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All Journal Teknika Jupiter International Journal of Economics, Business and Accounting Research (IJEBAR) Journal of Applied Data Sciences JIKI (Jurnal Ilmu Komputer dan Informatika) Jurnal Alih Teknologi Sistem Informasi (JATSI) Jurnal Informatika: Jurnal Pengembangan IT Jurnal Ilmu Siber dan Teknologi Digital Vokasi UNESA Bulletin of Engineering, Technology and Applied Science
Herwanto, Riko
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Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mechanical Engineering Transportation Other

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An Artificial Neural Network-Based Geo-Spatial Model for Real-Time Flood Risk Prediction Using Multi-Source High-Resolution Data Aziz, RZ Abdul; Nurpambudi, Ramadhan; Herwanto, Riko; Hasibuan, Muhammad Said
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.913

Abstract

Flood prediction presents a pressing challenge in disaster management, especially in regions vulnerable to extreme weather events. In response, this study offers a novel approach to flood risk prediction by developing a deep learning-based Geo-Spatial Artificial Neural Network (ANN). The model actively integrates high-resolution satellite imagery, meteorological data, and topographic indicators, such as rainfall, elevation, and land use to capture complex spatial and environmental relationships that influence flood risk. This study conducted data preprocessing using Principal Component Analysis (PCA) and normalization to ensure consistency across datasets. It built the ANN with multiple hidden layers and trained it using the backpropagation algorithm on historical flood data. Furthermore, it designed the ANN model with multiple hidden layers and trained it using the backpropagation algorithm. The model achieved a notable 92% prediction accuracy, significantly outperforming traditional flood prediction methods, which typically yield 75–85% accuracy. Conventional metrics were Mean Squared Error (1.41) and R-squared (0.94). It confirmed the model’s superior ability to predict high-risk flood zones. The model also effectively captured non-linear patterns that conventional statistical or deterministic methods often failed to detect. The results showed that the model generalizes well and adapts effectively, making it suitable for real-time and data-driven flood forecasting. By integrating artificial intelligence with geo-spatial analytics, this study offers a scalable, accurate, and efficient tool for early warning systems and risk management. It recommends that future research should focus on incorporating additional data sources and refining model training techniques to further enhance scalability and performance.
Intelligent and Secure Package Receiver System Utilizing Internet of Things (IoT) Technology Handayani, Retno Dwi; Jamal, Zaidir; Alkahfiansyah, Muhammad; Rosmalia, Lia; H Sudibyo, Novi; Herwanto, Riko
Vokasi Unesa Bulletin of Engineering, Technology and Applied Science Vol. 2 No. 3 (2025)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v2i3.38254

Abstract

The rapid expansion of e-commerce has increased the demand for secure and efficient package delivery solutions, particularly for recipients who are frequently away from home. This study presents the development of a Smart Package Receiver Box, an Internet of Things (IoT)-based system that integrates sensors and remote control functionalities to enhance package security. The system incorporates an ESP32-CAM microcontroller, an ultrasonic sensor for courier detection, a PIR sensor for package counting, and a solenoid door lock for secure access control. These components are integrated with the Telegram messaging application, enabling real-time notifications, visual monitoring, and remote control of package deliveries. Experimental testing demonstrates that the system reliably detects couriers within a 5 cm to 30 cm range, accurately counts inserted packages, and ensures a secure locking mechanism with prompt response times. The collected data confirm stable system performance, minimal delays, and effective remote accessibility for users. Despite its advantages, the system presents certain limitations that warrant further improvements. The current implementation lacks encrypted communication, posing potential security vulnerabilities that could be mitigated by integrating AES-256 encryption and secure authentication protocols. Additionally, the system's reliance on Telegram makes it susceptible to disruptions in service availability, necessitating the incorporation of alternative communication channels such as SMS notifications or cloud-based APIs. Future enhancements will focus on strengthening data security, increasing system redundancy, and conducting comprehensive field testing to improve robustness and scalability for broader adoption in modern package delivery systems.
Komparasi Metode Apriori dan FP-Growth Data Mining Untuk Mengetahui Pola Penjualan Purwati, Neni; Pedliyansah, Yogi; Kurniawan, Hendra; Karnila, Sri; Herwanto, Riko
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 2 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i2.4876

Abstract

 Sales data is generally still rarely used, as well as the Perfume Corner shop just piling up in the database, even though there are problems experienced by the store regarding sales data for the best-selling products and to increase the number of sales of subsequent perfume products, so that the store can survive and develop even better. The algorithm that can be used to manage sales data to overcome this problem is Apriori. The research method used in this research is the KDD (Knowledge Discovery in Database) process. This research produces a high frequency pattern for itemsets with a minimum support value of 20% resulting in products that become The Most Tree Items namely Jo Malone 82.49%, Zarra 28.25%, and Zwitsal 20.34%. While the association rules formed from the value of Min. Supp 20% and Min. Conf 80%, get a combination of 2 itemsets, namely Jo Malone and Zarra. Whereas for the combination of 3 itemsets, namely Jo Malone, Zarra and Baccarte with valid and strong status, it is proven by a lift value greater than 1, therefore the association rules are very appropriate to be used.
WEB-BASED RESEARCH ARTICLE CLASSIFICATION USING THE RANDOM FOREST ALGORITHM Ahludzikri, Fiqqi; Herwanto, Riko; RZ , Abdul Aziz; Agus, Isnandar; Irianto, Suhendro Yusuf
Jurnal Ilmu Siber dan Teknologi Digital Vol 4 No 1 (2025): November
Publisher : Penerbit Goodwood

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/jisted.v4i1.5547

Abstract

Purpose: This study aims to develop a web-based system that classifies research articles using the Random Forest algorithm to address mismatches between article content and journal scope. Methodology/approach: The research employed the SDLC Waterfall model, with data sourced from 560 articles published by Goodwood Publishing (2019–2024) across four categories. Text preprocessing included case folding, stopword removal, stemming, and tokenization, with TF-IDF applied for feature extraction. Random Forest was trained with 80% training data and 20% testing data. Results/findings: The model achieved 91% accuracy, with high precision and recall across all categories. The system was successfully implemented as a web-based application, providing instant classification and journal recommendations. Limitations: The dataset was limited to one publisher and only Random Forest was applied, which may restrict the generalizability of findings. Contribution: This study contributes to the application of machine learning in scholarly publishing, offering a practical solution for editors to streamline article selection and improve efficiency.
Co-Authors Agus, Isnandar Ahludzikri, Fiqqi Alkahfiansyah, Muhammad Aziz, RZ. Abdul Bobby Bachry Dwi Handayani, Retno H Sudibyo, Novi Hendra Kurniawan Ketut Artaye, Ketut Lia Rosmalia M Said Hasibuan Ma'ruf, M. Hasan Muhammad Husaini Neni Purwati Nurpambudi, Ramadhan Pedliyansah, Yogi Ramaputra, Muhammad Galih Retno Dwi Handayani Rikarda, Willy Artanika RZ , Abdul Aziz Sabita, Hary Sri Karnila Suranti, Yuli Y. Suhendro YA, Firmansyah Yan Aditiya Pratama Yundari, Yundari Zaidir Jamal