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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 525 Documents
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Comparative Analysis of K-Means Clustering and K-Medoids Clustering Methods in Clustering Neonatal Infant Mortality Rates in West Java Province Intan Putri Septiyani
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.v5i3.2329

Abstract

Neonatal mortality rate is an important indicator in assessing public health conditions. This study aims to cluster neonatal mortality data in West Java Province using the K-Means Clustering and K-Medoids Clustering methods, as well as compare the performance of both methods in producing the best clusters. The study used secondary data obtained from Open Data West Java. The research stages included data selection, preprocessing, clustering, and evaluation using the Davies-Bouldin Index (DBI). The experiments were conducted using cluster variations (k) from 2 to 8. The results showed that the K-Means Clustering method produced the best performance with a DBI value of 0.430 at k = 3. The clustering results generated three categories: low-risk cluster with 408 data points, medium-risk cluster with 65 data points, and high-risk cluster with 13 data points. The differences in cluster characteristics indicate variations in neonatal mortality risk levels among regions in West Java Province. The findings of this study are expected to support decision-making and more targeted health policy planning.   Keywords: K-Means Clustering, K-Medoids Clustering, Davies-Bouldin Index, Neonatal Mortality.
Analysis and Design of the Nusa Graha Module for Village Asset Management and Facility Booking on the NUSAEKA Multi-Tenant SaaS Platform Purnia Setiawati; Azhari Shouni Barkah; Rizki Cahya Putri; Intan Nur Sifa; Aulia Suryaning Tyas; 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.v5i3.2331

Abstract

In most regions of Indonesia, village asset management and the process of booking village facilities are still carried out manually, which can lead to disorganized record-keeping, data loss, and a lack of access for village residents. This study was conducted to analyze and evaluate the Nusa Graha module as a component of the Nusaeka multi-tenant SaaS platform, focusing on village inventory management, automatic asset depreciation, and web-based village booking services. This research was conductes through a literature review and system analysis obtained through consultation with supervising lecturer as well as document analysis. The analysis results include business flowcharts, Data Flow Diagrams (DFDs) at levels 0 and 1, and Entity-Relationship Diagrams (ERDs), which consist of several main tables. The research findings indicate that the Nusa Graha module can support and streamline asset management and the structured process of facility rentals using multi-tenant data via tenant_id and a modular language. Additionally, the Nusa Graha module facilitates integration with the Nusa Artha financial module if the village subscribes to it.
Customers’ Loss of Confidence in Banking Security Systems: A Case Study of the Loss of BRI Customers’ Funds Aisyah Safitri; Sitti Nur Aini; Moh. Ali Fajar Sidiq; 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.v5i3.2333

Abstract

The phenomenon of customer funds going missing in the banking sector, particularly in the case of Bank Rakyat Indonesia (BRI), has raised concerns about the security of the digital banking system and has led to a decline in public confidence. This study aims to analyse the crisis of customer confidence in banking security systems by examining influencing factors, such as cyber risk, risk perception, and the role of social media. The research method employed is a qualitative approach using case studies, utilising secondary data obtained from academic journals, institutional reports, and case documentation.The research findings indicate that the loss of customer funds is influenced by vulnerabilities in digital security systems and the rise in cybercrime, such as phishing and social engineering. Furthermore, these incidents have led to a decline in customer trust, a trend exacerbated by the dissemination of information via social media. This study concludes that the crisis of customer trust is caused not only by technical factors, but also by risk perceptions and the dynamics of public information. Therefore, improvements in banking system security, strengthened consumer protection, and effective communication strategies are required to maintain customer trust.
Design of a Web Based Population Data Information System at Matawai Atu Village Office Jesika Prince Piri; Arini Aha Pekuwali
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.v5i3.2334

Abstract

The development of information technology has greatly influenced many sectors, including village administration. The Matawai Atu Village Office, located in Umalulu Subdistrict, East Sumba Regency, still uses a manual system to record population data such as births, deaths, new residents, and relocations. Data is recorded in a main register book and then processed using Microsoft Word to create reports. This method causes several problems, including the risk of data loss, data entry errors, slow data searching, and delays in report preparation. To solve these problems, this study aims to design a web-based population data information system that is effective and efficient. The study uses the Waterfall method, which includes the stages of requirements analysis, system design, implementation, testing, and maintenance. The system is developed using PHP and a MySQL database. Data collection is carried out through interviews, direct observation at the research location, and literature study. System testing is conducted using Black Box Testing to ensure that all features work properly, and the System Usability Scale (SUS) to measure how easy the system is for users. The results show that the developed system can manage population data more accurately, quickly, and securely. The system also makes it easier for staff to search data, manage documents, and prepare reports. With this system, it is expected that public services at the Matawai Atu Village Office will improve and better support the work of village staff.
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.

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