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All Journal INOVTEK POLBENG TEKNOKOM : Jurnal Teknologi dan Rekayasa Sistem Komputer
Muhamad Suhaedi
Universitas Sultan Ageng Tirtayasa
Computer Science & IT

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POTENSI ENERGI LAUT DI INDONESIA SEBAGAI SUMBER LISTRIK BARU TERBARUKAN Sanam Sanam; Inda Aidatul Azpah; Muhamad Suhaedi; Rasendriya Arya Guna; Deddy Supriyatna
INOVTEK POLBENG Vol 12, No 2 (2022): INOVTEK Vol 12, No 2
Publisher : POLITEKNIK NEGERI BENGKALIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/ip.v12i2.2862

Abstract

Indonesia dan negara-negara lain telah cukup lama menghadapi krisis energi. Salah satu sumber energi alternatif yang memanfaatkan sumber daya yang tidak hayati dan memiliki banyak potensi untuk dikembangkan adalah energi laut. Seharusnya tidak mengherankan bahwa Sejumlah negara maju berlomba memanfaatkan potensi laut yang diperkirakan mampu memenuhi empat kali lipat kebutuhan listrik dunia. Tinjauan literatur (study literature) digunakan untuk mengkaji penelitian ini, tujuan penelitian ini untuk menganalisis pemanfaatan energi laut di Indonesia sebagai sumber energi baru terbarukan atau energi listrik alternatif. Sehingga penelitian ini memiliki manfaat terhadap pentingnya potensi energi laut sebagai energi terbarukan yang biasa dimanfaatkan di Negara Indonesia. Adapun hasil dari penelitian ini ialah energi laut di Indonesia memiliki banyak potensi karena Indonesia sendiri Negara yang memiliki laut yang sangat luas dan memiliki rata rata gelombang laut yang cukup tinggi sehingga dapat menghasilkan sumber ebergi listrik yang melimpah. Serta pemanfaatan energi gelombang laut juga dapat dipadukan dengan teknologi lain seperti teknologi pemecah gelombang
SKIN CANCER IMAGE DETECTION SYSTEM USING THE CONVOLUTIONAL NEURAL NETWORK MODEL Muhamad Suhaedi; Hamid Abdillah; Annisa Rizki Liliandari
TEKNOKOM Vol. 6 No. 1 (2023): TEKNOKOM
Publisher : Department of Computer Engineering, Universitas Wiralodra

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (333.707 KB) | DOI: 10.31943/teknokom.v6i1.106

Abstract

The development of science and technology (IPTEK) in the current era is growing very rapidly in various fields such as industry, education, especially the health sector. Many technologies can be used, one of which is artificial intelligence technology. This study aims to detect skin cancer images using CNN so that they can be efficient and precise. This research method uses the convolutional neural network (CNN) method, namely image processing, the development of a multilayer perceptron (MLP), in which the neurons of the data are propagated in two dimensions. Because this method has very high accuracy compared to the fuzzy k-nearest neighbors. The results of this study are that there are 7 classes of skin cancer images including actinic keratosis, basal cell carcinoma, dermatofibroma, benign keratosis, melanocytic nevi, vascular lesions and melanoma. From the results of testing the 7 classes using the convolutional neural network (CNN) method with a very high accuracy rate of 99%, 96%, 98%, 99%, 100%, 99% and 96%, respectively. With the conclusion that using the convolutional neural network (CNN) method produces an average accuracy of 98% compared to the Mobilnetv2, Resnet50 and VGG16 models, which means that the CNN model is proven to be more accurate. So it is hoped that this detection system can be applied as a skin cancer detection system for the world of health.
Co-Authors Annisa Rizki Liliandari Deddy Supriyatna Hamid Abdillah Inda Aidatul Azpah Rasendriya Arya Guna Sanam Sanam