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All Journal Informatika IKA BINA EN PABOLO : PENGABDIAN KEPADA MASYARAKAT
Mila Nirmala Sari Hasibuan
Manajemen Informatika, Fakultas Sains dan Teknologi, Universitas Labuhanbatu
Aerospace Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Environmental Science Library & Information Science Public Health Other

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Journal : informatika

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Penerapan Metode KNN untuk Menentukan Minat Calon Mahasiswa Tiara Riyanto; Gomal Juni Yanris; Mila Nirmala Sari Hasibuan
Jurnal Informatika Vol 12, No 3 (2024): INFORMATIKA
Publisher : Fakultas Sains & Teknologi, Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/informatika.v12i3.6153

Abstract

This study focuses on the implementation of data mining to determine the interests of prospective male and female students in the Informatics Management Department using the K-Nearest Neighbors (KNN) method. The analysis process is carried out through the Knowledge Discovery in Databases (KDD) stages, which include data selection, pre-processing, transformation, data mining, and pattern evaluation. The KDD stage ensures that the data used has been prepared and processed properly to produce an accurate and relevant model. The KNN method is used to classify sample data consisting of 82 prospective male and female students. The results of this study indicate that 63 out of 82 prospective students are interested in the Informatics Management Department, while 19 other prospective students are not interested. This classification process shows that the KNN method is able to identify the interests of prospective students with a high level of accuracy, providing useful information for universities in understanding the preferences of their prospective students. Evaluation of the research results using two evaluation tools, namely Test and Score and Confusion Matrix, showed perfect results with an accuracy of 100%. Both of these evaluation tools are consistent in assessing the performance of the KNN model, confirming that this model works very well in classifying prospective student interests. In conclusion, the KNN method is proven to be effective and reliable in determining prospective students' interest in the Informatics Management Department, providing a strong foundation for similar applications in the future.
Implementasi Deep Learning Untuk Menentukan Harga Buah Sawit Romtika Manurung; Volvo Sihombing; Mila Nirmala Sari Hasibuan
Jurnal Informatika Vol 12, No 3 (2024): INFORMATIKA
Publisher : Fakultas Sains & Teknologi, Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/informatika.v12i3.6029

Abstract

This study aims to analyze the price of palm oil using Convolutional Neural Network (CNN) method in deep learning. CNN was chosen for its ability to process complex data and recognize patterns from diverse data. The stages of research include data analysis, data pre-processing, predictive model design for CNN method, CNN classification model prediction results, CNN method evaluation, and CNN method evaluation results. This study aims to produce a model that can predict the price of oil palm with high accuracy, based on data covering a variety of characteristics of farmers and the quality of oil palm crops. Prediction results were conducted using data from 50 oil palm farmers. From the prediction, as many as 23 data farmers get a price of IDR 2,300, 13 other farmers get a price of IDR 2,000, and the remaining 14 data farmers get a price of IDR 1,800. The results of this prediction are based on data from farmers and the quality of oil palm crops they grow and produce. By utilizing the CNN method, the model can capture various factors that affect the price of palm oil, including the quality of palm fruit and agricultural conditions. Evaluation of the CNN method showed very good results, with almost perfect accuracy. This method managed to predict palm oil prices very precisely, showing that CNN can be an effective tool in the analysis of palm oil prices. The results of this evaluation confirmed that the CNN method can be relied upon to provide accurate predictions, helping farmers and palm oil industry players in determining prices that are in accordance with the quality and condition of the crop.
Co-Authors Ali Akbar Ritonga Bangun, Budianto Budi Febriani Fitri Aini Nasution Gomal Juni Yanris Irmayanti Irmayanti Romtika Manurung Tiara Riyanto Volvo Sihombing