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All Journal Voteteknika (Vocational Teknik Elektronika dan Informatika) Syntax Jurnal Informatika KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Sinkron : Jurnal dan Penelitian Teknik Informatika International Journal of Artificial Intelligence Research JURNAL MEDIA INFORMATIKA BUDIDARMA Faktor Exacta JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Sisfokom (Sistem Informasi dan Komputer) Conference on Innovation and Application of Science and Technology (CIASTECH) JURIKOM (Jurnal Riset Komputer) Building of Informatics, Technology and Science Systematics Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi TIN: TERAPAN INFORMATIKA NUSANTARA Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) Scientific Student Journal for Information, Technology and Science Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Buana Pengabdian Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Pengabdian Masyarakat Nasional Inovasi Gagasan Abdimas & Kuliah Kerja Nyata Scientific Journal of Informatics
Baihaqi, Kiki Ahmad
Universitas Buana Perjuangan Karawang
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Journal : Syntax Jurnal Informatika

 1 
PENGENALAN JENIS CANDI BERDASARKAN BENTUK DAN MODELNYA MENGGUNAKAN MOTODE CONVOLUTIONAL NEURAL NETWORK (CNN) PADA YOLLO v3 Kiki Ahmad Baihaqi; Candra Zonyfar; Bagja Nugraha
SYNTAX Jurnal Informatika Vol 10 No 02 (2021): Oktober 2021
Publisher : Universitas Singaperbangsa Karawang

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

Abstract

Berkembangnya teknologi yang cepat dan merambah semua sendi kehidupan. Termasuk sistem cerdas yang digunakan pada segmen cagar budaya yang penting untuk diperkenalkan kepada generasi muda bahkan memperkenalkan kepada bangsa lain bahwa ada cagar budaya berupa situs candi yang memiliki beberapa jenis candi dalam 1 komplek situs percandian candi jiwa yang terletak di Kabupaten Karawang. Tentu dengan model dan ciri khas bangunan candi tersebut, perlu pembuktian algoritma Convolutional Neural Network (CNN) dapat diterapkan dalam pendeteksian jenis candi dimana candi memiliki cirinya sendiri. Ada sumber yang menjelaskan bahwa CNN ini merupakan algoritma terbaik dalam sistem deep learning. Proses penarikan kesimpulan dalam algoritma melewati tahap klasifikasi dan tahap pembelajaran menggunakan backpropagation, yaitu setiap neuron dipresentasikan dalam bentuk dua dimensi. Metode pengembangan sistem berdasarkan kecerdasan buatan ini akan belajar dari data yang diinputkan sehingga semakin banyak inputan yang diberikan maka semakin cerdas dalam menarik kesimpulan. Sehingga sistem ini nantinya menghilangkan tahap feature extraction yang dilakukan secara manual pada machine learning. Hasil dari penelitian ini memiliki akurasi lebih dari 70%, sehingga CNN yang ada pada YOLO v3 ini baik untuk mendeteksi bentuk candi.
Artikel Implementasi Algoritma Best First Search untuk Pencarian Rute Terpendek pada Aplikasi Cerdas Pendaftaran Santri Baru: Aplikasi Cerdas Pendaftaran Santri Baru Herfandi Herfandi; Ulfatus Soleha; Agung Susilo Yuda Irawan; Kiki Ahmad Baihaqi; Reza Maulana
SYNTAX Jurnal Informatika Vol 11 No 01 (2022): Mei 2022
Publisher : Universitas Singaperbangsa Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35706/syji.v11i01.6398

Abstract

Pesantren in Indonesia is a traditional Islamic educational institution that is able to exist until now. A less structured registration process allows errors and delays in the new santri registration process will be easy to occur. Pondok Pesantren Manbaul Ulum in carrying out the education administration process still uses conventional means this causes the service system used has not been efficient and the problem of dormitory limitations, registration regulations must be made in accordance with the distance zone of residence that has been determined. Therefore this study conducted the implementation of the best first search algorithm for the search of the shortest route on the new registration intelligent application. This research resulted in a new santri registration intelligent application by applying the best first search algorithm. The development method uses waterfall. Testing software with black box testing method with test case equivalence partitioning technique gets successful conclusions from various types of testing. With features for santri namely estimation data, home, instructions, contact us, register, login, fill out registration form, see verification, print proof of pass, and see the shortest tute, for admin setting school year, see registration, see registration accepted and rejected and see reports. As for the superadmin, crud admin data and see the report. This application is expected to help pesantren administration and prospective students see the route and support education in the Industrial 4.0 era.
Deteksi Lahan Pertanian Yang Terdampak Hama Tikus Menggunakan Yolo v5: Indonesia Kiki Ahmad Baihaqi; Candra Zonyfar
SYNTAX Jurnal Informatika Vol 11 No 02 (2022): Oktober 2022
Publisher : Universitas Singaperbangsa Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35706/syji.v11i02.7226

Abstract

Rats are one of the pests that cause damage that can be seen by the naked eye, where the damage is in the form of poor growth and development of rice plants [1]. Because the shoots of rice or the fruit are eaten and damaged. In addition, rice plants are the staple food of the Indonesian people from the upper to the lower classes[2], Karawang Regency is the largest contributor to food availability in West Java with data obtained from the agricultural office, the total area of ​​rice fields amounted to 95,906 hectares in 2016 [3]. The number is likely to continue to decrease along with the conversion of land, which is one of the factors for the decline in rice production. With the development of digital image processing technology, a system can be made to detect affected or unaffected rice fields. So that farmers can calculate the yield of their rice harvest in the future. The results of the research using CNN in Yolo v5, from 260 photo data taken from the drone were divided into 230 datasets and 30 testing data. Which is then obtained an accuracy of 88% on average. An error occurs if the testing data uses rice plants that have just started to bear fruit because usually the growth and development of fruit does not coincide, causing it to almost resemble rice plants affected by rat pests.
Implementasi Algoritma Logistic Regression Untuk Klasifikasi Penyakit Stroke suhliyyah; Hanny Hikmayanti Handayani; Kiki Ahmad Baihaqi
SYNTAX Jurnal Informatika Vol 12 No 01 (2023): Mei 2023
Publisher : Universitas Singaperbangsa Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35706/syji.v12i01.8329

Abstract

Stroke menyebabkan kerusakan pada bagian otak yang muncul secara mendadak akibat dari gangguan peredaran darah non traumatik. Gangguan tersebut dapat menimbulkan gejala antara lain kelumpuhan seisi wajah atau anggota badan, bicara tidak jelas, bicara tidak lancar, gangguan penglihatan dan perubahan kesadaran. Penyakit stroke merupakan penyakit yang menjadi penyebab kematian nomor tiga tertinggi di indonesia setelah penyakit kanker dan jantung. Di indonesia, jumlah kasus dan prevalensi stroke belum diketahui secara jelas. Diperkirakan 500.000 penduduk terkena stroke setiap tahunnya, sekitar 2,5% atau 12.500 orang meninggal dunia dan sisanya mengalami cacat ringan. Hampir setiap hari, atau minimal rata-rata tiga hari sekali ada seseorang penduduk indonesia baik tua maupun muda meninggal dunia karena serangan penyakit stroke. Penelitian ini dibuat menggunakan metode Confusion matrix dan pengujian menggunakan algoritma Logistic Regression, penelitian ini dilakukan dengan pengumpulan data dan hasil analisis untuk meningkatkan akurasi, berdasarkan variabel berpengaruh meliputi jenis kelamin, hipertensi, penyakit jantung, kadar gula darah, berat badan dan status merokok. Berdasarkan hasil pengumpulan data yang telah dilakukan sebanyak 4981 data diperoleh hasil akurasi sebesar 94%.
Co-Authors Adi Rizky Pratama Adinata, Abdul Rohim Aditya Zatnika Agung Susilo Yuda Irawan Agusti, Anggi Renata Agustin, Rachmayanti Tri Ahmad Fauzi Ahmad Fauzi April Lia Hananto Awal, Elsa Elvira Azhari Ali Ridha Azhari, Febrian Akbar Bagja Nugraha Bagus Setiawan Bambang Sukowo Billy Ibrahim Hasbi Candra Zonyfar Candra Zonyfar Danny Manongga Deden Wahiddin Dendi Prana Yudha Deny Adi Faldano Devi Fitrianah Didi Juardi Direja, Azhar Ferbista Eko Purwirawansyah Faisal, Sutan Fariz Duta Nugraha Fauzi Ahmad Muda Fitria, Denisa Gozali, Gozali Habib Abdullah Hanny Hikmayanti Handayani Hardjianto, Mardi Hendry Herfandi Herfandi Heryana, Nono Hilda Yulia Novita Iman Permana Indah Listiyowati Indra, Jamaludin Iwan Setyawan Juardi, Didi Krisna Perdana Jaya Sitompul Kusumaningrum, Dwi Sulistya Lestari, Santi Arum Puspita Madani, Puja Milenia Sriwildan Mayasari, Rini Muhammad Raja Nurhusen Narwan Nahrudin Nisa Utami, Nisa Nofie Prasetiyo Nurdin, Cherry Januar Nurlaelasari, Euis Pertiwi, Anggun Purnomo, Hendryanto Dwi Rafikoh, Zahra Anggun Rahmat Rahmat Reza Avrizal Reza Maulana Rini Mayasari Rini Mayasari Rizki Septian Akbar Rohana, Tatang Salsa Desmalia Saruni Dwiasnati Siregar, Amril Mutoi suhliyyah Suja Priyanto Sukmawati, Cici Emilia Sulistya, Dwi Sutan Faisal Tatang Rohana Ulfatus Soleha Vandelweiss, Dita Aura Wahiddin, Deden Waiddin, Deden Wardi Karto Destian Yana Cahyana Yudo Devianto Zonyfar, Candra