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Journal Of Artificial Intelligence And Software Engineering
Published by Politeknik Negeri Lhokseumawe
ISSN : 2797054X     EISSN : 2777001X     DOI : http://dx.doi.org/10.30811/jaise
Core Subject : Science,
Computer Science & IT
Artificial Intelligence Natural Language Processing Computer Vision Robotics and Navigation Systems Decision Support System Implementation of Algorithms Expert System Data Mining Enterprise Architecture Design & Management Software & Networking Engineering IoT
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 254 Documents
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Analisis Komparatif Optimasi Hyperparameter Metaheuristik pada Model Deep Learning untuk Deteksi Berita Hoaks Berbahasa Indonesia Ramadhani, Rafli; Nurchim, Nurchim; Pramono, Pramono
Journal of Artificial Intelligence and Software Engineering Vol 6, No 2 (2026): Juni (OnProgress)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v6i2.9497

Abstract

The spread of hoax news on digital platforms demands an automatic detection system based on Neural Networks. However, the performance of these models is heavily influenced by hyperparameter configuration, where manual determination is time-consuming and prone to overfitting. This study aims to compare the performance of Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Hybrid GA-PSO in optimizing hyperparameters of Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) architectures for detecting hoax news in Indonesian. Quantitative experiments were conducted using 4,132 news data, extracted through TF-IDF and Word2Vec, then trained and evaluated based on the level of classification accuracy and convergence quality. The test results show that the effectiveness of the optimization algorithm is highly dependent on the model architecture. In the baseline ANN model, PSO performed optimally with an accuracy of 96.3% and an F1-Score of 96.3% due to its ability to exploit parameters stably. In the CNN model, PSO experienced overfitting, making GA the best method with 95.4% accuracy due to its advantage in maintaining model generalization on new data. Meanwhile, for the LSTM sequential model, pure GA experienced model collapse with accuracy dropping to 49.9%. The Hybrid GA-PSO approach proved to be the most optimal method for LSTM, dominating with the highest accuracy of 96.5%, a precision rate of 97.2%, and an F1-score of 96.5%. In conclusion, algorithm hybridization is highly recommended for complex architectures to overcome the weaknesses of a single algorithm while producing sharp predictions that precisely distinguish facts from hoaxes.
Analysis of Donor Behavior Based on Donation Patterns in the Nur Hidayah Foundation. Affandi Catur Pamungkas; Herliyani Hasanah; Nibras Faiq Muhammad
Journal of Artificial Intelligence and Software Engineering Vol 6, No 2 (2026): Juni (OnProgress)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v6i2.9218

Abstract

The increasing amount of donation data requires a data analysis process to identify information patterns that can be utilized to support decision-making. The problem in donation data management at Yayasan Nur Hidayah is the absence of data utilization to identify donor behavior patterns based on donation time, donation amount, and donation location. Therefore, this study aims to implement the Apriori algorithm to discover relationships among donation data through the association rule process. The research method used is Knowledge Discovery in Databases (KDD), which consists of data selection, preprocessing, transformation, data mining, and evaluation stages. The data used consisted of 50 donation transactions with attributes including donation date, donation amount, and donation location. The analysis process was carried out using the Apriori algorithm by measuring support and confidence values to generate association rules. The results showed that the Apriori algorithm successfully identified relationship patterns among donation data. The association rule with the highest confidence value was found in the pattern “TD3 → Mid-Month” with a confidence value of 84.61%, indicating that donation transactions at the TD3 donation location tended to occur in the middle of the month. The results of this study can be utilized as a basis for decision support in determining more effective and data-driven donation fundraising strategies.
Implementation Of Content-Based Filtering For a Cat Food Recommendation System Amanda Putri Setiawan; Dwi Hartanti; Pramono Pramono
Journal of Artificial Intelligence and Software Engineering Vol 6, No 2 (2026): Juni (OnProgress)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v6i2.9305

Abstract

This research discusses the implementation of the Content-Based Filtering method in a cat food recommendation system. The problem in this research is motivated by the difficulty of cat owners and pet shops in determining appropriate food, because there are many variations of products that differ in cat age, type of food, brand, price, composition, nutritional content, and product characteristics. This research was conducted to create a recommendation system that can help users in determining the appropriate cat food choice, by looking at product characteristics and specific needs of cats. This research utilizes the Content-Based Filtering method to analyze and recommend products, through the stages of data collection, data preprocessing, attribute weighting using Term Frequency-Inverse Document Frequency, and similarity calculation using Cosine Similarity. The research data was obtained through observation, interviews, literature study, and documentation of cat food products. The results show that the system built can help users determine cat food based on the suitability between user criteria, cat needs, and product characteristics. This system can also facilitate users in obtaining information and appropriate cat food choices. Based on this, the Content-Based Filtering method is considered effective to be applied in a recommendation system, to assist the cat food selection process in a more structured and efficient manner.
Design and Implementation of a Human Capital Information System Front-End Using CodeIgniter 4 at PT Pertamina Geothermal Energy Area Kamojang Amelia Mutiara Rahmi; Yopi Nugraha
Journal of Artificial Intelligence and Software Engineering Vol 6, No 2 (2026): Juni (OnProgress)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v6i2.9566

Abstract

Advances in information technology are driving organizations to undergo digital transformation in order to improve the efficiency and effectiveness of administrative processes. PT Pertamina Geothermal Energy Tbk’s Kamojang Area still faces challenges in managing human capital administration, such as data management that is not yet optimally integrated and administrative processes that are still carried out manually. This study aims to design and implement a web-based Human Capital Information System front-end using the CodeIgniter 4 framework. The research methods employed include observation, interviews, and literature review to identify system and user requirements. The front-end development of the system was implemented using the Model-View-Controller (MVC) architecture via the CodeIgniter 4 framework. In addition, the Bootstrap framework was integrated to ensure that the user interface is responsive and offers optimal readability across a range of devices. The developed system provides various Human Capital administration features, such as the management of employee data, business partners, attendance, internships, dispositions, correspondence, and monthly reports. The research results indicate that the designed front-end is capable of presenting information in a more structured manner, enhancing the system’s usability, and supporting the efficiency of Human Capital administration processes. Consequently, the developed system can support digital transformation and improve the effectiveness of administrative management at PT Pertamina Geothermal Energy Tbk Kamojang Area.

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