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All Journal Brahmana : Jurnal Penerapan Kecerdasan Buatan
Yuegilion Pranavarna Purba
STIKOM Tunas Bangsa, Pematangsiantar – Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering

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Analisa Jaringan Saraf Tiruan Backpropagation Untuk Memprediksi Prestasi Siswa SMA Muhammadiyah Serbelawan Aulia Ichwanda Ramadhan; Jaya Tata Hardinata; Yuegilion Pranavarna Purba
Brahmana : Jurnal Penerapan Kecerdasan Buatan Vol 3, No 1 (2021): Edisi Desember
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/brahmana.v3i1.88

Abstract

Achievements achieved by graduates from an educational institution show the quality and quality. One of them is seen from one of the assessment criteria for assessing the achievement of graduates at the secondary school level, namely through the average score. This average value is often used as a measure to assess students who will enter the next level of education. In addition, the acceptance of students at a level of education is also adjusted to the capacity of the school in question. The high average score at the high school level does not guarantee student achievement at the tertiary level. So that this study aims to obtain an output architecture prediction of student achievement at SMA Muhammadiyah Serbelawan which correlates between the average value and the total score of class XII (twelve) high school students according to the data trained using Artificial Neural Network Analysis using the Backpropagation method. The data taken in the form of the average value of students and the total value of the second semester of class XII students. Furthermore, the data were analyzed using Backpropagation ANN method, with the help of MATLAB software. From the results of testing the Student Achievement data above, we can see in the 5-5-5-1 architecture which shows from the target minus the ANN output that SSE is 0.17625 which shows that there is a measuring tool in predicting the best students using academic value data as a target. From the data obtained, the computational performance of artificial neural networks with the Backpropagation Algorithm is 85%.
Analisis Metode K-Medoids Pada Penjualan Produk Smartphone Vivo Di Kota Pematangsiantar Mita Yustika; Agus Perdana Windarto; Yuegilion Pranavarna Purba
Brahmana : Jurnal Penerapan Kecerdasan Buatan Vol 3, No 1 (2021): Edisi Desember
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/brahmana.v3i1.89

Abstract

Globalization is a very promising era of openness and freedom, where a communication which is a means to obtain information is expected to be carried out easily and effectively. One of the communication media in question is a cellphone. The users of cellphones or cellphones used to be limited to the elite, but for now it has begun to penetrate various circles of society ranging from students, university students, civil servants and even ordinary people who have used it... K-Medoids Clustering is one of the one technique of one of the data mining functionality, the clustering algorithm is an algorithm for grouping a number of data into certain data groups (clusters). While data mining, often referred to as Knowledge Discovery In Database (KDD) is an activity that includes the collection, use of historical data to find regularities, patterns or relationships in large data sets aimed at finding out the Vivo Smartphone brand that is selling well in the market so that it can be done. early procurement. From this study, it was found that Vivo smartphones with the brand S1 Pro 8+128, V17 Pro, V19 8+128GB, V19 8+256GB, Y11 2+32GB, Y12 3+64GB, Y17 4+128GB, Y19 6+128GB are brands which sold a lot. With the results obtained can provide information to PT. Vivo is improving its service even better by increasing the stock of Vivo smartphones which are in great demand, especially in the city of Pematangsiantar
Co-Authors Agus Perdana Windarto Aulia Ichwanda Ramadhan Jaya Tata Hardinata Mita Yustika