Journal of Applied Intelligent System <p>Journal of Applied Intelligent System (JAIS) is published by the Department of Informatics Universitas Dian Nuswantoro Semarang and IndoCEISS, which focuses on research in Intelligent System.</p><p>Topics of interest include, but are not limited to:</p><p>Biometric, image processing, computer vision, knowledge discovery in the database, information retrieval, computational intelligence, fuzzy logic, signal processing, speech recognition, speech synthesis, natural language processing, data mining, adaptive game AI, smart city technology and framework.</p> en-US <div id="gt-input-tool"> </div>Authors who publish with this journal agree to the following terms:<br /><br /><ol type="a"><li>Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a <a href="" target="_new">Creative Commons Attribution License</a> that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.</li><li>Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.</li><li>Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See <a href="" target="_new">The Effect of Open Access</a>).</li></ol> (Heru Agus Santoso) (Eko Hari Rachmawanto, M.Kom) Tue, 02 Apr 2024 07:05:11 +0000 OJS 60 Implementation of Feature Selection Chi-Square to Improve the Accuracy of the Classification Model Using the Random Forest Algorithm on Coronary Artery Disease <em>Coronary heart disease is a disease in which the occurrence of blockages in the blood vessels in the heart. Coronary heart disease is a fatal disease, it is better to get as much information about this disease as possible. Data Mining can classify whether a person has heart disease or not based on symptoms. Data mining builds a model that can predict whether a person has heart disease or not. How well a model performs classification can be determined from its accuracy value, but this accuracy value can still be improved. Increasing the accuracy value can be done by performing Feature Selection. The research object used in this research is a dataset about coronary heart disease obtained from the Kaggle website. The classification method used in this modeling is the Random Forest algorithm to classify whether a person has coronary heart disease or not. The Random Forest Algorithm is a classification algorithm consisting of Decision Trees for classifying. The Random Forest algorithm is used because it has been proven to produce good accuracy in several previous studies. The Feature Selection method used in this modeling is the Chi-Square hypothesis test to determine whether there is an effect of each independent variable on the dependent variable. This research compared the value of modeling accuracy without using Feature Selection with modeling using Feature Selection. The result of this study is that the model without Chi-Square Feature Selection produced an accuracy value of 96,05% and the model with Chi-Square Feature Selection produced an accuracy value of 97,33%.</em> Ida Bagus Satya Mahendra, Tatik Widiharih, Fajar Agung Nugroho, Priyo Sidik Sasongko Copyright (c) 2024 Ida Bagus Satya Mahendra, Fajar Agung Nugroho, Tatik Widiharih, Priyo Sidik Sasongko Tue, 02 Apr 2024 00:00:00 +0000 Utilization Of Principal Component Analysis To Improve Emotion Classification Performance In Text Using Artificial Neural Networks <span lang="EN-US">Emotions, being transient and variable, differ across locations, times, and individuals. Automatic emotion identification holds significant importance across various domains, such as education and business. In education, emotional analysis contributes to intelligent electronic learning environments, while in business, it aids in assessing customer satisfaction with products. This study advocates the application of Principal Component Analysis (PCA) to enhance the performance of text emotion classification using the Artificial Neural Network (ANN) method. PCA, a pattern identification method, reduces text dimensions, improving the classification process by determining word similarities. PCA offers the advantage of dimension reduction without compromising information integrity. The classification approach involves two stages: one after PCA dimension reduction and the other without PCA post TF-IDF stage. The study's conclusive findings, incorporating PCA in ANN classification, demonstrated a notable increase in recall for the happy class, reaching 0.92 compared to the pre-PCA score of 0.91. Furthermore, precision in the sadness class improved to 0.90, surpassing the pre-PCA precision of 0.80. This affirms the efficacy of integrating PCA in enhancing the accuracy and performance of emotion classification in text analysis.</span> Mahazam Afrad, Muljono Muljono, Pujiono Pujiono Copyright (c) 2024 Mahazam Afrad, Muljono Muljono2, Pujiono Pujiono Tue, 02 Apr 2024 00:00:00 +0000 Predicting Gold Price Movement Using Long Short-Term Memory Model <span lang="IN">Gold, as a valuable commodity, has been a primary focus in the global financial market. It is often utilized as an investment instrument due to the belief in its potential price appreciation. However, the unpredictable and complex movement of gold prices poses a significant challenge in investment decision-making. Therefore, this research aims to address this issue by proposing the use of the Long Short-Term Memory (LSTM) model in time series analysis. LSTM is a robust approach to understanding patterns and trends in gold price data over time. In the context of time series analysis, historical gold price data includes daily, weekly, and monthly datasets. Each model with its respective dataset is useful for identifying patterns in gold prices. The daily model achieves an MSE of 452.2284140627481 and an RMSE of 21.26566279387379. The weekly model achieves an MSE of 1346.1816584357384 and an RMSE of 36.69034830082345. The monthly model achieves an MSE of 11649.597907584808 and an RMSE of 107.93330305139747. With these RMSE results, the LSTM model can predict gold prices effectively. Based on the trained models, it can also be concluded that gold prices exhibit long-term temporal dependence.</span> Azaria Beryl Nagata, Moch Sjamsul Hidajat, Dibyo Adi Wibowo, Widyatmoko Widyatmoko, Noorayisahbe Bt Mohd Yaacob Copyright (c) 2024 Azaria Beryl Nagata, Moch Sjamsul Hidajat, Dibyo Adi Wibowo, Widyatmoko Widyatmoko, Noorayisahbe Bt Mohd Yaacob Tue, 02 Apr 2024 00:00:00 +0000 Counselor Application Frontend with Personality- matching Using Android-Based K-Means Clustering Algorithm <p>Education is one of the most important things for people to have. Many people are competing for education to increase their abilities. Technology plays a big role in developing access to education to make it easier with many online applications and online classes education becomes easier. However, there are still many unresolved problems in this field of education, namely the emergence of the phenomenon of incompatibility between educators and students so that student interest decreases dramatically because of this. And also, the lack of learning materials taught that are not school subjects such as programming. Therefore, the author and team designed an application where this application can find students a learning mentor outside of school so that they can increase their knowledge. The application also provides a matching feature based on the student's personality so that the student can find a suitable tutor.</p> Ifan Perdana Putra, Eko Hari Rachmawanto, Wellia Shinta Sari, Tri Esti Rahayuningtyas, Choerul Umam, Mahadika Pradipta Himawan, Noorayisahbe Bt Mohd Yaacob Copyright (c) 2024 Ifan Perdana Putra, Eko Hari Rachmawanto Tue, 02 Apr 2024 00:00:00 +0000 Optimization of Yolov5 Hyperparameter Using Adam Optimizer in Vehicle Object Detection Utilization of computer vision can be applied in various aspects of daily life, reducing dependence on human labor. One of its implementations is in industry, such as in the production process of motorized vehicles, to sort or classify parts or goods. The computer vision process involves many stages, such as image capture, image processing, image analysis, image recognition, and decision-making. In the automotive industry, computer vision has been used in autonomous or driverless electric vehicles, as well as in creating intelligent transportation systems. To detect objects in real-time, one of the options that can be used is to use the YOLO algorithm, which can detect objects in one stage with predictions of bounding boxes and class probabilities simultaneously. However, although YOLO has good performance, the architecture has some drawbacks, such as complexity and complicated hyperparameter congurations. To remedy this, the Adam optimization algorithm was introduced, which combines the momentum and RMSprop algorithms to adjust the learning rate adaptively and provide faster convergence in model training. This is evidenced by an increase in the value of mAP on Yolov5. These results prove that the Yolov5 method with Adam`s optimization is better than the Yolov5 method without optimization. Bambang Irawan, Pulung Nurtantio Andono, Ruri Suko Basuki Copyright (c) 2024 Bambang Irawan Tue, 02 Apr 2024 00:00:00 +0000 Optimization Of Neural Network Method Using Chi-Square Feature Selection In Poverty Data Classification <p>Poverty is a fundamental problem that has become the center of attention in several aspects, for example from the government. The government needs poverty data and analyzes it to determine which poverty alleviation programs should be delivered to the right target or the poor. The aim of this study is to determine the accuracy of the classification of poverty in Batang District using the Neural Network method using the chi square feature selection. The dataset used in this study uses poverty data sourced from the Batang district BPS based on the results of the Susenas survey (National Economic Survey) for the 2022 time period. The results of this study indicate that the accuracy obtained for poverty classification using a neural network is 96.38% , with a precision value of 100%, and a recall value of 89.38%. Whereas when using a neural network with feature selection chi square, it gets an accuracy value of 93.68%, with a precision value of 91.07%, and a recall value of 90.26%. The contribution of this research is to develop a neural network method using feature selection chi square to improve the results of the accuracy of the classification is not poor or poor.<strong> </strong></p><p> </p> Tresi Aprilia Aprilia Copyright (c) 2024 Tresi Aprilia Aprilia Tue, 02 Apr 2024 00:00:00 +0000 Person Re-Identification Using CNN Method With Combination of SVM and Semantic Segmentation <p><strong>Abstract – </strong><em>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.</em> <em>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. </em></p><p><strong><em> </em></strong></p><p><strong>Keywords </strong>Person re-identification; Feature extraction; CNN; SVM; Semantic segmentation;</p> Kristian Adhi Kurniawan, Moch Arief Soeleman Copyright (c) 2024 Kristian Adhi Kurniawan, Moch Arief Soeleman Tue, 02 Apr 2024 00:00:00 +0000 Classification Email Spam using Naive Bayes Algorithm and Chi-Squared Feature Selection <span>Spam email is a problem that disturbs and harms the recipient. Machine learning is widely used in overcoming email spam because of its ability to classify emails into spam or non-spam. In this research, the Naïve Bayes algorithm is initiated with the Chi-Squared selection feature to classify spam emails. So that the implementation is able to increase accuracy for better performance in classification. The feature selection method is used to direct the model's attention to features that are related to the target variable. In this study, the chi squared feature uses a value of K = 2500, with an accuracy of 98.83% which shows an increase in model performance compared to previous research. So that the Naïve Bayes model with the Chi-Squared selection feature is proven to provide better performance. </span> Maylinna Rahayu Ningsih, Jumanto Unjung, Habib al Farih, Much Aziz Muslim Copyright (c) 2024 Maylinna Rahayu Ningsih Tue, 02 Apr 2024 00:00:00 +0000 Completing Sudoku Games Using the Depth First Search Algorithm <p>Sudoku is a digital game that is included in the type of logic-based puzzle game where the goal is to fill in the puzzle with random numbers. Therefore, in this research it is proposed to use Artificial Intelligence which contains the Depth First Search Algorithm to track the number of possible solutions that lead to only one so that it becomes efficient. This game has different levels of difficulty such as easy, medium and difficult. The time and complexity of execution will vary depending on the difficulty so it is proposed to use Android Studio software. The experimental results prove that there is an increase in playing the Sudoku game quickly and accurately by applying the Depth First Search Algorithm method. This is proven by the ability to complete this game using the Depth First Search Algorithm using the Android Studio programming language. The average time at the easy level is 11:04 minutes, at the normal level is 10:52 minutes, at the hard level is 25:46 minutes, and at the extreme level is 38 minutes.</p> Fauzan Maulana Alfany, Christy Atika Sari, Cahaya Jatmoko, Deddy Award Widya Laksana, Candra Irawan, Solichul Huda Copyright (c) 2024 Fauzan Maulana Alfany, Christy Atika Sari Tue, 02 Apr 2024 00:00:00 +0000 Analysis of Inter-Subject and Session Variability using Brain Topographic Map <span lang="EN-US">The study described investigates the application of Brain-Computer Interface (BCI) technology, focusing on Motor Imagery (MI) signals which enable individuals to control movements through mental visualization. A major challenge in this field is accurately distinguishing between different movements, particularly when dealing with data from multiple subjects and recording sessions, known as inter-subject and inter-session variability. To address this, the authors employ the Wavelet Packet Transform-Common Spatial Patterns (WPT-CSP) method to enhance the resolution of MI signals. They visualize the results using Brain Topographic Maps (Topomaps) to depict brain activity during MI tasks, facilitating the analysis of variability across subjects and sessions. Utilizing dataset 2a from the Brain-Computer Interface Competition (BCIC) IV, the study demonstrates the efficacy of this approach in identifying variability patterns. This research holds promise for improving BCI technology applications in various domains, and future work could explore refining signal processing techniques and validation on larger datasets.</span> Topomap. Fachruddin Ari Setiawan, Dio Alif Pradana, Iim Nandang Copyright (c) 2024 Dio Alif Pradana, Fachruddin Ari Setiawan, Iim Nandang Tue, 02 Apr 2024 00:00:00 +0000 Implementation Of The Base64 Algorithm For Text Encryption And Decryption Using The Python Programming Language The exchange of information on the Internet requires increased protection to avoid potential threats to privacy and security. This study identified the main issues in this regard: the need for simple and effective tools for encoding and decoding messages, and the need to understand Base64 encoding algorithms and concepts. However, to overcome this problem the author developed an application to encode and decode messages/text using the Base64 algorithm and the Python programming language. This application allows users to send secret messages/text securely via and convert the data into Base64 format for secure transmission via text media. It also covers the basics of cryptography, Base64 algorithms, and how to use the Python programming language to develop secure applications. The result of this research is a simple and effective encryption and decryption application. This application provides a solution for users to protect messages or text when they want to change confidential information by converting it to Base64 format. With this application, you can send secret messages or texts with the confidence that only authorized parties can read them. Implementing message encryption and decryption using the Base64 algorithm using Python is an important step in maintaining message privacy and security in the current digital era. This research succeeded in developing an application suitable for this purpose. Therefore, the next step is to improve the security of your application by implementing stronger encryption algorithms. Additionally, we provide a more comprehensive user guide to help users better understand cryptographic concepts. Further research may focus on integrating applications with broader Internet security protocols to address increasingly complex security threats. Caroko Aji Pamungkas, Zudha Pratama, Ichwan Setiarso, Mohamed Doheir Copyright (c) 2024 Caroko Aji Pamungkas, Zudha Pratama, Ichwan Setiarso, Mohamed Doheir Tue, 02 Apr 2024 00:00:00 +0000 Data Mining Application Analyzing Customer Purchase Patterns Using The Apriori Algorithm The study aims to implement Data Mining with Apriori Algorithm and Association Methods (shop cart analysis) to analyze the sales pattern of Kaffa Beauty Shop stores as a case study. Sales information obtained from stores is used to find out the repeated buying habits of cosmetic products. This analysis provides store owners with valuable information to make more useful decisions about product inventory management, marketing strategies, and other aspects of their business. The Apriori Algorithm implementation follows steps including data preprocessing, subsetting, frequent dataset search, and strong association rules (strong Association Rules). The results of the analysis show that there are important purchasing patterns among some cosmetic products that can be the basis of a more effective sales strategy. The study helps understand how data mining and Apriori Algorithms can be applied in business contexts such as Kaffa Beauty Shop stores. Therefore, the results of this analysis are expected to contribute greatly to improving business efficiency and optimizing marketing strategies for store owners and stakeholders. The research is also expected to show the enormous potential of data analysis to support optimal business decision making. Moh. Lambang Prayugo, Dibyo Adi Wibowo, Moch. Sjamsul Hidajat, Ery Mintorini, Rabei Raad Ali Copyright (c) 2024 Moh. Lambang Prayugo, Dibyo Adi Wibowo, Moch. Sjamsul Hidajat, Ery Mintorini, Rabei Raad Ali Tue, 02 Apr 2024 00:00:00 +0000 Naive Bayes Sentiment Analysis Study On Street Boba And Gildak Kediri Consumer Reviews Streetboba &amp; Gildak Kediri outlet is a restaurant that serves a variety of Korean food menus and various kinds of drinks with boba and jelly toppings that are sold at low prices that suit the student's budget. This restaurant is located in East Java province which is precisely on Jalan Yos Sudarso No.43, Kediri City. With technological advances that continue to grow to affect various aspects, especially in the business and industrial world. Sentiment analysis is a technology that extracts or manages text to be expressed using text that can also be classified into positive and negative polarity. Consumer reviews are a form of communication that occurs in the sales process, the stage where potential buyers receive an explanation of the product posted and buyers receive reviews that explain the advantages or disadvantages of purchasing the product. In this study, sentiment analysis was conducted based on consumer opinions regarding social media accounts. The study aimed to use social media data to assess the service, cleanliness and quality of products offered by categorizing companies as having positive and negative reviews. To classify sentiment, the Naive Bayes method is used, which combines survey data collection methods, questionnaires, and observation data. Cindy Aprilia Wijaya Prasentya, Didik Hermanto, Wana Pramudyawardana Kusuma Negar, Folasade Olubusola Isinkaye Copyright (c) 2024 Cindy Aprilia Wijaya Prasentya, Didik Hermanto, Wana Pramudyawardana Kusuma Negar, Folasade Olubusola Isinkaye Tue, 02 Apr 2024 00:00:00 +0000 Customer Segmentation Using K-Means Clustering with RFM Method (Case Study : PT. Dewangga Travindo Semarang) <span lang="EN-US">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.</span> Hida Sekar Winaryanti, Heru Pramono Hadi, Eko Hari Rachmawanto Copyright (c) 2024 Hida Sekar Winaryanti, Heru Pramono Hadi, Eko Hari Rachmawanto Tue, 02 Apr 2024 00:00:00 +0000 Mobile-Based Interactive Learning Media Design Using Augmented Reality Concepts <span lang="EN">One of the natural disasters that currently occurs frequently in Indonesia is earthquakes. An earthquake is a situation where the earth shakes due to volcanic activity, or collisions due to the movement of the earth's plates. Earthquake activity causes many problems. One of them is liquefaction. Liquefaction is an event that shows a loss of soil shear strength caused by an increase in pore water pressure. This occurs because the earthquake load occurred so quickly and briefly. Liquefaction is a description of the effects of an earthquake so that the soil layer loses its strength. Not many people know the process of earth movement which is the precursor to earthquakes. Especially people who have hearing limitations. For this reason, education is needed to be conveyed to the public. Educational techniques in virtual form will be a special attraction for people, especially deaf people. Augmented Reality (AR) technology is a technology where virtual objects and real objects are combined. In this research, an application was produced in the form of learning media that utilizes Augmented Reality (AR) technology with the Marker Based Tracking method. To make learning media more interactive, the technology was developed using the MDLC (Multimedia Development Life Cycle) method. The results of the tests carried out concluded that the application could be used as an interactive learning medium to increase knowledge about the occurrence of earthquakes and the effects of liquefaction for the community</span> Natalinda Pamungkas, Bonifacius Vicky Indriyono, Wildan Mahmud, Iqlima Zahari Copyright (c) 2024 natalinda linda pamungkas, Bonifacius Vicky Indriyono Tue, 02 Apr 2024 00:00:00 +0000 Web-Based Public Street Lighting Complaint Application with Realtime Whatsapp Notification Using Prototype Method in Pemalang Regency <p>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.</p> Arifinza Eska Nugraha, Elkaf Rahmawan Pramudya, Abdussalam Abdussalam Copyright (c) 2024 Arifinza Eska Nugraha, Elkaf Rahmawan Pramudya, Abdussalam Abdussalam Tue, 02 Apr 2024 00:00:00 +0000