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All Journal Perfecting a Video Game with Game Metrics Paradigma JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Ilmiah Media Sisfo The IJICS (International Journal of Informatics and Computer Science) JURIKOM (Jurnal Riset Komputer) Building of Informatics, Technology and Science Journal of Computer System and Informatics (JoSYC) TIN: TERAPAN INFORMATIKA NUSANTARA Bulletin of Computer Science Research Jurnal Pustaka Data : Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer Jurnal Informatika dan Rekayasa Komputer PROCEEDING OF INTERNATIONAL CONFERENCE ON BUSINESS MANAGEMENT AND ACCOUNTING Bulletin of Informatics and Data Science Jurnal Informatika: Jurnal Pengembangan IT
Yovi Pratama
Universitas Dinamika Bangsa
Aerospace Engineering Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

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Eksperimen Estimasi Biaya Proyek Perangkat Lunak Menggunakan Metode Function Point Analysis (FPA) Hendrawan, Hendrawan; Jusia, Pareza Alam; Rasywir, Errissya; Pratama, Yovi
JURIKOM (Jurnal Riset Komputer) Vol 6, No 4 (2019): Agustus 2019
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (217.06 KB) | DOI: 10.30865/jurikom.v6i4.1329

Abstract

In building software, it should start with project planning. This activity is initialized with estimation activity. When estimation is done, it is necessary to make predictions in the future and handle the uncertainty that will be passed in running a software project. There are several options in project estimation. However, in this study, we used the most common and familiar technique to be tested on project data that had been running in the field. From the results of experiments conducted in the study on 20 (twenty) data software development projects in one of the software houses in Jambi city using the Function Point Analysis (FPA) method, the results of the calculation of the RMSE (root mean squared error) of Rp 1,073,001, - and an error value of 0.25. This means that it can be concluded that the difference in RMSE from the real price comparison transacted in the field with the price estimated using the Function Point Analysis (FPA) method is equal to the value mentioned above, which value includes a value with a fairly small gap. As well, the error value produced is also quite small at 0.25.
Sistem Manajemen Absensi dengan Fitur Pengenalan Wajah dan GPS Menggunakan YOLO pada Platform Android Hartiwi, Yessi; Rasywir, Errissya; Pratama, Yovi; Jusia, Pareza Alam
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 4, No 4 (2020): Oktober 2020
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v4i4.2522

Abstract

This study offers an attendance system that can be run with the Global Positioning System (GPS) feature to automatically check the location of the face owner. Recently, the YOLO algorithm is the world's most popular method of facial recognition. Currently the You Only Look Once (YOLO) algorithm toolbox has been provided in various programming language platforms for use. The system we offer is also able to check the position or whereabouts of objects using Global Positioning System (GPS) technology. The results of this test obtained an accuracy of 0.93435 and the lowest was within the range of 93%, while the average accuracy values were 93.26%. Of the 20 assessment data carried out by the Attendance Management System with Face Recognition and GPS Features using YOLO on the Android Platform. The evaluation of the accuracy of student attendance is expected to support the process of academic activities on campus. In addition, this product is expected to be able to assist management who require evaluation results as well as an effort to improve business processes in an agency in order to improve their performance. This research proves that the use of the tool library with the You Only Look Once (YOLO) algorithm is the most popular method in the world of facial recognition and is proven to be tough and very good at this time.
Pengukuran Perangkat Lunak Untuk Effort Estimation Dengan Teknik Pembelajaran Mesin Borroek, Maria Rosario; Rasywir, Errissya; Pratama, Yovi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 4, No 2 (2020): April 2020
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v4i2.2083

Abstract

Software effort estimation is to estimate the amount of resources needed in developing the software. For that software effort estimation is important so need to see the effect of software measurement to software effort estimation which is done by machine learning technique. Based on this the researcher tries to build a system capable of measuring software. In this study experiments on software measurement techniques (FPA, FPA with Sugeno fuzzy and FPA with mamdani fuzzy). The three types of techniques are compared with the three project data for further software effort estimation. For evaluation, this study evaluates using the assessment of the Developeras Analyst of the Project. The results of the study that the LOC and effort values on a similar system can be different if calculated by the use of FPA, Fam Mamdany fuzzy and FPA Sugeno Fuzzy. The highest LOC and Effort values are generated by FPA Mamdany Fuzzy on Project DUMAS POLDA SUMSEL. While the lowest effort value and lowest LOC produced by FPA Sugeno Fuzzy. This can be traced from the calculation mechanisms performed by FPA Sugeno Fuzzy where this method does not count the input, output, file, query and interface values at all. The calculation of FPA Sugeno fuzzy is done by roughly judging only from the difficulty of making the system. To raise the price of a project in order to be rewarded higher FAT methods Mamdani Fuzzy is recommended
Analisis Usability Pada Implementasi Sistem Pengelolaan Keuangan Masjid Menggunakan USE Questionnaire Fachruddin, Fachruddin; Pahlevi, Muhammad Riza; Ismail, Muhammad; Rasywir, Errissya; Pratama, Yovi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 4, No 4 (2020): Oktober 2020
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v4i4.2520

Abstract

The financial management of mosques with the use of technology can make financial data more organized, filed neatly and transparently. Moreover, financial reports are data that must be accounted for in order to be trusted by the public. However, it is necessary to know how good a financial management system is. By using the USE Questionnaire we can find out that an application can still run in accordance with applicable business processes without changing the data flow and some rules and reports that have been running previously. The need to analyze usability testing on financial applications is to support automatic and computerized mosque financial management and is considered very good in user testing. This study resulted in an average rating for the "Usefulness" instrument, the "Ease of Use" instrument, the "Ease of Learning and Satisfaction" instrument, which scored well above 93%. The “Usefulness” instrument received an average of 99.00%, the “Ease of Use” instrument received an average of 94.55%, the “Ease of Learning and Satisfaction” instrument received an average of 93.82%. Thus it can be stated that the mosque financial application built for mosque management is able to meet good criteria in the rules of the USE Questionnaire method.
Network and layer experiment using convolutional neural network for content based image retrieval work Fachruddin Fachruddin; Saparudin Saparudin; Errissya Rasywir; Yovi Pratama
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 1: February 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i1.19759

Abstract

In this study, a test will be conducted to find out how the results of experiments on the network and layer used on the convolutional neural network algorithm. The performance and accuracy of the retrieval process method that was tested using the algorithm approach to do an object image retrieval. The expected results of this study are the techniques offered can provide relatively better results compared to previous studies. The results of the classification of object images with different levels of confusion on the Caltech 101 database resulted an average accuracy value. From the experiments conducted in the study, content based image retrieval work (CBIR) work using convolutional neural network (CNN) algorithm in terms of execution time, loss testing and accuracy testing. From several experiments on layers and networks shows that, the more hidden layers used, then the result is better. The graph of validation loss decreases at fewer epochs, slightly fluctuating at more epochs. Likewise, validation accuracy increases insignificantly on epochs with small amounts, but tends to be stable on more epochs.
Extraction of object image features with gradation contour Fachruddin Fachruddin; Saparudin Saparudin; Errissya Rasywir; Yovi Pratama; Beni Irawan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 6: December 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i6.19491

Abstract

Image retrieval using features has been used in previous studies including shape, color, texture, but these features are lagging. With the selection of high-level features with contours, this research is done with the hypothesis that images on objects can also be subjected to representations that are commonly used in natural images. Considering the above matters, we need to research the feature extraction of object images using gradation contour. From the results of the gradation contour test results, there is linearity between the results of accuracy with the large number of images tested. Therefore, it can be said that the influence of the number of images will affect the accuracy of classification. The use of contour gradation can be accepted and treated equally in all image types, so there is no more differentiation between image features. The complexity of the image does not affect the method of extracting features that are only used uniquely by an image. From the results of testing the polynomial coefficient savings data as a result of the gradation contour, the highest result is 81.40% with the highest number of categories and the number of images tested in the category is also higher.
Eksperimen Pengenalan Wajah dengan fitur Indoor Positioning System menggunakan Algoritma CNN Yessi Hartiwi; Errissya Rasywir; Yovi Pratama; Pareza Alam Jusia
Paradigma Vol 22, No 2 (2020): Periode September 2020
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (621.118 KB) | DOI: 10.31294/p.v22i2.8906

Abstract

Facial recognition work combined with the facial owner's position estimation feature can be utilized in various everyday applications such as face attendance with position detection. Based on this, this study offers a system testing experiment that can be run with facial recognition features and an Indoor Positioning System (IPS) to automatically check the location of the owner of the face. Recently, deep learning algorithms are the most popular method in the world of artificial intelligence. Currently, the Deep Learning algorithm toolbox has provided various programming language platforms. Departing from research findings related to deep learning, this study utilizes this method to perform facial recognition. The system we offer is also capable of checking the position or whereabouts of objects using Indoor Positioning System (IPS) technology. Facial recognition evaluation using CNN obtained a maximum value = 92.89% and an accuracy error value of 7.11%. Meanwhile, the average accuracy obtained is 91.86%. In the evaluation of the estimated position tested using DNN, the highest value of r2 score is 0.934, the lowest is 0.930 and an average is 0.932 and the highest value is MSE is 4.578, the lowest is 4.366 and the average is 4.475. This shows that the facial recognition process that is tested is able to produce good values but not the position estimation process. Keywords: Face Recognition, IPS, CNN, MSE, Accuraccy.
Analisis dan Implementasi Diagnosis Penyakit Sawit dengan Metode Convolutional Neural Network (CNN) Errissya Rasywir; Rudolf Sinaga; Yovi Pratama
Paradigma Vol 22, No 2 (2020): Periode September 2020
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (700.673 KB) | DOI: 10.31294/p.v22i2.8907

Abstract

Jambi Province is a producer of palm oil as a mainstay of commodities. However, the limited insight of farmers in Jambi to oil palm pests and diseases affects oil palm productivity. Meanwhile, knowing the types of pests and diseases in oil palm requires an expert, but access restrictions are a problem. This study offers a diagnosis of oil palm disease using the most popular concept in the field of artificial intelligence today. This method is deep learning. Various recent studies using CNN, say the results of image recognition accuracy are very good. The data used in this study came from oil palm image data from the Jambi Provincial Plantation Office. After the oil palm disease image data is trained, the training data model will be stored for the process of testing the oil palm disease diagnosis. The test evaluation is stored as a configuration matrix. So that it can be assessed how successful the system is to diagnose diseases in oil palm plants. From the testing, there were 2490 images of oil palm labeled with 11 disease categories. The highest accuracy results were 0.89 and the lowest was 0.83, and the average accuracy was 0.87. This shows that the results of the classification of oil palm images with CNN are quite good. These results can indicate the development of an automatic and mobile oil palm disease classification system to help farmers.
Pengujian Algoritma MTCNN (Multi-task Cascaded Convolutional Neural Network) untuk Sistem Pengenalan Wajah Yovi Pratama; Marrylinteri Istoningtyas; Errissya Rasywir
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 3, No 3 (2019): Juli 2019
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v3i3.1324

Abstract

Measurement of facial similarity or checking similarity is done using features. The algorithm for describing the most up-to-date and best face features for generating features is Deep Convolutional Neural Network (DCNNs). Based on this, this study uses MTCNN (Multi-task Cascaded Convolutional Neural Network) as one variation of the DCNN method. In this research, we built a research system to test results with javascript. Given the many needs that are based on mobile or can be run on a smartphone. One of them is to support the absent feature that is used in a mobile manner such as the reporting system of sales and marketing performance or members of the police personnel who normally work on a mobile basis. From the results of the tests carried out automatically using several variation models testing the image of the Aberdeen dataset as many as 60 images from 30 different people used in the face recognition research system using MTCNN with influencing image parameters such as lighting variations, object position variations, then the position taken and expression face on the object image, the research system managed to do face recognition by 100%. Thus, true positive values are equal to the amount of data tested and zero negative true values.
Evaluasi Pembangunan Sistem Pakar Penyakit Tanaman Sawit dengan Metode Deep Neural Network (DNN) Errissya Rasywir; Rudolf Sinaga; Yovi Pratama
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 4, No 4 (2020): Oktober 2020
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v4i4.2518

Abstract

The limited knowledge of oil palm farmers on oil palm pests and diseases is related to oil palm productivity. Jambi Province is one of the largest oil palm producers on the island of Sumatra. Usually, to find out the types of pests and diseases in oil palm in the field, farmers need knowledge like that of experts about oil palm diseases. However, the limitation of facilities and capabilities becomes an obstacle. This study offers an expert system to analyze oil palm disease using deep learning. This method is deep learning with excellent accuracy. Various recent studies using DNN state that the classification accuracy results are very good. The data used for the expert system using the DNN algorithm comes from oil palm diagnostic data from the Jambi Provincial Plantation Office. After the oil palm disease diagnosis data is trained, the training data model will be stored for the oil palm disease diagnosis testing process. With a total of 11 classes (Leaf Spot Disease, Anthrox Leaf Blight, Leaf Rust Disease, Leaf Canopy Disease, Bud Rot Disease, Root Rot Disease, Fire Caterpillar or Setora Nitens, Red Mites or Oligonychus, Horn Beetle or Orycte rhinoceros, Bunch Borer Fruits and Nematodes Rhadinaphelenchus Cocophilus), with test variables including the number of classes, TP, TN, FP, FN, precision, recall, F1-score, accuracy, and Missclassificaion rate. The highest accuracy value was 0.88, while the lowest value was 0.83 and the average accuracy was 0.86. This shows that the results of expert system diagnosis on oil palm disease data with DNN are quite good.
Co-Authors Abdul Haris Abdul Harris Achpal Haddid Afrizal Nehemia Toscany Akbar Ramadhan Akwan Sunoto Alvito Widianto Annisa putri Anton Prayitno Arahmad Taupiq asih asmarani Babel Tio Carenina Bayu saputra Borroek, Maria Rosario Cahyana Putra Pratama Candra Adi Rahmat Chindra Saputra Defrin Azrian Desi Kisbianty Despita Meisak Dila Riski Anggraini Dimas Pratama elvi yanti Elvi Yanti Eni Rohaini Errissya Rasywir Evan Albert Fachruddin Fachruddin Fachruddin Fachruddin Fachruddin Fachruddin Fachruddin, Fachruddin farchan akbar Feranika, Ayu Fingki Lamhot Pasaribu fiqri ansyah Hartiwi, Yessi Hendrawan Hendrawan Hendrawan Hendrawan Hendrawan Hendrawan Hilda Permatasari Ilham Adriansyah ilham permana Imelda Yose Irawan Irawan Irawan, Beni Janu Hadi Susilo Jopi Mariyanto Julia Triani khalil gibran ahmad Kholil Ikhsan Luthfi Rifky M Fikrul Hakimi M Reihan Al Fajri M.Rizky Wijaya Maria Rosario Borroek Marrylinteri Istoningtyas Marrylinteri Istoningtyas Marrylinteri Istoningtyas Marshal` Koko Anand masgo Maulana Qaedi Aufar Mayang Ruza Muhammad Afif Dzaky Khairullah Muhammad Diemas Mahendra Muhammad Irwan Bustami Muhammad Ismail Muhammad Riza Pahlevi Muhammad Wahyu Prayogi Muhammad Zulfi Tisna Tama Mumtaz Ilham S Mumtaz Ilham Syafatullah NAIBAHO, RONALD Najmul Laila Naldi Irfan Nanda Ghina Nur Aini Pareza Alam Jusia Pareza Alam Jusia, Pareza Alam Raden Tio Putra Sudewo Reza Pahlevi Riki Bayu Andhika Rio Ferdinand Rudolf Sinaga Sandi Pramadi Santoso Saparudin Saparudin Saparudin Saparudin Steven Ie Verwin Juniansyah virginia casanova andiko andiko Warcita Warcita Xaverius Sika Yessi Hartiwi Yessi Hartiwi Yoga Rizki Yuga Pramudya Zahlan Nugraha