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INDONESIA
Jurnal Teknologi Informasi dan Multimedia
Published by Puslitbang Sekawan Institute Nusa Tenggara
ISSN : 27152529     EISSN : 26849151     DOI : https://doi.org/10.35746/jtim.v2i1
Core Subject : Science,
Computer Science & IT
Cakupan dan ruang lingkup JTIM terdiri dari Databases System, Data Mining/Web Mining, Datawarehouse, Artificial Integelence, Business Integelence, Cloud & Grid Computing, Decision Support System, Human Computer & Interaction, Mobile Computing & Application, E-System, Machine Learning, Deep Learning, Information Retrievel (IR), Computer Network & Security, Multimedia System, Sistem Informasi, Sistem Informasi Geografis (GIS), Sistem Informasi Akuntansi, Database Security, Network Security, Fuzzy Logic, Expert System, Image Processing, Computer Graphic, Computer Vision, Semantic Web, Animation dan lainnya yang serumpun dengan Teknologi Informasi dan Multimedia.
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Articles 15 Documents
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Search results for , issue "Vol. 7 No. 1 (2025): February" : 15 Documents clear
Media Pembelajaran Jenis Jamur Berbasis Augmented Reality Menggunakan Metode Marker Based Tracking Tedi Wardiana; Eka Wahyu Hidayat; Euis Nur Fitriani Dewi
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 1 (2025): February
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i1.608

Abstract

Mushrooms are a plant-based food source with considerable potential and are often found around us. However, some types of mushrooms have similar characteristics and morphology. Based on these problems, an application is made that aims as a medium for the introduction of mushroom types through the use of Augmented Reality (AR) technology. By utilizing Augmented Reality as an interactive educational media, it is expected to facilitate users in obtaining information about the type of fungus accompanied by visualization in the form of 3D objects. The research method used consists of three stages, namely data collection, multimedia product creation, and evaluation. This mushroom type learning application is made using AR marker based on tracking technology with the MDLC (Multimedia Development Life Cycle) method according to Luther Sutopo. Based on the results of Black Box testing, angle, distance and light intensity testing, the application can function properly. Evaluation using the System Usability Scale (SUS) was conducted on the general public with an age range of 17-45 years in RW 05 Panyingkiran Village, Indihiang District, Tasikmalaya City with a population of 356 people, determining the number of respondents using the slovin formula which is 32 people. The test results show that the application obtained an average score of 74.78, which indicates that the application is in the “Acceptable” category for Acceptability Range, Grade C in Grade Scale, and “Good” in Adjective Rating.
Implementasi E-Commerce Bisnis Warung Klontong berbasis Android dengan menggunakan Algoritma Haversine Formula Khairil Anam; Bayu Charisma Putra; Muhammad Aldi Firmansyah; Ferdy Muhammad Firdaus
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 1 (2025): February
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i1.615

Abstract

The influence and role of information technology in the Indonesian business sector is very large. With this technology, in today's era, people are required to live an efficient lifestyle, for example when making buying and selling transactions. However, this has an impact on small shops that are less competitive than online stores that can provide convenience in finding products and making shopping transactions. Grocery shops in Sidoarjo Regency are less competitive and experience a decline in consumer purchasing power due to the minimal use of E-Commerce on the development of their shop business. This study aims to determine the effect of Android-based Grocery Shop Business ECommerce using the Haversine Formula Algorithm. The targeted results of this activity are that by implementing this E-commerce application, people will be facilitated in various ways in making buying and selling transactions starting from finding the distance to the nearest shop, digital purchases, and the time spent. The test results from this study were carried out using the black box testing method. The testing process will be carried out on all functional needs that have been designed at the application design stage. The results of this research trial obtained a system accuracy of 100%, and received a rating of 74% as very good, 23% received a good rating and 3% received a sufficient rating from the results of user testing conducted on the haversine formula application.
Implementasi Data Mining dalam Menentukan Prediksi Status Resiko Persalinan pada Ibu Hamil menggunakan Algoritma C4.5 Dwidya Poernareksa; Nina Rahmadiliyani
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 1 (2025): February
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i1.619

Abstract

High-risk of pregnancy refers to a situation where pregnancy will have a negative impact on the safety of the mother and baby. Since the beginning of pregnancy, high-risk pregnancy can be predicted by various factors such as the physical and psychological condition of the pregnant woman, nutritional intake, and congenital diseases. According to WHO, Indonesia ranks 5th in premature birth rates with 675,700 babies and this figure is 15.5% of the total birth rate in Indonesia. Estimates of high-risk pregnancies can be observed from patient medical record data, in this case, pregnancy data from pregnant women. Data that is processed into knowledge can be processed through the data mining process. The main objective of this study is to determine how data mining is implemented in determining the prediction of the birth process in pregnant women using the C4.5 algorithm. This research can provide knowledge about the combination of the Two Crows model and the C.45 algorithm to predict the risk status of childbirth in pregnant women. The C.45 algorithm is one of the most popular prediction techniques because it is easy for humans to interpret. The data analysis technique in this study uses the Two Crows model which is a development of the CRISP-DM model. The flow of the Two Crows model includes Understanding Business Problem, Building Data Mining Database, Data Explore, Prepare Data For Modeling, Building Model, and Evaluate Model. The data taken is examination data on pregnant women at the Health Center. Based on the results of the study, it was found that the highest root of the application of the C4.5 algorithm is in the height variable. The evaluation was carried out using a confusion matrix. From the evaluation results, it was found that the accuracy value reached 98.44%, the precision value reached 96%, and the recall value reached 100%.
Desain UI/UX e-Marketplace UMKM Pastry & Bakery (Bakehouse) dengan Metode Lean UX Radinka Frisia Mulia; Agussalim Agussalim; Rizka Hadiwiyanti
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 1 (2025): February
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v6i4.624

Abstract

Micro, small, and medium enterprises (MSMEs) currently play a crucial role in the Indonesian economy. However, there are several challenges faced by MSME managers in Indonesia such as marketing and digitalization problems. In overcoming these challenges, MSMEs need help in managing business processes more effectively and efficiently, by using e-marketplaces. This e-marketplace application was designed using the Lean UX approach, which involves four key stages: declare assumption, create an Minimum Viable Product (MVP), run an experiment, feedback and research. The Lean UX method supports efficient digital platform development, focuses on customer needs, accelerates trend adaptation, and enhances customer experience and loyalty through feedback-driven iterations. The primary objective of this process is to facilitate the management of information about registered MSMEs in the pastry and bakery sector. Based on this study, the results showed that the design of the BakeHouse e-marketplace interface with the Lean UX approach was able to meet the declare assumption needs of users. After going through two iterations with design improvements, the evaluation results indicated a significant improvement compared to the first iteration. This is evidenced by the increase in score from 70 to 93 for the evaluation from MSMEs owners and a score of 67.5 to 89.5 for the evaluation from customers on the System Usability Scale assessment.
Penerapan User Centered Design untuk Optimisasi User Experience Aplikasi Virtusee Nabila Octavianti; Rizka Hadiwiyanti; Abdul Rezha Efrat Najaf
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 1 (2025): February
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i1.627

Abstract

This study aims to redesign Virtusee, an employee performance monitoring platform, using the User-Centered Design (UCD) methodology. Virtusee features functionalities such as leave requests, monthly performance tracking, and self-service payslip access. The application, last updated in 2014, was described by users as outdated and not aligned with current trends. Based on the System Usability Scale (SUS) questionnaire, the application scored 40, categorized as poor with a grade of F, indicating the need for significant improvements. Employing the UCD approach, this study prioritized user needs and preferences in the redesign process, following these stages: (1) defining the usage context, (2) identifying user and organizational requirements, (3) design and implementation, and (4) usability evaluation. Testing the new design involved users who had used Virtusee more than once in the past month. The testing scenarios included exploring the new design, completing SUS and QUIS (Questionnaire for User Interaction Satisfaction) surveys via Google Forms, and conducting brief interviews to gather suggestions and critiques. Data were collected through observation, interviews, literature review, and questionnaire dissemination. Results showed a significant improvement, with the SUS score increasing to 80.3, indicating acceptable usability. The QUIS evaluation revealed average scores ranging from 5.9 to 6.8 across various indicators, exceeding the expected median. These findings highlight that the UCD methodology is effective in designing applications that are more user-centered, enhancing productivity and user satisfaction. This study provides valuable insights for developers aiming to create applications that are not only functional but also adaptive to evolving user needs, serving as a reference for designing solutions that align with user-centric principles.
Perbandingan Support Vector Machine, Random Forest Classifier, dan K-Nearest Neighbour dalam Pendeteksian Anomali pada Jaringan DDos Haeruddin Haeruddin; Erick Erick; Heru Wijayanto Aripradono
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 1 (2025): February
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i1.628

Abstract

A Distributed Denial of Service (DDoS) attack poses a serious threat to network security and can disrupt online services by overwhelming the target server with excessive traffic. Effective detection of DDoS attacks requires a system capable of identifying anomalies in network traffic. In this context, Machine Learning (ML) offers an effective approach for classification and anomaly detection. However, different ML algorithms have varying strengths and weaknesses when processing large and complex network data. Therefore, this study aims to evaluate the performance of three ML algorithms: Support Vector Machine (SVM), Random Forest Classifier (RFC), and K-Nearest Neighbors (KNN) in detecting DDoS anomalies. The dataset used consists of 225,745 data points with 85 attributes that describe various characteristics of network traffic, such as destination port, flow duration, packet count, and packet size. This dataset is classified into two classes, BENIGN and DDoS, representing normal traffic and DDoS attacks, respectively. Evaluation is performed using several performance metrics, including accuracy, precision, recall, MCC (Matthews Correlation Coefficient), F-Measure, ROC Area, PRC Area, True Positive Rate (TPR), and False Positive Rate (FPR). The results show that the Random Forest Classifier (RFC) delivers the best performance with an accuracy of 99.99%, precision of 99.98%, recall of 100%, and a very low FPR of 0.02%. This is followed by the Support Vector Machine (SVM) with an accuracy of 99.91%, and the K-Nearest Neighbor (KNN) with an accuracy of 99.98%. All three algorithms demonstrate strong performance in detecting DDoS anomalies, with RFC slightly outperforming others in terms of consistency and higher classification capability. The findings of this study provide valuable insights for selecting the best algorithm to detect DDoS attacks in networks.
Implementasi Software-Defined Network Terintegrasi Firewall pada Proxmox untuk Pengontrolan Konfigurasi Jaringan dan Pengamanan Layanan Container I Putu Hariyadi; I Made Yadi Dharma; Raisul Azhar; Suriyati Suriyati
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 1 (2025): February
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i1.644

Abstract

Virtualization technology has helped companies consolidate various server roles into a single physical server, reducing hardware costs. Hypervisor is a software in virtualization that is used to manage server hardware, allowing multiple Virtual Machines (VM)/Containers (CT) to run on a single physical machine. Companies face various challenges to remain competitive in the digital era, such as the need for rapid deployment of virtual guests and virtual networks on hypervisors in development, testing, and production environments, as well as securing network services. The purpose of this study is to implement SDN on hypervisors to centrally control virtual network configurations with a simple design, reducing setup and maintenance costs and time. In addition, it also implements a firewall and Virtual Private Network (VPN) based on OpenVPN and a reverse proxy to secure the hypervisor and VM/CT so that services remain available. This study presents a new approach that integrates Software-Defined Network (SDN)-based network management with comprehensive security solutions on hypervisors. This approach combines efficiency in network management and security that have rarely been focused on simultaneously in previous studies. The research method uses the Network Development Life Cycle (NDLC). The hypervisor used is Proxmox Virtual Environment (PVE) which is installed on the Virtual Private Server (VPS) provider IDCloudHost. Based on the results of the trials that have been carried out, it can be concluded that the simple zone type SDN on PVE can be used to control network configurations centrally and more simply such as routing, Dynamic Host Configuration Protocol (DHCP), Source Network Address Translation (SNAT), hostname registration and Internet Protocol (IP) from CT to forward lookup zone on the Domain Name System (DNS) server. Activating the firewall and creating rules at the cluster and CT levels from PVE and OpenVPN can protect the infrastructure when accessed both internally and externally. While the implementation of nginx reverse proxy can secure access to HTTP/HTTPS services on CT in PVE.
Implementasi Teknologi Interactive Projection Sebagai Media Tampilan Produk UMKM Ashafidz Fauzan Dianta; Fony Revindasari; Zakha Maisat Eka Darmawan; Aris Sudaryanto; Kholid Fathoni
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 1 (2025): February
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i1.645

Abstract

To increase the attractiveness of promotion for small & medium enterprises, the use of exhibition booths is an effective tool to promote a product or service. With a limited booth size, it can limit the number of products or information that can be displayed. The number of participants at an exhibition makes booth owners have to compete hard to attract visitors' attention. The use of immersive technology through interactive booths is an innovative solution that answers marketing challenges in today's digital era. This study develops an interactive booth using Interactive Projection technology to create a new experience for visitors. This interactive booth creates an environment that seems real and allows visitors to interact directly with the product. Creating product information content that is presented in an interesting and interactive way can attract visitors' attention. The Villamil-Molina method is used in development, where this method is one of the popular methods in multimedia software development. The stages of this method consist of: development, pre-production, production, post-production, and delivery. The results show that interactive booths can be used as a realistic and attractive small & medium enterprises product promotion media for visitors, and can increase engagement, and provide an immersive experience. This technology can be used as a promotional media, as well as supporting the introduction of modern technology to the wider community. Thus, interactive booths have the potential to be an efficient alternative solution for small & medium enterprises in strengthening their brands and expanding their market reach.
Comparative Analysis of Stock Price Prediction Using Deep Learning with Data Scaling Method I Nyoman Switrayana; Rifqi Hammad; Pahrul Irfan; Tomi Tri Sujaka; Muhammad Haris Nasri
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 1 (2025): February
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i1.650

Abstract

The dynamic and unpredictable nature of stock prices makes accurate forecasting an important challenge in financial analysis. This study aims to compare the performance of three deep learning models, namely, Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM) in predicting stock prices on historical daily banking data from Yahoo Finance. The main objective is to determine the model that is best able to capture sequential patterns and temporal dependencies in stock price movements. Each model was trained and op-timized through data scaling, namely MinMax Scaler and Standard Scaler, with performance evaluated using Root Mean Square Error (RMSE) as the primary metric. Results show that while the RNN provides a basic approach, the GRU and LSTM models produce higher prediction accuracy, with GRU achieving the lowest RMSE thanks to its better ability to maintain long-term depend-encies. The RMSE achieved by RNN, GRU, and LSTM were 211.47, 158.89, and 197.45, respectively. The lowest error results were achieved when using MinMax Scaler. The use of MinMax Scaler here shows a better performance improvement with an average improvement of 22.57% compared to using Standard Scaler. This comparative analysis contributes to providing empirical insight into the relative effectiveness of the tested architectures. The findings suggest that the combination of GRU and MinMax Scaler can be a more reliable tool for financial forecasting, with the potential to develop more robust stock prediction applications under fluctuating market conditions.
Evaluasi Keamanan Sistem Autentikasi Biometrik pada Smartphone dan Rekomendasi Implementasi Optimal Felix Yeovandi; Sabariman Sabariman; Stefanus Eko Prasetyo
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 1 (2025): February
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i1.653

Abstract

Biometric authentication on smartphones is a modern solution for more practical and secure login security. This technology offers advantages such as speed of access and resistance to forgery compared to password-based methods. However, there are various weaknesses, such as the potential for exploitation through malware, spoofing, or brute force attacks that exploit security holes, such as Cancel-After-Match-Fail (CAMF) and Match-After-Lock (MAL). Additionally, hacked biometric data cannot be replaced, leaving users vulnerable to long-term security threats. To overcome these weaknesses, this article recommends a security approach based on Trusted Execution Environment (TEE), AES-256 encryption, spoofing detection based on liveness recognition, anti-tamper mechanisms, and the application of rate limiting. The secure authentication flow implementation is designed to protect biometric data locally without transmission to external servers, ensuring user integrity and privacy is maintained. This flow includes suspicious activity detection, login encryption, and data protection with advanced encryption. Through a combination of these technologies, the biometric authentication system is characterized as being able to significantly maximize security by minimizing the risk of attacks on user data. This research provides evaluation results that the DNN deep neural network model trained with AES-256 is characterized as being able to produce accuracy above 99.9% with less than 5,000 power traces. Then, the implementation of liveness detection is characterized as being able to produce an F1-Score of 97.78% and an HTER of 8.47% in the intra-dataset scenario, as well as an F1-Score of 74.77% and an HTER of 29.05% in the cross-dataset scenario. This combination of technologies provides secure and efficient biometric authentication without compromising user comfort.

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