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All Journal Jurnal Mantik Journal of Computer Networks, Architecture and High Performance Computing Data Sciences Indonesia (DSI)
Yennimar Yennimar
Universitas Prima Indonesia, Indonesia
Computer Science & IT Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Physics

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Implementation of Water Conditions in Soil with Artificial Neural Network Method using Backpropagation Toppan Sintio; Steven Steven; Yennimar Yennimar
Journal of Computer Networks, Architecture and High Performance Computing Vol. 3 No. 2 (2021): Journal of Computer Networks, Architecture and High Performance Computing, July
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v3i2.950

Abstract

In agriculture and plantations, the land is an important thing, but sometimes the soil needs to be measured for its fertility, so measuring instruments are used. In this study, the authors tried to collect data for measuring using the Backpropagation method to determine the prediction of fertility in the soil. The backpropagation method is used to predict and also Backpropagation is a Neural Network algorithm. In using this method, input is a sensor that will take data in the form of soil moisture, pH when wet, and pH when dry, followed by this method which processes the data to be generated. The results of research with the Backpropagation algorithm get 80% accuracy of the 10 test data used for test results. The results tested initially were not as expected but with several trials, it was almost as expected but needed to be further developed. With the hope that there are people who can develop better for more knowledge and hopefully it can be useful for more. The suggestion in this is for readers who want to develop their suggestions to collect more data from this research to get more satisfying results. If the data is not more efficient, more efficient or accurate methods or means of data collection are expected to be used.
Analisis Wawasan Penjualan Supermarket dengan Data Science Mawaddah Harahap; Fachrul Rozi; Yennimar Yennimar; Saut Dohot Siregar
Data Sciences Indonesia (DSI) Vol. 1 No. 1 (2021): Article Research Volume 1 Issue 1, June 2021
Publisher : ITScience (Information Technology and Science)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (771.16 KB) | DOI: 10.47709/dsi.v1i1.1173

Abstract

Data science atau ilmu data adalah suatu disiplin ilmu yang khusus mempelajari data, khususnya data kuantitatif (data numerik), baik yang terstruktur maupun tidak terstruktur. Pemanfaatkan siklus dalam pengembangan analisis untuk membuat keputusan bisnis yang praktis dan berbasis data, dan menerapkan perubahan berdasarkan keputusan tersebut. Makalah ini menyajikan analisis wawasan yang berguna pada kumpulan transaksi penjualan supermarket selama 3 bulan dari 3 cabang yang berbeda. Berdasarkan hasil analisis nilai rating terting adalah 10, terendah 4 dengan rata-rata rating produk 6.9 dan wanita lebih dominan membeli produk Aksesoris Fashion dan pria Kesehatan & Kecantikan
Implementation of artificial neural network and support vector machine algorithm on student graduation prediction model on time Yennimar Yennimar; M. Rafi Faturrahman; Siwa Nesen; M. Anhar Guci; Samuel Rifaldi Pasaribu
Jurnal Mantik Vol. 7 No. 2 (2023): Agustus: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v7i2.3992

Abstract

This research aims to evaluate how Artificial Neural Network (ANN) and Support Vector Machine (SVM)  algorithms can be used to predict student graduation on time. This research uses student data from Universitas Prima Indonesia (UNPRI) Medan to build a prediction model. ANN and SVM methods have been applied and compared to see the performance of each model. The test results show that the SVM model is superior in terms of accuracy and computational speed compared to the ANN model. In addition, the test results also show that the SVM model can be used to predict student graduation on time with an accuracy of 96.34%. This result shows that the SVM model is more effective in predicting student graduation on time compared to the ANN model
Co-Authors Fachrul Rozi M. Anhar Guci M. Rafi Faturrahman Mawaddah Harahap Samuel Rifaldi Pasaribu Saut Dohot Siregar Siwa Nesen Steven Steven Toppan Sintio