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Journal : Paradigma

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
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 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 Nainggolan 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