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All Journal Jurnal EECCIS KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal)
Sjahrunnisa, Anita
Unknown Affiliation
Computer Science & IT Engineering

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Lost and Found: An Application to Search and Find Lost Items Romadhona, Yasinta; Sjahrunnisa, Anita; Yuhana, Umi Laili
KLIK- KUMPULAN JURNAL ILMU KOMPUTER Vol 10, No 3 (2023)
Publisher : Lambung Mangkurat University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/klik.v10i3.566

Abstract

Carrying personal items is a must for everyone, especially to the campus area. It can cause people to lose things, and many lost or found items pile up without the owners. However, to communicate this information, only broadcasts were implemented through chain messages on social media which were considered less effective. Based on these problems, a website-based information system is proposed to assist in managing reports of lost and found items and transactions between finders and searchers of the items. This system has three main functions: inserting data on lost or found items, deleting items that have been returned to their owners, and making claims for items by exchanging messages to each user’s account. This system is built using PHP, HTML, CSS, JavaScript and is referred to the waterfall model. This information system can be accessed through a web browser on a device, so there is no need to install many applications because a browser is installed by default there. The purpose of this study is to measure the usability scale. This information system is expected to facilitate interaction for searchers and finders of the items and be a convenient alternative so that cases of lost items in the campus area can be handled effectively and efficiently. Evaluation of the system using the System Usability Scale (SUS) has a score of 77 which means it is acceptable or feasible.
Combination of Historical Stock Data and External Factors In Improving Stock Price Prediction Performance Sjahrunnisa, Anita; Suciati, Nanik; Hidayati, Shintami Chusnul
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 18 No. 2 (2024)
Publisher : Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jeeccis.v18i2.1707

Abstract

Stock price prediction continues to be a major focus for investors today, some previous studies often focus on technical analysis using historical stock price data and ignore external factors that can affect stock prices. The purpose of this research is to overcome the shortcomings of previous research by creating a stock price prediction model that combines historical stock data consisting of date, high, low, open, close, adj close, volume and external factors such as days, interest rates, inflation, and dividends. The data used came from 33 companies from 11 industrial sectors in Indonesia for 2267 trading days and evaluated the prediction performance using MSE, MAPE and R-squared. The results show a significant improvement in the evaluation metrics when external factors are added. This shows the importance of such factors in improving the prediction analysis and increasing the reliability of the prediction model. This approach is expected to not only overcome the limitations of traditional methods but also utilize a combination of deep learning and machine learning to improve prediction accuracy. Thus, this research not only provides new insights in the field of financial analysis but also provides new insights and solutions for investors to make more informed and less risky decisions.
Co-Authors Hidayati, Shintami Chusnul Nanik Suciati Yasinta Romadhona Yuhana, Umi Laili