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All Journal M A T H L I N E : Jurnal Matematika dan Pendidikan Matematika Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Media Computer Science
Parwati, Lusiana Sani
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Computer Science & IT Education Mathematics

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Rancang Bangun Sistem Informasi Tracer Study Pada Teknik Informatika Universitas Nusa Putra Nugraha, Nugraha; Anggun Fergina; Alamsyah, Zaenal; Parwati, Lusiana Sani; Iskandar, Alyanissa Putri
Computer Science and Information Technology Vol 5 No 1 (2024): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v5i1.6691

Abstract

This research aims to implement the Tracer Study information system in the Department of Information Engineering, Nusa Putra University, with a focus on needs analysis, system design, application development, and testing. This system is designed to manage alumni data, monitor career development, and collect relevant information for study program evaluation. The successful implementation of the system is expected to increase the efficiency and effectiveness of Tracer Study, make a positive contribution to curriculum development, and improve the quality of education at universities.
Forecasting Stock Price Using Armax-Garchx Model During The Covid-19 Pandemic Parwati, Lusiana Sani; Nugrahani, Endar Hasafah; Budiarti, Retno
Mathline : Jurnal Matematika dan Pendidikan Matematika Vol. 8 No. 2 (2023): Mathline: Jurnal Matematika dan Pendidikan Matematika
Publisher : Universitas Wiralodra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31943/mathline.v8i2.413

Abstract

The Covid-19 pandemic, which was proclaimed by the World Health Organization (WHO) on March 2020, has impacted stock risk on the capital market. Stock price forecasting can be used to provide future stock projection prices in order to reduce risk. The ARMA GARCH model and its development model can forecast stock prices by incorporating exogenous factors such as the ARMAX GARCH, ARMA GARCHX, and ARMAX GARCHX models. PT Mitra Keluarga Karyasehat Tbk's stock price is analyzed in this study, along with the exogenous factors of total daily positive cases and total daily fatalities cases of Covid-19 from March 16, 2020, to January 31, 2022.The results of several models show that based on MAPE value the ARMAX GARCH model has better accuracy in forecasting stock price.
Implementation of Convolutional Neural Network for Soil Type Category Detection in a Web-Based Plant Recommendation System Sanjaya, Imam; Wahyuni, Yulinar Sri; Parwati, Lusiana Sani
Jurnal Media Computer Science Vol 4 No 2 (2025): Juli
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v4i2.8637

Abstract

The growth of the agricultural sector in Indonesia is highly dependent on soil fertility, as soil is an important factor in the agricultural sector. However, conventional identification of soil types often takes a long time and requires high costs. To overcome this problem, this research develops a soil classification system using an optimized Convolutional Neural Network (CNN) model to improve soil classification accuracy. The results of this classification become the basis for a Content-Based Filtering (CBF) based recommendation system, in order to provide suggestions for crop types that are suitable for soil types. This research was conducted through several main stages, namely soil image data collection, data preprocessing, CNN model training and CBF-based recommendation system implementation. The CNN model is used to recognize soil texture and color patterns, while CBF is used to match soil characteristics with suitable plant species. System evaluation is conducted using confusion matrix to assess the accuracy of the classification model as well as the effectiveness of the recommendation system. The soil type classification process using CNN with MobileNetV2 architecture achieved an accuracy rate of 96%. This result shows that the architecture is effective in recognizing soil types precisely and can be used to provide appropriate crop recommendations. Thus, this system has the potential to support increased agricultural productivity, both on a small and large scale.
Co-Authors adang badru jaman,anggun fergina, adang badru jaman,anggun fergina Alamsyah, Zaenal Endar Hasafah Nugrahani Iskandar, Alyanissa Putri Nugraha Nugraha Retno Budiarti Sanjaya, Imam Wahyuni, Yulinar Sri