A Hybrid Approach Support Vector Machine (SVM) – Neuro Fuzzy for Fast Data Classification

In recent decade, support vector machine (SVM) was a machine learning method that widely used in several application domains. It was due to SVM has a good performance for solving data classification problems, particularly in non-linear case. Nevertheless, several studies indicated that SVM still has some inadequacies, especially the high time complexity in testing phase that is caused by increasing the number of support vector for high dimensional data. To address this problem, we propose a hybrid approach SVM – Neuro Fuzzy (SVMNF), which neuro fuzzy here is used to avoid influence of support vector in testing phase of SVM. Moreover, our approach is also equipped with a feature selection that can reduce data attributes in testing phase, so that it can improve the effectiveness of time computation. Based on our evaluation in real benchmark datasets, our approach outperformed SVM in testing phase for solving data classification problems without significantly affecting the accuracy of SVM

Pengenalan Ucapan Kata sebagai Pengendali Gerakan Robot Lengan secara Real-Time dengan Metode Linear Predictive Coding – Neuro Fuzzy

Sejak beberapa dekade terakhir ini, peran robot dalam industri maupun kehidupan sehari-hari semakin meningkat. Hampir tidak ada cabang industri teknologi tinggi yang tidak dibantu robot. Dalam kehidupan sehari-hari, berbagai bentuk robot diciptakan untuk membantu atau memudahkan aktivitas manusia. Namun seiring dengan tingkat kebutuhan manusia terhadap robot, tingkat resiko kesulitan manusia dalam menggunakan teknologi tersebut semakin tinggi. Hal ini ditunjukkan dengan banyaknya kecelakaan akibat tidak adanya teknologi yang memudahkan manusia dalam berinteraksi dengan robot secara interaktif. Pada umumnya robot-robot tersebut dikendalikan melalui input keyboard dari Personal Computer (PC) atau remote control analog, dan bukan melalui suara ucapan. Oleh karena itu perlu dirancang suatu robot yang bergerak sesuai perintah suara ucapan. Jika suara ucapan digunakan untuk mengendalikan suatu robot, maka sistem yang dipakai harus berjalan secara realtime sehingga robot dapat dikendalikan secara interaktif. Pada tugas akhir ini akan dikembangkan sebuah suatu perangkat lunak sistem pengenalan suara menggunakan metode Linear Predictive Coding (LPC) dan Neuro-Fuzzy. Perangkat lunak tersebut akan digunakan untuk mengendalikan robot lengan yang terhubung pada kabel serial RS-232 suatu PC melalui komunikasi serial. Dalam penelitian ini diharapkan dengan menerapkan metode Linear Predictive Coding (LPC) dan Neuro-Fuzzy pada sistem pengenalan suara dapat digunakan untuk mengidentifikasi perintah suara dengan tingkat keberhasilan yang tinggi sehingga dapat digunakan sebagai pengendali robot yang handal. Berdasarkan dari hasil pengujian yang dilakukan pengenalan jaringan untuk data baru lebih rendah terhadap data latihan. Prosentase pengenalan suara dari dalam database sebesar 100 %, dan prosentase untuk pengenalan suara dari luar database 12,5%

Analysis of the Effect of the MBKM Internship Program and Certified Independent Study (MSIB) on University Performance Universitas 17 Agustus 1945 Surabaya

Universitas 17 Agustus 1945 Surabaya as one of the leading private campuses in Indonesia has a high commitment to improving the quality of higher education.  One of the efforts of the Universitas 17 Agustus 1945 Surabaya in improving the quality of higher education is through the development and improvement of every aspect of activities that refer to the Higher Education Main Performance Indicators (KPI) that have been set by the Ministry of Education and Culture.  The activity program that refers to the Main Performance Indicators (IKU) is the Merdeka Learning Campus Merdeka (MBKM) activity program, which has been well implemented by the Universitas 17 Agustus 1945 Surabaya since 2019. However, in an effort to continuously improve the MBKM program, analysis is needed  an in-depth look at readiness and the impact obtained at the level of study programs, faculties, and universities.  One of the MBKM programs that refers to the KPI and needs to be analyzed is the Certified Independent Study & Internship MBKM program. On this basis, this study conducted an in-depth analysis of the readiness and impact of the MBKM Internship & Certified Independent Study program at the Universitas 17 Agustus 1945 Surabaya.  Based on the results obtained, the level of readiness of the Universitas 17 Agustus 1945 Surabaya in supporting the MBKM program is quite good with an average percentage of readiness above 50%.  In measuring the impact of MBKM, it is found that the MBKM policy, Internship Program and Independent Studies greatly affect the performance of the Universitas 17 Agustus 1945 Surabaya significantly with an impact effect of 75.4%