Evaluasi Sistem Pendeteksi Intrusi Berbasis Anomali Dengan N-Gram Dan Incremental Learning

Keberadaan teknologi informasi yang terus berkembang dengan pesat menjadikan kebutuhan akan penggunaannya semakin hari semakin meningkat. Transaksi data melalui internet telah menjadi kebutuhan wajib hampir dari semua perangkat lunak yang ada saat ini. Perangkat lunak seperti media social, colud server, online game, aplikasi layanan pemerintah, aplikasi pengontrol suatu tempat secara remote, dsb. Tentu dengan berbagai macam penggunaan internet tersebut dibutuhkan metode untuk mengamankan jaringannya. Sistem pendeteksi intrusi atau yang pada umumnya disebut IDS (Intrusion Detection System) merupakan solusi untuk mengamankan suatu jaringan. Sistem ini nantinya bertugas untuk menentukan apakah suatu paket merupakan bentuk serangan atau paket biasa sesuai dengan kondisi tertentu. Saat ini telah banyak dikembangkan aplikasi IDS (Intrusion Detection System), namun sebagian besar yang dikembangkan berbasis signature atau menggunakan rule, dan sebagaian kecil menggunakan anomali. Anomali adalah suatu metode untuk mencari penyimpangan dalam sebuah data. Pada aplikasi ini konsep IDS yang diterapkan adalah IDS berbasis anomali dimana analisis datanya pada infromasi paket data yang dikirimkan. Pada tugas akhir ini menggunakan dua metode, yaitu metode n-gram yang digunakan untuk mengitung distribusi byte karakter pada paket data sedangkan metode mahalanonis distance digunakan untuk menghitung jarak antara paket data normal dan paket data yang berupa intrusi. Metode mahalanobis distance dapat membedakan paket data yang normal dan paket data yang berupa intrusi dengan menghitung rata-rata dan standar deviasi dari paket data ================================================================================================ The rapid development of information technology is inevitable wich made its necessity is growing every single day. Data transaction through internet has become the primary need of most software nowadays. Software like social media, cloud server, online game, e-government, remote application, etc. With the various needs of the internet, it is obvious that we need a method that can guarantee its safety. IDS which stands for Intrusion Detection System is the solution to protect the internet network. This system will decide wether a packet is safe or dangerous for the network depends on certain condition. Nowadays many IDS (Intrusion Detection System) has been developed, but most are developed base signature or use the rule, and a small part sing anomaly. Anomaly is a method to look for irregularities in the data. In this application IDS concept that is applied is based anomaly in which the data analysis on the data packets transmitted. In this thesis using two methods, the n-gram method used to calculate the distribution of byte character data paket while the mahalanobis distance methods used to calculated the distance between the normal data packets and intrusion data packets. Mahalanobis distance methods can distinguish between normal data packets and intrusion data packets by calculating the average and standar deviation of the data packet

The challenges of emotion recognition methods based on electroencephalogram signals: a literature review

Electroencephalogram (EEG) signals in recognizing emotions have several advantages. Still, the success of this study, however, is strongly influenced by: i) the distribution of the data used, ii) consider of differences in participant characteristics, and iii) consider the characteristics of the EEG signals. In response to these issues, this study will examine three important points that affect the success of emotion recognition packaged in several research questions: i) What factors need to be considered to generate and distribute EEG data?, ii) How can EEG signals be generated with consideration of differences in participant characteristics?, and iii) How do EEG signals with characteristics exist among its features for emotion recognition? The results, therefore, indicate some important challenges to be studied further in EEG signals-based emotion recognition research. These include i) determine robust methods for imbalanced EEG signals data, ii) determine the appropriate smoothing method to eliminate disturbances on the baseline signals, iii) determine the best baseline reduction methods to reduce the differences in the characteristics of the participants on the EEG signals, iv) determine the robust architecture of the capsule network method to overcome the loss of knowledge information and apply it in more diverse data set

Oversampling Approach Using Radius-SMOTE for Imbalance Electroencephalography Datasets

Several studies related to emotion recognition based on Electroencephalogram signals have been carried out in feature extraction, feature representation, and classification. However, emotion recognition is strongly influenced by the distribution or balance of Electroencephalogram data. On the other hand, the limited data obtained significantly affects the imbalance condition of the resulting Electroencephalogram signal data. It has an impact on the low accuracy of emotion recognition. Therefore, based on these problems, the contribution of this research is to propose the Radius SMOTE method to overcome the imbalance of the DEAP dataset in the emotion recognition process. In addition to the EEG data oversampling process, there are several vital processes in emotion recognition based on EEG signals, including the feature extraction process and the emotion classification process. This study uses the Differential Entropy (DE) method in the EEG feature extraction process. The classification process in this study compares two classification methods, namely the Decision Tree method and the Convolutional Neural Network method. Based on the classification process using the Decision Tree method, the application of oversampling with the Radius SMOTE method resulted in the accuracy of recognizing arousal and valence emotions of 78.78% and 75.14%, respectively. Meanwhile, the Convolutional Neural Network method can accurately identify the arousal and valence emotions of 82.10% and 78.99%, respectively. Doi: 10.28991/ESJ-2022-06-02-013 Full Text: PD

Continuous Capsule Network Method for Improving Electroencephalogram-Based Emotion Recognition

The convolution process in the Capsule Network method can result in a loss of spatial data from the Electroencephalogram signal, despite its ability to characterize spatial information from Electroencephalogram signals. Therefore, this study applied the Continuous Capsule Network method to overcome problems associated with emotion recognition based on Electroencephalogram signals using the optimal architecture of the (1) 1st, 2nd, 3rd, and 4th Continuous Convolution layers with values of 64, 128, 256, and 64, respectively, and (2) kernel sizes of 2×2×4, 2×2×64, and 2×2×128 for the 1st, 2nd, and 3rd Continuous Convolution layers, and 1×1×256 for the 4th. Several methods were also used to support the Continuous Capsule Network process, such as the Differential Entropy and 3D Cube methods for the feature extraction and representation processes. These methods were chosen based on their ability to characterize spatial and low-frequency information from Electroencephalogram signals. By testing the DEAP dataset, these proposed methods achieved accuracies of 91.35, 93.67, and 92.82% for the four categories of emotions, two categories of arousal, and valence, respectively. Furthermore, on the DREAMER dataset, these proposed methods achieved accuracies of 94.23, 96.66, and 96.05% for the four categories of emotions, the two categories of arousal, and valence, respectively. Finally, on the AMIGOS dataset, these proposed methods achieved accuracies of 96.20, 97.96, and 97.32% for the four categories of emotions, the two categories of arousal, and valence, respectively. Doi: 10.28991/ESJ-2023-07-01-09 Full Text: PD

Modified Weighted Mean Filter to Improve the Baseline Reduction Approach for Emotion Recognition

Participants' emotional reactions are strongly influenced by several factors such as personality traits, intellectual abilities, and gender. Several studies have examined the baseline reduction approach for emotion recognition using electroencephalogram signal patterns containing external and internal interferences, which prevented it from representing participants’ neutral state. Therefore, this study proposes two solutions to overcome this problem. Firstly, it offers a modified weighted mean filter method to eliminate the interference of the electroencephalogram baseline signal. Secondly, it determines an appropriate baseline reduction method to characterize emotional reactions after the smoothing process. Data collected from four scenarios conducted on three datasets was used to reduce the interference and amplitude of the electroencephalogram signals. The result showed that the smoothing process can eliminate interference and lower the signal's amplitude. Based on the three baseline reduction methods, the Relative Difference method is appropriate for characterizing emotional reactions in different electroencephalogram signal patterns and has higher accuracy. Based on testing on the DEAP dataset, these proposed methods achieved accuracies of 97.14, 99.70, and 96.70% for the four categories of emotions, the two categories of arousal, and the two categories of valence, respectively. Furthermore, on the DREAMER dataset, these proposed methods achieved accuracies of 89.71, 97.63, and 96.58% for the four categories of emotions, the two categories of arousal, and the two categories of valence, respectively. Finally, on the AMIGOS dataset, these proposed methods achieved accuracies of 99.59, 98.20, and 99.96% for the four categories of emotions, the two categories of arousal, and the two categories of valence, respectively. Doi: 10.28991/ESJ-2022-06-06-03 Full Text: PD

Evaluasi Sistem Pendeteksi Intrusi Berbasis Anomali dengan N-gram dan Incremental Learning

Keberadaan teknologi informasi yang terus berkembang dengan pesat menjadikan kebutuhan akan penggunaannya semakin hari semakin meningkat. Transaksi data melalui internet telah menjadi kebutuhan wajib hampir dari semua perangkat lunak yang ada saat ini. Perangkat lunak seperti media social, colud server, online game, aplikasi layanan pemerintah, aplikasi pengontrol suatu tempat secara remote, dsb. Tentu dengan berbagai macam penggunaan internet tersebut dibutuhkan metode untuk mengamankan jaringannya. Sistem pendeteksi intrusi atau yang pada umumnya disebut IDS (Intrusion Detection System) merupakan solusi untuk mengamankan suatu jaringan. Sistem ini nantinya bertugas untuk menentukan apakah suatu paket merupakan bentuk serangan atau paket biasa sesuai dengan kondisi tertentu. Saat ini telah banyak dikembangkan aplikasi IDS (Intrusion Detection System), namun sebagian besar yang dikembangkan berbasis signature atau menggunakan rule, dan sebagaian kecil menggunakan anomali. Anomali adalah suatu metode untuk mencari penyimpangan dalam sebuah data. Pada aplikasi ini konsep IDS yang diterapkan adalah IDS berbasis anomali dimana analisis datanya pada infromasi paket data yang dikirimkan. Pada tugas akhir ini menggunakan dua metode, yaitu metode n-gram yang digunakan untuk mengitung distribusi byte karakter pada paket data sedangkan metode mahalanonis distance digunakan untuk menghitung jarak antara paket data normal dan paket data yang berupa intrusi. Metode mahalanobis distance dapat membedakan paket data yang normal dan paket data yang berupa intrusi dengan menghitung rata-rata dan standar deviasi dari paket data

DSS for "E-Private" Using a Combination of AHP and SAW Methods

Private tutoring was non-formal education and it was needed to help student in learning.There were already tutoring system developed where the selection of private tutors was done by filtering peocess. However, filtering process was not suitable with needs and desires of students.Besides the filtering process, to support the solution in making decisions on the selection of private tutors on the E-Privat system it also used the Decision Suport System (DSS) concept, namely a combination of AHP and SAW methods. AHP method was used to find the weights in each criterion, and the ranking calculation with the SAW method.E-Privat aimed to help parents / students in choosing private tutors that suit the needs and desires of students by involving multi-criteria and various alternative. This system was also developed to help private tutors to get the opportunity to fill out private lessons.  The testing process results showed that the system had been successful and suitable for used. There were 5 testing processes : (1)black box testing, (2)white box testing, (3)accuracy test which showed a percentage of 87%, and (4)user's response test whichused the SUS method showed a percentage 92.08% with best imaginable category

PENINGKATAN PRODUKSI INDUSTRI RUMAH TANGGA JAJE BEGINA DI DESA TEGAL LINGGAH KABUPATEN KARANGASEM

The partner in this community partnership program activity is Mrs. Ni Nengah Resti whose address is in Tegallinggah Village, Karangasem District. Jaje begina production is produced using sufficient and simple equipment, both in processing and in the packaging process. Marketing is carried out conventionally by partners by leaving products in nearby stalls and selling them to the market. Another obstacle faced by partners in the packaging process is still using staples without labels. The solutions provided in this service activity are based on the principles of intensification, extensification and rationalization in three activity methods of awareness, capacity building and mentoring. The aim of this community service is how to increase the quantity and quality of products from partners. The results of the service show that partners are able to increase the quantity and quality of products, where the time needed to produce such snacks is shorter than before. Products produced by partners already have the label "jaje begina me' resti" with marketing using the social media fan page Facebook. The partner response was very satisfying with a percentage of 72,14%

LEVEL KINERJA STRUKTUR GEDUNG FAKULTAS PARIWISATA UNIVERSITAS UDAYANA BERDASARKAN FEMA 356 DAN ATC-40

Desain berbasis kinerja adalah desain struktur yang menunjukkan tingkat kerusakan suatu struktur akibat beban gempa lateral. Tingkat kerusakan harus dinyatakan dengan tingkat kriteria atau performance level. Penelitian ini mengambil studi kasus Gedung Fakultas Pariwisata Universitas Udayana yang terletak di Jalan Kampus Bukit Jimbaran, Badung, Bali. Menurut SNI 1726:2012, fungsi gedung ini sebagai fasilitas pendidikan termasuk dalam kategori risiko IV, yang berarti gedung ini memiliki risiko tinggi terhadap nyawa manusia saat terjadi gempa. Bangunan ini perlu dianalisis dalam keadaan non-linier dengan menggunakan metode analisis static-pushover, sehingga dapat ditentukan tingkat kinerjanya. Analisis kinerja dilakukan dengan mengikuti ketentuan ATC-40 dan FEMA 356 yang sudah tersedia dalam software SAP2000. Performance point dengan analisis pushover ditentukan menggunakan metode kapasitas spektrum ATC-40 dan menunjukkan nilai total drift maksimum 0,0056 m pada arah X dan 0,0049 m pada arah Y. Berdasarkan kriteria pada ATC-40, kinerja struktur gedung berada pada tingkat Immediate Occupancy (IO). Sedangkan berdasarkan metode koefisien perpindahan FEMA 356 didapatkan hasil target perpindahan pada arah X sebesar 0,125 m dan pada arah Y sebesar 0,098 m. Target perpindahan pada arah X dan Y kurang dari 1% dari tinggi bangunan, sehingga bangunan tersebut masuk dalam level kinerja Immediate Occupancy (IO)

Pemrograman Berorientasi Objek

vii., 109 hlm.: 26 c