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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 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 The Indonesian Journal of Computer Science Scientific Journal of Informatics Jurnal Pengabdian Masyarakat Unama Jurnal Lentera Ilmiah Pengabdian Masyarakat
Yovi Pratama
Universitas Dinamika Bangsa
Aerospace Engineering Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry 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 Public Health Social Sciences Veterinary Other

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Journal : Paradigma

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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.
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (649.729 KB) | DOI: 10.31294/p.v22i2.8909

Abstract

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.
Co-Authors Abdul Haris Abdul Harris Achpal Haddid Adelia Putri, Ananda Afrizal Nehemia Toscany Agus Siswanto Akbar Ramadhan Akwan Sunoto Alvito Widianto Amroni, Amroni Angelica, Felicia Anggraini, Dila Riski Anggy Utama Putri Annisa putri Anton Prayitno Arahmad Taupiq asih asmarani Bayu saputra Beni Irawan Borroek, Maria Rosario Bustami, M Irwan Cahyana Putra Pratama Candra Adi Rahmat Carenina, Babel Tio Chindra Saputra Defrin Azrian Desi Kisbianty, Desi Despita Meisak Dimas Pratama Dimas Yudha Prawira Dinata, Despan elvi yanti Emelia, Shinta Enjelina, Mia Errissya Rasywir Evan Albert Fachruddin 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 Hussaein, Ahmad Ilham Adriansyah ilham permana Imelda Yose Indana Arum , Refi Irawan Irawan Irawan Irawan Irawan, Beni Istoningtyas, Marrylinteri Janu Hadi Susilo Jopi Mariyanto Julia Triani khalil gibran ahmad Kholil Ikhsan Luthfi Rifky M Fikrul Hakimi M Reihan Al Fajri M.Rizky Wijaya Manyu, Dimas Abi 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 SURYA Muhammad Wahyu Prayogi Muhammad Zulfi Tisna Tama Mumtaz Ilham S Mumtaz Ilham Syafatullah NAIBAHO, RONALD Najmul Laila Naldi Irfan Nanda Ghina Nia Azzahra Nur Aini Nurhadi Nurhadi Pahlevi, M. Riza Pahlevi, M.Riza Pareza Alam Jusia Pareza Alam Jusia, Pareza Alam Ramadhan Saputra, Tri Ramadhani, Utari Reza Pahlevi Rezky Pramudia, Muhammad Riki Bayu Andhika Rio Ferdinand ROBY SETIAWAN Rohaini, Eni Rosario B, Maria Rosario, Maria Rudolf Sinaga Sandi Pramadi Santoso Saparudin, Saparudin Sariyani SIKA, XAVERIUS Steven Ie Sudewo, Raden Tio Putra Sutoyo, Mochammad Arief Hermawan Suyanti taupiq, Arahmad Toscany, Afrizal Verna Anatasya, Rara Verwin Juniansyah virginia casanova andiko andiko Warcita Warcita WILLY RIYADI Xaverius Sika Yaasin, Muhammad Yanti, Elvi Yessi Hartiwi Yessi Hartiwi Yoga Rizki Yuga Pramudya Zahlan Nugraha Zulia, Restutik