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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 : Jurnal Teknik Informatika (JUTIF)

 1 
CLASSIFICATION OF RICE PLANTS AFFECTED BY RATS USING THE SUPPORT VECTOR MACHINE (SVM) ALGORITHM Nofie Prasetiyo; Baihaqi, Kiki Ahmad; Lestari, Santi Arum Puspita; Cahyana, Yana
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 2 (2024): JUTIF Volume 5, Number 2, April 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.2.1949

Abstract

In the era of Indonesia's agrarian economy which is supported by the agricultural sector, rice plants play an important role in meeting food needs. However, pest attacks, especially field mice, can cause significant losses in rice production. To overcome this, this research proposes the use of the Support Vector Machine (SVM) algorithm with the Particle Swarm Optimization method in predicting rat pest attacks on rice plants. This research involves the process of collecting data from drone photos to identify affected agricultural land. The preprocessing stage involves changing colors from RGB to GRAY and zoom augmentation. Feature extraction is carried out using Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP). Testing was carried out involving the SVM/SVC model and performance evaluation was carried out using accuracy, precision and recall metrics. The preprocessing test results showed an increase in performance with training accuracy of 68.33%. However, the actual prediction on the original image results in a low accuracy of around 25%. However, image testing after involving the entire process, including preprocessing and model prediction, shows a higher level of accuracy, reaching around 90%.
IMPLEMENTATION OF THE YOLOV8 METHOD TO DETECT WORK SAFETY HELMETS Direja, Azhar Ferbista; Cahyana, Yana; Rahmat, Rahmat; Baihaqi, Kiki Ahmad
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 3 (2024): JUTIF Volume 5, Number 3, June 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.3.2005

Abstract

Work safety helmets are an important tool in OHS (Occupational Health and Safety) that must be used by workers. Workers who work with heavy equipment must wear work safety helmets as an obligation. Unfortunately, there are still many workers who do not comply with this rule. They will only wear helmets if there is supervision from a supervisor. However, if the supervisor is not on site, many workers will remove their helmets. The need for supervision of workers is important in reducing work accidents. From these problems, a work safety helmet detection model was created using the YOLOv8 method. This implementation aims to increase the accuracy values ​​obtained and can reduce workload and increase efficiency in checking violations of the use of work safety helmets among workers. The method used consists of several stages, namely image acquisition of 670 images, image labeling, preprocessing, augmentation in roboflow, YOLOv8x model training with 100 epochs, image testing with a distance of 1, 3, 5 meters between the object and the camera, evaluation of test results. Based on the results of training with 467 images, the mAP50 reached 99.5%. Meanwhile, the test results with 100 images showed an accuracy of 99%.
ANALYSIS AND IMPLEMENTATION OF AES-128 ALGORITHM IN SUKAHARJA KARAWANG VILLAGE SERVICE SYSTEM Fariz Duta Nugraha; Kiki Ahmad Baihaqi; Hilda Yulia Novita; Siregar, Amril Mutoi
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 3 (2024): JUTIF Volume 5, Number 3, June 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.3.2038

Abstract

Data security in databases is needed in the industrial era 4.0 to prevent attacks and unwanted things from happening, one of the biggest cases that has been widely reported is data leakage, in this study aims to implement and analyze the Advanced Encryption Standard Algorithm, one of the data security algorithms with a block chiper type that has 4 transformations (SubByte, ShiftColumn, MixColumn, AddRoundKey), or what we usually call the Cryptography method. Cryptography is a method that is often used to secure important data in databases, in this article the Advanced Encryption Standard Algorithm is used to secure citizen data and family card data in the Sukaharja Karawang Village service system. The method in this research is the observation method, the data is obtained from each head of the neighborhood in Sukaharja Karawang Village with the permission of the head of Sukaharja Karawang Village. Citizen data and family cards were encrypted and analyzed for resource requirements in storing encryption results and time in returning and displaying original data. The results of the analysis obtained the amount of resources required 1.5MB to store family card data, which before encryption required 352KB. Citizen data requires a resource of 6.5MB, before encryption it takes 1.5MB. As for the AES resilience test stage using the Bruteforce attack method with the help of Hashcat software version 6.2.5 with 4 trial processes, One encrypted address data was taken for this test, but out of 4 attempts none of them showed that the data could be cracked.
Co-Authors Adi Rizky Pratama Adinata, Abdul Rohim Aditya Zatnika Agung Susilo Yuda Irawan Agusti, Anggi Renata Agustin, Rachmayanti Tri Ahmad Fauzi Ahmad Fauzi Amril Mutoi Siregar 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 Dwiasnati, Saruni 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