Proceeding of International Conference on Informatics, Engineering, Science & Technology
Proceeding of International Conference on Informatics, Engineering, Science & Technology is an open-access, peer-reviewed publication that serves as the official scientific output of the International Conference on Informatics, Engineering, Science, and Technology (INCITEST) organized by Universitas Komputer Indonesia. This proceeding publishes selected papers presented during the conference, focusing on notable contributions, innovations, and developments in the fields of informatics, engineering, science, and technology. The Proceeding of INCITEST aims to provide a reputable platform for researchers, academics, industry professionals, and students to disseminate their research findings. All submitted papers undergo a rigorous peer-review process to ensure high academic standards and the quality of published works. The proceedings are published annually in conjunction with the INCITEST conference and are accessible to a global audience. By facilitating the exchange of knowledge, ideas, and research advancements, the Proceeding of INCITEST supports the ongoing growth of scientific inquiry and the development of technology-driven solutions to address real-world challenges.
A Systematic Literature Review of Process Mining Adoption: Insights from Technology, Organization, Process, and Trust Dimensions
Process mining adoption has received growing academic attention in recent years. This study presents the literature on process mining adoption. We divided the 34 papers found into four dimensions: Technology, Organization, Process, and Value/Trust. Based on the analysis of the findings, the implementation of process mining is not only a technical issue, but also requires organizational readiness, contextualization of the process, and mechanisms to build process awareness and trust in the results. Existing and cutting-edge technologies can expand the potential of process mining, such as ERP, AI, and Robotic Process Automation (RPA). Organizational factors and user trust play a crucial role in ensuring continued adoption. These findings are expected to provide a series of research directions for government organizations.
Quality Assesment of The Kerinci District Social Services Website Using The Webqual 4.0 Model
The Kerinci Regency Social Service is a local government agency tasked with administering social welfare programs and community assistance. Its official website serves as a central public information platform and a channel for interaction between citizens and the local government. However, in practice, this platform still faces several shortcomings, including incomplete or outdated information, inaccessible features, and the lack of an active public complaints service. Therefore, this study aims to assess the quality of the Kerinci Regency Social Service website using the WebQual 4.0 framework, which focuses on three main dimensions: usability, information quality, and interaction quality and their impact on user satisfaction. A quantitative research design was employed, with data collected through an online questionnaire distributed to active users of the Kerinci Regency Social Service website. User responses were analyzed using multiple linear regression with SPSS 26. This study found that usability (coefficient = 3.633) and interaction quality (coefficient = 2.365) have a significant positive influence on user satisfaction. In contrast, information quality (coefficient = –0.687) showed no significant effect. These results suggest that ease of navigation and the quality of service interactions play a greater role in shaping user satisfaction than the completeness of the information provided. Based on these insights, website administrators are advised to prioritize improving usability and user interaction features to enhance overall service quality.
Designing Educational E-Comic of Qurban for Junior High School Students
In Indonesia, there are several religions recognized by the government, including Islam, Hinduism, Buddhism, Protestant Christianity, Catholic Christianity, and Confucianism, with the majority of the population adhering to Islam. There are two major Islamic celebration, namely Eid al-Fitr and Eid al-Adha, also known as Qurban. The goal of this research is to design an engaging educational medium about Qurban that aligns with Islamic law and supports junior high school learning outcomes. A qualitative method was applied, combining interviews with teachers and students with literature review to identify learning challenges and design needs. The results show that students often lose interest when learning about Qurban through text-based materials, while the use of visual and story-based media significantly increases their motivation and comprehension. The findings indicate that e-comics through PDF format in Google Drive can provide an effective supplementary tool by presenting procedures, permissible animal conditions, and the values of sincerity in Qurban through interactive visual storytelling. In conclusion, the designed e-comic offers a practical solution for enhancing student’s engagement in religious education. This research lies in providing teachers with a creative learning tool and contributing to the development of innovative media for Islamic education through e-comic
Ethics and Competence Analysis of AI Usage Among Higher Education Students in Bandung Indonesia
This research aims to conduct a comprehensive assessment of students' AI competency levels in Bandung, using the AI Competency Framework for Students from UNESCO as its primary foundation. Employing a quantitative approach with a descriptive survey method, the research instrument was distributed to students from various universities in Bandung. The findings indicate that while students possess a strong ethical foundation (Ethics of AI mean score 3.12), there are significant weaknesses in the Human Centered Mindset (2.94) and AI Techniques & Applications (2.98) dimensions. The AI System Design dimension shows an adequate score (3.01), yet has not reached its maximum potential. The analysis also identified differing AI usage patterns among student demographic groups. This study found that students' ethical awareness aligns with national (UU PDP) and global (GDPR) regulations, but a critical implementation gap exists. This gap suggests that students do not yet fully possess the practical skills and systematic understanding necessary to meet the demands for accountability and human oversight emphasized by advanced regulations such as the Circular Letter of the Minister of Communication and Informatics and the EU AI Act. Thus, this research provides an empirical basis for policy recommendations aimed at developing a more holistic and balanced curriculum, in order to prepare students to be not only technically competent but also ethically responsible users of artificial intelligence.
Implementation of Prototyping Method in Web-Based POS System Development for MSMEs: A Case Study in Airmadidi District, North Sulawesi, Indonesia.
This study aims to design a web-based Point of Sale (POS) system for MSMEs in Airmadidi District, North Sulawesi, Indonesia using the prototyping method. This POS system was developed to address the challenges faced by MSMEs in managing sales transactions, stock recording, and financial reporting quickly and accurately in the era of digital transformation. This study mapped user needs, designed the system interface, developed a prototype, and conducted direct evaluations with MSMEs to ensure the resulting system truly meets real-world needs. A descriptive method is employed with a prototyping approach in five stages, namely communication, quick planning, quick design, prototype development, as well as testing and feedback. The results of this study indicate that the developed POS system aligns with user needs, featuring key functionalities such as product management, sales transactions, payment processing, and data recap, all operating effectively. The system is also considered user-friendly, easy to access, and supports the operational efficiency of small and medium-scale MSMEs. The system’s relevance to user needs is achieved through iteration and direct user involvement in each phase. Thus, the application of the prototyping method provides a relevant experience and a system that is adaptive to the needs of MSMEs, while also supporting business digitalization more effectively, efficiently, and sustainably.
Performance Evaluation of Hyperparameter-optimized Random Forest Regressor for Used Car Price Prediction
This study evaluates the performance of a hyperparameter-optimized random forest algorithm for predicting used car prices. The primary objective is to develop a robust model capable of delivering accurate price predictions. The random forest technique was selected for this study due to its proven effectiveness in handling non-linear regression problems. We utilized a public dataset from Kaggle, sourced via web scraping from Mobil123. The GridSearchCV method was employed to tune the hyperparameters and identify the optimal model configuration. The resulting model demonstrates strong predictive power, explaining over 93% of the price variance (R² score > 0.9349). Furthermore, the model's robustness is confirmed by an average cross-validation score of 0.9418. These results affirm that the optimized random forest model is a highly effective tool for this application. This research has practical implications for the automotive market, providing both buyers and sellers with a data-driven tool for more accurate price valuation
Investigating User Perceptions and Usability of Academic Information System Among Public Health Undergraduates in Kuningan
This research is designed to review user perceptions and usability of Academic Information System (SIAKAD) in undergraduate at Public Health Students, Kuningan. The aim is to determine how users perceive the usability of the system (ease, effectiveness and satisfication) and where there can be improvements. We assesed using the User Experience Questionare (UEQ), capturing six standard dimensions and two locally adapted ones: Security and Accessibility. A total of 51 respondents participated in the survey. The results indicated that all standard UEQ dimensions were rated in the "Above Average" category, with Perspicuity and Efficiency being the most notable strengths. Conversely, the Novelty dimension received lower scores, suggesting limited innovation in the system’s design. The other two dimensions of Security and Accessibility performed well above industry standards as users feel more secure with their data and access the system. Overall, the system performed well in pragmatic aspects, particularly clarity and reliability. These outcomes provide useful lessons for future system modifications such as improving interface design, multi-device accessibility, and reinforcing security features
Session-Based Segmentation of Music Listening Behaviour on Digital Platforms Using K-Means Clustering
Gaining insight into digital music listening behaviour is essential for optimizing personalization and enhancing user engagement. Instead of using traditional aggregated data, we employed a session-based methodology to reveal detailed interaction dynamics. A "session" was simply defined as a set of tracks played sequentially within a 30-minute window. By processing playback data into features and applying K-Means clustering, we successfully delineated four unique listener profiles. These profiles differ significantly across three dimensions: session length, skipping frequency, and listening time patterns. The practical relevance of these findings is high, directly informing platform recommender systems, feature strategy, and marketing efforts, while also contributing to the academic case for session-based user modelling.
Evaluating Information Quality in E-Government Implementation: A Case Study of City Government Websites in North Sulawesi
This study aims to evaluate the quality of information on local government websites in cities across North Sulawesi. This evaluation is important because official government websites are essential tools for providing public services, enhancing transparency, and encouraging community participation. The research employs a qualitative descriptive method, which allows for an in-depth analysis of website content and characteristics from the perspective of information quality. Data collection techniques include literature review, articles, and direct observation of the websites. This study was conducted using ten dimensions of information quality—Accuracy, Consistency, Timeliness, Completeness, Reliability, Availability, Relevancy, Believability, Efficiency, and Value-Added— to assess and evaluate the websites. Based on the findings, recommendations are provided for improving information quality, including system maintenance, data updates, and consistency enhancements. The results of this study, the quality of information on local government websites varies; there are several cities with good information quality and some that still need improvement in the information quality on their websites. This difference is primarily due to municipalities' differing commitments to data updates, consistency in information management, and the technical infrastructure available in each city. Therefore, this study presents recommendations for improving information quality through system maintenance, regular data updates, and greater consistency. By following these recommendations, it is expected that efficiency and user satisfaction with e-government websites will increase.
Performance Evaluation of Naïve Bayes and SVM for Detecting Mental Health and Depression Sentiment in Social Media X
Highly effective strategies for early detection of mental health issues are urgently needed, given the increasing incidence of mental health issues among adolescents. This study investigates sentiment analysis as a tool for identifying depression in social media accounts. Selected reviews about depression will be used to compare two machine learning classifiers: Naive Bayes and Support Vector Machines (SVM). Data will be processed and vectorized using the TF-IDF technique, divided into 70% training and 30% testing. Algorithm performance will be evaluated using Recall, accuracy, precision, and F1 score. Support Vector Machines significantly outperformed the Naive Bayes model. SVM achieved 92.93% accuracy compared to 79.01% accuracy, 93.00% precision, and 92.93% recall. For Naive Bayes, the precision and recall were 80.71% and 80.71%. The determining factor for the superior machine learning performance of SVM is its ability to detect complex and non-linear linguistic aspects present in expressions of depression and mental health. These results demonstrate how advanced machine learning algorithms such as Support Vector Machines (SVMs) can form the basis for proactive and scalable mental health support systems.
Simulation Prediction of the Number of Foreign Tourist Visits to Indonesia
One of the main drivers of the Indonesian economy is the tourism sector. The Indonesian Ministry of Tourism targets the number of foreign tourists visiting Indonesa in 2025 to be around 14 to 16 million tourists. The fluctuating number of foreign tourist visits must be predictable to anticipate any decreases or increases in the number of visits. This allows the government to establish relevant policies regarding the infrastructure that must be prepared and the promotional strategies to be implemented. The purpose of this study is to simulate the estimated number of foreign tourists visiting Indonesia per entry point per month in the coming year using the Monte Carlo method and measure the error rate using MAPE. The simulation results showed an average growth rate in foreign tourist visits of 30.9% from air entry points, 24.2% from sea entry points, and 53.9% from land entry points in 2024. The average accuracy rate of the Monte Carlo reached 66.24%, with an error rate (MAPE) of 45%. The results of the study indicate that the Monte Carlo method proved effective in predicting trends in foreign tourist arrivals, although its accuracy still need improvement. This study provides a strategic reference for Indonesian tourism growth
Marketing Strategy Selection System Based on Purchasing Decisions at Company X
The appropriate marketing strategy selection system in this case is the design of a decision support system to quickly and accurately identify which elements of the marketing mix should be maintained or improved based on consumer responses. The research method involved collecting data through questionnaires designed from marketing mix variables, namely product, promotion, process, price, distribution, and physical evidence. The questionnaire results were compiled and interpreted using multiple linear regression analysis. Each calculation and analysis result was then stored in a database, supported with interactive forms, and presented through visual reports in Microsoft Access. This design provides two categories of information: first, users obtain the percentage of simultaneous influence, namely the combined effect of the seven marketing mix variables along with the hypothesis testing results; second, users obtain information on the partial influence of each marketing mix variable on purchasing decisions, including their significance levels. The second type of information is presented by separating the significant group of variables from the non-significant ones. The final outcome of the system design is a ranked list of significant variables, from the largest to the smallest percentage, providing users with clear guidance on which variables should be maintained and which should be improved. Thus, this decision support system concept helps the management of Company X transform data into high-quality information that can serve as the basis for strategic decision-making.
Design of A 2d Animated Public Service Announcement Video for The Balanced Lunch Campaign for Office Workers
This design study addresses the issue of poor nutritional habits among young office workers, particularly during lunch, which is essential for maintaining productivity and overall health. Skipping meals or consuming fast food due to time pressure and financial constraints is common in this age group, leading to unbalanced nutrition and reduced work performance. The aim of this study is to design a persuasive social communication campaign that promotes balanced lunch habits among office workers aged 23–25. Using a qualitative and case study approach, data were collected through literature review and interviews with office workers in the Bandung area to understand the behavioral, psychological, and economic factors influencing their food choices. The population of this study consists of young office workers in Bandung, with a sample of 68 respondents selected through purposive sampling. The intervention was developed into a campaign titled GIZSI (Gizi Siang), applying the AISAS model (Attention, Interest, Search, Action, Share) as a strategic framework. The campaign integrates various media, including posters, billboards, animations, and social media content, to deliver persuasive and educational messages. The results indicate that visual communication can increase awareness of balanced nutrition, encourage healthier choices, and support behavioral change. This multi-platform campaign highlights the role of design in health promotion and aims to support long-term well-being and work productivity through sustainable lunch practices
User Requirements Analysis for an Android-Based Consignment Sales Reporting System for MSMEs
This research system aims to identify user needs for a consignment sales reporting system as the basis for developing an Android-based application. The method used was descriptive qualitative with an exploratory approach through questionnaires, participant observation, and semi-structured interviews with MSMEs in Bandung City. The analysis results indicate that manual reporting still dominates consignment practices, resulting in obstacles such as late reporting, miscalculations, difficulties in monitoring stock, and low transparency between the consignor and consignee. Thematic analysis yielded two groups of needs: consignees emphasized simple transaction recording, automated commission calculations, reminders, and transaction history; while consignors emphasized real-time reporting, detailed documentation, stock monitoring, and data security. From these results, three overarching themes emerged: the need for mobile-based accessibility, transparent and accountable reporting, and a clean user interface. Based on the findings, a conceptual system design was proposed with user-centered design principles as the basis for the development of the application. The results of this study will potentially enhance the effectiveness, accountability, and confidence in consignment-based MSME business relationship
Data Mining Approach to Predict Bank Saving Decisions Employing Classification Method
This study aims to predict which individuals would opt for saving their money in a bank by looking at matters concerning their age, their current occupation, gender, and how much income they provided each month. The authors employ the Naïve Bayes classification method, which is a frequently observed yet occasionally straightforward enough approach to mine data. The very initial stages were a questionnaire via the web that got 109 responses. After a rigorous data clean-up procedure that included dropping features and grouping continuous variables, the dataset was split up. About 70% of it was used to train the model, and its remaining 30% was used to test to determine how well it performed in circumstances it hadn't experienced previously. The outcomes pointed out that the Naïve Bayes classifier exceeded the threshold of 81% accuracy, with precision and recall around 85%. These were attained because the model indicated a strong ability to identify the majority of positive cases (high recall), while also showing a tendency to predict positives slightly more often than necessary, leading to some false positives, as indicated by the confusion matrix. This suggests that the model prioritizes minimizing missed detections over avoiding errors. These findings contribute to banks that use this type of data mining classification method may be able to improve their marketing strategies, prioritize specific leads, make decisions more quickly while still obtaining results that are thought to be fairly accurate, or optimize services to make hesitant customers feel more likely to open accounts. But this relies substantially on how properly the model indicates the way individuals conduct themselves in their actual lives.
Optimizing Product Promotion with Smart Bundling: An FP-Growth and IT-Enabled Analytical Framework
In a business, product promotion is necessary. This is done to increase sales and enhance the competitiveness of the business amid similar market competition. One strategy that can be used in promotion is smart bundling, which combines products into a single package offering that can increase the value of consumer purchases. Problems arise when determining the optimal product combinations is done subjectively without the support of systematic and strong data analysis. The aim of this research is to obtain suitable product bundling based on customer transaction history data, so that the produced products can increase the sales volume itself. In this study, product bundling will be carried out using an information technology-based analytical framework using the Frequent Pattern Growth (FP-Growth) algorithm. This method can identify customers' usual purchasing patterns and generate smart and data-driven product bundling recommendations. The results of the study show that 50% of consumers order Bengawan rice along with signature coffee, indicating that the two products can be sold together at a competitive price. This will impact an increase in sales volume as well as customer loyalty.
Partial KM Roadmap Implementation in a Garment Industry Marketing Division
The purpose of this research is to overcome the challenges of unstructured information flow, lack of systematic documentation, and limited knowledge sharing in the marketing division of AG Apparel through partial implementation of the Knowledge Management Roadmap. Using a qualitative case study method, this research applies the initial stage of Tiwana's 10-step KM roadmap combined with enterprise knowledge infrastructure principles and the SECI model to design the conceptual framework and modelling of the Knowledge Management System (KMS). The research findings show that the proposed model has the potential to improve the efficiency of information retrieval, enable more systematic knowledge documentation, and strengthen collaboration between marketing personnel. This study shows that a contextually tailored KM framework can improve knowledge-based decision-making, drive innovation, and support the formulation of more adaptive marketing strategies in the garment industry. This study concludes that by producing a conceptual KMS model that is still at the design stage, it can offer a structured yet flexible foundation for further empirical implementation and become a reference for similar labour-intensive industries
Security Optimization of JSON Web Token (JWT) Authentication in Laravel Framework Against Cross Site Scripting (XSS) Threats
This research aims to optimize the security of the Laravel JWT authentication library created by tymondesign. The security improvement focuses on complying with RFC 6749 by using distinct tokens for access and refresh, and on mitigating Cross-Site Scripting (XSS) attacks by preventing stolen access tokens from being reused in an attacker’s browser. The approach to improve security is by separating the access token and refresh token in accordance with OAuth 2.0 as defined by RFC 6749, and by performing token binding on the access token through an access token verifier stored in an HttpOnly cookie. This token is used to verify that the access token being sent is valid from the same browser that issued it. The access token verifier stored in the HttpOnly cookie cannot be stolen via XSS attacks because HttpOnly cookies cannot be read by JavaScript. In this way, the access token remains secure, and even if stolen, it cannot be used because the attacker does not possess the access token verifier. The research was conducted using the Waterfall methodology with a proof-of-concept implementation and comparative testing against the original tymondesign/jwt-auth library. The results show that the proposed approach successfully mitigates the reuse of stolen access tokens in an attacker’s browser and prevents stolen access tokens from being used multiple times to refresh tokens, because the new approach uses separate access and refresh tokens stored in HttpOnly cookies. This improvement contributes to more secure JWT authentication practices in RESTful APIs and provides insight for enhancing web security against XSS attacks.
Website-based Arabic Conjugation (Sharf) Learning: ArabicMorph
Learning Arabic morphology (sharf), particularly verb conjugation, presents a significant challenge for students due to its complexity and reliance on rote memorization. This research aims to design and develop ArabicMorph, an interactive web application to facilitate the automatic and efficient learning of sharf. The system was developed using the Waterfall model, with requirements prioritized through the MoSCoW method. It is built on the Laravel framework and integrates the Qutrub API for verb conjugation and the DeepSeek AI API for contextual translation. System evaluation was conducted via beta testing with a questionnaire administered to 17 respondents to measure user satisfaction. The results show that all core features were successfully implemented, and the system achieved a very high user satisfaction level with an average score of 91.8%, covering functionality, appearance, and ease of use. These findings indicate that the integration of rule-based linguistic technology with artificial intelligence in a modern interface is effective in overcoming the difficulties of leaning sharf. In conclusion, ArabicMorph has been successfully realized as a practical, adaptive, and user-friendly tool for independent training. The impact of this research is a digital solution that can potentially enhance student interest and understanding of Arabic morphology, providing a foundation for future development with additional educational features
Comparative Analysis of Logistic Regression and Support Vector Machine for Sentiment Classification of Shopee App Reviews on the Play Store
The rapid advancement of information technology has significantly transformed consumer shopping behavior on mobile e-commerce platforms, with Shopee standing out as one of the most popular choices. User reviews provide essential insights into customer sentiment and preferences, thereby highlighting the importance of automated sentiment analysis for capturing public opinion. This study aims to evaluate and compare the performance of two supervised learning algorithms Support Vector Machine (SVM) and Logistic Regression (LR) in classifying sentiments from 3,000 Shopee app reviews obtained from the Playstore. The research employed a 5-fold cross-validation method, with performance measured using accuracy, precision, recall, and F1-score. The findings indicate a clear trade-off between the models: SVM achieved a slightly higher test accuracy of 80.67% compared to 80.33% for Logistic Regression, but it showed greater susceptibility to overfitting. Conversely, Logistic Regression demonstrated more stable performance, maintaining a balanced trade-off between precision and recall without noticeable overfitting. This trade-off is likely due to SVM's complexity and sensitivity to high-dimensional feature spaces, whereas Logistic Regression's simpler linear model offers greater generalizability on this dataset. Based on these results, Logistic Regression is recommended for applications that prioritize consistency and robustness, while SVM may be preferable in contexts where maximizing precision is the primary objective.
Machine Learning Analysis of Patient Age Classification Based on Inpatient Visit Data Using Random Forest and SVM Algorithms
The increase in inpatient care visits in hospitals has led to a growing need for data driven analysis to classify patient demographics, particularly age groups. This study applies supervised machine learning algorithms Random Forest and Support Vector Machine (SVM) to classify inpatient visit data based on age categories: children, adults, and elderly. A dataset from hospital information systems, consisting of features such as gender, diagnosis, length of stay, and referral source, was used for model training and evaluation. The system was developed using Python and integrated into a web interface to allow real-time predictions. Evaluation results show that Random Forest slightly outperforms SVM in accuracy and recall. The findings of this study should help hospitals make decisions, especially when it comes to allocating resources and differentiating services according to patient age.
Understanding Consumer Resistance to AI-Powered Marketing in Indonesia
As the largest digital economy in Southeast Asia, Indonesia has followed global digitalization trends, including digital marketing that is changing due to Artificial Intelligence (AI) to become more personalized, efficient, and competitive. Therefore, this article aims to explain a more localized understanding of technology adoption in marketing, by highlighting the unique challenges faced in emerging markets including Indonesia which faces its own challenges regarding AI acceptance. Through a qualitative approach with descriptive methods, the research findings show that AI-based marketing uses technologies such as machine learning, natural language processing (NLP), and predictive analytics to create highly personalized, automated, and data-driven strategies that improve consumer interactions, simplify operations, and help businesses grow in various fields. It can be concluded that the adoption of AI-based marketing in Indonesia is still low due to limited digital literacy, emotional and cultural resistance, lack of trust and transparency, inadequate infrastructure, and the absence of local communication strategies that align with consumer values ​​and expectations. The impact of this research is expected to enriches the literature on AI adoption in marketing by providing a localized, Indonesian perspective so that companies can design AI-powered marketing campaigns that are not only technologically advanced but also culturally sensitive and aligned with Indonesian consumer values.
WireGuard Based Secure Network Design to Protect Backend Access at Prolov Office
This study is driven to develop and implement secure VPN network based on WireGuard for enhance the backend access in Prolov Office. Approach I am using in this post is experimental, including install and configure Wiregurd with easy-wg-quick, set up client peers, plus implementing firewall rules to restrict access to backend thru the VPN network. Testing was done with ping (latency) iperf3 (throughput), wireshark to verify packet encryption and nmap for external port scanning. "The results of our analysis indicate average latencies of 187–279 ms, which can be considered moderate but stable, a predictable throughput rate (constant bitrate) in the range of 4 Mbps, and all encrypted traffic without any sensitive data in plaintext. Security testing also confirms that the backend page can only be accessed through wireguard, and the backend ports (80 443) cannot be found from outside. In summary, the realization of WireGuard enhances the security of backend access, including encrypted data transmission, fine-grained access control and ease in deployment that is ready to be applied in aid of maintaining a modern enterprise network security.
Utilizing Generative AI Technology for the Development of Cinema Ticketing Information Systems: A Case Study on Lovable.IA Implementation
The development of artificial intelligence has enabled new methods for information system development, such as the use of prompt-based generative platforms like Lovable.IA (a generative AI platform). This research is to create a web-based cinema ticketing information system with Lovable.IA and Supabase as the backend database. The methodology used is a system development approach with six phases: user interface design, movie data input, seat selection, payment integration (QRIS – Quick Response Code Indonesian Standard), admin login, and contact information display. The results show that Lovable.IA supports a rapid and cost-effective system development process that delivers responsive and user-centric interfaces. Supabase was integrated for real-time stable data interaction so that the system can respond to the user’s input swiftly. The results of this study suggest that a generative AI-based approach can simplify information system development, particularly for non-technical users. Although there are minor constraints such as daily usage limitations and the requirement for multiple prompts, this method still proves its dominance in developing effective web-based systems. This study contributes to the body of knowledge by advancing the application of generative AI technology in digital information system development, specifically in the education and business domains
IoT–Based Smart Agriculture System Using ESP32, DHT22, and Soil Moisture Sensors with Relay Control, MySQL–Bootstrap (Without PDO), and Chart.js for Water–Scarce Environments
The Internet of Things (IoT) presents a transformative opportunity to address escalating water scarcity in agriculture, particularly in tropical regions where inefficient irrigation remains prevalent. This study aimed to design, implement, and evaluate a low-cost IoT-based smart irrigation system to optimize water usage while maintaining crop health. The system employed an ESP32 microcontroller integrated with DHT22 (temperature and humidity) and capacitive soil moisture sensors, coupled with a relay module to automate a water pump. Real-time sensor data was transmitted via Wi-Fi to a remote web server using PHP and MySQL (without PDO), and visualized through a responsive dashboard built with Bootstrap and Chart.js, enabling remote monitoring and control. Field testing over 30 days on a Capsicum annuum plot demonstrated a 37.2% reduction in water consumption compared to conventional manual irrigation, with sensor accuracy averaging 92.4%. The system achieved 99.8% operational uptime and an average data transmission latency of 1.18 seconds, confirming its reliability and responsiveness. These findings indicate that real-time, sensor-driven irrigation significantly enhances water efficiency without compromising agricultural output. The solution proves to be scalable, affordable, and accessible — particularly for smallholder farmers in resource-limited settings. By bridging technology and sustainable farming, this IoT implementation not only conserves vital water resources but also promotes climate-resilient agricultural practices, offering a replicable model for precision agriculture in water-stressed tropical environments