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All Journal JUTECH : Journal Education and Technology Jurnal Pengabdian Bukit Pengharapan
Valentinus, Valentinus
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Computer Science & IT Education Languange, Linguistic, Communication & Media Other

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PENINGKATAN KETERAMPILAN DAN KUALITAS PEMBELAJARAN MAHASISWA MELALUI PROGRAM INTERNSHIP DI SUJAYA PROCESSING FOOD SINGKAWANG Habel, Habel; Valentinus, Valentinus; Dedy, Dedy
Jurnal Pengabdian Bukit Pengharapan Vol. 5 No. 1 (2025)
Publisher : LPPM Institut Teknologi dan Bisnis Kristen Bukit Pengarapan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61696/jurdian.v5i1.809

Abstract

Internship program skills and quality of student learning internship is an educational strategy to combine theory with practical work in the industrial world. This program aims to analyze the impact of internship programs on the development of skills and improving the quality of learning of students participating in PT Sujaya Processing Food, this internship program is a learning approach that combines theoretical knowledge with practical experience in the industrial work environment. This study shows that student internship programs can effectively develop student skills. This includes technical skills, such as operating production machines, quality control in the food industry, and a better understanding of industrial work systems. In addition, soft skills such as discipline in working, effective communication, and teamwork have increased significantly for students.
IMPLEMENTASI DATA MINING DALAM PREDIKSI HARGA SAHAM BBNI DENGAN PEMODELAN MATEMATIKA MENGGUNAKAN METODE LSTM DENGAN OPTIMASI ADAM Rahman, Alrafiful; Istiyowati, Lucia Sri; Valentinus, Valentinus; Ivan, Ivan; Azis, Zainal
JUTECH : Journal Education and Technology Vol 5, No 2 (2024): JUTECH DESEMBER
Publisher : STKIP Persada Khatulistiwa Sintang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31932/jutech.v5i2.4137

Abstract

Stock price prediction plays a crucial role in investment decision-making, allowing investors to maximize profits and minimize risks. This study implements the Long Short-Term Memory (LSTM) method with Adam optimization to predict the stock price of Bank Negara Indonesia (BBNI) based on historical stock price data from the Indonesia Stock Exchange (2001-2023). LSTM is chosen for its ability to handle sequential data and identify long-term patterns in time series. Meanwhile, the Adam optimization algorithm is used to accelerate model convergence and improve prediction accuracy. The data used includes daily stock prices (closing prices), and the research process involves data collection, preprocessing, LSTM model creation, Adam optimization, training, evaluation, and prediction. The experimental results show that the model with a batch size of 64 and 100 epochs yields an R² of 0.9928 and a MAPE of 1.53%, indicating a very high prediction accuracy. With an accuracy of 98.46%, the LSTM model with Adam optimization proves to be effective in predicting stock prices, providing excellent results for applications in investment strategies. This study demonstrates the great potential of applying data mining and machine learning techniques in more informed and data-driven stock market analysis.
Co-Authors Azis, Zainal dedy dedy, dedy Habel, Habel Ivan Ivan Lucia Sri Istiyowati Rahman, Alrafiful