Vol. 5 No. 1 (2026): June 2026

Indoor air quality plays an important role in students' comfort and academic performance in learning environments. High carbon dioxide (CO₂) concentrations caused by inadequate ventilation can reduce learning concentration. This study aims to develop an Internet of Things (IoT)-based indoor air quality monitoring system using an ESP32 microcontroller and an MQ135 sensor, as well as to analyze the relationship between CO₂ concentration and students' academic performance. The research employed a Research and Development (R&D) approach, with CO₂ concentration data collected every minute from May to October in a classroom environment. The data were processed into monthly averages and compared with students' average academic scores. The results show that the highest average CO₂ concentration occurred in September at 997.78 ppm, accompanied by the lowest average student score of 74.37, while the lowest CO₂ concentration was recorded in July at 712.10 ppm, with an average score of 83.76. The findings indicate a tendency for increased CO₂ concentrations to be followed by a decrease in students' academic performance. The developed system is capable of displaying real-time indoor air quality data through a web-based dashboard and can support the creation of a healthier learning environment.  

The advancement of information technology has encouraged various innovations in the education sector, including web-based student violation recording systems. This study designed and developed a web-based platform to support the recording of student violations at SMK Muhammadiyah Tasikmalaya, employing the Research and Development (R&D) method alongside the ADDIE model — Analysis, Design, Development, Implementation, and Evaluation. Built on the Laravel framework, the system provides structured features for managing violation categories and types, recording individual student violations, and generating discipline evaluation reports, enabling teachers to monitor and evaluate student conduct more accurately. Two evaluation approaches were applied: the TELOS framework (Technical, Economic, Legal, Operational, Schedule) to assess overall feasibility, and the Black Box method to verify functional performance. Results across all five TELOS dimensions placed the system in the "highly feasible" category, with an aggregate score of 86.45. These findings confirm that the system is ready for institutional deployment as a reliable, technology-based tool for student discipline management.  

Bosque Barbershop operates without a structured reservation system and lacks an official website capable of presenting its business profile in a centralized manner. The existing process relies on Linktree — a link-aggregation tool that cannot handle scheduling logic or booking confirmation, leaving customers without scheduling certainty. This study developed an integrated website serving two functions: online service reservation and business profile presentation. The Research and Development (R&D) method was applied using the ADDIE development model. Data were gathered through direct observation and semi-structured interviews with the owner and administrator. The website was built using the Laravel framework, incorporating features for service reservation, scheduling, barber selection, and booking management. Functional testing via Black Box Testing confirmed that all primary features operate as specified. Feasibility assessment through TELOS analysis — covering Technical, Economic, Legal, Operational, and Schedule dimensions — placed the system in the "Very Feasible" category. The system meets its functional requirements and is operationally viable for both administrators and customers.  

Manual attendance recording at SMKN 1 Tasikmalaya has produced recurring problems: clerical errors, delayed recapitulation, and limited administrative visibility into student presence patterns. A web-based student attendance information system was developed using the CodeIgniter framework to address these operational shortcomings. The study followed a Research and Development (R&D) approach structured around the ADDIE model, proceeding through five stages — analysis, design, development, implementation, and evaluation. The system was built on PHP and MySQL, supported by standard web technologies. Functional testing was conducted using Black Box Testing, while system feasibility was assessed through the TELOS method covering technical, economic, legal, operational, and schedule dimensions. The TELOS assessment returned a composite score of 93.6, placing the system in the Very Feasible category. Black Box Testing confirmed that all primary functions performed as specified. The resulting system supports structured attendance recording, data management, and recapitulation, and was judged suitable for operational deployment within the school environment.  

Building safety monitoring has grown increasingly dependent on low-cost sensing solutions — and for good reason. Structural tilt, when left undetected, accumulates into deformation patterns that compromise both occupant safety and long-term building integrity. This study developed an IoT-based room inclination monitoring system by pairing an MPU6050 inertial sensor with an ESP32 microcontroller to capture, process, and transmit tilt data across three measurement axes in real time. The system computes pitch and roll angles from raw accelerometer outputs and delivers the results through a web-based interface, allowing users to record wall and floor orientations independently before calculating the combined inclination angle via the dot product method. Ten repeated trials were conducted against a calibrated 90° wall-to-floor reference angle to assess system reliability. The mean recorded angle was 88.06°, reflecting a deviation of 1.94° from the reference — a gap narrow enough to confirm basic functional validity, yet wide enough to warrant concern in precision-critical contexts. All measured values fell consistently below the 90° threshold, suggesting a systematic offset rather than random error. The absence of digital filtration — no Kalman or Complementary filter was applied to the raw data — appears to be the primary contributor to this pattern, compounded by minor sensor placement inconsistencies during testing. Future work must address calibration offset correction and signal noise attenuation before the system can be considered adequate for structural health monitoring or smart building applications.