Kerangka Kerja Sistem Kecerdasan Buatan dalam Meningkatkan Kompetensi Sumber Daya Manusia Indonesia

Devianto, Yudo; Dwiasnati, Saruni

doi:10.22441/incomtech.v10i1.7460

Cited by 22 publications

(21 citation statements)

References 5 publications

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“…In order for a system to emulate the human brain's ability to think, associate, differentiate and interpret what is experienced, the system must understand how the human brain model draw the conclusions. (Devianto & Dwiasnati, 2020)…”

Section: Artificial Intelligencementioning

confidence: 99%

Rice Plants Disease Identification Using Deep Learning with Convolutional Neural Network Method

Jatmika¹,

Saputra²

2022

SinkrOn

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Indonesia is an agricultural country where most of the population grows rice and most farmers cannot detect early if there is a pest attack on rice plants . This research discuss about deep learning implementation to classify or identify diseases in rice leaves using mobile application. This system will make users easily to diagnose diseases by displaying diagnostic results in the form of the name of the disease along with its taxonomy, disease description and drug recommendations for disease solutions. There are four classes of leaves used in this research, including healthy leaves, leaf blight, brown spot and potassium deficiency. The design of the model uses two approaches, one of them are modeling convolutional neural network from the scratch and modeling with transfer learning using inception v3 architecture. Both models will go through training process to produce a model that is ready to be used for classification. In application testing, a comparison is made between two models. From the tests that have been carried out, it is concluded that the system with model made using transfer learning approach, produce good accuracy with an accuracy of 90%. Meanwhile the System with the other model gain an accuracy of 62%. So when the data used in research are extremely low, it is best to use transfer learning as an approach to design a mode.

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Section: Artificial Intelligencementioning

confidence: 99%

Rice Plants Disease Identification Using Deep Learning with Convolutional Neural Network Method

Jatmika¹,

Saputra²

2022

SinkrOn

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“…Pada dasarnya, ada tiga jenis pekerjaan yang mengharuskan penggunaan komputer untuk meningkatkan efektivitas serta efisiensinya, diantaranya adalah: 1) Pekerjaan yang memiliki tingkat ketelitian tinggi; 2) Pekerjaan berulang; serta 3) Pekerjaan yang melibatkan data yang besar (Devianto and Dwiasnati, 2020;Ghufron, 2018). Di era revolusi industri 4,0 pekerjaan-pekerjaan tersebut yang selama ini dikerjaan secara manual, mulai ditinggalkan dan beralih dengan menerapkan teknologi komputer dalam menyelesaikannya, baik secara hardware, software maupun kombinasi dari keduanya .…”

Section: Pendahuluanunclassified

Kombinasi Metode Entropy Dan Simple Additive Weighting (Saw) Dalam Penentuan Kepala Sekolah Terbaik

Rahmat

2022

TIKA

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Sektor pendidikan merupakan salah satu sektor yang paling banyak menerapkan teknologi infromasi terkomputerisasi. Hal ini tentu berdampak kepada pengembangan teknologi komputer secara terus menerus baik dari sisi perangkat keras maupun perangkat lunak. Salah satu kasus dalam bidang pendidikan yang melibatkan komputer adalah pemilihan kepala sekolah terbaik dengan menggunakan metode pendukung keputusan. Metode pendukung keputusan merupakan metode yang dapat digunakan untuk memberikan rekomendasi terhadap alternatif yang telah ditetapkan sebelumnya berdasarkan kriteria yang telah ditentukan. Dalam penelitian ini dilakukan pengembangan model pendukung keputusan dengan pendekatan kombinasi dua metode yaitu metode Entropy dan Simple Additive Weighting (SAW). Metode entropy digunakan untuk menghitung nilai entropy dan normalisasi entropy, sedangkan metode SAW digunakan untuk melakukan normalisasi serta perangkingan alternatif berdasarkan ketieria yang ada. Adapun hasil yang didapatkan adalah penggunaan dua metode ini dinilai lebih baik daripada menggunakan salah satu metode saja, baik entropy maupun SAW

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“…The research method used is an artificial neural network with machine learning methods (Eddy et al, 2018). Machine learning is a branch of artificial intelligence or artificial intelligence that allows systems to adapt human abilities to learn (Devianto & Dwiasnati, 2020). This algorithm is also trained to make predictions in data development through the use of statistics (Saifudin & Wahono,…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Implementation of Backpropagation ANN in Predicting Long Bean Crop Production in Sumatra Island Province

Siallagan¹,

Solikhun²

2022

Computational

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The production of long bean vegetable crops in Indonesia is very high, this is because this plant is easy to cultivate. Predicting the production of long bean vegetable crops on the island of Sumatra, where the data source comes from BPS (Central Bureau of Statistics). In predicting the use of ANN (Artificial Neural Networks) and the method used in this study is the backpropagation algorithm, this method will be used to predict or predict the production of long bean vegetable crops on the island of Sumatra. The results have been obtained using 4 models, namely the 6-5-1, 6-10-1, 6-15-1, and 6-20-1 models. Among the 4 existing models, the 6-5-1 model has the more accurate accuracy or the lowest error value with an MSE of 0.00711838.

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Kerangka Kerja Sistem Kecerdasan Buatan dalam Meningkatkan Kompetensi Sumber Daya Manusia Indonesia

Cited by 22 publications

References 5 publications

Rice Plants Disease Identification Using Deep Learning with Convolutional Neural Network Method

Rice Plants Disease Identification Using Deep Learning with Convolutional Neural Network Method

Kombinasi Metode Entropy Dan Simple Additive Weighting (Saw) Dalam Penentuan Kepala Sekolah Terbaik

Implementation of Backpropagation ANN in Predicting Long Bean Crop Production in Sumatra Island Province

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