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JAIS (Journal of Applied Intelligent System)
Published by Universitas Dian Nuswantoro
ISSN : 25020493     EISSN : 25029401     DOI : -
Core Subject :
Journal of Applied Intelligent System (JAIS) is published by LPPM Universitas Dian Nuswantoro Semarang in collaboration with CORIS and IndoCEISS, that focuses on research in Intelligent System. Topics of interest include, but are not limited to: Biometric, image processing, computer vision, knowledge discovery in database, information retrieval, computational intelligence, fuzzy logic, signal processing, speech recognition, speech synthesis, natural language processing, data mining, adaptive game AI.
Arjuna Subject : -
Articles 191 Documents
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Person Re-Identification Using CNN Method With Combination of SVM and Semantic Segmentation Kurniawan, Kristian Adhi; Soeleman, Moch Arief
(JAIS) Journal of Applied Intelligent System Vol. 9 No. 1 (2024): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jais.v9i1.10345

Abstract

Abstract – Person re-identification is a mechanized procedure of video investigation which has been widely studied in contemporary years. Research problems that are often raised in the field of a person's re-identification research are characteristic representations that are easily affected by closure (abhorrent to other objects). Furthermore, after extracting local features by means of a boundary box, the background image still contains and does not focus on the human body parts. This study comes up with a method combination of CNN, SVM classification, and semantic segmentation. CMC (Cumulative Matching Characteristics) and mAP (mean Average Precision) are measurements of assessment that will be utilized to measure the operation of re-identification. The ResNet + SVM + SSP-ReID technique performed best in the Market dataset, with a CMC increase of 3-10% (rank-1 through rank-20). The Market and CUHK03 (D) datasets both showed improvements of 1-4.1% in mAP.  Keywords Person re-identification; Feature extraction; CNN; SVM; Semantic segmentation;
Artificial Intelligence Chatbot for Customer Service in E-Commerce Using Telegram Based on Node.js Nabyla, Fuaida; Saraswati, Nurul Mega; Nursetyo, Arif; Hariyono, Rito Cipta Sigitta; Septi, Ariani Dwi
(JAIS) Journal of Applied Intelligent System Vol. 9 No. 2 (2024): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jais.v9i2.10432

Abstract

Currently, the traditional market is increasingly being supplanted by numerous online markets. The fierce competition in the online market necessitates excellent service from sellers to buyers. As a result, many online stores now offer round-the-clock service, which can be financially burdensome if handled manually. Chatbots offer a promising solution by automating the online shopping process, thereby reducing costs and enhancing customer service. To address the need for accurate and prompt responses, this study proposes an intelligent chatbot system built on Artificial Intelligence (AI), specifically tailored to function as an e-commerce assistant. Integrated seamlessly into the Telegram application, the chatbot efficiently processes user input questions through three essential stages: parsing, pattern matching, and data crawling, all powered by AI technology. Within the AI process, user requests are systematically categorized into three primary domains: general questions, calculations, and stock checks. Notably, the calculation category encapsulates both order and payment processes. The effectiveness of the proposed method is substantiated by the results of 200 trials, demonstrating its adeptness in accurately addressing all user inquiries.
Customer Segmentation Using K-Means Clustering with RFM Method (Case Study : PT. Dewangga Travindo Semarang) Winaryanti, Hida Sekar; Hadi, Heru Pramono; Rachmawanto, Eko Hari
(JAIS) Journal of Applied Intelligent System Vol. 9 No. 1 (2024): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jais.v9i1.10440

Abstract

PT. Dewangga Travindo is a company that operates in the field of travel services which includes tours, travel, and Hajj and Umrah pilgrimages which is based in the city of Semarang and has received permission from the Ministry of Religion No. D/606 of 2013. Every year there is always an increase in sales of services. Hajj and Umrah. The higher transaction activity every day results in a large buildup of data in the database which will only become data waste. The ability to process data is increasingly sophisticated using data mining, which is an activity of looking for relationships between items to obtain patterns as information to assist in decision making. However, considering the large number of competitors offering the same services, it is necessary to increase competitiveness to overcome market segmentation at PT Dewangga Travindo. For this reason, this research was carried out which aims to overcome customer segmentation using the Clustering method with the K-Means algorithm which produces a visual cluster model with RStudio tools using RFM attributes applied to carry out segmentation. The data used in this research is data on Hajj and Umrah pilgrims in the 2018-2020 period.
Web-Based Public Street Lighting Complaint Application with Realtime Whatsapp Notification Using Prototype Method in Pemalang Regency Nugraha, Arifinza Eska; Pramudya, Elkaf Rahmawan; Abdussalam, Abdussalam
(JAIS) Journal of Applied Intelligent System Vol. 9 No. 1 (2024): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jais.v9i1.10445

Abstract

Public Street Lighting (PJU) plays an important role in transportation infrastructure, especially at night. Currently, complaints about PJU damage are only made using social media. This research designs and builds a web-based PJU complaint system with real-time notifications via WhatsApp in Pemalang Regency. Data was collected through interviews, observations, and questionnaires. This system is built with PHP and MySQL, with WhatsApp notification integration to ensure accurate and real-time complaint information. The system trial involved the community, showing the system's effectiveness in increasing reporting efficiency and officer response. The system provides easy online reporting and real-time notifications via the website and WhatsApp. This system is expected to improve community services and PJU management. The results can be a reference for the development of similar systems in other areas.
Smart Waste Management and Recycling Based on IoT using Machine Learning Algorithm Ginting, Gerinata; Apnena, Riri Damayanti
(JAIS) Journal of Applied Intelligent System Vol. 9 No. 2 (2024): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jais.v9i2.10766

Abstract

Smart waste management and recycling have become critical issues in urban planning and environmental sustainability due to the increasing volume of waste generated by modern societies. In this study, we investigated the performance of Support Vector Machine (SVM) and Neural Network (NN) methods in an Arduino-based waste sorting system. Our testing phase revealed exceptional performance, with SVM achieving an accuracy of 92% and NN achieving 96%, alongside perfect precision, recall, and F1-score metrics. The consistent True Positive (TP) results across all waste categories underscored the system's capability to accurately direct waste into correspondingcolored bins. These findings highlight the significance of automated waste management systems in promoting effective waste sorting practices and facilitating sustainable recycling efforts. Moreover, advancements in technology and machine learning algorithms offer promising prospects for further enhancing the efficiency and scalability of such systems, thereby contributing to a cleaner and healthier environment for future generations. Future research in smart waste management could focus on exploring additional machine learning algorithms, advanced sensor technologies, and Internet of Things integration. Investigating alternative algorithms beyond SVM and NN, adopting advanced sensors like hyperspectral imaging or lidar, and incorporating IoT devices for real-time monitoring could enhance waste sorting accuracy and scalability.
Pneumonia Detection on X-rays Image using YOLOv8 Model Hyperastuty, Agoes Santika; Pradana, Dio Alif; Widayani, Aisyah; Putra, Fadli Dwi; Mukhammad, Yanuar
(JAIS) Journal of Applied Intelligent System Vol. 9 No. 2 (2024): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jais.v9i2.10865

Abstract

Pneumonia is an acute inflammatory disease of lung tissue. It is usually caused by microorganisms such as bacteria, fungi and viruses. The young children are particularly vulnerable to this illness. Report in 2019 shows that pneumonia kills almost 2,000 children under the age of five every day worldwide and affects over 800,000 children under the age of five annually. Analyzing the chest X-ray results of the patient's body is one method of diagnosing pneumonia. Therefore, this research was done to deploy a deep learning to identify the healthy and pneumonia affected lungs from chest X-ray images in order to aid in the diagnosing process. This research was done by using 2000- chest X-ray dataset—of which 1500 pneumonia lung data and 500 normal lung data. The computer vision model YOLOv8 is used in this study. The accuracy results from the training process were 56.15% in the pneumonia class and 92.03% in the normal class. Wether in the testing process yielded an average value of 0.482 (48, 2%) for the pneumonia class and 0.675 (67,5%) for the normal class. From these results, there are promising possibilities for developing a pneumonia detection system using YOLO in the future.
K-MEANS ALGORITHM IN CLUSTERING SALES DATA FOR CALCULATING ESTIMATED HOUSE PRICES Pranoto, Gatot Tri
(JAIS) Journal of Applied Intelligent System Vol. 9 No. 2 (2024): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jais.v9i2.11027

Abstract

Determination of the value of the guarantee to the Bank in the process of applying for Home Ownership Credit (KPR) submitted by prospective customers still refers to the provisions of the Financial Services Authority, where the assessment must follow the existing rules and be carried out by public appraisals or commonly called the Office of Public Appraisal Services (KJPP). Currently the analyst credit officer cannot validate the results of the assessment report from KJPP, so if an error occurs either intentionally or not by KJPP or appraisal parties continue to process according to the given value. In the event of default of payment by the customer due to the lower collateral value of the loan provided, the bank violates Bank Indonesia Regulation number 18/16/PBI/2016 concerning loan to value ratio. This study aims to apply the K-Means algorithm in grouping home sales so that it can be used for the calculation of the estimated value of house prices, and develop a prototype of the house price estimation information system. Data retrieval using crawling or scrapping techniques on the website makes it easier to fulfill data on a dataset. The result of this study is the data of home sales for kebon Jeruk area spread across the internet can be grouped into 3 clusters with the value of David Bouldin Index in duri Kepa sub area, which is 0.096, in South Kedoya sub area of 0.087, in North Kedoya sub area of 0.071, and Kelapa Dua sub area of 0.117. By combining clusterization results using K-Means methodology with land price calculation formula obtained land price estimation in sub area. Keywords: K-Means, KPR, Data Scraping, KJPP, MAPPI
Mapping System Design of a Genetic Mapping System for the White Nest Swallow (Collocalia Fuciphaga) in Java Kasih, Patmi; Utomo, Budi; Ramadhani, Risky Aswi; Firliana, Rina; Tanjungsari, Ardina
(JAIS) Journal of Applied Intelligent System Vol. 9 No. 2 (2024): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jais.v9i2.11093

Abstract

The distribution of the white swift (Collocalia fuciphaga) is on several large islands in Indonesia. The white swallow has an advantage over the nests of the black swallow and sriti, having edible parts reaching 85-100% of the total nest. Many types of white swallow's nests in Indonesia are known for their different physical characteristics, color and weight. It is not known that these differences occur due to genetic differences or simply due to differences in the type of food and living environment. Differences due to food and environmental factors do not really affect the health function of the nest. Differences due to genetics greatly influence the function of the nest for health. The design of this system is initial research to start mapping white nest swiftlets in Indonesia. The results of the design will be used as a data storage system for genetic mapping of white nest swallows on the island of Java by taking DNA samples of swallows from various habitat areas. The system will store data on habitat areas, record location points and take bird samples from these areas to then carry out laboratory tests to determine the DNA code test of each bird sample. Furthermore, it is hoped that clear genetic mapping results can be used to determine the quality and function of the bioactive components of white swift nests on the island of Java. The mapping results will also be a source of knowledge about the richness of the germplasm of native Indonesian swiftlets. Keywords: GIS, Bioactive, genetic, Java, mapping, white swallow's nest
C4.5 Algorithm Based on Forward Selection and Particle Swarm Optimization for Improving Accuracy in Heart Disease Patient Classification Setiawan, Aji Awang
(JAIS) Journal of Applied Intelligent System Vol. 9 No. 2 (2024): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jais.v9i2.11099

Abstract

Early detection of heart disease is crucial given the high number of cases occurringin advanced stages and affecting individuals in their productive years. Utilizing data mining, the C4.5 Algorithm is one method capable of detecting the onset of heart disease, prompting timely awareness and early prevention. The dataset employed is the Heart Disease Cleveland UCI from Kaggle, featuring 13 input attributes and 1 target attribute. Using the Decision Tree method results in decision-making by constructing a decision tree. The test outcomes revealed an accuracy rate of 77.11% with the C4.5 algorithm, 83.69% with the C4.5 algorithm employing Forward Selection, and 84.73% with the C4.5 algorithm based on Forward Selection and Particle Swarm Optimization.
Vehicle Detection Using Image Conversion Percentage to Binary Method Based on K-Means Irawan, Candra; Ningrum, Amanda Prawita; Nohan, Rejendra
(JAIS) Journal of Applied Intelligent System Vol. 9 No. 2 (2024): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jais.v9i2.11178

Abstract

Vehicle detection is the artificial intelligence that can help us in transportation highway systems like counter vehicles passing through the road on Eid Mubarak day etc. The object in this case is divided into six classifications there are car, motorbike, van, truck, and three-wheel. On the dataset vehicle is mostly an image of a car that we get from Kaggle. To solve vehicle detection problems such as poor vehicle detection and reduced detection accuracy, we provide a new vehicle detection with a dataset at kaggle. The clustering process consists of steps in which input images are transformed into morphometrics. The next step is to classify the image data using the K-Means algorithm. The images grouped by this detection are vehicles. The first step is to determine the randomly drawn mean or center point of two image data values ​​in the database. If there is no data transfer, the group is considered stable and group creation is completed. Seven vehicle image data are used to test this application. And the result of our experiment on vehicle detection is about 85.7 % accurate

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