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All Journal Jurnal Surya Teknika
Rahmawati, Asde
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Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Comparison of MDKA Stock Price Prediction using Multi-Layer Perceptron, Long Short-Term Memory, and Gated Recurrent Unit Wajhi Akramunnas, Bastul; Hakim, Legisnal; Marta Putri, Dita; Fahrizal, Fahrizal; Rahmawati, Asde; Purbolingga, Yoan
JURNAL SURYA TEKNIKA Vol. 10 No. 1 (2023): JURNAL SURYA TEKNIKA
Publisher : Fakultas Teknik UMRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jst.v10i1.5004

Abstract

Shares are rights owned by a person against a company due to the delivery of capital, either in part or in whole. Investors invest in stocks and try to get maximum results, but many investors are still unsure about the risks involved in investing. To minimize risk, investors need to predict stock prices with an accurate method. Several methods that can be implemented to predict stock data include Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The research objective to be achieved in this study is to compare the performance of each algorithm in producing a more accurate stock price forecasting model by testing neurons (10, 20, 30) and epochs (50, 75, 100). The research was conducted on the stock price data of PT. Merdeka Copper Gold Tbk (MDKA) which is a mining sector share with the largest capitalization value. Tests on some of the algorithms above got the best results using 82% training data and 18% test data, namely the MLP model with 10 neurons and 100 epochs with a MAPE training data result of 2.325 and a MAPE test data of 2.014. Based on the test results, MLP can predict MDKA stock prices for the 2018-2022 period with good performance and a relatively small error rate, while tests using the LSTM and GRU methods still produce large errors. Thus, it can be concluded that MLP can predict stock prices with more accurate results.
A CNN-based Approach for Breast Cancer Classification from Ultrasound Images Marta Putri, Dila; Ikhsan, M; Nurjanah, Siti; Fahrizal, Fahrizal; Akramunnas, Bastul Wajhi; Rahmawati, Asde
JURNAL SURYA TEKNIKA Vol. 12 No. 1 (2025): JURNAL SURYA TEKNIKA
Publisher : Fakultas Teknik UMRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jst.v12i1.9378

Abstract

Breast cancer is one of the most frequently diagnosed cancers and remains a leading cause of cancer-related mortality among women worldwide. According to WHO Globocan 2020, breast cancer ranks second globally, with 2,262,419 cases out of a total of 19,292,289 cancer cases, accounting for approximately 11.7%. Early detection plays a critical role in reducing breast cancer mortality. In this study, a machine learning-based approach using Convolutional Neural Networks (CNN) was employed to classify breast cancer using ultrasound imaging. The dataset, collected by Al-Dhabyani et al. at Baheya Hospital in 2018, consists of ultrasound images of women aged between 25 and 75 years. The proposed CNN model includes stages of data input, preprocessing, training, testing, and performance evaluation. The model achieved an accuracy of 85%, demonstrating promising results for automated breast cancer detection. Further optimization is recommended to improve classification performance.
Pengembangan Sensor Elektrokimia Berbasis Material Nano untuk Deteksi Ion Timbal (Pb²⁺) Menggunakan Sistem Elektronika Terintegrasi Rahmawati, Asde; Nurjanah, Siti; Fahrizal, Fahrizal; Marta Putri, Dila; Ikhsan, M; Wajhi Akramunnas, Bastul
JURNAL SURYA TEKNIKA Vol. 12 No. 1 (2025): JURNAL SURYA TEKNIKA
Publisher : Fakultas Teknik UMRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jst.v12i1.9544

Abstract

Electrochemical sensors are a reliable method for detecting the presence of heavy metal ions such as lead (Pb²⁺) in aquatic environments. In this study, a sensor was developed based on a carbon paste electrode modified with ZnO nanomaterials and polyaniline, and integrated with a data acquisition system using a microcontroller. Voltammetric characterization results showed that the sensor could detect Pb²⁺ with high sensitivity at low concentrations. This system is expected to be applied for real-time and portable water quality monitoring.
Co-Authors Akramunnas, Bastul Wajhi Dila Marta Putri Fahrizal Fahrizal Legisnal Hakim M Ikhsan Marta Putri, Dita Siti Nurjanah Wajhi Akramunnas, Bastul Yoan Purbolingga