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INDONESIA
Jurnal Software Engineering and Computational Intelligence
Published by Universitas Indo Global Mandiri
ISSN : -     EISSN : 29882028     DOI : https://doi.org/10.36982/jseci.v1i1
Core Subject : Science,
Computer Science & IT
Journal of Software Engineering and Computational Intelligence (JSECI) is a scientific journal in software engineering and computational intelligence containing the scientific literature on studies of pure and applied research in informatics and computer sciences, public review of the development of theory, method, and applied sciences related to the subject. The topics covered include but are not limited to: Artificial Intelligence, Computer Vision, Cryptography, Genetic Algorithm, Human-Computer Interaction, Image Processing, Intelligent Home Environments, Machine Learning, Natural Language Processing, Neural Network, Pattern Recognition, Software Engineering (Implementation of Computational Intelligent), Steganography
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 3 Documents
 1 
Search results for , issue "vol 4 no 01 (2026)" : 3 Documents clear
Perancangan Robot Penyiraman pada Tanaman Tomat Beef (Lycopersicum esculentum Mill) menggunakan Metode Forward Chaining Baginda siregar; Daren Sulistio
Jurnal Software Engineering and Computational Intelligence Vol 4 No 01 (2026)
Publisher : Informatics Engineering, Faculty of Computer Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jseci.v4i01.6808

Abstract

Plants, especially tomatoes, have high economic value and require regular maintenance, especially watering, to maintain optimal growth. This study developed an automatic tomato plant watering robot named "Farmbot" based on the forward chaining method, which allows for intelligent monitoring and regulation of water needs. The application of forward chaining as a rule-based expert system simplifies decision-making in watering, and this tool has the potential to be applied to other plants by reconfiguring environmental parameters. This study was conducted to prove the reliability of the forward chaining method in the process of designing a plant watering robot. The main microcontroller used in this study is the NodeMCU ESP32, then for the input device uses a soil moisture sensor to generate humidity data and a DS18B20 temperature sensor to generate temperature data on the soil. Various facts and rules are used to control the movement and watering process on the Farmbot robot. The results of this study show that the accuracy of the forward chaining method is 75.472% which is a good result and is not far from the fuzzy logic method which produces an MAE value of 4.5535 and has an accuracy value in the range of 92.149%. From these results, it can be concluded that the forward chaining method can be relied upon as a method for designing watering robots.
Sistem E-Cuti Berbasis Website Berbasis Intelligent System untuk Meningkatkan Efisiensi Administrasi Sekolah Akhsani Taqwiym; Nurussama
Jurnal Software Engineering and Computational Intelligence Vol 4 No 01 (2026)
Publisher : Informatics Engineering, Faculty of Computer Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jseci.v4i01.7073

Abstract

Leave administration management in elementary schools is still largely conducted manually, resulting in various issues such as unstructured documentation, difficulties in data retrieval, and delays in the approval process. This study aims to design and develop a web-based e-leave application to improve the efficiency and effectiveness of leave management for teaching staff. The system was developed using the Waterfall method, which consists of requirement analysis, system design, implementation, and testing stages. Data collection was carried out through observation, interviews, and documentation to understand the existing system. The results show that the developed e-leave application successfully integrates the entire leave submission process into a structured digital system. The system provides key features such as user authentication, teacher data management, department and position management, leave variables, as well as leave submission and monitoring. System testing was conducted using the Black Box Testing method, and the results indicate that all system functions operate correctly without any errors. The implementation of this system has a positive impact on improving administrative efficiency, accelerating the leave submission and approval process, and enhancing data accuracy and transparency. Therefore, the web-based e-leave application can serve as an effective solution to support digital transformation in educational administration.
Prediksi Saham IHSG Menggunakan Extreme Gradient Boosting, Support Vector Machine, K-Nearest Neighbors Dan Autoregressive Integrated Moving Average Renaldy Pratama; Shinta Puspasari; Muhammad Haviz Irfani
Jurnal Software Engineering and Computational Intelligence Vol 4 No 01 (2026)
Publisher : Informatics Engineering, Faculty of Computer Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jseci.v4i01.7141

Abstract

The Jakarta Composite Index (JCI) is a key indicator in assessing the performance of the Indonesian capital market. Dynamic stock price fluctuations require accurate prediction methods to assist investors in decision making. This study aims to compare the performance of four prediction algorithms, namely Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), Extreme Gradient Boosting (XGBoost), and Autoregressive Integrated Moving Average (ARIMA) in predicting the closing price of JCI. The data used is the JCI daily historical data for the 2019-2024 period obtained from Yahoo Finance. The research process includes data pre-processing, prediction model implementation, model training and testing, and performance evaluation using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) metrics on a 0-1 normalization scale.The results showed that the ARIMA model provided the most stable results with an RMSE value of 0.4038 and MAE of 0.3945, followed by K-NN and SVM. Although SVM has the lowest MAE value, its RMSE is still higher than ARIMA and K-NN. SVM showed the lowest performance in this experiment. Based on the evaluation results, ARIMA is recommended as the best algorithm in predicting JCI closing price based on historical data. The Jakarta Composite Index (JCI) is a key indicator in assessing the performance of the Indonesian capital market. Dynamic stock price fluctuations require accurate prediction methods to assist investors in decision making. This study aims to compare the performance of four prediction algorithms, namely Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), Extreme Gradient Boosting (XGBoost), and Autoregressive Integrated Moving Average (ARIMA) in predicting the closing price of JCI. The data used is the JCI daily historical data for the 2019-2024 period obtained from Yahoo Finance. The research process includes data pre-processing, prediction model implementation, model training and testing, and performance evaluation using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) metrics on a 0-1 normalization scale.The results showed that the ARIMA model provided the most stable results with an RMSE value of 0.4038 and MAE of 0.3945, followed by K-NN and SVM. Although SVM has the lowest MAE value, its RMSE is still higher than ARIMA and K-NN. SVM showed the lowest performance in this experiment. Based on the evaluation results, ARIMA is recommended as the best algorithm in predicting JCI closing price based on historical data.

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