International Journal of Applied Sciences and Smart Technologies

Thermal Investigation on Lithium-Ion Battery Using Liquid Cooling Method

2026-06-25T09:12:11+07:00

An electric battery is an energy storage device that has a high energy density, a long lifetime and has a high rate of charging and discharging the battery. The performance of the Lithium-Ion battery greatly affects the temperature of charging and discharging, so a cooling method is needed that is able to maintain the working temperature of the battery at (25⁰C – 50⁰C) conditions. In this study, the Computational Fluid Dynamics method was used to investigate the effect of Depth of Discharge (DOD) on the cooling performance of BTMS (Battery Thermal Management System) Liquid Cooling using ethylene-glycol as the working fluid to be flowed on the curved cooling tube. The results of this study indicated that the maximum temperature and temperature difference of the battery module decreased as the mass flow rate of the cooling fluid increased, the lowest temperature value occured at the mass flow rate of 18×10-4kg·s-1 with a temperature value of 303.6 K. The maximum temperature of the battery module fluctuated as the DOD value increased.

Psychrometric Performance of the Air Dehumidifier Base Clothes Dryer

2026-06-25T09:08:07+07:00

Drying clothes with dehumidifier machine and additional IR light experiments were conducted to show the psychrometric performance of dehumidification drying. Experiment of dehumidification drying 7.07 kg wet clothes to 4,78 kg dry clothes and 9.9 kg wet clothes to 7.67 kg dry clothes were applied. Variation of 715 W dehumidifier and 375 W IR were conducted. Measurement of room and environment temperatures and humidity were performed. The results show that additional IR make the drying process faster. However, it reduces drying effectivity. The efficiencies of drying are 109%, 96%, 100%, and 79% for drying 4.78 kg dry clothes with dehumidifier, drying 4.78 kg dry clothes with dehumidifier + IR, drying 7.67 kg dry clothes with dehumidifier, and drying 7.67 kg dry clothes with dehumidifier + IR. The numbers also reveal need of adopting drying COP using closed loop heat pump dehumidification to be ratio of heating and cooling energy over the supplied energy plus one.

Design of a Portable Air Quality Monitoring System Using Internet of Things Based Multisensor

2026-08-04T10:51:13+07:00

The rise in air pollution in the modern era, particularly in urban areas, has hurt public health. Current conventional systems can only measure a limited number of parameters. While several air quality monitoring systems exist, they are extremely expensive and therefore inaccessible to the general public. The objective of this system is to monitor air quality by detecting harmful gases and fine particles in indoor air at a low cost. This system uses a multisensor setup consisting of a DHT11 sensor, an MQ-135 sensor, and a GP2Y1010AU0F sensor to measure temperature, humidity, CO gas levels, and fine particle levels in the air. The method applied is an engineering research approach to design a portable air quality monitoring system. The result of this research is a portable air quality monitoring system based on the Internet of Things. The results of the characterization tests for each sensor in the system indicate that the system can measure the parameters accurately. The DHT11 sensor has a coefficient of determination of 0.998 and 0.998, the MQ-135 sensor has a coefficient of 0.986 and 0.957, and the GP2Y1010AU0F sensor has a coefficient of 1. Based on the regression equations obtained, the performance of the DHT11, MQ-135, and GP2Y1010AU0F sensors indicates that they can effectively respond to changes in temperature, CO levels, and dust levels occurring in the environment.

Handling Highly Imbalanced Flood Data Using K-Means Clustering in Skyline Query Dominance Testing

2026-06-25T09:05:12+07:00

Skyline query is a recommendation algorithm used to select objects based on multi-attribute preferences, but a key challenge is that its results can be highly imbalanced, where only a small number of objects meet the preferred criteria. This imbalance reduces the reliability of spatial decision-making, including in flood vulnerability assessment. This study addresses the issue by applying a modified Sort-Filter Skyline method that considers maximum and minimum attribute preferences during sorting. The skyline output shows a strong class imbalance, with only 18 areas identified as flood-prone compared to 1,574 non-flood-prone areas. To mitigate this, K-Means clustering is used as a refinement step. The Elbow and Gap Statistic methods recommend three clusters as optimal, while the Silhouette method suggests eight. Cluster distribution analysis shows that three clusters produce a more balanced representation, with Scheme 1 and Scheme 3 showing better balance ratios and lower variation than Scheme 2. Thus, clustering into three groups helps achieve a more representative mapping of flood-prone areas.

A Simulation-Based Approach To Spiral Pump Design

2026-06-25T09:02:14+07:00

The consistent availability of irrigation water in agricultural areas remains a challenge to this day, due to the fact that rivers are always located at a lower elevation than farmland. As an alternative solution, a spiral pump has been introduced that operates solely using the kinetic energy of river water flow, unlike conventional water pumps that always require electricity or fossil fuels. This study aims to develop a simulation-based spiral pump design by integrating findings from previous analyses regarding fluid flow behavior and water wheel performance. Accordingly, using SolidWorks software, a comprehensive geometric model of the spiral pump was created, consisting of a 1.2-meter-diameter undershot water wheel, an eight-coil spiral hose with a 2-inch diameter, and a support frame. Through static structural simulation, it was also determined that a hydraulic force of 72.63 N and a torque of 32.68 Nm act on the water wheel, with the shaft withstanding a force of 174.06 N. The von Mises stress on the shaft is 3.746 × 10⁶ N/m², while on the blades it is 1.711 × 10⁷ N/m²; these stress values are well below the yield strength of A36 steel for the shaft and aluminum alloy for the blades. Thus, based on CFD-based flow rate analysis, hydraulic head evaluation, and structural validation, this study has produced an integrated design that ensures operational efficiency and structural safety. This spiral pump design can be developed into a prototype for field application as a cost-effective renewable energy solution.

Modelling and Analysis of Ziegler-Nichols PID Tuning on Water Tank Control System

2026-06-25T09:05:51+07:00

In the industrial world, the water level in the tank is often controlled using a PID controller. The main problem in using a PID controller is the problem of tuning the three PID parameters (Proportional gain, Integral gain, and Derivative gain). In this study, the parameter controller tuning is done using the heuristic tuning method that is applied on the water level plant and the Ziegler-Nichols tuning method that is simulated using MATLAB software. The results of both tuning methods will be compared. To simulate the control system, the water level system in the tank is modeled in the form of a first-order plus dead time modified with the Pade approach. Tuning using the Ziegler-Nichols method produced a good response, where the final value of the water level was the same as the set point value (SSE = 0%) with a settling time of 6.18 minutes.

Technical and Cost Barriers in Accessing Smart Educational Technologies in Rural Areas

2026-06-25T09:04:26+07:00

This research study explore how data costs and technical issues affect the use of LowDataMode, based on insights from 100 people living in a resource-limited setting. Using chi-square tests and logistic regression, I analyzed how device types, network access, barriers, and platform usability influence LowDataMode use. Results shows high data consumption (41%) as the primary barrier, significantly linked to LowDataMode use (p = 0.023), with marginal significance in regression (p = 0.086). Technical barriers, including lack of affordable devices (28%) and offline learning options (19%), hinder adoption, particularly for shared family device and laptop/desktop users (p = 0.052). 3G users (55%) face greater data cost challenges, while Wi-Fi users (15%) rarely need LowDataMode. Higher platform usability was associated with a greater likelihood of skipping LowDataMode (β = 1.48, p = 0.008), suggesting that intuitive, feature‑rich apps may encourage more data‑intensive activity and thus raise users’ data costs. And, non-significant was technical barriers include network stability (p = 0.315) and technical problems (p = 0.545).

Analysis of Key Features in PCOS Diagnosis Using Random Forest and XGBoost with SMOTE and SHAP

2026-06-25T09:10:21+07:00

Polycystic Ovary Syndrome (PCOS) is a hormonal disorder in women of
reproductive age characterized by irregular cycles, hyperandrogenism, and
polycystic ovarian morphology. Diagnosis is challenging because symptoms overlap with other endocrine disorders. This study proposes an interpretable machine learning approach for PCOS diagnosis using Random Forest and XGBoost. The Synthetic Minority Oversampling Technique (SMOTE) was applied to handle class imbalance, while Shapley Additive Explanations (SHAP) enhanced model interpretability. The dataset included 541 samples with 45 clinical and hormonal features, processed through preprocessing and hyperparameter tuning with GridSearchCV. XGBoost with SMOTE and GridSearchCV achieved the best performance, with 93% accuracy, 92% precision, 89% recall, and 90% F1-score. Random Forest obtained comparable results with 93% accuracy, 94% precision, 87% recall, and 90% F1-score. SHAP analysis highlighted key features such as follicle count, Anti Müllerian Hormone (AMH), skin darkening, weight gain, and irregular cycles. Global SHAP interpretation identified the most influential predictors, while local SHAP provided patient-specific explanations that improved transparency. The consistency of SHAP results with the Rotterdam criteria supports the model’s clinical validity and strengthens trust in AI-assisted tools. Overall, combining SMOTE, GridSearchCV, and SHAP not only improved predictive performance but also ensured transparent outcomes, indicating potential use for early PCOS screening.

Principal Component Analysis-Driven Feature Reduction for Predicting Coffee Quality Using a Machine Learning Approach

2026-06-25T09:08:41+07:00

Coffee quality assessment using a machine learning approach faces major challenges, including high data dimensionality and redundancy between features. Therefore, PCA is proposed as a feature reduction technique to improve the efficiency and accuracy of coffee quality prediction models. The research phase began with data acquisition, data cleaning, feature engineering, explanatory data analysis, testing the normalization of coffee parameter profiles, implementing PCA on Random Forest and XGBoost models, and then evaluating model performance. Model evaluation using MAE and MAPE showed that Random Forest provided more precise predictions than XGBoost, particularly when applying PCA. This resulted in a 39% performance increase for Random Forest from 0.11903 to 0.08542 and an 8% increase for XGBoost, shifting the score from 0.12511 to 0.11570. Prediction visualization reinforced the consistency and precision of the Random Forest model, regardless of whether PCA was used. The findings of this study highlight the importance of feature cleaning and engineering, and the role of PCA in improving the precision of coffee quality predictions. The use of the Random Forest model with PCA is recommended as an efficient method for modeling the quality of Arabica coffee, taking into account sensory and environmental factors.

Development of an Arduino-Based Water Rocket Launcher in Physics Experiments

2026-06-25T09:09:42+07:00

The effective science education requires practical methods that allow students to explore complex physics concepts. One promising approach is the use of physics experiment as an interactive media. This research focuses on the development of water rocket launcher using an Arduino as an innovative physics experiment. Arduino in water rocket launcher is used for making the precise control and relevant measurement of variables, such as angle of projection, speed of launch, maximum altitude of launch, and air pressure. The research process followed the ADDIE instructional design model and involved hardware, software prototyping, work testing, and user instruction. The launcher’s performance was tested with 33 engineering students and assessed by 5 experts. Expert evaluations rated the relevance, design, and usability of the kit highly (3.4–4.0 on a 4-point Likert scale). User responses from 33 students indicated strong agreement on ease of use and engagement (mean scores 3.79–3.91), with a high reliability (Cronbach’s alpha = .964). Experimental launches, using three and four finned rockets, showed maximum height percentage differences between theoretical and observed values ranging from 0.0%–52.6% (three fins) and 1.2%–51.8% (four fins); range errors were 3.4%–36.8% (three fins) and 2.1%–42.7% (four fins). The findings confirm that the Arduino-based water rocket launcher provides effective, interactive learning, though further refinement in data accuracy and instructional materials is recommended to maximize its classroom impact and is needed for improved accuracy.

IoT Based Nozzle Actuation System Design for Automated Fish Feed Distribution

2026-06-25T09:07:00+07:00

This study presents the design and development of an IoT-based nozzle actuator system intended to improve the accuracy and efficiency of automatic fish-feed distribution. The system was designed using an ESP32 microcontroller as the central controller, a servo motor as the nozzle-direction actuator, and the Blynk application as the remote monitoring and control interface. This configuration enables users to adjust the nozzle’s direction and feed-dispersion intensity through both manual control and scheduled timer modes. A series of experiments was conducted to evaluate mechanical performance, IoT connectivity stability, response time, and cross-device application compatibility. The experimental results indicate that the proposed system improves feed-distribution efficiency by 32.6% compared to conventional manual methods. Feed waste was reduced by 28.4% due to more uniform distribution and minimized overfeeding. The average command-to-actuator response time was measured at 0.82 seconds, demonstrating stable real-time performance. Application testing across five smartphone devices (Redmi 12, Huawei P30, Redmi Note 9, Samsung M23, and Little M3) achieved a 100% success rate for login, timer functions, and manual ON commands, confirming the reliability of the IoT control interface across multiple platforms. Compared with traditional automatic feeders, the developed prototype offers more precise nozzle orientation, flexible remote operation, and an adaptive feed-dispersion pattern. The integration of actuation mechanisms with IoT-based control provides a smarter and more efficient automation solution suitable for small- to medium-scale aquaculture systems. Overall, the findings demonstrate that the proposed design delivers superior distribution performance and operational flexibility, representing a meaningful advancement over existing feeding technologies.

A Sub-Saharan African Airport Mechanical Equipment Failure Assessment Using Joint FMECA-GRA Method Based On Technical Process Efficiency

2026-06-25T09:09:11+07:00

This article establishes how airport ground equipment fails in operations to enhance failure prediction, reduce maintenance and costs. Airports are essential to enhance the economy of a developing country and these failures affect the efficiency of the airport system. Consequently, this paper deals with utilising the risk priority number of equipment failures to create ranks for selected airport ground equipment and study the performance of these equipment using the grey relational analysis. Three methods were established to evaluate the failures of five selected major equipment (elevators, travellator, escalator, baggage handling equipment and air-conditioners): the risk priority number (RPN), RPN with same weights and RPN with different weights for the risk factors. All the methods approved elevator 8, travellator 6, escalator 11, baggage handler 3 and air conditioner 9 as the best, indicating reduced risk factors from the equipment. The worst equipment approved by all the methods are elevator 2, baggage handler 6 and air-conditioner 7. However, while the RPN, and RPN (with an equal weight of risk factors) approve travellator 3 and escalator 9 as the worst equipment, a divergent choice of travellator 1 and escalator 5 is mapped RPN method with different weights for the risk factors as the worst equipment. This work contributes to the airport maintenance literature by applying three models to evaluate the failure of ground equipment in airports to identify equipment that should be given the utmost priority and those that warrant the least attention of the airport maintenance management.

Using the Six Sigma DMAIC Approach to Improve Maintenance Practices of Ground-Service Mechanical Equipment within a Sub-Saharan African Airport

2026-06-25T09:06:28+07:00

Despite its ambitions for the most effective and world-class standard attainment, the ground-service equipment literature for airports is limited in engagements with the question of how to ascertain the effectiveness of maintenance services for critical ground-service equipment, to improve company goodwill and passenger comfort. By borrowing ideas from the process improvement literature, the DMAIC approach was instituted in a sub-Saharan African airport to capture the five critical ground-services mechanical equipment of package air-conditioner, escalator, travelator, baggage handling equipment and elevator. The methodology deployed to solve the problem is technical action research conducted in a large international airport. The DMAIC improvement framework was designed, installed and regularly managed for outcomes in discussions with the maintenance ground-service team from the delegated department. The cause and effect analysis was deployed to establish the frontline reasons for the equipment failures. The results revealed that 80% of the studied ground-service equipment exhibits the C_p/C_pk values above 1.33. Consequently, the maintenance procedure adopted to maintain the equipment is capable of maintaining a large majority of the equipment studied. However, there is scope to improve the performance of the escalator, whose value of the C_p/C_pk ratio falls below the 1.33 benchmark. A DMAIC framework appropriate to analyse the maintenance efficiency of ground-service mechanical equipment in airports is contributed for the first time to the airport setting.

Tourism News Classification Using Convolution Long Short-Term Memory (C-LSTM)

2026-06-25T09:07:33+07:00

Along with the rapid development of information technology, news about Indonesian tourism destinations can now be accessed widely through various platforms such as social media and online news, making news easily accessible. With the increase in tourism news, manual news classification is less effective in dividing data into various subcategories, such as natural tourism, artificial tourism, cultural tourism, and non-tourism. An algorithm is needed to address this problem, one of which uses an algorithm from deep learning. This study developed a tourism news classification model using Convolutional Long Short-Term Memory (C-LSTM) and Word2Vec Representation with Continuous Bag of Words (CBOW) architecture to obtain better accuracy and computational efficiency, and is used to produce better word vectorization, so that semantic relationships between words can be captured. This study used a news dataset of 5261 and news with an 80:20 ratio for training and testing. With this approach, the highest accuracy value of 94% was obtained with a time of 1140 seconds.

The Influence of Printing Parameter Variations on the Dimensional Accuracy of 3D Nylon Carbon FDM Prints Using the Grey-Taguchi Method

2026-06-25T09:03:46+07:00

The dimensional accuracy of the printed specimen in the process of forming a workpiece with carbon fiber material using the Creality K1 Max printer machine requires a combination of parameters in the printing process. The printing type using Fused Deposition Modeling (FDM) is a very popular type of printing process, in this process the filament of the raw material is heated to form the desired product. Nylon carbon was chosen because it has high strength and heat resistance, so it is quite potential for various types of applications. Dimensional accuracy is a major problem in the printing process using 3D printing, especially if the printed product will be assembled either in a mechanical system or a non-mechanical system. The results of the dimensional accuracy of the object printing process are greatly influenced by the selection of parameters used in the printing process. The printing parameter optimization process uses the Grey-Taguchi method, this method was chosen because this method combines multi-dimensional workpieces according to product quality. The specimen or workpiece uses the ASTM D638 standard. Dimensions are 165 length, 13 neck, 19 width, and 7 mm thick. Deviation or dimensional accuracy is based on the largest GRG value in the parameter composition. The research results obtained the following printing parameter factors: a layer thickness of 0.2 mm, a nozzle temperature of 280°C, and a printing speed of 60 mm/s, also layer thickness is the most influence parameter that increasing the dimensional accuracy.

Grouping Weekly Weather Based on Weather Elements in Pagaralam by Using K-Means Clustering Analysis

2026-06-25T09:11:45+07:00

Weather is the result of the interaction and combination of various atmospheric elements that occurs in a relatively small place or region over a short period of time. This study aims to analyze weekly weather characteristics in Pagaralam using the K-Means Clustering method. The data used was weekly weather in 2022 and 2023 with 15 variables, namely Maximum temperature, Minimum temperature, Temperature, Dew, Humidity, Precipitation, Precipitation cover, Wind gust, Wind speed, Wind direction, Sea pressure, Cloud cover, Solar radiation, UV Index, and Moon phase. The analysis process began with data standardization, followed by clustering using K-Means Clustering with several K values to observe variations in cluster structure. The optimal number of clusters was determined using the elbow and silhouette methods. The best optimal K value for each of the 2022 and 2023 data was K=3. A small number of weeks in both years had high temperatures and solar radiation and accompanied by lower dew, humidity, precipitation, and cloud cover than other weeks. A small number of weeks in 2022 had low minimum temperature and were also accompanied by lower cloud cover, dew, and humidity, but they had higher wind gusts and wind speeds than other weeks. Meanwhile, a small number of weeks in 2023 had lower temperatures and accompanied by higher cloud cover, wind gusts, and wind speeds than other weeks.

Durability of Silica Sand-Based Self-Compacting Mortar: A Study on Sorptivity and Chemical Attack

2026-06-25T09:11:14+07:00

Cement is an essential construction material which serves as the binder in the production of concrete, mortar and bricks production. The production of cement poses a serious issue as it results in the emission of greenhouse gases directly responsible for climate change. In this study, Self-Compacting Mortar (SCM) mixes were developed by incorporating Ground Silica Sand (GSS) of 0 - 10% at a step of 2.5% and Raw Ground Silica Sand (RSS) of 0 - 100% at a step of 25% as replacement for cement and fine aggregate, respectively. The sorptivity and chemical attack via the use of Sodium Chloride (NaCl) on SCM specimens produced from different mixes were determined after 28 and 56 days of curing. The sorptivity of SCM specimens increased at all levels compared to the control when RSS is used as replacement for fine aggregate while the same was experienced when GSS was used as replacement for cement except at the 5% replacement level. All specimens fell under the ASTM standard for sorptivity states that all construction materials must have a water absorption value within 20%. When exposed to chemical attacks, there was an increase in weight loss and compressive strength loss with every increase in percentage replacement while the SCM does not deteriorate rapidly after exposure to NaCl. It can be concluded that GSS and RSS exhibited the potential for application as binder and sand contents in SCM production.

Kinematics and Mathematical Modelling of Wheeled Mobile Robots: a compact review

2026-08-04T10:49:04+07:00

This paper presents a comprehensive review on the kinematics and mathematical modelling of wheeled mobile robots. Wheeled mobile robots are a class of robots whose locomotion is facilitated by a set of wheels. The paper delves into the aspects that have proved to be the center of research as far as wheeled mobile robots are concerned, that is, stability, control and path planning. By examining the works in these areas, this paper evaluates the proposed solutions and discusses the findings in various works.