Keystroke Dynamic Authentication used a behavior to authenticate the user and one of biometric authentication. The behavior used a typing speed a character on the keyboard and every user had a unique behavior in typing. To improve classification between user and attacker of Keystroke Dynamic Authentication in this research, we proposed a combination of MHR (Mean of Horner’s Rules) and standard deviation. The results of this research showed that our proposed method gave a high accuracy (93.872%) than the previous method (75.388% and 75.156%). This research gave an opportunity to implemented in real login system because our method gave the best results with False Acceptance Rate (FAR) is 0.113. The user can be used as a simple password and ignore a worrying about an account hacking in the system.

Madrasah Muhammadiyah Al-Munawarroh adalah madrasah yang terletak di Malang dan salah satu madrasah yang berkembang. Untuk menunjang perkembangan tersebut, dibuat sebuah website profile yang dapat memberikan informasi kepada masyarakat secara cepat. Metode yang digunakan dalam pembuatan website adalah metode RAD (Rapid Application Development). Metode ini digunakan karena kami ingin melibatkan pihak madrasah dalam pembuatan website, agar website yang dibuat dapat bermanfaat secara penuh dan sesuai dengan kebutuhan penyebaran informasi bagi madrasah. Hasil dari pembuatan website profile menunjukkan bahwa pihak madrasah dapat dengan mudah menyebarkan informasi-informasi penting seperti jadwal pendaftaran siswa, informasi mengenai madrasah hingga informasi lowongan pekerjaan yang ada di madrasah.

A significant method should be deployed in OpenFlow environment for reducing the complexity during the implementation of IPv6 neighbor discovery protocol (NDP) in multicast manner. This paper was performed for deploying reactive-based application in controller’s northbound layer for handling as well as cutting the Neighbor solicitation packet’s journey. The application had a capability for storing each of the incoming Neighbor Solicitation (NS) and Neighbor Advertisement (NA) packet information. Therefore, the controller could reply the NS packet directly by using OFPT_PACKET_OUT message that contained the NA packet extracted from the reactive application. The experiment’s result showed that the proposed approach could reduce the NS response time up to 71% than the normal result produced by the traditional/learning switch application.

Account security was determined by how well the security techniques applied by the system were used. There had been many security methods that guaranteed the security of their accounts, one of which was Keystroke Dynamic Authentication. Keystroke Dynamic Authentication was an authentication technique that utilized the typing habits of a person as a security measurement tool for the user account. From several research, the average use in the Keystroke Dynamic Authentication classification is not suitable, because a user's typing speed will change over time, maybe faster or slower depending on certain conditions. So, in this research, we proposed a combination of the Scaled Manhattan Distance method and the Mean of Horner's Rules as a classification method between the user and attacker against the Keystroke Dynamic Authentication. The reason for using Mean of Horner's Rules can adapt to changes in values over time and based on the results can improve the accuracy of the previous method.

At the beginning of 2020, the world was shocked by the coronavirus, which spread rapidly in various countries, one of which was Indonesia. So that the government implemented the Work from Home policy to suppress the spread of Covid-19. This has resulted in many people writing their opinions on the Twitter social media platform and reaping many pros and cons of the community from all aspects. The data source used in this study came from tweets with keywords related to work from home. Several previous studies in this field have not implemented feature selection for sentiment analysis, although the method used is not optimal. So that the contribution in this study is to classify public opinion into positive and negative using sentiment analysis and implement PSO for feature selection and Naïve Bayes for classifiers in building sentiment analysis models. The results showed that the best accuracy was 81% in the classification using Naive Bayes and 86% in the classification using naive Bayes based on PSO through a comparison of 90% training data and 10% test data. With the addition of an accuracy of 5%, it can be concluded that the use of the Particle Swarm Optimization algorithm as a feature selection can help the classification process so that the results obtained are more effective than before.

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Sebuah instansi pemerintah yang bergerak di bidang pendidikan khususnya pendidikan smp tentunya Dinas Pendidikan Kabupaten Malang yang merupakan instansi penting sebagai penggerak pendidikan di wilayah kabupaten malang untuk mengukur suatu mutu dan kualitas - kualitas sekolah yang ada di Dinas Kabupaten Malang yaitu berdasarkan standart penilaian pendidikan dan Standart pengelolaan yang berupa Nilai Ujian Nasional, Nilai Ujian Sekolah, dan Nilai Akreditasi Sekolah, namun demikian mengingat banyaknya jumlah Sekolah Menengah Pertama atau dalam kasus ini adalah SMP yang ada di Kabupaten Malang, nilai Ujian Nasional, Ujian Sekolah, serta Nilai Akreditasi Sekolah dari tiap tiap sekolah tidak seragam tentu pihak Dinas Kabupaten Malang akan kesulitan dalam mencari dan memilah tiap tiap sekolah berdasarkan karakteristik tersebut. Maka penelitian ini membahas tentang penerapan data mining menggunakan metode Algoritma K-Means untuk menghasilkan tampilan profil yang memiliki atribut sama , atribut atau parameter nilai yang digunakan adalah rata rata nilai dari setiap sekolah dari nilai Ujian Nasional, nilai Ujian Sekolah, serta nilai Akreditasi Sekolah, dengan menghasilkan cluster sejumlah 3 (k = 3) dengan cluster1 sebanyak 33 data , cluster2 sebanyak 56 data, cluster3 sebanyak 49 data. Hal ini menunjukan nilai SSE paling besar dengan jumlah cluster sebanyak 3 dimana dengan jumlah cluster tersebut  yang paling ideal untuk melakukan clustering sekolah SMP berdasarkan data di Dinas Pendidikan Kabupaten Malang.

The exchange rate is the value or price of a currency in front of other currencies divided into selling rates and buying rates. The differences and alteration of exchange rates are caused by interest rates, inflation, and many other factors. The General Regression Neural Network method is applied to build a prediction system for the Yuan to IDR exchange rate, using the input to determine the output. The dataset is taken from the Bank Indonesia website with 191 records after pre-processing. Based on the resulting test, we found that the MSE score is 106.13, the RMSE score is 10.30, and the MAE score is 8.73. The model can find and recognize training data patterns to provide excellent data output with the results given.