Article Per Year (5 Year)
p-Index From 2020 - 2025
1.945
P-Index
This Author published in this journals
All Journal Scientific Journal of Informatics Journal of Economic Education J-SAKTI (Jurnal Sains Komputer dan Informatika) Nusantara Science and Technology Proceedings J-SAKTI (Jurnal Sains Komputer dan Informatika) Recursive Journal of Informatics International Journal of Active Learning Indonesian Journal of Advocacy and Legal Services Journal of Advances in Information Systems and Technology
Anggyi Trisnawan Putra
Universitas Negeri Semarang
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Medicine & Pharmacology

Published : 19 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 19 Documents
Search

 1   2 
Pengembangan E-Lecture menggunakan Web Service Sikadu untuk Mendukung Perkuliahan di Universitas Negeri Semarang Putra, Anggyi Trisnawan
Scientific Journal of Informatics Vol 1, No 2 (2014): November 2014
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v1i2.4023

Abstract

Proses penjadwalan di Universitas Negeri Semarang yang sedemikian rumit menghasilkan data penjadwalan yang tersimpan di dalam database Sikadu (Sistem Informasi Akademik Terpadu) berupa keterkaitan antara data dosen, mahasiswa, dan mata kuliah. Namun, data ini tidak diintegrasikan secara langsung ke dalam aplikasi/sistem e-learning yang disediakan oleh Unnes, mengakibatkan adanya proses/kegiatan yang tidak perlu sebelum dapat menggunakan aplikasi e-learning. Dengan fakta bahwa data penjadwalan dapat diakses secara online, dapat dirancang aplikasi pendukung e-lecture dengan memanfaatkan data tersebut. Pertama-tama, dirancang web service yang akan menyajikan akses aman ke dalam data Sikadu. Lalu, dirancang database e-lecture yang akan memanfaatkan web service yang telah dibuat tersebut. Data akan disajikan dalam interface yang dibuat dengan HTML, bermesin PHP. Dosen dan mahasiswa dapat menggunakan akses login yang sama dengan Sikadu untuk dapat langsung memanfaatkan aplikasi ini. Dengan adanya aplikasi ini, proses perkuliahan meliputi sharing bahan ajar, pemberian tugas/aktivitas kuliah, integrasi pengumpulan tugas, koreksi nilai tugas, pembatasan waktu pengumpulan tugas secara tegas (tersistem) dan lain sebagainya dapat dilakukan secara mudah dan efisien. 
Forecasting Inflation Rate Using Support Vector Regression (SVR) Based Weight Attribute Particle Swarm Optimization (WAPSO) Priliani, Erlin Mega; Putra, Anggyi Trisnawan; Muslim, Much Aziz
Scientific Journal of Informatics Vol 5, No 2 (2018): November 2018
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v5i2.14613

Abstract

Data mining is the process of finding patterns or interesting information in selected data by using a particular technique or method. Utilization of data mining one of which is forecasting. Various forecasting methods have progressed along with technological developments. Support Vector Regression (SVR) is one of the forecasting methods that can be used to predict inflation. The level of accuracy of forecasting is determined by the precision of parameter selection for SVR. Determination of these parameters can be done by optimization, to obtain optimal forecasting of SVR method. The optimization technique used is Weight Attribute Particle Swarm Optimization (WAPSO). The use of WAPSO can find optimal SVR parameters, so as to improve the accuracy of forecasting. The purpose of this research is to implement SVR and SVR-WAPSO to predict the inflation rate based on Consumer Price Index (CPI) and to know the level of accuracy. The data used in this study is CPI Semarang City period January 2010-February 2018. Implementation experiments using Netbeans 8.2 gives results, SVR method has an accuracy of 94.654%. SVR-WAPSO method has an accuracy of 97.459%. Thus, the SVR-WAPSO method can increase the accuracy of 2,805% of a single SVR method for inflation rate forecasting. This research can be used as a reference for the next researcher can make improvements in determining the range of SVR parameters to get the value of each parameter more effective and efficient to get more optimal accuracy.
Implementation of Expert System for Diabetes Diseases using Naïve Bayes and Certainty Factor Methods Ilham Insani, Muhammad; Alamsyah, Alamsyah; Putra, Anggyi Trisnawan
Scientific Journal of Informatics Vol 5, No 2 (2018): November 2018
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v5i2.16143

Abstract

Expert Systems is a computer systems that has been entered the base knowledge and a set of rules used to solve problems like an expert. Methods that can be used in the expert systems which is Naïve Bayes and Certainty Factor. Naïve Bayes method can handle quantitative calculations and discreate data and only requires a little research data to estimate the parameters needed in the clasification and Certainty Factor which is suitable for measuring something whether it is certain or not in diagnosing. Diabetes is one of the most frequent diseases suffered in Indonesia. The purpose of this research is implementation expert systems used Naïve Bayes and Certainty Factor in diagnosing diabetes and knowing the level of accuracyof the systems. Data that is used by researchers as much 100 data medical record, obtained from the medical record RSUD Bendan Kota Pekalongan. The variabels used in this research is age, gender, the symptoms of the desease diabetes and result diagnose desease from expert. The accuracy rate of this system derived from the scenario distribution data 70 training data and 30 testing data that is equal to 100% according to the doctors diagnosis.
Comparison of PCA and 2DPCA Accuracy with K-Nearest Neighbor Classification in Face Image Recognition Sutarti, Sri; Putra, Anggyi Trisnawan; Sugiharti, Endang
Scientific Journal of Informatics Vol 6, No 1 (2019): Mei 2019
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v6i1.18553

Abstract

Face recognition is a special pattern recognition for faces that compare input image with data in database. The image has a variety and has large dimensions, so that dimension reduction is needed, one of them is Principal Component Analysis (PCA) method. Dimensional transformation on image causes vector space dimension of image become large. At present, a feature extraction technique called Two-Dimensional Principal Component Analysis (2DPCA) is proposed to overcome weakness of PCA. Classification process in 2DPCA using K-Nearest Neighbor (KNN) method by counting euclidean distance. In PCA method, face matrix is changed into one-dimensional matrix to get covariance matrix. While in 2DPCA, covariance matrix is directly obtained from face image matrix. In this research, we conducted 4 trials with different amount of training data and testing data, where data is taken from AT&T database. In 4 time testing, accuracy of 2DPCA+KNN method is higher than PCA+KNN method. Highest accuracy of 2DPCA+KNN method was obtained in 4th test with 96.88%. while the highest accuracy of PCA+KNN method was obtained in 4th test with 89.38%. More images used as training data compared to testing data, then the accuracy value tends to be greater.
Pengembangan E-Lecture menggunakan Web Service Sikadu untuk Mendukung Perkuliahan di Universitas Negeri Semarang Putra, Anggyi Trisnawan
Scientific Journal of Informatics Vol 1, No 2 (2014): November 2014
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v1i2.4023

Abstract

Proses penjadwalan di Universitas Negeri Semarang yang sedemikian rumit menghasilkan data penjadwalan yang tersimpan di dalam database Sikadu (Sistem Informasi Akademik Terpadu) berupa keterkaitan antara data dosen, mahasiswa, dan mata kuliah. Namun, data ini tidak diintegrasikan secara langsung ke dalam aplikasi/sistem e-learning yang disediakan oleh Unnes, mengakibatkan adanya proses/kegiatan yang tidak perlu sebelum dapat menggunakan aplikasi e-learning. Dengan fakta bahwa data penjadwalan dapat diakses secara online, dapat dirancang aplikasi pendukung e-lecture dengan memanfaatkan data tersebut. Pertama-tama, dirancang web service yang akan menyajikan akses aman ke dalam data Sikadu. Lalu, dirancang database e-lecture yang akan memanfaatkan web service yang telah dibuat tersebut. Data akan disajikan dalam interface yang dibuat dengan HTML, bermesin PHP. Dosen dan mahasiswa dapat menggunakan akses login yang sama dengan Sikadu untuk dapat langsung memanfaatkan aplikasi ini. Dengan adanya aplikasi ini, proses perkuliahan meliputi sharing bahan ajar, pemberian tugas/aktivitas kuliah, integrasi pengumpulan tugas, koreksi nilai tugas, pembatasan waktu pengumpulan tugas secara tegas (tersistem) dan lain sebagainya dapat dilakukan secara mudah dan efisien.
Forecasting Inflation Rate Using Support Vector Regression (SVR) Based Weight Attribute Particle Swarm Optimization (WAPSO) Priliani, Erlin Mega; Putra, Anggyi Trisnawan; Muslim, Much Aziz
Scientific Journal of Informatics Vol 5, No 2 (2018): November 2018
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v5i2.14613

Abstract

Data mining is the process of finding patterns or interesting information in selected data by using a particular technique or method. Utilization of data mining one of which is forecasting. Various forecasting methods have progressed along with technological developments. Support Vector Regression (SVR) is one of the forecasting methods that can be used to predict inflation. The level of accuracy of forecasting is determined by the precision of parameter selection for SVR. Determination of these parameters can be done by optimization, to obtain optimal forecasting of SVR method. The optimization technique used is Weight Attribute Particle Swarm Optimization (WAPSO). The use of WAPSO can find optimal SVR parameters, so as to improve the accuracy of forecasting. The purpose of this research is to implement SVR and SVR-WAPSO to predict the inflation rate based on Consumer Price Index (CPI) and to know the level of accuracy. The data used in this study is CPI Semarang City period January 2010-February 2018. Implementation experiments using Netbeans 8.2 gives results, SVR method has an accuracy of 94.654%. SVR-WAPSO method has an accuracy of 97.459%. Thus, the SVR-WAPSO method can increase the accuracy of 2,805% of a single SVR method for inflation rate forecasting. This research can be used as a reference for the next researcher can make improvements in determining the range of SVR parameters to get the value of each parameter more effective and efficient to get more optimal accuracy.
Implementation of Expert System for Diabetes Diseases using Naïve Bayes and Certainty Factor Methods Ilham Insani, Muhammad; Alamsyah, Alamsyah; Putra, Anggyi Trisnawan
Scientific Journal of Informatics Vol 5, No 2 (2018): November 2018
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v5i2.16143

Abstract

Expert Systems is a computer systems that has been entered the base knowledge and a set of rules used to solve problems like an expert. Methods that can be used in the expert systems which is Naïve Bayes and Certainty Factor. Naïve Bayes method can handle quantitative calculations and discreate data and only requires a little research data to estimate the parameters needed in the clasification and Certainty Factor which is suitable for measuring something whether it is certain or not in diagnosing. Diabetes is one of the most frequent diseases suffered in Indonesia. The purpose of this research is implementation expert systems used Naïve Bayes and Certainty Factor in diagnosing diabetes and knowing the level of accuracyof the systems. Data that is used by researchers as much 100 data medical record, obtained from the medical record RSUD Bendan Kota Pekalongan. The variabels used in this research is age, gender, the symptoms of the desease diabetes and result diagnose desease from expert. The accuracy rate of this system derived from the scenario distribution data 70 training data and 30 testing data that is equal to 100% according to the doctor's diagnosis.
Pengembangan E-Lecture menggunakan Web Service Sikadu untuk Mendukung Perkuliahan di Universitas Negeri Semarang Putra, Anggyi Trisnawan
Scientific Journal of Informatics Vol 1, No 2 (2014): November 2014
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v1i2.4023

Abstract

Proses penjadwalan di Universitas Negeri Semarang yang sedemikian rumit menghasilkan data penjadwalan yang tersimpan di dalam database Sikadu (Sistem Informasi Akademik Terpadu) berupa keterkaitan antara data dosen, mahasiswa, dan mata kuliah. Namun, data ini tidak diintegrasikan secara langsung ke dalam aplikasi/sistem e-learning yang disediakan oleh Unnes, mengakibatkan adanya proses/kegiatan yang tidak perlu sebelum dapat menggunakan aplikasi e-learning. Dengan fakta bahwa data penjadwalan dapat diakses secara online, dapat dirancang aplikasi pendukung e-lecture dengan memanfaatkan data tersebut. Pertama-tama, dirancang web service yang akan menyajikan akses aman ke dalam data Sikadu. Lalu, dirancang database e-lecture yang akan memanfaatkan web service yang telah dibuat tersebut. Data akan disajikan dalam interface yang dibuat dengan HTML, bermesin PHP. Dosen dan mahasiswa dapat menggunakan akses login yang sama dengan Sikadu untuk dapat langsung memanfaatkan aplikasi ini. Dengan adanya aplikasi ini, proses perkuliahan meliputi sharing bahan ajar, pemberian tugas/aktivitas kuliah, integrasi pengumpulan tugas, koreksi nilai tugas, pembatasan waktu pengumpulan tugas secara tegas (tersistem) dan lain sebagainya dapat dilakukan secara mudah dan efisien. 
Comparison of PCA and 2DPCA Accuracy with K-Nearest Neighbor Classification in Face Image Recognition Sutarti, Sri; Putra, Anggyi Trisnawan; Sugiharti, Endang
Scientific Journal of Informatics Vol 6, No 1 (2019): May 2019
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v6i1.18553

Abstract

Face recognition is a special pattern recognition for faces that compare input image with data in database. The image has a variety and has large dimensions, so that dimension reduction is needed, one of them is Principal Component Analysis (PCA) method. Dimensional transformation on image causes vector space dimension of image become large. At present, a feature extraction technique called Two-Dimensional Principal Component Analysis (2DPCA) is proposed to overcome weakness of PCA. Classification process in 2DPCA using K-Nearest Neighbor (KNN) method by counting euclidean distance. In PCA method, face matrix is changed into one-dimensional matrix to get covariance matrix. While in 2DPCA, covariance matrix is directly obtained from face image matrix. In this research, we conducted 4 trials with different amount of training data and testing data, where data is taken from ATT database. In 4 time testing, accuracy of 2DPCA+KNN method is higher than PCA+KNN method. Highest accuracy of 2DPCA+KNN method was obtained in 4th test with 96.88%. while the highest accuracy of PCA+KNN method was obtained in 4th test with 89.38%. More images used as training data compared to testing data, then the accuracy value tends to be greater.
Sipp Application Development Effort in Supporting the Digitalization of Tri Dharma at The Universitas Negeri Semarang Setiawan, Avi Budi; Widyastuti, Ariyani; Putra, Anggyi Trisnawan; uminar; Wicaksana, Tania
Nusantara Science and Technology Proceedings The 3rd International Conference on Vocational Innovation and Applied Sciences (ICVIAS) 2021
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Technology has helped humans in various fields, for example in the field of research and service. The Institute for Research and Community Service (LPPM) Universitas Negeri Semarang (UNNES) in its role in realizing the responsibility for the Tri Dharma of Higher Education, has changed the procedure for evaluating the instrument which was originally manual to be more effective and efficient through an application called SIPP (Management Information System). Research and Service) which can be accessed on the website. This change is expected to create even better conditions. However, these existing facilities are not well received. This can be seen from the many researchers who are late in collecting research instruments and even less familiar with this SIPP application. Data analysis techniques used in this study include transcripts of the results of field surveys, observations and interviews, data reduction, analysis, data interpretation and triangulation.
Co-Authors Adityatama, Resta Akhmad Munawar Alamsyah - Alfath Yanuarto Amaliyah, Tazkiyatun Aeni Andika Enggal Ramadhan Avi Budi Setiawan Bhekti Kumorowati, Bhekti Cahya Wulandari Dinar Diaz Septian Emha, Zidney Ilma Fazaada Endang Sugiharti, Endang Hassan, Muhamad Sayuti Ilham Insani, Muhammad Ilham Insani, Muhammad Martanto Setyo Husodo Much Aziz Muslim Muh. Sholeh Priliani, Erlin Mega Priliani, Erlin Mega Putra, Deonisius Germandy Cahaya S.Pd. M Kes I Ketut Sudiana . Sugianto Sugianto Sutarti, Sri Sutarti, Sri uminar Vivi Wulandari Wicaksana, Tania Widyastuti, Ariyani