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All Journal JURNAL TEKNOLOGI DAN OPEN SOURCE Jurnal Ilmu Teknologi Informasi Indonesia (JITIFNA)
Abdul Rezha Efrat Najaf
UPN Veteran Jawa Timur
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering

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Rancang Bangun Sistem Informasi Kelola Dokumen Berbasis Website Pada Dinas Tenaga Kerja Dan Transmigrasi Provinsi Jawa Timur Muhammad Zakky Ulil Amry; Chandra Febryan Saputra; Jonathan Alexander Christian; Abdul Rezha Efrat Najaf
Jurnal Ilmu Teknologi Informasi Indonesia Vol. 2 No. 2 (2026): JITIFNA - Juli
Publisher : CV. SINAR HOWUHOWU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70134/jitifna.v2i2.1737

Abstract

Document management plays a key part in how government institutions run their administrative processes and information services, yet at the Department of Manpower and Transmigration of East Java Province it was still handled through physical archives and digital files scattered across different locations, making retrieval slow and inconsistent. This study designs and develops a web-based document management information system using the Waterfall method, built with the Laravel framework and MySQL as the database. Development covered requirements analysis, system design, implementation, and Black Box Testing, resulting in a system that provides document management, category and document type management, document relationship management, publication, search, and usage statistics. Testing results showed that all functions worked according to the specified requirements, resolving the fragmentation that had previously slowed down the institution's document-handling process.
BCA Stock Price Prediction Using Time Series Method With GRU (Gated Recurrent Unit) Rizky Nugraha; Abdul Rezha Efrat Najaf; Reisa Permatasari
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 2 (2025): Jurnal Teknologi dan Open Source, December 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i2.4500

Abstract

Stock price prediction is a crucial component in investment decision-making, enabling investors to plan strategies more accurately and minimize risks. This study applies the Gated Recurrent Unit (GRU) model to predict the stock prices of blue-chip banking companies in Indonesia using data from the period 2019 to 2024. The model utilizes historical stock data to forecast future trends. The results from the first testing scheme, with a data split ratio of 70% / 30%, using GRU units (128,256) with the Adam optimizer, show that the GRU model is the most optimal in terms of prediction, measured by metrics such as MSE, RMSE, and MAPE. This study also proposes a web-based dashboard that visualizes the predicted stock prices and provides decision-support tools for investors. The findings highlight the effectiveness of deep learning in financial forecasting and underscore its potential to enhance investment strategies.
House Price Prediction in Surabaya Using Backpropagation Neural Network Dimas Fajri Pamungkas; Abdul Rezha Efrat Najaf; Reisa Permatasari
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 2 (2025): Jurnal Teknologi dan Open Source, December 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i2.4829

Abstract

This research develops a house price prediction system in Surabaya using the Backpropagation Neural Network (BPNN) method. The dataset was obtained through web scraping of property listings, resulting in 3,435 records with 52 attributes. To improve stability, the target variable (house price) was transformed using natural logarithms. Several neural network architectures were tested, and the best configuration [32, 64, 32] achieved Mean Absolute Error (MAE) of 0.3125, Root Mean Squared Error (RMSE) of 0.4201, R² of 0.7138, and Mean Absolute Percentage Error (MAPE) of 1.46%. A multi-run evaluation of 20 iterations confirmed consistency of results. The model was implemented as a web-based application using Flask, allowing users to predict house prices in real-time. This research shows that BPNN is reliable for property price forecasting and can support decision-making in the housing market.
Co-Authors Chandra Febryan Saputra Dimas Fajri Pamungkas Jonathan Alexander Christian Muhammad Zakky Ulil Amry Permatasari, Reisa Rizky Nugraha