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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 215 Documents
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Rainfall Classification Based on El-Niño and La-Niña Climate Phenomenon Using Naive Bayes Classifier Algorithm Erlinda, Mely; Andrianingsih, Andrianingsih
Journal of Artificial Intelligence and Software Engineering Vol 5, No 1 (2025): March
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i1.6552

Abstract

As a tropical country, Indonesia faces significant challenges due to global climate phenomena such as El Niño and La Niña that impact rainfall patterns. This research aims to classify daily rainfall in major Indonesian cities such as, DKI Jakarta, Surabaya, Medan, Makassar, and Bandung, into three main categories, namely moderate rain, extreme rain, and no rain. In addition, it identifies climate conditions based on El Niño, La Niña, and Normal categories by applying the Naïve Bayes Classifier algorithm. In this study, the CRISP-DM (Cross-Industry Standard Process for Data Mining) method was used as a framework for processing daily rainfall data for the period January to December 2023, obtained from BMKG. The analysis results show that the Naïve Bayes Classifier algorithm has high performance with 93.15% accuracy, 98% precision, 93% recall, and 94% F1-score. Further analysis, this study found that El Niño causes a significant decrease in rainfall, while La Niña increases extreme rainfall, especially in Makassar and Medan. This research contributes to the development of rainfall classification models that can help the government to anticipate the impacts of climate change and improve the efficiency of water resources management in urban areas.
Application Of The Smart Method In Decision Making For Kip Scholarship Recipients Faculty Of Faculty Of Business Technology And Science Muri, Adinda Cantika; Muharni, Sita
Journal of Artificial Intelligence and Software Engineering Vol 5, No 1 (2025): March
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i1.6447

Abstract

The selection process for the Indonesia Smart Card (KIP) scholarship at the Faculty of Business and Science Technology faces challenges in ensuring an objective and transparent selection of recipients. The number of applicants often exceeds the available quota, necessitating a fair decision-making method. This study applies the Simple Multi-Attribute Rating Technique (SMART) to support the decision-making process in determining KIP scholarship recipients. SMART is used to assign weights to various criteria, such as parental income, land ownership, and the number of dependents. Using this method, candidates are ranked based on their total scores, calculated from the weighted criteria. The results indicate that the SMART method provides a more objective and systematic selection process, assisting universities in identifying the most eligible scholarship recipients. The implementation of this method is expected to enhance transparency and accountability in scholarship selection at higher education institutions.
Implementation of the Least Square Method in the Work Plan and Budget Application (SIREKA) at Politeknik Negeri Lhokseumawe Rizqillah, Rizqillah; Arhami, Muhammad; Abdi, Musta’inul; Arifai, Muhammad; Meilvinasvita, Dwi
Journal of Artificial Intelligence and Software Engineering Vol 5, No 1 (2025): March
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i1.6654

Abstract

Every year, all units at the Lhokseumawe State Polytechnic (PNL) are required to plan activity plans and budgets (RKA). This includes Library units, Departments, SPI, P3M, P4M, and other units. However, the process of planning activities to budget realization still depends on Drive services, causing complexity and lack of coordination in managing data. To overcome this problem, a solution is needed in the form of SIREKA (Work Plan and Budget Management Information System). In this application, the least squares method is used as a prediction tool for activity budget data. From the results of the calculations carried out, it is known that the least squares method has a high level of accuracy of 97.71% with a MAPE value of 2.29%, so this method is considered successful. The implementation of SIREKA has been successful in overcoming RKA obstacles in PNL, as well as providing budget predictions that are useful for decision making. SIREKA has proven itself to be a solution that has a positive impact on the Politeknik Negeri Lhokseumawe.
An Integrated Approach of Informatics Engineering and Information Systems in the Modernization of Public Administration Abdurrohim, Iim; Sindrawati, Sindrawati; Lesmana, Dina; Kurniawati, Kurniawati; Sumiati, Nia
Journal of Artificial Intelligence and Software Engineering Vol 5, No 1 (2025): March
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i1.6367

Abstract

Modernisasi administrasi publik menjadi kebutuhan mendesak di tengah meningkatnya tuntutan terhadap layanan publik yang cepat, transparan, dan efisien. Penelitian ini mengeksplorasi pendekatan terintegrasi antara Teknik Informatika dan Sistem Informasi untuk menghadapi tantangan birokrasi tradisional, seperti fragmentasi data dan kurangnya efisiensi. Teknik Informatika berperan dalam pengembangan infrastruktur teknologi, sementara Sistem Informasi memfasilitasi pengelolaan data secara terpusat dan mempercepat pengambilan keputusan. Studi ini menggunakan metode kuantitatif deskriptif dengan data yang diperoleh melalui kuesioner, melibatkan responden dari kalangan pemerintah, masyarakat, dan pengembang sistem. Hasil penelitian menunjukkan bahwa integrasi teknologi ini meningkatkan efisiensi waktu, mengurangi biaya operasional, dan memperbaiki transparansi. Namun, tantangan seperti keterbatasan infrastruktur, resistensi terhadap perubahan, dan kurangnya pelatihan pengguna masih menjadi hambatan. Dengan rekomendasi strategis untuk meningkatkan pelatihan dan infrastruktur, penelitian ini memberikan dasar bagi pengembangan administrasi publik yang lebih responsif dan adaptif terhadap era digital.
Bidirectional Long Short-Term Memory Model for Intent Classification in Customer Service Chatbot Cahyadi, Yagus; Redjeki, Sri; Almagrib, Ahmad; Satriani, Bayu; Naufal, Nabil
Journal of Artificial Intelligence and Software Engineering Vol 5, No 1 (2025): March
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i1.6520

Abstract

The demand for responsive and efficient customer service is a crucial aspect of enhancing customer satisfaction, particularly in Indonesian government offices abroad. To address this challenge is implementing a chatbot system based on Bidirectional Long Short-Term Memory. This model can understand conversational contexts more comprehensively, enabling it to generate relevant and timely responses. This study aims to optimize chatbot performance in enhancing customer experience by implementing the Bi LSTM algorithm to handle intent classification of customer input data. Experimental results demonstrate that this model successfully improves evaluation metrics, achieving an accuracy of 84.64%, precision of 85%, recall of 85%, and an F1-score of 85%.
Analysis and Optimization of a Buffet Pricing Strategy in the Telecommunication Industry Using the Particle Swarm Optimization (PSO) Algorithm Merdikawati, Silvia; Oktaviani, Revina Dwi; Salahuddin, Salahuddin; Khadijah, Afni
Journal of Artificial Intelligence and Software Engineering Vol 5, No 2 (2025): June
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i2.6954

Abstract

Flat-rate pricing (buffet pricing) is a common strategy in the global telecommunications industry, yet its adoption in Indonesia remains limited due to regulatory challenges, network capacity constraints, and diverse customer preferences. This study aims to optimize buffet pricing by considering user segmentation and varied service consumption patterns. A metaheuristic approach, specifically Particle Swarm Optimization (PSO), is employed to determine the optimal pricing that maximizes operator profit while maintaining customer satisfaction. A customer demand model is developed using a triangular distribution to reflect the asymmetric variability of usage. Results indicate that heavy users benefit significantly from flat-rate plans, whereas light users are better served by a hybrid pricing scheme. PSO demonstrates superior adaptability and efficiency compared to conventional methods, particularly when parameter tuning accelerates convergence. The study also highlights the importance of pricing flexibility to address heterogeneous customer needs. This study offers practical contributions to the development of data-driven, competitive pricing strategies in the evolving telecommunications market.
Smart Fisheries: Real-Time Water Quality Management and Automated Feeding System Design for Tilapia Farming using ESP32 Micro Controller Yudistira, Bagus Gede Krishna; Hapsari, Cindy; Adnyana, Gede Defry Widhi; Nath, Wiswa; Putra, I Putu Romyadhy Maha
Journal of Artificial Intelligence and Software Engineering Vol 5, No 2 (2025): June
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i2.7288

Abstract

The fisheries sector in Jinengdalem Village, Buleleng, Bali holds considerable potential but continues to face challenges related to operational efficiency and unstable production outcomes. This study proposes an innovative solution through Smart Fisheries: The AI-Powered IoT in Smart Fisheries, an intelligent aquaculture system powered by Artificial Intelligence (AI) and the Internet of Things (IoT). The system is designed to perform real-time monitoring of water parameters, automate feeding processes, and analyze fish growth in order to enhance aquaculture productivity and sustainability. The research methodology follows a Research and Development (RD) framework, utilizing the ADDIE model (Analysis, Design, Development, Implementation, Evaluation). Preliminary results indicate that the system provides accurate environmental data and supports data-driven decision-making in fishery management. This project is expected to serve as a replicable model for implementing digital aquaculture technologies in similar regions.
Edge Implementation of Vehicle Plate Identification using Haar Classifier and Convolutional Neural Networks Wibowo, Risky Ari; Muhammad, Fadil; Ahendyarti, Ceri; Alimuddin, Alimuddin; Muttakin, Imamul
Journal of Artificial Intelligence and Software Engineering Vol 5, No 2 (2025): Juni On-Progress
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i2.6570

Abstract

The increase in vehicle ownership every year causes a lack of information monitoring on each vehicle. As one of the methods used to find vehicle information, recognizing each number plate is a solution for recognizing vehicles. Utilizing object detection techniques using computer vision in recognizing vehicle number plates can simplify the plate recognition process. The process of identifying and classifying the characters on the plates is conducted simultaneously with a simple implementation which is a benefit of using computer vision in recognizing vehicle plates. The use of the Haar cascade classifier algorithm in this research overcomes the problem of plate detection combined with the Convolutional Neural Networks (CNN) to conduct Optical Character Recognition (OCR) on vehicle plates. The results of vehicle plate recognition in-situ experiments in four real-time tests obtained an average accuracy value of 42.67%.
Instagram Influencer Recommendation System Based On Content-Based Filtering To Support Digital Marketing Strategy Az Zahra, Erika Oktaviana; Pramono, Pramono; Suryani, Fajar
Journal of Artificial Intelligence and Software Engineering Vol 5, No 2 (2025): Juni On-Progress
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i2.6939

Abstract

Influencer marketing is a popular promotional strategy on Instagram that involves influential individuals or public figures to promote products. However, there are problems where companies still find it difficult to find the right influencers. This research aims to build a Content-Based Filtering-based Instagram influencer recommendation system to support digital marketing strategies. The system development method used is Rapid Application Development (RAD) with 4 stages, namely requirements planning, system design, development, and implementation. With this system, users can recommend other influencers who have similar characteristics such as number of followers, average likes, and comments, engagement rate, and growth rate. System testing was conducted on 10 test data with different inputs. The results showed that 9 out of 10 tests matched the user input, indicating a system accuracy of 90% and has the potential to assist users in selecting relevant influencers.
Clustering of Accounts Receivable Billing Data Based on Customer Tariff Categories at PT PLN UP3 Palembang Ramadhan, Dimaz Gymnastiar; Yulistia, Yulistia
Journal of Artificial Intelligence and Software Engineering Vol 5, No 2 (2025): Juni On-Progress
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i2.6511

Abstract

The purpose of writing this final assignment is to group customers based on late payment patterns by applying the K-Means Clustering algorithm. The data used are late receivables and arrears of PT PLN Palembang customers. The results of writing this final assignment show that Cluster 1 has 10 data, Cluster 2 has 36 data, and Cluster 3 has 326 data on late payments. While in the risky payment arrears, Cluster 1 has 26 data, Cluster 2 has 36 data, and Cluster 3 has 312 data. From the evaluation results using Silhouette Score, it shows that there are 3 clusters with a value of 0,880 (Highest), which means that the clustering that was formed was successful and can be used.

Page 11 of 22 | Total Record : 215

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