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All Journal Perfecting a Video Game with Game Metrics Annual Research Seminar Paradigma JURNAL MEDIA INFORMATIKA BUDIDARMA PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer 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 Journal of Applied Business and Technology Jurnal Informatika dan Rekayasa Komputer Jurnal Manajemen Teknologi dan Sistem Informasi PROCEEDING OF INTERNATIONAL CONFERENCE ON BUSINESS MANAGEMENT AND ACCOUNTING Bulletin of Informatics and Data Science
Errissya Rasywir
Universitas Dinamika Bangsa
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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.
Pengujian Implementasi Sistem Pengelolaan Keuangan Masjid Berbasis Web Dan Android Fachruddin Fachruddin; Muhammad Riza Pahlevi; Muhammad Ismail; Errissya Rasywir
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 (1145.465 KB) | DOI: 10.31294/p.v22i2.8908

Abstract

Manual financial management is one of the causes of data loss and report files. Meanwhile, financial reports are data that must be accounted for. As with our observations, the Darusalam Mosque (Pakuan Baru Village in Jambi City) quite often experiences these classic problems. With the use of Android-based application technology, it is hoped that the mosque's financial data will be more organized, neatly archived and transparent. The digital financial management or accounting system allows flexibility in accessing mosque financial reports. Therefore it is necessary to build a Mosque Financial Management System Based on the Android Platform. The Android-based financial management application will later be launched on Google Playstore, so that all parties who need this system can download this application for free. The application of applications with a whole series of good software engineering must be carried out in accordance with applicable business processes and do not change the flow of data and reports that have been running for years. The application of the Website-based application and the Android Platform that we did, was able to produce automatic and computerized mosque financial management and was considered very good in user testing
Prediksi mahasiswa Drop Out dengan menggunakan Algoritma Klasifikasi Data Mining Jasmir Jasmir; Dodo Zaenal Abidin; Pareza Alam Jusia; Errissya Rasywir
Annual Research Seminar (ARS) Vol 4, No 1 (2018): ARS 2018
Publisher : Annual Research Seminar (ARS)

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

Abstract

Setiap tahun lembaga perguruan tinggi pasti menerima mahasiswa baru, termasuk STIKOM Dinamika Bangsa Jambi. Kehadiran mahasiswa baru ini lambat laun akan menjadi tumpukan data yang luar biasa. Dengan adanya tumpukan data ini penulis mencoba memanfaatkannya untuk mencari informasi baru. Di STIKOM Dinamika Bangsa tumpukan data yang digunakan merupakan data akademik yaitu data IP (Indeks Prestasi) mahasiswa dari semester satu sampai semester empat. Dengan adanya data ini, penulis melakukan prediksi mahasiswa drop out. Data dari semester satu sampai semester empat ini akan menjadi dasar perhitungan prediksi yang penulis lakukan dengan tujuan dapat menemukan informasi mahasiswa yang kemungkinan drop out dengan menggunakan salah satu algoritma data mining yaitu k-Nearest Neighbor.  Hasil dari penelitian ini adalah nilai prediksi mahasiswa yang akan terkena drop out.
IMPLEMENTASI SISTEM PENENTUAN SKRIPSI MAHASISWA STIKOM JAMBI DENGAN EMCLUSTERING DAN NAÏVE-BAYES Errissya Rasywir
Jurnal Processor Vol 12 No 1 (2017): Processor
Publisher : LPPM STIKOM Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1851.361 KB)

Abstract

Selama ini proses penentuan topik skripsi mahasiswa dilakukan secara manual. Yakni baik dosen yangmemberi masukan atau ide yang diperoleh dari berbagai makalah penelitian. Dalam arti kata, proses yangtelah berjalan masih tradisional dan manual tanpa menggunakan sistem yang terkomputerisasi.Berdasarkan hasil penelitian sebelumnya, terdapat korelasi antara nilai mahasiswa pada matakuliahtertentu dengan topik skripsi. Berdasarkan hal tersebut peneliti mencoba membangun sebuah sistemyang mampu membantu mahasiswa Sekolah Tinggi Ilmu Komputer (STIKOM) untuk menentukan topikskripsi yang akan diambil. Sistem yang dibangun dalam penelitian ini merupakan sistem yangmemberikan penentuan topik skripsi mahasiswa berdasarkan nilai mata kuliah yang telah dicapaimahasiswa. Hasil penelitian ini berupa sistem penentuan topik skripsi mahasiswa menggunakan EMClustering dengan Algoritma Naïve Bayes yang mampu memberi contoh referensi topik skripsi.
Co-Authors Abdul Haris Abdul Harris Abdurrahman Ade Saputra Akwan Sunoto Anita Anita Nurjanah Annisa putri Anton Prayitno Arya Atmanegara asih asmarani Babel Tio Carenina Bayu saputra Betantiyo Prayatna Borroek, Maria Rosario Briyan Chairullah Candra Adi Rahmat Clara Zuliani Syahputri Defrin Azrian Desi Kisbianty Despita Meisak desy ayu ramadhanty Dila Riski Anggraini Dimas Pratama Dodo Zaenal Abidin Elsa Charolina L Siantar eni rohaini Eni Rohaini Evan Albert Fachruddin Fachruddin Fachruddin Fachruddin Fachruddin Fachruddin Fachruddin, Fachruddin farchan akbar Feranika, Ayu Fernando Fernando fiqri ansyah Fradea Novi Ramadhayanti Hani Prastiwi Hartiwi, Yessi Hendrawan Hendrawan Hendrawan Hendrawan Hendrawan Hendrawan Hilda Permatasari Ilham Adriansyah Ilham Fahrozi ilham permana Imelda Yose Iqbal Pradibya Irawan Irawan Irawan, Beni Jasmir Jasmir Jeny Pricilia Jopi Mariyanto khalil gibran ahmad Kholil Ikhsan Li Sensia Rahmawati Lies Aryani Luthfi Rifky M.Rizky Wijaya Macharani Raschintasofi Maliyatul Khasanah Maria Rosario Borroek Marrylinteri Istoningtyas Marrylinteri Istoningtyas Marrylinteri Istoningtyas Mayang Ruza Migi Sulistiono Muhammad David Adrilyan Muhammad Diemas Mahendra Muhammad Ismail Muhammad Ismail Muhammad Riza Pahlevi Muhammad Satria Mubin Muhammad Wahyu Prayogi Mulyadi Mumtaz Ilham S Mumtaz Ilham Syafatullah Muttaqin Nabila Khumairo Najmul Laila Nanda Ghina Nasrul Ahlunaza Nilu Widyawati Nungky Septia Kurnicova Nur Aini Nurul Aulia Pareza Alam Jusia Pareza Alam Jusia Pareza Alam Jusia, Pareza Alam Renita Syafitri Reza Pahlevi Rio Ferdinand Rts CiptaNingsi Rudolf Sinaga Sandi Pramadi Saparudin Saparudin Saparudin Saparudin Satria Oldie Versileno Sri Wahyuni Nainggolan Sulistia Ramadhani Tasya Basalia Sihombing Tedy Hardiyanto Tondy Maulana Tambunan Verwin Juniansyah virginia casanova andiko andiko Wahid Hasyim Wahyudi Nasutioni Yessi Hartiwi Yessi Hartiwi Yoga Rizki Yovi Pratama Yuga Pramudya Zahlan Nugraha