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JTIM : 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 10 Documents
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Search results for , issue "Vol 5 No 4 (2024): February" : 10 Documents clear
Pengembangan Aplikasi Pose Detection untuk Asesmen Kemajuan Fisioterapi Pasien Pasca Stroke dari Jarak Jauh Febry Putra Rochim; Nugroho, Anan; Sukamta, Sri; Wafi, Ahmad Zein Al; Fathurrahman, Muhammad; Damayanti, Amelia; Wardah, Hildatul
JTIM : Jurnal Teknologi Informasi dan Multimedia Vol 5 No 4 (2024): February
Publisher : Puslitbang Sekawan Institute Nusa Tenggara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v5i4.415

Abstract

Assessment has an important role in determining the diagnosis and subsequent treatment plan. In an effort to increase access and effectiveness of rehabilitation, this research aims to develop a mobile application that is able to report the results of post-stroke patient pose assessment remotely. Telemedicine approaches in post-stroke rehabilitation have become increasingly popular, allowing patients to access rehabilitation services remotely. This is especially beneficial for patients who live in remote areas or have limited mobility. Telemedicine also allows for real-time patient monitoring, allowing adjustments to rehabilitation plans as needed. The mobile app is designed to provide easy access to rehabilitation programs that can be tailored to individual patient needs. In addition to making access easier, this application is equipped with a monitoring feature that allows health professionals to follow patient progress in detail. Data collected from patients' daily exercise and activities provides valuable insight into their progress, which can be used in tailoring rehabilitation plans in real-time. The development of this mobile application technology has great potential to improve rehabilitation outcomes for post-stroke patients. Testing by three experts with two experts as healthy patients and stroke patients, as well as one patient who acts as a medical personel to monitor, shows that from the graph, healthy patients tend to be consistent. On the other hand, post-stroke patients tend to be inconsistent. These results indicate that this application is effective for identifying patient movements during the rehabilitation process. Although there are several obstacles, such as delays in predictions on some devices, this application has great potential to improve the quality of life of post-stroke patients. Thus, the development of a pose detection application for remotely assessing the progress of physiotherapy in post-stroke patients has great potential in improving rehabilitation outcomes. The app facilitates patient access to appropriate, personalized and effective care, while providing medical personnel with objective and accurate data for monitoring and adjusting rehabilitation plans. This is a significant step in advancing the care of post-stroke patients.
Analisis Segmentasi Pelanggan pada Bisnis dengan Menggunakan Metode K-Means Clustering pada Model Data RFM Djun, Sisilia Fhelly; Gunadi , I Gede Aris; Sariyasa, Sariyasa
JTIM : Jurnal Teknologi Informasi dan Multimedia Vol 5 No 4 (2024): February
Publisher : Puslitbang Sekawan Institute Nusa Tenggara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v5i4.434

Abstract

The development of business strategies, particularly in the marketing of SMEs, requires the utilization of business intelligence as the foundation for objective decision-making. This research aims to develop a business intelligence scheme for SMEs and design targeted assistance strategies for SME support institutions. The implementation of business intelligence involves leveraging transactional data from SMEs to ascertain customer segmentation and correlating it with Customer Relationship Management (CRM) strategies. Transactional data is processed into a Recency, Frequency, Monetary (RFM) data model. Customer segmentation is achieved through a clustering process using the K-Means algorithm, and the results yield distinct profiles for SME customers. Evaluation processes are conducted to determine the optimal solution for the number of customer segments. Evaluation methods, including the Elbow Method, Silhouette Scores, and Davies–Bouldin Index, are employed to determine the optimum cluster. The evaluation results indicate that the optimum cluster is 3, with the best Silhouette Score being 0.548 and Davies–Bouldin Index at 0.76. The first customer segment exhibits the highest shopping frequency and monetary value, categorizing them as active and profitable customers. Special loyalty services are recommended for this segment. The second segment, despite having the largest number of customers, exhibits a shopping frequency of only 1-2 times, with an average recency of approximately the last 2 months. These customers require effective after-sales service. The third segment consists of customers who last shopped more than 6 months ago, making them a low-priority segment. Re-engagement strategies, such as email marketing, are suggested for this segment. Support institutions can focus on CRM assistance targeting these three identified segments.
Pengembangan Laboratorium Multimedia Virtual sebagai Media Pembelajaran Audio Digital menggunakan Model Game First Person Shooter Sonjaya, Iwan; Marcheta, Noorlela; Segara, Prayoga Bayu Lail
JTIM : Jurnal Teknologi Informasi dan Multimedia Vol 5 No 4 (2024): February
Publisher : Puslitbang Sekawan Institute Nusa Tenggara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v5i4.435

Abstract

The laboratories at PNJ cannot be operated optimally because the available equipment does not yet have equipment with the latest technology. Whereas students should be able to practice theories related to the latest technology so that equipment is needed that continues to change in line with current technological developments. However, the situation in changing the existing technology requires a bureaucracy that is quite complicated and long. Virtual laboratories are media used to help understand a subject matter and can be a solution to the limitations or absence of laboratory equipment that can be upgraded more easily. Acceleration in the world of education is also re-sponded to by shifting the function of the laboratory. In addition, this virtual laboratory can also be utilized as a distance learning tool. This application is made using the MDLC (Multimedia De-velopment Life Cycle) method which has 6 stages, namely, concept, design, material collecting, assembly, testing, and distribution. The virtual laboratory in audio multimedia learning has been successfully created using the First Person View model. This success is based on the results of testing from 37 responders through 11 questions posed to users who have tried the application found that the respondent's interpretation of the ease of use of the application in learning is 85.40%. While 81.62% stated that the Digital Audio Laboratory already had a feel like a real lab and 85.94% stated that participants were interested in learning through a virtual digital audio laboratory.
Aplikasi Virtual Reality untuk Media Terapi Phobia Ketinggian Fadillah, Muhamad Akbar Triadi; Aldya, Aldy Putra; Hidayat, Eka Wahyu
JTIM : Jurnal Teknologi Informasi dan Multimedia Vol 5 No 4 (2024): February
Publisher : Puslitbang Sekawan Institute Nusa Tenggara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v5i4.438

Abstract

This research aims to develop a therapy application using Virtual Reality technology and a smartwatch to view heart rate data as an assessment in dealing with and assessing how bad Arcophobia (phobia of heights) is in users. The application built is a Virtual Reality (VR) application on the Android platform and applies the Exposure-Based Therapy (EBT) method, the Exposure-Based Theraphy (EBT) method is a method that applies an introduction to phobias suffered gradually according to the ability of the sufferer, in this case the EBT method is proven effective in reducing phobias including Arcophobia. The use of multimedia elements is given gradually to individuals who have phobia. The results show that the development of Android-based Virtual Reality applications is able to help individuals efficiently and effectively overcome Arcophobia by using simulated altitude situations. This application has 3 stages of desensitization (exposure) in a Virtual Reality environment that is made in such a way that individuals can slowly get used to their phobia. In addition, this application collects heart rate data to assess how severe the phobia experienced by the individual there are 3 objective assessments to determine how bad the phobia suffered by the user is if the user's heart rate ranges from 60bpm - 100bpm (normal) while 105bpm - 115bpm is included in the category (quite normal) and 120bpm-200bpm is categorized (bad), from these 3 assessment categories provide additional information that is useful for objectively evaluating the progress of therapy.
Deteksi Nodul Paru pada Citra CT dengan Klasifikasi Pseudo Nearest Neigbour Rule Jaya, I Nyoman Surya; Aryanto, Kadek Yota Ernanda; Divayana, Dewa Gede Hendra
JTIM : Jurnal Teknologi Informasi dan Multimedia Vol 5 No 4 (2024): February
Publisher : Puslitbang Sekawan Institute Nusa Tenggara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v5i4.463

Abstract

This research aims to obtain the classification performance of the Pseudo Nearest Neighbor Rule (PNNR) algorithm in detecting lung nodules in CT scan images. The PNNR classification algorithm is used to reduce the influence of noise or outliers in the classification process so that false positives (prediction of an object that is not a nodule as a nodule) can be reduced. The data set used is 200 patient data obtained from the public data of The Lung Image Database Consortium and Infectious Disease Research Institute (LIDC/IDRI) where 4 fold Cross Validation will be carried out. The preprocessing stage is carried out by segmenting the otsu image, where from the segmentation results the two largest blobs are then searched for to determine the area of ​​the lung to be analyzed. Next, the feature extraction process from the candidate nodules (white pixels / foreground) is obtained from the Otsu segmentation process again. The results of this second segmentation contain information from the candidate nodules to then calculate the value of the shape features of the candidate nodules such as area, eccentricity, equivalent diameter, major axis length, minor axis length and perimeter which produces feature set values ​​as the basis for training data and data test for the classification process in PNNR The results of the classification proposed in this research, namely using the PNNR classification method, obtained an Accuracy value of , which is included in the excellent classification level or the Accuracy level is very good but with a lower level of sensitivity or recognition of true positives, namely . In further research, classification optimization can be carried out by selecting the feature set usedThis research aims to obtain the classification performance of the Pseudo Nearest Neighbor Rule (PNNR) algorithm in detecting lung nodules in CT scan images. The PNNR classification algorithm is used to reduce the influence of noise or outliers in the classification process so that false positives (prediction of an object that is not a nodule as a nodule) can be reduced. The data set used is 200 patient data obtained from the public data of The Lung Image Database Consortium and Infectious Disease Research Institute (LIDC/IDRI) where 4 fold Cross Validation will be carried out. The preprocessing stage is carried out by segmenting the otsu image, where from the segmentation results the two largest blobs are then searched for to determine the area of ​​the lung to be analyzed. Next, the feature extraction process from the candidate nodules (white pixels / foreground) is obtained from the Otsu segmentation process again. The results of this second segmentation contain information from the candidate nodules to then calculate the value of the shape features of the candidate nodules such as area, eccentricity, equivalent diameter, major axis length, minor axis length and perimeter which produces feature set values ​​as the basis for training data and data test for the classification process in PNNR The results of the classification proposed in this research, namely using the PNNR classification method, obtained an Accuracy value of , which is included in the excellent classification level or the Accuracy level is very good but with a lower level of sensitivity or recognition of true positives, namely . In further research, classification optimization can be carried out by selecting the feature set used
Perbandingan Metode Prediksi untuk Nilai Jual USD: Holt-Winters, Holt's, dan Single Exponential Smoothing Rosita, Yesy Diah; Moonlight, Lady Silk
JTIM : Jurnal Teknologi Informasi dan Multimedia Vol 5 No 4 (2024): February
Publisher : Puslitbang Sekawan Institute Nusa Tenggara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v5i4.473

Abstract

In the ever-changing landscape of the global economy, the role of the United States Dollar (USD) as the backbone of the international financial system significantly influences market stability and dynamics. The close correlation between fluctuations in the USD exchange rate and internal and external factors demands effective prediction methods to understand and manage associated risks. This study aims to compare the performance of three main prediction methods: Single Exponential Smoothing (SES), Holt's Method, and Holt-Winters Method, in forecasting USD exchange rates. Utilizing historical data from the Central Statistics Agency (BPS) and testing under three training data distribution scenarios (45%, 55%, and 75%), this research provides in-depth findings on the strengths and weaknesses of each prediction method. Performance evaluations include the time required, Mean Absolute Error (MAE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), R-Squared, and correlation for the implementation of each method. If averaged, the results are as follows for SES, Holt’s, and Holt’s Winter, respectively: SES (1.58; 284.20; 68,768.26; 440.07; 0.03; -2.12; Nan), Holt’s (1.39; 890.23; 426,377.44; 1,043.28; 0.06; -24.28; -0.66), and Holt’s Winter (1.20; 997.45; 513,657.58; 1,168.00; 0.07; -30.62; -1.55). Overall, this indicates that the Holt-Winters Method stands out with significant performance, especially in scenarios with larger training data distributions, with a low R-Squared value (-4.618) and satisfactory correlation (0.417). Holt's Method also shows improved accuracy, while Single Exponential Smoothing (SES) offers time efficiency, albeit with limitations in explaining data variations. In conclusion, this research provides valuable guidance for business stakeholders, investors, and policymakers in selecting prediction methods suitable for their data characteristics and analysis goals, with the potential for a positive impact on business strategies, competitiveness, and risk management amid the uncertainty of USD exchange rate fluctuations.
Twitter Sentiment Analysis in Tourism with Polynomial Naïve Bayes Classifier Rizal, Ahmad Ashril; Nugraha, Gibran Satya; Putra, Rian Asmara; Anggraeni, Dara Puspita
JTIM : Jurnal Teknologi Informasi dan Multimedia Vol 5 No 4 (2024): February
Publisher : Puslitbang Sekawan Institute Nusa Tenggara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v5i4.478

Abstract

Lombok has become a favorited tourist destination in the world. Therefore, tourism is a mainstay sector in regional development in West Nusa Tenggara. The contribution of the tourism sector shows an increasing trend. Tourist expenditures are distributed to various sectors. The tourism sector has a positive impact on the regional economy. The local government has prepared to improve the quality and quantity of tourism in Lombok. The results of local government efforts need to be analyzed so that future policies are on target. Analysis can be done on the satisfaction of tourists who travel to Lombok. It would be very difficult to get satisfaction data from all tourists through questionnaires. But on the other hand, tourist satisfaction is usually posted on their social networks. One of the social media that is widely used by tourists is Twitter. Their tweets contain not only expressions of natural beauty but also criticism, suggestions, and complaints during their visit. In addition, the tweet data on twitter is open access. This study tries to analyze the sentiment on Twitter which contains tweets of tourists who have visited Lombok. Sentiment analysis is performed using the Polynomial Naive Bayes Classifier. Sentiment results are classified into positive and negative sentiments. The results of this sentiment are expected to help related agencies or other tourism actors to improve the quality and quantity of regional tourism. The results showed that the positive sentiment on the security factor were 50.65%, the cost 75.32%, accommodation 62.33% and the cleanness factor 77.92%.
Implementasi Smart Home pada Platform Apple Homekit dan Google Home dengan Raspberry Pi 4B Robbani, Abdul Jabbar; Alfiaturrohmah, Fifi; Nurdiansyah, Maulana Rafi; Maharani, Amanda Salsabila; Putro, Aditya Dwi
JTIM : Jurnal Teknologi Informasi dan Multimedia Vol 5 No 4 (2024): February
Publisher : Puslitbang Sekawan Institute Nusa Tenggara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v5i4.480

Abstract

This research examines the significant impact of technological advances, especially in the Internet of Things (IoT) paradigm, on various aspects of human life strongly manifested during the In-dustrial Revolution Era 4.0. The main focus of this research is on the application of advanced and innovative IoT concepts in the context of smart homes, integrating popular platforms such as Apple HomeKit and Google Home. The temperature sensor (DHT11) and light sensor (LDR) play a key role as important input elements, enabling the optimization of smart home automation functions. Raspberry Pi 4B was chosen as the main platform, the "brain" of the system, providing reliable computing capabilities. Using four-channel relays, this research specifically aims to increase ef-ficiency and integration in controlling devices in the smart home ecosystem. By emphasizing the concept of optimization, this research proposes a smart solution that is expected to not only provide an integrated and efficient experience for smart home users but also unlock the potential for further development of IoT technology. As a result, the implementation of the solutions proposed in this research is expected to create a smart home that is not only technologically smart but also responsive to the needs and preferences of its occupants, paving the way for further in-novation in the development and application of the Internet of Things in various contexts of human life.
Implementasi Chatbot Kesehatan Kucing Melalui Dialogflow dan Telegram untuk Pemberian Informasi Penyakit dan Perawatan Haryanto, Iqbal Dwi; Saefurrahman, Saefurrahman
JTIM : Jurnal Teknologi Informasi dan Multimedia Vol 5 No 4 (2024): February
Publisher : Puslitbang Sekawan Institute Nusa Tenggara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v5i4.484

Abstract

Cats are an integral part of human life, but their health needs are often complicated for their owners. This research proposes and implements a Chatbot system using Natural Language Processing (NLP) through the Dialogflow framework to provide advice on cat care and treatment. This method is intended to help cat owners understand basic care needs and identify symptoms of illness their cat may be exhibiting. Using Dialogflow as the main framework enables intuitive and responsive interactions between chat owners and Chatbots. This system has gone through comprehensive stages of analysis, development and testing to ensure reliability and accuracy in providing cat health care information. This test also includes several real-life cases to validate the Chatbot's ability to provide appropriate treatment advice and solutions for cat health problems. The results of this research show that the application of Chatbots via Dialogflow has great potential in helping cat owners in caring for and dealing with diseases in cats. With the ability to understand natural language and provide accurate information, this Chatbot can be a useful tool to increase cat owners' understanding of proper care and early treatment of possible illnesses in their pets.
HABERTAN: Game Petualangan 3D Dengan Tema Pemilahan Sampah Sebagai Upaya Pendekatan Inovatif Untuk Pengenalan Lingkungan Revindasari, Fony; Dewayanti, Athallia; Syahrazad, Emirel Ihsan
JTIM : Jurnal Teknologi Informasi dan Multimedia Vol 5 No 4 (2024): February
Publisher : Puslitbang Sekawan Institute Nusa Tenggara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v5i4.485

Abstract

The increasing awareness of the importance of maintaining forest cleanliness as a primary ecosystem is becoming more urgent, especially in remote areas with limited deep socialization. This research aims to develop an innovative game titled "HABERTAN" focusing on waste sorting in forest areas to raise public awareness of the waste present in the forest. By utilizing a 3D model for visual assets with stages of 2D sketch, 3D modeling, UV Mapping, and Texture Baking. The development of this game is conducted using the Analysis, Design, Development, Implementation, and Evaluation (ADDIE) method. The ADDIE method is used to ensure a systematic game development process, starting from needs analysis to performance evaluation. The goal of this game is to provide players with an interactive experience that not only entertains but also provides a deep understanding of the impact of waste on forest sustainability. The game "HABERTAN (Let's Clean the Forest)" is designed to provide players with a fun and educational learning experience. The results of the development of this game show that "HABERTAN" provides a unique experience for players, with the ability to learn while playing. It is hoped that this game can be an effective tool in raising public awareness of the importance of maintaining forest cleanliness, especially in the context of waste sorting. Through this innovative approach, it is hoped to encourage active participation from the community in preserving forests as extremely valuable natural resources.

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