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All Journal JOURNAL OF ICT APLICATIONS AND SYSTEM Jurnal Informatika Ekonomi Bisnis Galaksi
Oktafanda, Ego
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering

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Implementation of a Web-Based Information Management System for Masjid Al-Mukhlisin Muslim; Santosa, Firman; Amelia Chandra, Detri; Oktafanda, Ego; Zainab, Siti
Journal of ICT Applications System Vol 3 No 1 (2024): Journal of ICT Aplications and System
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56313/jictas.v3i1.341

Abstract

Al-Mukhlisin Mosque is a mosque located in Rantau Panjang Village. The mosque has a function as a place of worship, therefore it can be found various religious activities such as paying Zakat Mal, Zakat Fitrah, Infak, Sacrifice, Islamic Holiday Celebration, cult (Seven Minute Lecture) in Ramadan, and so on and so forth. At this time the problem that exists in Al-Mukhlisin Mosque is that data recording is still using a manual method written in books and then recorded and making reports from the data that has been collected. And the mosque has difficulty in recapitulation and searching for data in the mosque if needed at any time because it uses a manual process. The design of the mosque management information system at Al-Mukhlisin Mosque aims to make it easier to manage and search for data and data recapitulation. This mosque information system is web-based using PHP program tools, databases, XAMPP, MYSQL.
Klasifikasi Citra Kualitas Bibit dalam Meningkatkan Produksi Kelapa Sawit Menggunakan Metode Convolutional Neural Network (CNN) Oktafanda, Ego
Jurnal Informatika Ekonomi Bisnis Vol. 4, No. 3 (September 2022)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (481.191 KB) | DOI: 10.37034/infeb.v4i3.143

Abstract

Palm oil is one of the plantation commodities that has a strategic role in Indonesia's economic development. Lack of employee knowledge about the types of diseases in oil palm seedlings resulted in errors in handling them. In the selection of seeds to be planted in plantations, sick will cause unstable palm growth and even die. This study aims to find the best solution according to experts when the emergence of pests or diseases is identified through the pattern and color of the leaves. The data used in this study comes from image data of PT.Gatipura Mulya which is conducting a nursery as many as 612 images of oil palm seedlings and can be divided into 4 classes. The method that can be used in this identification is Convolutional Neural Network (CNN) which can study objects in image patterns. The result of this research is that the accuracy of image recognition is very good. So that this research can be recommended in the introduction of oil palm image patterns.
Advanced Long Short-Term Memory (LSTM) Models for Forecasting Indonesian Stock Prices Santosa, Firman; Oktafanda, Ego; Setiawan, Hendrik; Latif, Abdul
Jurnal Galaksi Vol. 1 No. 3 (2024): Galaksi - Desember 2024
Publisher : Yayasan Sraddha Panca Widya Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70103/galaksi.v1i3.42

Abstract

The Indonesian stock market is a key indicator of national economic dynamics. Blue-chip stocks, including Bank Central Asia (BBCA), Bank Rakyat Indonesia (BBRI), and Bank Mandiri (BMRI), hold significant influence due to their liquidity and impact on the market index. However, their price volatility, driven by global economic conditions, monetary policies, and market sentiment, poses challenges for accurate forecasting. This study employs the Long Short-Term Memory (LSTM) model to address these challenges. LSTM, a deep learning technique, effectively handles time series data by capturing long-term dependencies and complex price patterns. Using historical stock data from 2019 to 2024, the model was trained and optimized. Evaluation metrics, including Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE), were used to assess performance. BBCA stocks achieved the best results, with a MAPE of 0.0099 and RMSE of 128.02.The findings demonstrate LSTM's robustness in forecasting stock price trends, providing investors with valuable tools for informed decision-making. This research advances predictive analytics in financial markets, particularly in emerging economies like Indonesia, and highlights LSTM’s potential to improve accuracy in volatile environments.
Co-Authors Abdul Latif Amelia Chandra, Detri Hendrik Setiawan, Hendrik Muslim Santosa, Firman Siti Zainab, Siti