Nowadays, enhancing campus environments through mitigations of air pollutions is an essential endeavor to support academic achievements, health, and safety of students and staffs in higher educational institutes. In laboratories, pollutants from welding, auto repairs, or chemical experiments can drastically degrade the air quality in the campus, endangering the respiratory and cognitive health of students and staffs. Besides, in universities in Indonesia, automobile emissions of harmful substances such as carbon monoxide (CO), nitrogen dioxide (NO₂), and hydrocarbon (HC) have been a serious problem for a long time. Almost everybody is using a motorbike or a car every day in daily life, while the number of students is continuously increasing. However, people in many campuses including managements do not be aware these problems, since air quality is not monitored. In this paper, we present a real-time air quality monitoring system utilizing Internet of Things (IoT) integrated sensors capable of detecting pollutants and measuring environmental conditions to visualize them. By transmitting data to the SEMAR IoT application server platform via an ESP32 microcontroller, this system provides instant alerts through a web application and Telegram notifications when pollutant levels exceed safe thresholds. For evaluations of the proposed system, we adopted three sensors to measure the levels of CO, NO₂, and HC and conducted experiments in three sites, namely, Mechatronics Laboratory, Power and Emission Laboratory, and Parking Lot, at the State Polytechnic of Malang, Indonesia. Then, the results reveal Good, Unhealthy, and Dangerous for them, respectively, among the five categories defined by the Indonesian government. The system highlighted its ability to monitor air quality fluctuations, trigger warnings of hazardous conditions, and inform the campus community. The correlation of the sensor levels can identify the relationship of each pollutant, which provides insight into the characteristics of pollutants in a particular scenario.

3-Dimensional (3D) Printing Technology (3D printer) has become a Standard Acceleration Tool for Industry 4.0. Until now, 3D Printing system material melting technology has been using Fused Deposition Modelling (FDM) method. FDM is the melting of plastic materials through melting temperature and the formation of 3D objects in layer by layer manner. Meanwhile, the feeding for melting material is in the form of rolling filament. Currently, in this research FDM uses plastic pellete feeding method. Its exstrusion is called extruder, that is, plastic pellet processed through heater zone and pushed out by screw to the nozzle where the nozzle is a Computer Numerical Control (CNC) drives 3-Degree-of-Freedom (3DOF) applying cartesian axes. In this research, the printing uses high density polyethylene plastic pellete (HDPE). Temperature control in melting heater using proportional-integral-derivative (PID) method is controlled by microntroller and programmed through marlin firmware. The design and implementation result showed that the extruder system is capable to melt HDPE pellet at 200°C with heater power 300Watt and the screw can rotate to push out melted plastic at 2mm of nozzle.

At the moment, there are some inaccuracies in manual classification for tobacco leaf quality selection, which are influenced by some factors, such as human fatigue or poor lighting. These cases lead to the need for another method that is more consistent, faster and reliable. This research is an implementation of CNN (Convolutional Neural Network) in the classification of fresh tobacco leaves in terms of maturity grades. The primary objective is to develop an efficient CNN model capable to automize the classification of tobacco leaves into three maturity criteria: immature, mature, and old. This methodology consists of some key factors, including color thresholding strategies to purge the noise from the background, Basic Image Manipulation approaches, the systemized screening of different input sizes, and CNN models to enhance the results. The result of this research proves that the developed CNN model has 97.9 % accuracy achieved following 200 training sessions. The model is trained on a dataset comprising 1,249 fresh leaf photos, with a balanced 80:10:10 for train, validation and test ratio. However, the study emphasizes that the CNN model has successfully supported the tobacco leaf discrimination on a Jetson Nano Single-Board Computer with a Graphic Processing Unit (GPU). The study extends beyond the mere theoretical contribution to practical applications in sorting “Gagang Rejeb Sidi” tobacco leaf, the highest quality tobacco variety in South Malang, East Java, Indonesia. Classification using a webcam as an input device shows the fastest processing time of 203.17 ms and the maximum is 1,363 ms. This CNN model algorithm will be applied to a tobacco leaf selector machine, which has a high-speed conveyor and a three-position selector arm. The machine will be operated close to the field in post-harvest time under uniform lighting conditions. Overall, the result of this research is highly relevant in terms of the short duration and accuracy for understanding the commodity classification. It provides a new angle toward speeding up the classification process and improving Indonesian tobacco quality

Nuraksa Tahura is an area that has potential to develop as tourist destination with its natural waterfall Segenter. For its development, same effort were need for the waterfall. This study aims to determine the feasibility of Segenter waterfalls, its development strategies and future development solution. The research were conducted through data collection, observation, interviews, Focus Group Discusion. The data were analyzed using natural tourist attraction Guidelines of Director General of Forest Protection and Nature Conservation 2003 and SWOT analysis. The results of the research showed that Segenter waterfall is feasible to be used as a tourist destination. The development is using the SO strategy those were utilizing its opportunities with the own potential by the Nuraksa Tahura region.

The use of interactive learning media in the pandemic period is deemed necessary to be developed better, besides being able to increase the desire to learn from students who already feel bored just doing learning at home, interactive modules are considered more effective in delivering material. However, there are still many kindergarten teachers who are not optimal in applying interactive modules. This is because there is still rarely counseling or training in making interactive modules. So, it is necessary for counseling to be carried out by lecturers from Electronic Engineering who in the process will be assisted by students to introduce and teach the process of making to the application of augmented reality modules that can be used as interactive learning media for early childhood. It is hoped that after the counseling is held, learning media using augmented reality can be applied to subjects that require 3D visuals in delivering material.

Air pollution poses a significant threat to human health and environmental sustainability, exacerbated by anthropogenic activities such as industrial emissions, vehicle exhaust, and fossil fuel combustion. Major pollutants include carbon monoxide (CO), nitrogen dioxide (NO2), hydrocarbons (HC), sulfur dioxide (SO2), ozone (O3), particulate matter (PM2.5), particulate matter (PM10), and etc. In this study, an Early Warning System (EWS) was developed integrating sensors, microcontroller, IEEE 802.11 network, web interface monitoring, and Instant Messaging. The system uses the CART algorithm to analyze the data, with sensors that detect CO, NO2, and HC. The evaluation of the system demonstrated its effectiveness, with recorded pollutant concentrations of 1.401 ppm for CO, 0.639 ppm for NO2, and 0.860 ppm for HC, and identified correlations between these pollutants. The EWS proved capable of providing timely alerts, ensuring continuous monitoring and management of indoor air quality, and contributing to public health and environmental protection.