ejournal.nusamandiri.ac.id (STMIK Nusa Mandiri)
Not a member yet
1504 research outputs found
Sort by
IMPLEMENTATION OF DESIGN THINKING IN THE UMKM TIGA PUTRI RESTAURANT IN THE UNIVERSITY OF RIAU AREA
No full textThis study examines Rumah Makan Tiga Putri, a culinary business located near Universitas Riau that offers Minang dishes at affordable prices. Despite its strategic location and a market potential dominated by students, the business faces challenges in optimizing its marketing strategy, especially in visual media promotion. The promotional banner, which is unattractive and lacks clear information, reduces the effectiveness of visibility in attracting and engaging consumers. This study explains the implementation of the design thinking process in redesigning the promotional banner for Rumah Makan Tiga Putri. The purpose of this study is to describe the stages of design thinking in optimizing visual communication effectiveness to increase consumer reach and visibility. The research uses a descriptive qualitative approach by applying the stages of design thinking: empathize, define, ideate, prototype, and test. Data were collected through direct observation, interviews with owners and consumers, comparisons with academic colleagues, and questionnaires using a Likert scale to measure perceptions of the banner's visual aspects. The findings show that the implementation of design elements related to imagery, color, typography, information clarity, and the integration of visual elements with Minang cultural characteristics can improve visual communication effectiveness and strengthen brand identity. The proposed solution is a redesigned banner that is informative, aesthetically pleasing, and culturally representative of Minang identity. Positive responses from participants indicate that applying design thinking is effective in optimizing banner design as visual promotional media, improving visibility and accessibility of business information for potential consumers around the Universitas Riau area.The Rumah Makan Tiga Putri, located near the University of Riau, faces challenges in marketing due to its limited and unremarkable visual promotional media, particularly in banner design, restricting its visibility beyond the immediate local community. The study aimed to enhance the restaurant’s competitive edge and customer reach through a redesigned promotional banner using the Design thinking process. This human-centered, approach comprised five stages: Empathize, Define, Ideate, Prototype, and Test. Data were gathered through customer questionnaires, direct observations, and literature reviews on visual design. Diverse feedback from academic peers and loyal customers helped refine the design to better address user needs and visual aesthetics. The results demonstrated that integrating Minangkabau cultural elements with clear, attractive information in the banner significantly increased its marketing effectiveness. The final banner design featured comprehensive product details, visually appealing typography, and balanced colors reflecting Minang cultural identity. Validation via Likert scale questionnaires showed improved positive responses concerning image representation, color harmony, typography, product information clarity, and overall cohesiveness of design elements. This research underscores that applying an iterative, user-focused Design thinking methodology can substantially improve promotional media for small and medium enterprises like Rumah Makan Tiga Putri, leading to enhanced market presence and customer engagement in a competitive environment around the University of Riau. The study illustrates how Design thinking effectively solves marketing design challenges for UMKM in the culinary business sector by aligning product presentation with customer preferences and cultural identity to boost business sustainability and growt
DIGITALIZATION OF HR AT ONIP: INFORMATION SYSTEMS URBANIZATION AND STRATEGIC ALIGNMENT AS KEY LEVERS
Full text linkThis article examines the digitalization of human resources (HR) at the Office National d’Identification de la Population (ONIP) in the Democratic Republic of Congo (DRC), emphasizing the pivotal role of information systems (IS) urbanization and strategic alignment as key levers. Using a qualitative methodology that combines semi-structured interviews with 15 stakeholders (HR managers, IT specialists, directors) and process analysis, we demonstrate the following outcomes: 40% reduction in HR processing time (from 7 to 4.2 days), 30% decrease in data entry errors through administrative task automation, 29% optimization of annual IT expenditures (from 120,000 to 85,000 USD), Increase in employee satisfaction scores from 58% to 82% (based on an internal survey of 200 employees). These results, derived from the implementation of a secure and modular HR information system (HRIS), underscore the efficacy of a structured approach in a fragile context. The article contributes to the literature on HR digital transformation in the African public sector by proposing a reproducible framework grounded in IS interoperability and collaborative governance
MEASURING PERCEIVED USABILITY OF ARTIFICIAL INTELLIGENCE-BASED QUIZZES IN A VIRTUAL MUSEUM
Full text linkThe transformation of modern museums through digital technology offers added value to visitors, especially in the context of education. Virtual museums, in particular, complement physical museums by providing accessibility and enhancing the learning experience. The SMBII virtual museum includes an AI-based quizzes feature designed to assess the knowledge level of visitors regarding the museum's history and collections as an educational feature. In addition to physical museums, virtual museums offer convenience and enrich the learning process for visitors. The quizzes adapts its questions based on the visitor's profile, leveraging AI to tailor content and maximize learning outcomes. This study aims to compare the effectiveness of two widely used usability metrics—System Usability Scale (SUS) and Usability Metric for User Experience (UMUX)—in evaluating the usability of the AI-driven quiz feature within the SMBII virtual museum. The study specifically seeks to determine whether there are significant differences between SUS and UMUX in measuring user perceptions of the quiz’s usability. The primary respondents of this study were students, who represent the museum's target audience for educational purposes. Hypothesis testing results show no significant difference between the SUS and UMUX scores (P > 0.05), indicating that both metrics offer similar evaluations of usability. Based on these findings, the study recommends the use of UMUX over SUS for future usability assessments in virtual museum systems, as UMUX is more time-efficient without compromising accuracy. This research contributes to advancing the understanding of usability testing methods for AI-based educational features in virtual museum environment
INTEGRATING AUGMENTED REALITY WITH C4.5 ALGORITHM TO ENHANCE TOURISM EXPERIENCE IN PEKALONGAN
Full text linkThe tourism industry demands interactive and personalized solutions to enhance the traveler experience. However, providing relevant and customized travel recommendations based on individual preferences remains a challenge. This study integrates Augmented Reality (AR) technology with the C4.5 algorithm to address this issue and improve the tourism experience in Pekalongan Regency. The research method involved collecting data from 500 respondents through an online questionnaire. The collected data underwent preprocessing, including handling missing data, data transformation, and class balancing. The C4.5 algorithm was applied to build a tourism recommendation model, while AR technology presented 3D visualizations of tourist destinations through an interactive application. The research results show that the recommendation model achieved an accuracy rate of 76.92%. The integration of AR provided an interactive experience that enhanced tourist engagement and satisfaction, although limitations were found in AR visualization quality and the completeness of destination information. Further improvements are needed to enhance AR realism, provide more detailed content, and optimize user satisfaction. This study contributes to the development of AR-based tourism technology integrated with the C4.5 algorithm. The findings encourage local tourism innovation and have the potential to enhance the traveler experience in Pekalongan Regency. This model can also be applied to other tourist destinations across Indonesia
PEMBUATAN DAN PENERJEMAHAN MEDIA PROMOSI ‘BIG FARMER’ DI DESA KERTAWANGI, KABUPATEN BANDUNG BARAT
Full text linkKertawangi Village, located in Cisarua District, West Bandung Regency, has great potential as a tourist destination due to its mountainous topography and cool climate. The majority of the village's population relies on agriculture and livestock. The local government has strived to develop the village into an international tourist destination by forming a tourism awareness group (POKDARWIS) and initiated the 'Big Farmer' program in 2020. However, the lack of promotional media in both Indonesian and English hinders the progress of promoting this program as well as the village. This community service program aims to provide bilingual promotional media for Kertawangi Village by creating and translating a program profile and brochure that can be used by POKDARWIS and the local community. The method involved three main stages: planning, implementation, and evaluation. The data was primarily obtained from interviews with POKDARWIS members as well as local residents. The result consists of a bilingual program profile and brochure containing information about the history, vision and mission, core values, activities, products and services, partnerships, markets and customers, as well as the team behind the ‘Big Farmer’ program. The development of these promotional materials may enhance the visibility of Kertawangi Village as a tourist destination, support the local economy's sustainability, and advance community-based tourism
UPSCALING BISNIS GULA SEMUT MELALUI SOSIALISASI KEAMANAN PANGAN DAN DIGITAL MARKETING
Full text linkPalm sugar is the flagship product of Pernasidi Village, Banyumas Regency, which is managed in an integrated manner by KUB Merci. KUB Merci faces several obstacles in the production and management process, including a lack of knowledge about food safety and digital marketing, which results in suboptimal turnover. Traditional production methods also affect the cleanliness of the production kitchen, increasing the risk of contamination. This community service aims to improve participants' understanding of Good Processed Food Processing (CPPOB) and digital literacy. The program is carried out through three stages: pre-service, implementation, and post-service, based on Participatory Rural Appraisal (PRA). The methods used include socialization and training, including material provision, hands-on practice, and discussion. The results of the program showed a significant increase in participants' understanding of CPPOB (72%) and digital marketing (52%). In addition, the evaluation of participants' satisfaction with the program showed positive results with an average score of 3.87. The long-term impact of this program is expected to improve the production standards and competitiveness of palm sugar products from Pernasidi Village through the sustainable application of CPPOB and digital marketing. The program is also expected to contribute to an increase in KUB Merci's turnover and the economic welfare of the local community. However, a thorough implementation of CPPOB and intensive assistance in social media management is needed for the artisans to maximize marketing and expand market reach
OPTIMIZATION OF COVID-19 DETECTION THROUGH TRANSFER LEARNING ON CHEST X-RAY IMAGES
Full text linkThe urgent need for rapid and reliable identification of COVID-19 cases has highlighted the importance of auxiliary diagnostic tools. Chest X-ray imaging serves as a key resource in clinical settings, yet manual interpretation remains susceptible to inter-observer variability and diagnostic delays. This study introduces an optimized deep learning framework based on transfer learning to enhance the detection of COVID-19 from chest X-ray images. The aim is to improve classification accuracy and operational efficiency using pre-trained models tailored for radiographic analysis. We applied transfer learning with fine-tuning to convolutional neural networks pre-trained on large-scale image datasets. The models were adapted and evaluated on a curated collection of chest X-rays representing COVID-19 positive and negative cases. The proposed model achieved a test accuracy of 99% with a loss of 0.15, indicating high diagnostic performance and robustness in distinguishing COVID-19 cases from other pulmonary conditions. Transfer learning offers a viable and efficient strategy for COVID-19 screening using chest X-rays. This approach has the potential to support frontline clinical decision-making and scale public health response during outbreaks
FORECASTING UPWELLING IN LAKE MANINJAU USING VECTOR AUTOREGRESSIVE, SUPPORT VECTOR MACHINE AND DASHBOARD VISUALIZATION
Full text linkLake Maninjau experiences periodic upwelling events that disrupt water quality, harm fish stocks, and pose socioeconomic challenges to surrounding communities. This study aimed to enhance upwelling prediction accuracy by integrating Vector Autoregressive (VAR) time series modelling with Support Vector Machine (SVM) classification. A five-year dataset (2020–2024) of daily climate variables surface temperature, precipitation, and wind speed was collected from NASA. Data stationarity was confirmed using Box-Cox transformations and Augmented Dickey-Fuller tests, while Granger Causality analysis revealed bidirectional relationships among the variables. The optimal forecasting model, VAR(17), was selected based on the Akaike Information Criterion (AIC), ensuring residuals met white-noise criteria. K-means clustering then labelled potential upwelling days, and these labels were employed to train SVM classifiers. An interactive dashboard was developed using Python and Streamlit to facilitate real-time forecasts and classification outputs. The VAR(17) model produced highly accurate forecasts, reflected by minimal error metrics (e.g., RMSE < 0.60). SVM classification of potential upwelling events achieved strong performance, consistently attaining F1-scores above 0.95. By merging time series forecasts with event classification, the hybrid VAR–SVM framework outperformed single-method approaches in identifying and predicting upwelling episodes. This integrated modelling strategy effectively addresses the complexity of upwelling in Lake Maninjau, enabling timely decision-making for fisheries management and local tourism stakeholders. Future work may incorporate additional environmental indicators (e.g., dissolved oxygen, pH) and extend dashboard functionalities to bolster sustainable resource management and community resilienc
DEVELOPMENT OF A SMART PARKING SYSTEM USING AUTOMATIC DEBIT AND OPTICAL CHARACTER RECOGNITION
Full text linkThe current parking infrastructure predominantly relies on traditional or semi-automatic mechanisms, leading to significant inefficiencies during peak hours. This study proposes the development of a fully automated smart parking system utilizing locally sourced Indonesian components to reduce dependence on imported parts. The proposed Auto-Debit Smart Parking System incorporates Optical Character Recognition (OCR) for vehicle identification and automated payment, improving both accuracy and operational efficiency. The system consists of two primary modules: server software for gate control and an image-processing host application. Space Vector Pulse Width Modulation (SVPWM) is employed for switching control, and communication is facilitated via wired or wireless channels using the RS232C standard. Vehicle entry and exit are detected by sensors that transmit signals to the Command TX module. To evaluate real world applicability, the system was implemented and tested in various public and commercial environments, including office buildings, shopping malls, and open parking areas.These testing sites represent common urban parking conditions with varying lighting, network connectivity, and traffic density, allowing the system’s adaptability and reliability to be analyzed comprehensively. An experimental research method is adopted, encompassing prototype development, testing, data acquisition, and performance evaluation. The results indicate reduced operational costs and enhanced user convenience, validating the system’s effectiveness in supporting modern, efficient parking managemen
COMPARISON OF PRINCIPAL COMPONENT ANALYSIS AND RANDOM FOREST ALGORITHM FOR PREDICTING HOUSING PRICES
Full text linkHouse price predictions are an important thing in the property industry and are useful for buyers in making decisions. Principal Component Analysis (PCA) and Random Forest (RF) methods were used for accuracy analysis in predicting housing prices. Purpose of this research is to measure the accuracy of both methods also to compare RF method optimized with PCA and the one that has not been optimized. The data used is house prices in Karanganyar city based on data scraping results on the rumah123.com site. The analysis reveals that Jaten has the highest number of house sales, and sales of houses with land ownership certificates are also the highest. Of the 10 variables used, land area and buildings have the most influence on selling prices. The model training results show that the RF and PCA methods combination has more optimal value than only using the RF method. The error rate of the PCA method is smaller, averaging 0.0257, making its value more consistent than using only the RF method, which has a larger error value with an average of 0.0332. The model training time using PCA is faster (5005.75) than only using the RF method (6099.25