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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Annual Research Seminar Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 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 Jurnal Informatika, Komputer dan Bisnis (JIKOBIS) 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 Scientific Journal of Informatics Jurnal Pengabdian Masyarakat Unama

Errissya Rasywir

Universitas Dinamika Bangsa

Author-ID : 1207662

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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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

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

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

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.

Eksperimen Penerapan Sistem Traffic Counting dengan Algoritma YOLO (You Only Look Once) V.4. Yovi Pratama; Errissya Rasywir
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 4 (2021): Oktober 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i4.3309

Traffic counting is the activity of counting traffic (vehicles) that pass on the road in a certain period. The purpose of traffic counting is to collect traffic data, determine traffic characteristics, determine vehicle composition and measure traffic performance. With the YOLO V.4 algorithm, changes in the position, size and volume of the detected object can be carried out in several tests. Although not all the results of using this algorithm are perfect on all data, the results tend to be good. This is related to the services provided in the form of a convolutional layer on YOLO reducing downsample or reducing image dimensions by using anchor boxes, this algorithm can also increase accuracy. The YOLO V.4 algorithm utilizes an image feature scanning model using the concepts of angles and directions mathematically. From the results of experiments carried out in this study, obtained detection results that have a fairly good accuracy in the results of separating frames from video data. Irregular transformations of position, dimension, composition and direction can still be captured as the same feature. YOLO's ability in feature engineering is an acknowledgment that has been successfully proven in this research.

Sistem Pakar Diagnosis Penyakit Tanaman Karet dengan Metode Fuzzy Mamdani Berbasis Web Hendrawan Hendrawan; Abdul Harris; Errissya Rasywir; 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.v4i4.2521

Rubber plants can be attacked by various diseases originating from fungi, pests, animals and even cancer cells. A method that is able to diagnose rubber disease is needed so that it is hoped that it can help farmers detect symptoms early so that the productivity of rubber plantations can increase. This study developed an analysis of the results of the diagnosis of rubber plant disease using the Mamdany Fuzzy method. The choice of this method departs from the fuzzy mamdany research which states that the fuzzy mamdany method is able to resemble the workings of the human brain intuitively. With the implementation of the Expert System for Diagnosis of Disease in Rubber Plants with the Fuzzy Mamdani Algorithm, the work of diagnosing rubber plant diseases can be done more automatically. With 33 sympthon parameter data for rubber plant disease symptoms and 14 classes of rubber disease diagnosis tested using the Mamdany Fuzzy algorithm, the results obtained an accuracy of 81.74%, a value of 5-cross validation of 80.93% and a value of 10-cross validation of 82.30%. This shows that the application of the fuzzy mamdani algorithm produces good accuracy in diagnosing rubber plants.

Application of YOLO (You Only Look Once) V.4 with Preprocessing Image and Network Experiment Yovi Pratama; Errissya Rasywir; Akwan Sunoto; Irawan Irawan
The IJICS (International Journal of Informatics and Computer Science) Vol 5, No 3 (2021): November 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/ijics.v5i3.3386

In computer science, specifically in the field of image processing, many reliable algorithms have been found. Previously it was introduced that the YOLO (You Only Look Once) V.3 algorithm. In this case, the application of the YOLO algorithm that we carried out was applied experimentally by utilizing image preprocessing techniques. In this study, image preprocessing was carried out. The image of the Microsoft COCO dataset that was preprocessed in this study used the method of image dimension reduction and image quality improvement. The Microsoft COCO dataset image dimension reduction method used is the Principal Component Analysis (PCA) method and to improve the image quality of the Microsoft COCO dataset using Gaussian Smoothing. Then after the fine-tuning process, there is an increase in the mAP value by an average of 8.99% so that the five models can have an mAP above 80%. The highest mAP value is owned by the model using the schema after the fine-tuning process. From the results of experiments carried out in this study, obtained detection results that have fairly good accuracy in the dataset results. Irregular transformations of position, dimension, composition and, direction can still be captured as the same feature. YOLO's ability in feature engineering is an acknowledgment that has been successfully proven in this research. Although not all the results of using this algorithm are perfect on all data, the results tend to be good. This is related to the services available in the form of a convolutional layer on YOLO reducing downsample or reducing image dimensions by using anchor boxes, this algorithm can also improve accuracy

Komparasi Penilaian Kinerja Karyawan Dengan Menggunakan Pendekatan Pembelajaran Mesin Errissya Rasywir; Yessi Hartiwi; Yovi Pratama
JURIKOM (Jurnal Riset Komputer) Vol 6, No 4 (2019): Agustus 2019
Publisher : STMIK Budi Darma

Performance evaluation is an activity to measure whether a worker is able to do his job in accordance with his duties and responsibilities. The results of the assessment will be utilized and evaluated by the management managing the workers. The reason for using machine learning in research is because of its advantages in learning about machines with high accuracy results. There has been a lot of machine learning that has been tested, both guided such as decision trees, neural networks, bayesian learning and non-guidance such as clustering, as well as genetic algorithms and ant colony algorithms. The algorithms chosen in this research are Naïve Bayes, Perceptron and Support Vector Machine (SVM). Naïve Bayes was chosen because of the reliability of results with simple steps from the type of learning with the concept of probability. Perceptron is known to be reliable from the types of neural network-based learning. SVM is a well-known algorithm that is able to find hyperplane values more accurately than other algorithms. The results of the study state that the highest accuracy value is generated by the Perceptron algorithm which is equal to 99.33%. Followed by SVM of 96.64% and naive bayes of 94.63% as a result of the use of training data. For the results of testing using 10-folds cross validation consistently under the training data testing.

Diagnosis Penyakit Tanaman Karet dengan Metode Fuzzy Mamdani Hendrawan Hendrawan; Abdul Haris; Errissya Rasywir; Yovi Pratama
Paradigma Vol 22, No 2 (2020): Periode September 2020
Publisher : LPPM Universitas Bina Sarana Informatika

Like most plantation plants in general, rubber can be attacked by various diseases originating from fungi, pests, animals and even cancer cells. For that we need a method capable of diagnosing rubber disease. In previous research related to the diagnosis of plant diseases, among others, using the Dempster Shafer method, the Certainty factor method and forward chaining. This study developed an analysis of the results of the diagnosis of rubber plant disease using the Mamdany Fuzzy method. The choice of this method departs from research on fuzzy mamdany which states that the fuzzy mamdany method is able to resemble the intuitive way the human brain works. It is hoped that with this method, the diagnosis of rubber plant disease can help farmers detect symptoms earlier so that the productivity of rubber plantation products can be achieved. increased. This study used rubber plant disease data from the Jambi Provincial Plantation Office in Jambi City. From the results of calculations carried out in diagnosing rubber plant disease, as many as 161 rubber plant object data were equipped with 33 symptom identities and a diagnosis from plantation data, then tested 60 rubber plant data without a diagnostic label, we obtained an accuracy value of 81.28%. Likewise, testing by randomizing training data with Cross Validation obtained close results.

IMPLEMENTASI SISTEM PENENTUAN SKRIPSI MAHASISWA STIKOM JAMBI DENGAN EMCLUSTERING DAN NAÃVE-BAYES Errissya Rasywir
Jurnal PROCESSOR Vol 12 No 1 (2017): Jurnal Processor
Publisher : LPPM Universitas Dinamika Bangsa

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

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 NaÃ¯ve Bayes yang mampu memberi contoh referensi topik skripsi.

Penerapan Algoritma K-Means Pada Penyebaran Covid-19 Di Provinsi Jambi Reza Pahlevi; Muhammad Wahyu Prayogi; Errissya Rasywir; Yovi Pratama
Journal of Computer System and Informatics (JoSYC) Vol 4 No 1 (2022): November 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i1.2550

The purpose of this study using the K-Means Cluster method is to determine the level of distribution of Covid-19 cases in the high, medium, and low categories in each region in Jambi Province. There are several aspects that can be measured such as population, population density, positive cases infected with Covid-19, recovered patients, and patients who died. The data collection method used is the documentation method in the form of secondary data obtained from the Jambi Provincial Government website. The data used were positive, recovered, and died and were analyzed using the WEKA application. From the results of research with the K-Means method using 3 clusters. Cluster 0 is a cluster with a high level of distribution category, which is in the city of Jambi. Cluster 1 is a cluster with a medium level distribution category consisting of Batanghari, Merangin, Muaro Jambi, Tanjab Timur. Cluster 2 is a cluster with a low-level distribution category consisting of Bungo, Kerinci, Sarolangun, Sungai Penuh, Tanjab Barat, Tebo.

Co-Authors Abdul Haris Abdul Harris Abdurrahman Abidin, Dodo Zaenal Abrani, Sauti Ade Saputra Agus Siswanto Akwan Sunoto Anggraini, Dila Riski Anita Anita Nurjanah Annisa putri Anton Prayitno Arya Atmanegara Aryani, Lies asih asmarani Athalina, Ghita Bayu saputra Beni Irawan Betantiyo Prayatna Borroek, Maria Rosario Briyan Chairullah Candra Adi Rahmat Carenina, Babel Tio Clara Zuliani Syahputri Defrin Azrian Desi Kisbianty, Desi Despita Meisak desy ayu ramadhanty Dimas Pratama Dodo Zaenal Abidin Dwi Rosa Indah Elsa Charolina L Siantar Evan Albert Fachruddin Fachruddin Fachruddin Fachruddin Fachruddin Fachruddin Fachruddin Fachruddin, Fachruddin farchan akbar Feranika, Ayu Fernando Fernando fiqri ansyah Fradea Novi Ramadhayanti GILLIANI, WENNY Hani Prastiwi Hartiwi, Yessi Hendrawan Hendrawan Hendrawan Hendrawan Hendrawan Hendrawan Hilda Permatasari Hussaein, Ahmad Ilham Adriansyah Ilham Fahrozi ilham permana Imelda Yose Iqbal Pradibya Irawan Irawan Irawan Irawan Irawan, Beni Istoningtyas, Marrylinteri Jasmir Jasmir Jeny Pricilia Johari, Riyan Jopi Mariyanto khalil gibran ahmad Kholil Ikhsan Lazuardi Yudha Pradana 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 Mgs Afriyan Firdaus Migi Sulistiono Muhammad David Adrilyan Muhammad Diemas Mahendra Muhammad Ismail Muhammad Ismail Muhammad Riza Pahlevi Muhammad Satria Mubin Muhammad Wahyu Prayogi Mulyadi Mulyadi Mumtaz Ilham S Mumtaz Ilham Syafatullah Muttaqin Nabila Khumairo Najmul Laila Nanda Ghina Nasrul Ahlunaza Nasutioni, Wahyudi Nilu Widyawati Nungky Septia Kurnicova Nur Aini Nur Azmi Nurhadi Nurhadi Nurul Aulia OPHELIA, CHANDY Pahlevi, M. Riza Pahlevi, M.Riza Pareza Alam Jusia Pareza Alam Jusia Pareza Alam Jusia, Pareza Alam Putri Ratna Sari, Putri Ratna Rani Oktavia, Feby Renita Syafitri Reza Pahlevi Rio Ferdinand ROBY SETIAWAN Rofi'i, Imam Rohaini, Eni Rosario B, Maria Rosario, Maria Rts CiptaNingsi Rudolf Sinaga Sandi Pramadi Saparudin, Saparudin Satria Oldie Versileno Sri Wahyuni Sulistia Ramadhani Suyanti Tasya Basalia Sihombing Tedy Hardiyanto Tondy Maulana Tambunan Verwin Juniansyah virginia casanova andiko andiko Wahid Hasyim Yaasin, Muhammad Yessi Hartiwi Yessi Hartiwi Yoga Rizki Yovi Pratama Yuga Pramudya Zahlan Nugraha

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