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Moh. Diqi
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INDONESIA
International Journal of Informatics Engineering and Computing
Published by ASTEEC Publisher
ISSN : -     EISSN : 30909112     DOI : https://doi.org/10.70687/ijimatic
Core Subject : Science,
Computer Science & IT
International Journal of Informatics Engineering and Computing (IJIMATIC) is an international, peer-reviewed, open-access journal that publishes original theoretical and empirical work on the science of informatics and its application in multiple fields. Our concept of informatics encompasses technologies of information and communication, as well as the social, linguistic, and cultural changes that initiate, accompany, and complicate their development. IJIMATIC aims to be an international platform to exchange novel research results in simulation-based science across all computer science disciplines.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 6 Documents
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Search results for , issue "Vol. 2 No. 2 (2025): International Journal of Informatics Engineering and Computing [Preview]" : 6 Documents clear
An Internet of Things-Based Temperature and Humidity Monitoring System for Palm Sugar Storage Warehouses Izzurohman, Moh.; Mulyani, Sri Hasta; Ordiyasa, I Wayan
International Journal of Informatics Engineering and Computing Vol. 2 No. 2 (2025): International Journal of Informatics Engineering and Computing [Preview]
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/c3d6kr84

Abstract

The integration of Internet of Things (IoT) technology in warehouse management offers real-time monitoring and control to improve efficiency and minimize risk. This study develops an IoT-based system for a sugar warehouse to monitor environmental parameters such as temperature and humidity. The system uses sensors (DHT22, ultrasonic, and light sensors) connected to a NodeMCU ESP8266, with real-time data sent to a web dashboard via the internet. The results indicate that the system can detect environmental changes and send alerts when thresholds are exceeded, ensuring sugar quality and reducing manual supervision.
Weather Forecasting in Denpasar City Using Stacked Long Short-Term Memory Algorithm (LSTM) Hidayatulloh, M. Riyan; Diqi, Mohammad; Wijaya Sugiarto, R. Nurhadi
International Journal of Informatics Engineering and Computing Vol. 2 No. 2 (2025): International Journal of Informatics Engineering and Computing [Preview]
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/408j8q02

Abstract

Forecasting the weather is essential to sustaining everyday human activities, particularly in industries like tourism, agriculture, and transportation. The effects of extreme weather events can be lessened by timely and accurate weather forecasts. This research suggests a Denpasar City weather forecasts are made utilizing a deep learning technique and the Stacked Long Short-Term Memory architecture. The four main parameters of the model— temperature, humidity, wind speed, and pressure—were trained using historical weather data spanning 1990 to 2020. A sliding window method was used to organize the dataset into time-series sequences after it had been preprocessed using normalization techniques. The Adam optimizer was used to train the model over 50 epochs with a batch size of 64. Four regression measures were used for evaluation: Coefficient of Determination (R2), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE). The Stacked LSTM model's average MAE of 1.08, MAPE of 10.22%, RMSE of 1.93, and R2 of 0.86 demonstrate how well it captures temporal patterns and generates precise forecasts, according to the experimental data. These results show how the Stacked LSTM approach can be used to support decision-making in weather-sensitive domains and create automated weather forecasting systems.
Comparison of Hybrid CNN-LSTM, LSTM, and CNN Models for Stock Price Prediction (Case Study: PT. Indofood Sukses Makmur Tbk) Wijaya S, R. Nurhadi; Endah H, Marselina; Wanda, Putra; Rafitajudin, Rafitajudin
International Journal of Informatics Engineering and Computing Vol. 2 No. 2 (2025): International Journal of Informatics Engineering and Computing [Preview]
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/sdts5v08

Abstract

This study develops a hybrid deep learning model by combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) to predict the stock price of INDF.JK using historical data from 2015 to 2025. Feature extraction is performed using Conv1D, followed by MaxPooling1D to reduce dimensions, and LSTM to capture time-dependent patterns. The model is evaluated using the R², RMSE, MAE, and MAPE metrics. The CNN-LSTM model demonstrates the best performance with an R² of 0.9759, RMSE of 87.77, MAE of 63.97, and MAPE of 1.02%. As a comparison, the single CNN model produced an R² of 0.9711, RMSE of 96.18, MAE of 71.16, and MAPE of 1.12%, while the single LSTM model obtained an R² of 0.9752, RMSE of 89.13, MAE of 66.99, and MAPE of 1.07%. These results confirm that the hybrid approach is superior in terms of stock price prediction accuracy compared to the use of a single model.
Detecting DDoS Attacks on Network Traffic Using a Hybrid Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) Algorithm Nahak, Ivansius; Mulyani, Srihasta; Ordiyasa, I Wayan Ordiyasa
International Journal of Informatics Engineering and Computing Vol. 2 No. 2 (2025): International Journal of Informatics Engineering and Computing [Preview]
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/vd7kk061

Abstract

Distributed Denial of Service (DDoS) the attack had a significant impact risk for network security by flooding systems with excessive traffic, disrupting services, and causing potential financial harm [1]. As these attacks grow more frequent and sophisticated, effective detection methods are essential [2]. Machine learning techniques offer a powerful solution by identifying abnormal traffic patterns associated with DDoS attacks. This study focuses on developing a detection model that combines LSTM and SVM algorithms [3]. LSTM component analyzes time-based traffic trends, while the SVM distinguishes between normal and malicious activity [4]. Performance is assessed using metrics accuracy, precision, recall, and F1-score. This study shows that the hybrid LSTM-SVM model performs very well, achieving 95% accuracy, 91% precision, 96% recall, and 93% F1-score. These results highlight the model's potential as a powerful tool for improving DDoS attack detection and strengthening network security defenses.
Design of Automatic Metal and Non-metal Waste Sorting Based on Internet of Things (IoT) Diqi, Mohammad; Hasta Mulyani, Sri; Wijaya S, R Nurhadi; Junia Sipit, Marselina
International Journal of Informatics Engineering and Computing Vol. 2 No. 2 (2025): International Journal of Informatics Engineering and Computing [Preview]
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/31bqbr09

Abstract

Ineffective waste management, especially in the process of sorting between metal and non-metal waste, is still a serious environmental problem. This research aims to design and build a prototype of an Internet of Things (IoT)-based automatic waste sorting device that is able to identify and separate types of waste independently. The system uses an ESP32 microcontroller integrated with ultrasonic sensors, inductive proximity sensors, and MQ135 gas sensors, as well as the Blynk application as an IoT-based monitoring interface. The method used is Research and Development (RnD) with six stages: literature study, system design, tool making, testing, evaluation, and conclusion. The test results show that the system is able to classify metal and non-metal waste with an accuracy rate of 90%, precision reaches 85.71% for metals, and 95.45% for non-metals. The integration of IoT technology in waste sorting tools is proven to increase the efficiency of waste processing from the source and provide innovative solutions in smarter and more sustainable environmental management.
Automatic Detection of Cabbage Pest Attacks Based on Leaf Images with Machine Learning Approach Ni Wayan Surya Wardhani; Prayudi Lestantyo; Atiek Iriany; Nur Silviyah Rahmi
International Journal of Informatics Engineering and Computing Vol. 2 No. 2 (2025): International Journal of Informatics Engineering and Computing [Preview]
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/3szcd282

Abstract

Farmers in cabbage farming face many problems, one of which is pest attack. Plutella xylostella L. is a major pest on cabbage (known as cabbage leaf caterpillar) which can cause a decrease in production of up to 100 percent. Decision Support System (DSS) was developed to classify the attack rate of Plutella to reduce the negative effects of using various types of high doses of pesticides and short spraying intervals but causing residual effects and killing natural enemies. DSS has a role in helping farmers to make decisions regarding the time of pesticide treatment needed to minimize negative effects and increase productivity. In this study, DSS was developed to detect damage to cabbage (Brassica oleracea L) crops so that farmers can determine pesticide doses and spraying intervals based on a website. The results of the system is presented in the form of images and the percentage of damage to cabbage plants. Therefore, the CART method can be used to analyze the level of damage to plants that are attacked by pests.

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