offered by hospitals, such as medical examination, diagnosis, treatment, and rehabilitation. Generally, hospitals determine the LOS by calculating the difference between the number of admissions and the number of discharges. However, this procedure is shown to be unproductive for some hospitals. A cost-effective way to improve the productivity of hospital is to utilize Information Technology (IT).  In this paper, we create a system for predicting LOS using Neural Network (NN) using a sample of 3055 subjects, consisting of 30 input attributes and 1 output attribute. The NN default parameter experiment and parameter optimization with grid search as well as random search were carried out. Our results show that parameter optimization using the grid search technique give the highest performance results with an accuracy of 94.7403% on parameters with a value of Epoch 50, hidden unit 52, batch size 4000, Adam optimizer, and linear activation. Our designated system can be utilised by hospitals in improving their effectiveness and efficiency, owing to better prediction of LOS and better visualization of LOS done by web visualization

Classification Algorithms of Maternal Risk Detection For Preeclampsia With Hypertension During Pregnancy Using Particle Swarm Optimization

Preeclampsia is a pregnancy abnormality that develops after 20 weeks of pregnancy characterized by hypertension and proteinuria.  The purpose of this research was to predict the risk of preeclampsia level in pregnant women during pregnancy process using Neural Network and Deep Learning algorithm, and compare the result of both algorithm. There are 17 parameters that taken from 1077 patient data in Haji General Hospital Surabaya and two hospitals in Makassar start on December 12th 2017 until February 12th 2018. We use particle swarm optimization (PSO) as the feature selection algorithm. This experiment shows that PSO can reduce the number of attributes from 17 to 7 attributes. Using LOO validation on the original data show that the result of Deep Learning has the accuracy of 95.12% and it give faster execution time by using the reduced dataset (eight-speed quicker than the original data performance). Beside that the accuracy of Deep Learning increased 0.56% become 95.68%. Generally, PSO gave the excellent result in the significantly lowering sum attribute as long as keep and improve method and precision although lowering computational period. Deep Learning enables end-to-end framework, and only need input and output without require for tweaking the attributes or features and does not require a long time and complex systems and understanding of the deep data on computing