JTIM : Jurnal Teknologi Informasi dan Multimedia
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Sistem Informasi Persediaan Barang dengan Metode Perpetual pada Toko Mebel Sidarta Berbasis Web
No full textInventory of goods is an important component in company operations, especially for companies that sell finished goods such as furniture and household electronics. The aim of this research project is to create a goods management website using the Perpetual method at the Sidarta Furniture Store. Previously, this shop used manual recording through physical bookkeeping which was considered inefficient, prone to errors, and could hinder sales. To optimize inventory management, an in-tegrated system is needed. This system development uses the waterfall method, which consists of five stages, namely needs analysis, design, implementation, testing and maintenance. During the requirements analysis step, functional and non-functional system requirements are defined. System design involves developing use case diagrams, database design, and user interface design. The system is implemented using PHP as a programming language, CodeIgniter as a framework, and MySQL for database management. The black box testing method is used to carry out the system testing process, which ensures that all functionality operates according to predetermined speci-fications. The results of the tests carried out show that all system functionality functions in ac-cordance with the designed specifications. System maintenance, which is the final stage of the development cycle, is carried out periodically for the long-term sustainability of system opera-tions. This developed system allows the Sidarta Furniture Store to manage inventory data more efficiently and effectively by utilizing the perpetual method. This system is equipped with various features, including an inventory data management interface, supplier data management interface, purchase transaction recording interface, sales transaction recording interface, and the ability to produce comprehensive inventory recapitulation reports. Implementation of this system facili-tates the process of managing and updating inventory data more efficiently and accurately
Perancangan Media Pembelajaran Dasar Desain Grafis Menggunakan Teknologi Augmented Reality
No full textLearning basic graphic design materials at SMK Negeri 1 Godean is carried out with less conducive classroom conditions because the media used in learning is not appropriate so that students are not motivated to learn and there are some abstract materials that are difficult for students to understand. This research aims to design a learning media for basic graphic design materials using augmented reality technology. The method used in this research is the development method or research and development with the ADDIE development model (Analysis, Design, Development, Implementation, and Evaluation). The tools used in this research are Corel Draw X7, Blender 3D, Unity, Vuforia, and Visual Studio Code. The data analysis technique used in this research is quantitative descriptive analysis technique. This research produces a learning media for graphic design materials using augmented reality technology applied to SMK Negeri 1 Godean with the results of feasibility testing, namely the media expert test obtained a score of 92% which was classified in the very feasible category, the material expert test obtained a score of 80% which was classified as feasible, and the small group test obtained a score of 81.52% which was classified as very feasible. The effectiveness test obtained an N-Gain value of 0.8005 with a high category, the percentage value of increasing learning outcomes obtained a value of 80% with a moderate category, and the effectiveness value obtained a value of 80 with an effective category. Thus, it can be concluded that learning media for basic graphic design materials using augmented reality technology is feasible and effective for use in learning so that learning basic graphic design materials at SMK Negeri 1 Godean runs more effectively and can improve student learning outcomes
Penerapan Langchain Retriever dengan Model Chat Openai dalam Pengembangan Sistem Chatbot Hadis Berbasis Telegram
No full textIn Islamic studies, the Hadiths of Prophet Muhammad (SAW) hold significant value as guides for behavior and faith. However, access to understanding Hadiths often presents challenges, espe-cially for those who are not Hadith experts. The digitalization of Hadiths is still limited, making it time-consuming to find answers by sifting through the vast amount of available information. This research aims to create an efficient chatbot that provides answers related to Hadiths, including the original sources, quickly. The proposed solution is a technology-based approach through the development of a Hadith chatbot on Telegram, integrated with the LangChain Retriever and the GPT-4-1106-preview chat model from OpenAI. Using LangChain Retriever helps the chatbot find accurate answers by matching user questions with relevant Hadith databases, enhancing the ac-curacy of the chatbot\u27s responses. The GPT-4-1106-preview chat model enables the chatbot to generate natural and context-appropriate responses, improving user interaction. The Rapid Ap-plication Development (RAD) method is applied in system development, through stages of Re-quirement Planning, User Design, Construction, and Cut-Over, including data analysis of Hadiths from the Nine Imam Hadith Books, totaling 62,169 Hadiths. The chatbot\u27s performance evaluation uses the Scoring Evaluator framework with an average evaluation score of 0.97 and quality answer evaluation testing by five Hadith experts with an accuracy percentage of 90%. The Scoring Eval-uator test results indicate that the responses are highly accurate and aligned with Hadith refer-ences, and the quality answer evaluation test on a Likert scale shows respondents strongly agree with the system\u27s answers. This research contributes to laypersons wanting to learn Hadiths by utilizing the chatbot as an interactive and innovative learning medium. Further research can expand the focus to complex interpretations of Musykil al-Hadith and asbab al-wurud to address deeper questions about Hadith interpretation
Analisis Kinerja Logistic Regression Classifier Berdasarkan Seleksi Fitur Warna, GLCM (Gray Level Co-occurrence Matrix) dan Bentuk (Studi Kasus Jenis Ketupat Khas Bali)
No full textKetupat is a unique culture and tradition in Bali. Ketupat is often used as a banten offering. This unique procession of the ketupat war is held once a year to coincide with the Purnama Kapat. The ketupat war is a traditional event with participants throwing ketupat at each other. It aims to be grateful for all the gifts that the Creator has given to humans in this world. This tradition has existed since the 1970s with the beginning of its appearance involving two shirtless men. The ketupat war ceremony is part of the yadnya as the basis for the return of the Tri Rna. In today\u27s era of the development of science and technology, people are only focused on meeting material needs, understanding the meaning of rituals and religious events is decreasing, and it is considered an incriminating or even meaningless activity. In this study, a dataset of 90 was used with 18 types of ketupat used, namely: bagia, batu dam-pulan, bracelet, kale, mattress, kedis, kepel, kroso, lepet, pengambean, sai, cow, sari, sirikan, suna, tulud, tumpeng. This study aims to determine the per-formance of Logistic Regression Classifier by using color, HSV, GLCM and shape features. The dataset used is a typical Balinese ketupat type of 90 data. In this test, 4 test scenarios are used: 1) The first test scenario, the dataset input performs the preprocessing process, HSV imagery, and the color feature extraction process where the output results are in the form of hue, saturation and value values in the form of excel files. 2) The second test scenario, the input dataset performs a preprocessing process, grayscale, and performs the GLCM feature extraction process where the output results are in the form of feature values of angle 00, angle 450, angle 900 and angle 1350 in the form of an excel file. 3) The third test scenario, the dataset input performs the preprocessing process, binner, performs the form feature extraction process where the output results are in the form of metric, eccentricity in the form of excel files. 4) The fourth test scenario, from the 3 (three) feature extractions carried out, the new dataset, the next stage is to implement the Logistic Re-gression classifier method to obtain accuracy values. Based on the results of the classification, testing and analysis, the level of accuracy from each of them was obtained with a training accuracy value of 69.84% and a testing accuracy of 22.2%, which means that the classification method was declared ineffective in analyzing the features used in the ketupat classification, so it is necessary to compare it with other methods so that it gets an accuracy result above 90%
Reka Cipta Brand UI/UX Program Studi Desain Komunikasi Visual menggunakan Metode Design Thinking
No full textThe Visual Communication Design Study Program at Universitas Bumigora currently utilizes various digital platforms such as the website, Instagram, YouTube, and Facebook. However, branding across these platforms shows inconsistencies and insufficient interactivity between users and the system. This research aims to design a cohesive UI/UX solution for the Visual Communication Design Study Program by applying a design thinking approach, which includes stages of empathy, definition, ideation, prototyping, and testing. The study is grounded in interaction aesthetics theory, used to identify user needs and develop innovative, responsive design solutions. The results indicate that the new UI/UX design significantly improves user satisfaction and visual communication effectiveness. The resulting interface concept is more intuitive, with additional interactive features that support a more integrated user experience and more accurately reflect the institution\u27s identity. This study makes a significant contribution to user-centered design approaches in academic environments and offers a model that can be applied to branding development in other educational institutions. The novelty of this research lies in the deep integration of UI/UX design principles within a specific academic context, providing more effective and representative solutions for institutional branding needs
Optimalisasi Layanan Kesehatan di Puskesmas Melalui Pengembangan Chatbot Berbasis Web Menggunakan Flowise AI
No full textThe development of a web-based chatbot service for Puskesmas presents a potential solution to improve the accessibility and efficiency of healthcare services. This research uses Flowise AI, a chatbot development platform that leverages machine learning technology to support dynamic information processing and provide accurate and relevant responses to users. Flowise AI is integrated with Langchain Retriever to further enhance dynamic information processing, ensuring accurate and relevant responses to users. Using the Rapid Application Development (RAD) methodology, the chatbot development follows a fast-paced cycle, enabling early prototyping and continuous user feedback. The chatbot is tested using Black Box Testing to verify functionality and System Usability Scale (SUS) to evaluate usability. The test results show that the chatbot is able to provide accurate responses to patient queries, especially on relevant health topics, with an SUS score of 75, which falls within the "good" category. This score reflects that the chatbot is easy to use and acceptable to users. This technology allows the chatbot to provide more accurate, relevant, and contextual responses to patient inquiries, while dynamically accessing information from various sources, thereby improving the efficiency and effectiveness of healthcare services
Optimalisasi Model Ensemble Learning dengan Augmentasi dan SMOTE pada Sistem Pendeteksi Kualitas Buah
No full textFruit quality is an important factor in selecting fruit for consumption because it affects consumer health and satisfaction. Identification of fruit quality has become the focus of research, and one of the approaches used is a non-destructive approach through measuring the gases produced by the fruit. Machine learning can be used to process this gas data and build system models that can classify fruit quality. This research discusses the application of the DCS-OLA and Stacking dynamic ensemble learning algorithms to build a fruit quality detection system model. The basic methods used to build models are Logistic Regression, Decision Tree, Gaussian Naïve Bayes, and Mul-ti-Layer Perceptron. The fruit used is mango with a shelf life of 7 days and Srikaya (sugar apple) with a shelf life of 4 days. The condition of the initial dataset is unbalanced. The research results show that trimming the mango dataset to only 4 days according to the shelf life of sugar apple helps reduce the difference in shelf life between the two. Then jittering and balancing techniques are used to increase and balance the number of datasets between the two types of fruit. High accuracy is achieved by the DCS-OLA ensemble and stacking ensemble by combining the basic methods of Logistic Regression and Decision Tree, especially in balanced dataset conditions. In conclusion, the use of ensemble learning in detecting fruit quality has great potential for real-world applications. However, further validation is needed with larger datasets and a wider variety of conditions
Analisis Segmentasi Pelanggan pada Bisnis dengan Menggunakan Metode K-Means Clustering pada Model Data RFM
No full textThe 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
Implementasi Metode Prototype pada Pembuatan Web Portal TEFA House of Health Promotion
No full textHealth Promotion is a series of activities that strive for individuals to increase their knowledge related to health so that ultimately they can improve the quality of health. In order for the objectives of health promotion activities to be achieved, media is needed to support these activities. TeFa House of Health Promotion Polije is one of the TeFa (Teaching Factories) managed by the Health Promotion study program, Department of Health. TeFa was created as a forum for communication, information and health education, TeFa is also a means for lecturers to contribute to society by providing consultation services related to the development of public health promotion programs. To support this, a multifunctional platform is needed in the form of a webportal which is used to help TeFa. This research focuses on creating a web portal using one of the SDLC methods, namely prototype. The process of creating a web portal begins with extracting information through interviews with potential users. The results of this excavation process are information related to the features of the web portal, including articles, video, audio, pooling and chat. The process of creating a Web Portal is made using the PHP programming language and Laravel Framework. The testing process is carried out using the black box method to determine the functionality of each feature. The test results showed that the web portal was in accordance with the user\u27s required features and each feature was functioning according to its function
Perbandingan Metode Prediksi untuk Nilai Jual USD: Holt-Winters, Holt\u27s, dan Single Exponential Smoothing
No full textIn 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\u27s 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\u27s 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