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INDONESIA
JOURNAL OF APPLIED INFORMATICS AND COMPUTING
Published by Politeknik Negeri Batam
ISSN : -     EISSN : 25486861     DOI : 10.3087
Core Subject : Science,
Computer Science & IT
Journal of Applied Informatics and Computing (JAIC) Volume 2, Nomor 1, Juli 2018. Berisi tulisan yang diangkat dari hasil penelitian di bidang Teknologi Informatika dan Komputer Terapan dengan e-ISSN: 2548-9828. Terdapat 3 artikel yang telah ditelaah secara substansial oleh tim editorial dan reviewer.
Arjuna Subject : -
Articles 695 Documents
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Non-Playable Character (NPC) based on Behaviour Tree for Enhanced Immersive Experience in the Serious Game "Warik's Adventure" Siswoko, Tandicha Marchelputra; Haryanto, Hanny; Hastuti, Khafiizh; Kadiasti, Ristia
Journal of Applied Informatics and Computing Vol. 7 No. 2 (2023): December 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i2.6753

Abstract

Video games, or digital games, have transcended their traditional role of mere entertainment and are now being leveraged for more serious purposes, such as education and cultural preservation. Serious games offer the advantage of presenting content in an interactive and enjoyable manner. A crucial element in gameplay for creating interactivity and a pleasurable experience is the non-playable character (NPC). In serious games, replayability is pivotal for optimizing the comprehension of learning content, with the gameplay experience being a key factor influencing replayability. While games are known for their entertaining aspects, immersion design in serious games often falls short, resulting in a subpar experience. The NPC design in this study is grounded in appreciative learning, emphasizing positive outcomes such as possibilities, peak successes, exploration, and future optimism. The reward activity is structured across four levels: Discovery, Dream, Design, and Destiny. Behaviour Tree is employed to govern NPC behavior throughout all four stages. To evaluate the effectiveness of the immersive design, the Game Experience Questionnaire (GEQ) is employed, measuring three primary components"”sensory, imaginative, and challenge-based immersion. The GEQ result indicates a score of 3.2, showcasing a slight improvement from the previous version's score of 3.07. This research contributes to the enhancement of serious game design by focusing on NPC behavior and immersive experiences, ultimately fostering more effective and engaging learning environments.
Optimizing Driving Completeness Prediction Models: A Comparative Study of YOLOv7 and Naive Bayes at Institut Teknologi Sumatera Algifari, Muhammad Habib; Ashari, Ilham Firman; Nugroho, Eko Dwi; Afriansyah, Aidil; Vebriyanto, Mario
Journal of Applied Informatics and Computing Vol. 7 No. 2 (2023): December 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i2.6761

Abstract

The number of vehicles in Indonesia is increasing every year. The number of motor vehicle accidents in 2022 will be more than 100,000. It is hoped that several regulations regarding motorbike rider equipment will increase awareness of rider safety. By utilizing image recognition technology developed with artificial intelligence, it is possible to create digital image processing models or images that are fast and accurate for detecting driving equipment. The object detection model developed uses a dataset in the form of images of motorists who want to enter ITERA through the main gate. The object detection model will also be integrated with the classification model to create a program that can detect motorbike rider equipment, such as mirrors, helmets, not wearing a helmet, shoes, not wearing shoes, open clothes, and closed clothes. After detecting motorized rider equipment in the classification area, the results will be transferred to a classification model to determine the level of safety for motorized riders, either insufficient or sufficient safety. The test results show that the optimal object detection model was found at an epoch value of 70 with a batch-size of 16, producing a mAP value of 0.8914. The optimal classification model uses the naive Bayes method which has been trained with a dataset of 62 data and achieves an accuracy of 94%.
Re-Calibration of Model-Based Capacitive Sensor for IoT Soil Moisture Measurements Setiawan, Iman; Musa, Mohammad Dahlan Th.; Putri, Saskia Amalia
Journal of Applied Informatics and Computing Vol. 7 No. 2 (2023): December 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i2.6809

Abstract

Low-cost automatic irrigation systems require quality calibrated soil moisture sensors. The sensor is an indirect method of soil moisture measurement. The sensor works based on the change in the dielectric constant. So, it requires to be calibrated in terms of the soil water content. Polynomial and linear models are frequently used to calibrate soil moisture sensor data in the gravimetric test method. However, computational effort is required. This study aims to obtain a sensor calibration application that can provide the best model of the available models for model-based capacitive soil moisture sensor. This research was conducted using primary data from gravimetric test experiment on Internet of things (IoT) based soil moisture sensor. Web-based re-calibration application produced best model based on adjusted R Squared. Finally, model-based capacitive soil moisture sensor set up using best model coefficient. The results show that the web-based re-calibration application can provide the best model for model-based capacitive soil moisture sensor. Based on gravimetric test experiments and web applications, the best model is a polynomial regression model order 3 with 0.945 adjusted R Squared. The model predicted value for soil moisture is in the range 0 "“ 1.2 for raw sensor data values of 100 "“ 530. When the model coefficient configured in capacitive soil moisture sensor and Blynk application, soil moisture measurement can be done via mobile phone in real time.
Classification of COVID-19 Aid Recipients in Kasomalang District Using the K-Nearest Neighbor Method Permatasari, Ismi Aprilianti; Dermawan, Budi Arif; Maulana, Iqbal; Kurniawan, Dwi Ely
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.3279

Abstract

The impact of the Coronavirus, also known as COVID-19, which emerged in 2019, has not only threatened public health but also affected the global economy, including Indonesia. The government has initiated various aid programs to assist the community during the COVID-19 pandemic. These aids are expected to alleviate the economic burden on the affected population. One such aid program is the Direct Cash Assistance (Bantuan Langsung Tunai/BLT) from the Village Fund, which has been distributed since the onset of COVID-19 in Indonesia. However, the distribution of BLT has encountered several issues, including misidentification of recipients and double or excessive distribution beyond the established criteria. To address these issues, data mining for the classification of aid recipients can be employed. This study uses the K-Nearest Neighbor (KNN) method for data mining classification to classify residents' data with new patterns, ensuring aid distribution aligns with the criteria and eliminating double recipients. The application of K-Nearest Neighbor to the population data in Kasomalang District yields optimal performance, with evaluation results showing an accuracy of 96%, precision of 0.98, recall of 0.96, and F1 score of 0.97 using the confusion matrix method.
Comparative Study of Web Server Performance Testing with and without Docker Based on Virtual Machines Ramadhan, Fajar Kurnia; Garno, Garno; Solehudin, Arip
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.3884

Abstract

Web server development is often hindered by the cost and resources required, as developing a web server typically requires a bare-metal server. Container technology, which allows for the development of multiple web servers on a single bare-metal server, has become popular. One of the most widely used containers is Docker. Docker reduces the need for costs and resources. Beyond the issues of cost and resource requirements, the performance of web servers also needs to be considered. The performance of web servers with and without Docker needs to be verified. This research aims to test the performance of two web servers, one using Docker and one not using Docker, utilizing the native hypervisor VMware ESXi. The web server performance test items in this study include CPU and RAM resource usage. The method for developing infrastructure systems uses SIDLC (System Infrastructure Development Life Cycle). Performance testing (Load Test) was conducted using Apache JMeter as a tool, with the manipulation of the number of threads predetermined. Resource usage information was monitored using Prometheus and Grafana. The research results show that with the same resources for each virtual machine, the CPU resource usage of Virtual Machine 2 (Undockerized) is less than that of Virtual Machine 1 (Dockerized). Meanwhile, RAM resource usage is not affected by the number of users on both virtual machines. Virtual Machine 2 (Undockerized) is better at handling HTTP requests. Virtual Machine 1 (Dockerized) can handle only 2,790 users, while Virtual Machine 2 (Undockerized) can handle more than 2,790 users without errors.
Implementation of K-Means, Hierarchical, and BIRCH Clustering Algorithms to Determine Marketing Targets for Vape Sales in Indonesia Laurenso, Justin; Jiustian, Danny; Fernando, Felix; Suhandi, Vartin; Rochadiani, Theresia Herlina
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.4871

Abstract

In today's era, smoking is a common thing in everyday life. Along with the development of the times, an innovation emerged, namely the electric cigarette or vape. Electric cigarettes or vapes use electricity to produce vapor. The e-cigarette business is very promising in today's business world due to the consistent increase in market demand. However, determining the target buyer is one of the things that is quite important in determining the success of a business. In this analysis, the background of each region in Indonesia has different diversity; therefore, observation of data is needed to find out which regions in Indonesia have the potential to increase marketing based on profits (margins) to support the target market analysis process so that companies do not suffer losses and increase business success. In this study, the analysis will be carried out using vape quantity, margin, and purchasing power data in each region, which is processed using 3 algorithms: K-Means, Hierarchical, and BIRCH. The results of the clustering of the three algorithms produce two clusters. The K-means, Hierarchical, and BIRCH algorithms produce the same clusters: a potential cluster consisting of 18 cities and a non-potential cluster consisting of 45 cities. To see the performance of the model results, an evaluation was carried out using the Silhouette score, Davies Bouldin, Calinski Harabasz, and Dunn index, which obtained results of 0.765201, 0.376322, 315.949434, and 0.013554. From these results, it can be concluded that the clustering results are not too good and not too bad because the greater the Silhouette Score, Calinski Harabasz, and Dunn Index value, the better the clustering results while for Davies Bouldin the smaller the value means the better the clustering results.
Web-Based Mapping of Crime-Prone Areas in Samarinda Seberang and Loa Janan Ilir Districts, Samarinda Citys Karim, Syafei; Prasetya, F.V. Astrolabe Sian; Sundarti, Anisa
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.5151

Abstract

The development of Geographic Information System (GIS) technology has provided significant benefits in various fields, including the monitoring of crime-prone areas. GIS is used to minimize the traces of these crimes. This study aims to map crime-prone areas in the Samarinda Seberang and Loa Janan Ilir Districts to identify which areas are potentially vulnerable, allowing for analysis for prevention and handling. The data used were collected from theft cases that occurred in these districts in 2019 and 2020. The research employs a scoring technique where each parameter is rated according to its classification. The results of the scoring process are then analyzed to determine the level of crime-prone areas, categorizing them as very vulnerable, vulnerable, or not vulnerable. Based on respondents' feedback, the application facilitates users in locating crime-prone areas, with 94.34% of responses indicating agreement or strong agreement. These results suggest that the application is feasible for implementation.
Implementation of Identity Loss Function on Face Recognition of Low-Resolution Faces With Light CNN Architecture Mufid, Tsaqif Mu'tashim; Adam, Riza Ibnu; Jaman, Jajam Khaeru; Garno, Garno; Maulana, Iqbal
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.6274

Abstract

Face recognition in low-resolution images has seen significant advancements over the past few decades. Although extensive research has been conducted to improve accuracy in these conditions, one of the main challenges remains the difficulty in identifying unique facial features in low-resolution images, leading to high error rates in identification. The use of Deep Convolutional Neural Networks (DCNN) for low-resolution face recognition is still limited. However, employing super-resolution models like REAL-ESRGAN can enhance recognition accuracy in low-resolution images. This study utilizes the Light CNN architecture and applies the margin-based identity loss function AdaFace on low-resolution datasets. The model is trained using the Casia-WebFace dataset and evaluated using the LFW and TinyFace test datasets. Based on the evaluation results on the LFW test data, the best model is Light CNN9-AdaFace, achieving the highest accuracy of 97.78% at 128x128 resolution. For images with the lowest resolution of 16x16, an accuracy of 83.37% was achieved using super-resolution techniques. On the TinyFace test data, the use of super-resolution resulted in performance metrics with a Rank-1 accuracy of 47.26%, Rank-5 accuracy of 55.25%, Rank-10 accuracy of 58.61%, and Rank-20 accuracy of 61.90% using the Light CNN9-AdaFace architecture.
Comparison of Deep Learning Architectures in Identifying Types of Medicinal Plant Leaf Images Salsabila, Sarah; Suharso, Aries; Purwantoro, Purwantoro
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.6289

Abstract

This study focuses on the identification of 3500 images of medicinal plant leaves using Deep Learning CNN Transfer Learning models such as MobileNet, VGG16, DenseNet121, ResNet50V2, and NASNetMobile. The dataset used is the "Indonesian Herb Leaf Dataset 3500," consisting of 10 classes of medicinal plants. This research has the potential to efficiently and accurately recognize medicinal plants using machine learning workflow methods. The objective of this study is to compare the performance of these five methods in conducting plant identification. The testing phase involves various data handling schemes, dividing the data into two scenarios: 80:10:10 and 70:20:10. Performance comparison is also done between augmented and non-augmented data. The research findings indicate that MobileNet exhibits the best performance with an accuracy, precision, recall, and f1-Score of 98.86%. Accurate leaf identification supports further research on the properties and benefits of medicinal plants and can be applied in the development of decision support systems for plant recognition.
Taxpayer Awareness Classification Using Decision Tree and Naive Bayes Methods Maskur A, Moch Riyadi; Wibowo, Arief
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.6654

Abstract

Land and Building Tax (PBB) has a big influence on a region's PAD. Therefore, regions always strive to increase PBB income as much as possible. Many factors influence the increase in PBB, one of which is public awareness of taxes. Lack of public awareness of taxes causes PBB income to also decrease, and has implications for regional PAD. And conversely, if public awareness of taxes is high, PBB and PAD revenues will also increase. Therefore, a system is needed to measure public awareness of taxes in the region. If public awareness of taxes can be measured, then the relevant agencies can evaluate and map taxpayers in which sub-districts have high or below average awareness. There are several factors that influence taxpayer awareness, including ownership status, tax sector, assessment category, and the number of receivable payments over the past 5 years. By knowing the awareness of taxpayers, the relevant agencies can review the targets for achieving PBB revenue and issue warning letters to taxpayers whose awareness of PBB is lacking. This research uses decision tree and Naive Bayes methods to classify 666,580 datasets obtained from the Cianjur Regency Regional Revenue Management Agency. The stages are carried out by data collection, data preprocessing, training data labeling, classification process, and evaluation. The result of this research is a system that can predict whether taxpayers are aware or not in a sub-district and sub-district or rural area using decision trees and Naive Bayes. The accuracy obtained from the decision tree method was 93.73%, while the accuracy obtained from the Naive Bayes method was 85.61%.

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