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All Journal JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Uranus: Jurnal Ilmiah Teknik Elektro, Sains dan Informatika
Abdul Rabi
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Mathematics

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Rancang Bangun Pengendali Senjata SS1 Pemantau Keamanan Pos Perbatasan Berbasis Mikrokontroler Rochmat Apriyanto; Abdul Rabi; Jeki Saputra; Irfan Mujahidin
JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Vol 2, No 2 (2020): Desember 2020
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jasiek.v2i2.5341

Abstract

The problem of borders between countries and between regions, both on land and at sea, is very complex. The current state of using security personnel at the border posts still has many limitations (human factor) in carrying out monitoring in the area of the border post. The guard post staff cannot fully carry out regional monitoring at the border post, as the visibility of each guard post staff member is still limited. As technology advances in the world, especially technology in the military, with the advent of upgrades in weapons control systems operating in automatic mode. This final design aims to develop the design or design of the SS1 weapons control system, which controls the security of border posts, automatically controls movements in azimuth and elevation. In this study, electronic control is used as an SS1 weapons control system to monitor the security of border posts. This tool is capable of moving 90˚ in azimuth and 15˚ in elevation to match the expected system of the tool.
Perbandingan Akurasi Model ResNet50 dan VGG16 dalam Mengklasifikasi Penyakit Cacar Menggunakan Metode Convolutional Neural Network Isvine Zahroya Jazmine Marzuki Fahd; Abdul Rabi; Elta Sonalitha
Uranus : Jurnal Ilmiah Teknik Elektro, Sains dan Informatika Vol. 3 No. 1 (2025): Uranus : Jurnal Ilmiah Teknik Elektro, Sains dan Informatika
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/uranus.v3i1.679

Abstract

Pox disease is an infectious disease that attacks the immune system. This disease is often considered mild because it is usually harmless and does not cause death. With the lack of counseling and treatment of this disease, lacks an understanding of pox disease, the types and the treatment . Some of the types of pox disease are Monkeypox, Chickenpox, and Cowpox. Cowpox can cause complications such as keratitis and corneal melting due to persistent erosion. To avoid complications of pox disease arising, CNN algorithm is considered capable of classifying the type of pox disease. Therefore, this study was conducted using 2 CNN methods used, namely: ResNet50 and VGG16. Because the distinguishing features of pox disease classification involve a mixture of color, shape and texture, CNN algorithm approaches including ResNet50 and VGG16 were tried. The ResNet50 model showed quite good results with an average accuracy of 76% but VGG16 had better accuracy with a value of 93%. The ResNet50 model showed good results with an average accuracy of 76% but VGG16 had better accuracy with a value of 93%. Using further evaluation, VGG16 has superior values with good precision, recall, and F1-score  for each class. This proves that VGG16 is a superior model for classifying pox disease types. The ResNet50 model is good at identifying three classes, namely, Chickenpox, Cowpox, and Healthy compared to the Monkeypox class because that class has 25% recall value and 40% F1-score  value. Similarly, the VGG16 model where the monkeypox class still has a recall value of 76%. This shows the potential of using artificial intelligence technology to classify smallpox disease.  
Co-Authors Elta Sonalitha Isvine Zahroya Jazmine Marzuki Fahd Jeki Saputra Rochmat Apriyanto