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Journal of Artificial Intelligence and Engineering Applications (JAIEA)
Published by Yayasan Kita Menulis
ISSN : -     EISSN : 28084519     DOI : https://doi.org/10.53842/jaiea.v1i1
Core Subject : Science, Engineering,
Automotive Engineering Computer Science & IT Control & Systems Engineering
The Journal of Artificial Intelligence and Engineering Applications (JAIEA) is a peer-reviewed journal. The JAIEA welcomes papers on broad aspects of Artificial Intelligence and Engineering which is an always hot topic to study, but not limited to, cognition and AI applications, engineering applications, mechatronic engineering, medical engineering, chemical engineering, civil engineering, industrial engineering, energy engineering, manufacturing engineering, mechanical engineering, applied sciences, AI and Human Sciences, AI and education, AI and robotics, automated reasoning and inference, case-based reasoning, computer vision, constraint processing, heuristic search, machine learning, multi-agent systems, and natural language processing. Publications in this journal produce reports that can solve problems based on intelligence, which can be proven to be more effective.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 524 Documents
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Implementation of a Chatbot-Based AI Agent for Employee and Student Attendance Systems with Face Recognition and N8N Integration Muh. Dwicky P. Sanjaya; Adhy Rizaldy; Rahman; Asrul Ashari Muin; A. Mustika Abidin
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2337

Abstract

Students frequently rely on direct messaging to verify the presence of lecturers and staff on campus, a practice that often results in delayed responses due to the recipients' busy schedules. This study aims to design, implement, and evaluate an automated attendance system based on an AI agent utilizing face recognition technology and n8n as a centralized workflow automation platform. The research employs a Research and Development (R&D) approach with the Agile development method. Real-time face detection and recognition are performed from CCTV camera feeds using a Python module that integrates the InsightFace and MediaPipe algorithms. Identified attendance data is automatically stored in Google Sheets, subsequently processed by n8n to deliver information to users via a WhatsApp chatbot powered by the Gemini 2.5 Flash model. Testing conducted on 419 samples yielded an accuracy of 86.16%, with 275 True Negative values demonstrating the system's capability in filtering unregistered faces. The overall average system latency was 15.9 seconds, with a chatbot automation response time of only 9.3 seconds. This research demonstrates that the integration of workflow automation and AI agents is effective in improving the efficiency of academic attendance information access.
Selection of Outstanding Lecturers Using the Simple Multi-Attribute Rating Technique (SMART) Method Dede Irmayanti; Mochzen Gito Resmi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2338

Abstract

Lecturers play a crucial role as professional educators in the implementation of higher education through the Tridarma Perguruan Tinggi (Triple Dharma of Higher Education), which encompasses education, research, and community service. The selection of exemplary lecturers serves as both a form of recognition and a motivational instrument to enhance institutional quality. However, the selection process is often hindered by subjective assessments and the lack of standardized measurement, which may lead to dissatisfaction and diminish the objectivity of the results. This study aims to address these issues by implementing a Decision Support System (DSS) using the Simple Multi-Attribute Rating Technique (SMART) method. The SMART method was selected for its effectiveness in facilitating multi-criteria decision-making through weight assignment to priority parameters, such as scientific publications, educational qualifications, and external achievements. The results of this system implementation are provide structured, transparent, and accurate decision recommendations, ensuring that the selection of exemplary lecturers is conducted objectively based on measurable data.
Design of an Android-Based Sitting Posture Detection Application Using Deep Learning Jhonshen Lim; Octara Pribadi; Andy
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2341

Abstract

Prolonged poor sitting posture is a major cause of musculoskeletal disorders including lower back pain and spinal abnormalities. This study designs and implements PosturApp, a deep learning-based Android application for real-time sitting posture detection using Kotlin. A Multi-Layer Perceptron (MLP) model was trained on 3,526 keypoint datasets sourced from the Kaggle public dataset (Posture Recognition) and direct image capture using an Android front camera, extracting 66 coordinate values from 33 body landmarks via MediaPipe BlazePose. The model was converted to TensorFlow Lite (TFLite) format at approximately 78 KB for on-device inference without internet connectivity. Evaluation results show an accuracy of 97.81% with precision 0.99, recall 0.99, and F1-Score 0.98. The application provides real-time visual feedback through interface color changes and corrective notifications, along with a gallery-based classification feature. Functional testing across eight posture scenarios yielded entirely correct results with confidence values ranging from 59% to 99%.
Design of a Warehouse Inventory Management System Using FEFO Method in NUSA Niaga Multi-Tenant Lina Nur Afifah; Aulia Hamdi; Sri Rahayu; Intan Nur Sifa; Rizki Cahya Putri; Purnia Setiawati; Aulia Suryaning Tyas; Mayza Nurul Khasanatun Nisa
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2343

Abstract

Most village-based businesses still manage their inventory manually, from monitoring warehouse stock and generating reports and checking items based on expiration dates. This process is considered inefficient and carries the risk of errors in data recording and reporting. In the process of transferring inventory to the display on the web-based NUSA Niaga platform, the FEFO method is applied. Needs analysis, system design, design implementation, and system documentation were conducted. Literature review on management systems, the FEFO method, multi-tenant architecture, and RBAC were used for data collection. The system was designed to monitor inventory, manage products nearing expiration, record goods transfers, and implement multi-tenant functionality using flowcharts, ERDs, and DFDs. This system is expected to help manage BUMDes warehouse in a more connected and structured manner.
Analysis of JKN Mobile User Satisfaction using SVM and KNN Methods Through PSO Optimization Esty Purwaningsih; Ela Nurelasari
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2345

Abstract

This study was conducted to evaluate the service quality of the JKN Mobile application developed by the Health Social Security Administering Agency (BPJS Kesehatan) as a means of facilitating participants in accessing health services. Although the application provides convenience for users, there are still various complaints indicating that the service is not running optimally. Therefore, this study aims to analyze the positive and negative sentiments of JKN Mobile application users by comparing the Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) algorithms based on Particle Swarm Optimization (PSO). The research method was carried out by processing user review data using sentiment classification techniques. The test results showed that the SVM algorithm obtained an accuracy of 85.02% with an AUC value of 0.815, while the PSO-based SVM increased to 86.71% with an AUC of 0.831. The KNN algorithm obtained an accuracy of 39.54% with an AUC of 0.500, while the PSO-based KNN increased to 87.05% with an AUC of 0.736. The results of the study prove that the implementation of PSO is able to improve the accuracy performance of both algorithms.
Network Device Performance Monitoring Using the Simple Network Management Protocol (SNMP) Method Aldi Mulia Rismanto; Asrul Abdullah; Sucipto
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2346

Abstract

Network problems frequently occur at Politeknik Negeri Pontianak due to the increasing number and scale of network devices. These issues require continuous monitoring to ensure service availability across all network devices. To address this problem, the author conducted network monitoring using the SNMP (Simple Network Management Protocol) method and network performance measurement using the Wireshark application. SNMP is a standard protocol used to monitor and manage network devices such as routers, switches, servers, and other networking equipment. The research stages began with data collection, followed by monitoring and performance testing of the network. After testing the network in the Informatics Engineering Building, both satisfactory and unsatisfactory results were obtained. The results of SNMP measurements on MRTG showed the lowest throughput values on the second day of testing, with 485.6 kbps for daily traffic, 236.8 kbps for weekly traffic, 232 kbps for monthly traffic, and 121.6 kbps for yearly traffic. Meanwhile, the Quality of Service measurement produced the lowest throughput value of 0.225 kbps, packet loss of 0.354%, delay of 3.331 ms, and jitter of 8.763 ms.
The Effect of Mobile Banking Usage on Banking Customer Satisfaction Ayu Maulidia; Silvia Anita Dewi; Moh.Yogi Nuruzzalam; Achmarul Fajar
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2347

Abstract

The rapid development of information technology has encouraged the banking sector to innovate through digital-based services, one of which is mobile banking. This service provides convenience for customers in conducting banking transactions quickly, effectively, and efficiently through smartphones without having to visit bank offices. This study aims to determine the effect of mobile banking usage on customer satisfaction in banking services. The research method used is a quantitative descriptive approach with data obtained from previous journals and supporting literature related to mobile banking and customer satisfaction. The results indicate that the use of mobile banking has a positive and significant effect on customer satisfaction. Factors such as ease of use, transaction speed, security, service quality, trust, and digital banking innovation are proven to influence customer satisfaction in using mobile banking services. In addition, digital banking services are able to improve customer convenience and efficiency in carrying out financial transactions. Therefore, banks are expected to continuously improve the quality of digital services, strengthen transaction security systems, and provide sustainable innovations to maintain customer satisfaction and loyalty in the digital era.
Public Sentiment Analysis on the Issuance of Panda Bonds as an Effort for Rupiah Stability using SVM Algorithm on Youtube Social Media Junjung Rahmat Santosa; Rangga Apriwijaya; Ilham Ardiasyah; Rangga Apriansyah; Destiarini
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2350

Abstract

The stability of the Rupiah exchange rate is a crucial indicator of Indonesia's economic health, one of which is pursued through the issuance of Panda Bonds. However, this policy has triggered dynamic discourse on social media, particularly YouTube. This study aims to map public perception and test the performance of the Support Vector Machine (SVM) algorithm in classifying sentiments related to this issue. The research methodology includes scraping YouTube comment data, text preprocessing, automated labeling using the Lexicon-based method, and classification using SVM with a Linear kernel. From a total of 659 collected data, the results show that public sentiment is dominated by positive responses at 51.9%, followed by neutral sentiment at 29.0%, and negative sentiment at 19.1%. While public concerns focus on the debt burden and foreign currency dependence, there is overall support for economic stability efforts. The model evaluation demonstrates excellent performance, achieving an accuracy rate of 87.86%, precision of 88.79%, and an F1-score of 87.96%. This proves that a hybrid approach between Lexicon-based and SVM is effective in analyzing complex public opinions within the economic domain on social media.
Design of a Multi-Tenant SaaS-Based Centralized Financial System Using a Silent Accounting Approach Rizki Cahya Putri cahyaputri; Azhari Shouni Barkah; Aulia Suryaning Tyas; Intan Nur Sifa; Purnia Setiawati; Mayza Nurul Khasanatun Nisa; Sri Rahayu; Lina Nur Afifah
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2356

Abstract

Village financial management faces various fundamental challenges, including transaction recording that is still manual, a lack of integration between financial systems and village operational activities, and the absence of a platform capable of serving multiple villages within a single efficient infrastructure. These conditions result in financial reporting processes that are inefficient, error-prone, and difficult to account for. This study aims to design a centralized financial system based on a multi-tenant Software as a Service (SaaS) architecture using the Silent Accounting approach, defined as an automated transaction recording mechanism triggered by operational module activities without manual intervention. This study employs a qualitative descriptive method with a literature review approach. The design yields three main artifacts a system flowchart illustrating the workflow from user authentication, role assignment, and transaction validation through to automatic journal entry and posting to general ledger an Entity Relationship Diagram (ERD) modeling the database structure consisting of seven entities and a Data Flow Diagram (DFD) breaking down the system into five main processes A multi-tenant architecture with a ‘tenant_id’ column ensures data isolation between villages while allowing a single platform to serve multiple village simultaneously. The Silent Accounting mechanism ensures that all village financial activities are recorded consistently, accurately and in real time. The design is expected to serve as the foundation for the development and scalable village financial management platform.
Evolution and Impacts of AI-Based Rainfall Prediction Systems on Agricultural Management in Tropical Regions: A 20-Year Systematic Review Safrizal; Ika Safitri Windiarti
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2357

Abstract

Global climate change has significantly disrupted rainfall patterns in tropical regions, posing major challenges to agricultural productivity and food security. Accurate rainfall prediction has become a critical component of data-driven agricultural management. This study conducts a systematic literature review (SLR) following the PRISMA 2020 guidelines to analyze the evolution of AI-based rainfall prediction systems and their multidimensional impacts on tropical agricultural management over the period 2008–2026. Data were sourced from Scopus using three Boolean search strings, yielding 239 records, of which 235 articles were retained after duplicate removal and quality assessment using the Mixed Methods Appraisal Tool (MMAT) with a threshold score of ≥5. Bibliometric analysis was conducted using VOSviewer and Bibliometrix (R), while thematic narrative synthesis was performed using NVivo 14. Results reveal a clear four-phase technological evolution: conventional methods (2008–2015), machine learning adoption (2016–2020), deep learning and IoT integration (2021–2023), and multimodal and large language model era (2024–2026). Technical impacts dominated the corpus (accuracy improvements of 18–35%), while social and economic impact studies remain critically underrepresented (2.6% and 0.9%, respectively). Key research gaps identified include poor model interpretability (black-box problem), limited integration with decision support systems (DSS), inadequate tropical-specific model development, and the near-total absence of longitudinal impact evaluations. This study contributes a holistic synthesis integrating technological evolution with multidimensional impact analysis, offering strategic recommendations for developing more adaptive, transparent, and equitable AI rainfall prediction systems aligned with SDG 2, SDG 13, and SDG 15

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