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International Journal of Applied Sciences and Smart Technologies
Published by Universitas Sanata Dharma
ISSN : 26558564     EISSN : 26859432     DOI : 10.24071/ijasst
Core Subject :
nternational Journal of Applied Sciences and Smart Technologies (IJASST) is published by Faculty of Science and Technology, Sanata Dharma University Yogyakarta-Central Java-Indonesia. IJASST is an open-access peer reviewed journal that mediates the dissemination of academicians, researchers, and practitioners in engineering, science, technology, and basic sciences which relate to technology including applied mathematics, physics, and chemistry. IJASST accepts submission from all over the world, especially from Indonesia
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Articles 16 Documents
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Durability of Silica Sand-Based Self-Compacting Mortar: A Study on Sorptivity and Chemical Attack Toluwalope Dominion Zubair; Mutiu Kareem; Abibat Usman; Divine Adejumo; Glory Ponnle; David Adeyeye; Adeleye Akinbade-Ojo
International Journal of Applied Sciences and Smart Technologies Vol. 8 No. 1 (2026): Volume 08, Issue 1, June 2026
Publisher : Faculty of Science and Technology, Universitas Sanata Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24071/ebxrfg54

Abstract

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.
Grouping Weekly Weather Based on Weather Elements in Pagaralam by Using K-Means Clustering Analysis Sri Indra Maiyanti; Irmeilyana; Putri Nilam Cayo; Dinny Indah Angelia; Angelina
International Journal of Applied Sciences and Smart Technologies Vol. 8 No. 1 (2026): Volume 08, Issue 1, June 2026
Publisher : Faculty of Science and Technology, Universitas Sanata Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24071/w2m4fy05

Abstract

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.
The Influence of Printing Parameter Variations on the Dimensional Accuracy of 3D Nylon Carbon FDM Prints Using the Grey-Taguchi Method Gilang Argya Dyaksa; Felix Krisna Aji Nugraha; Heryoga Winarbawa; Kristian Ismartaya; Robertus Bellarmino Radya Ananda; Hadrianus Felin Saputra
International Journal of Applied Sciences and Smart Technologies Vol. 8 No. 1 (2026): Volume 08, Issue 1, June 2026
Publisher : Faculty of Science and Technology, Universitas Sanata Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24071/rxn01g31

Abstract

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.
Tourism News Classification Using Convolution Long Short-Term Memory (C-LSTM) Yoga Dwitya Pramudita; Husni; Mohammad Syarief; Eka Mala Sari Rochman; Arif Muntasa; Zahra Arwananing Tyas; Ika Oktavia Suzanti
International Journal of Applied Sciences and Smart Technologies Vol. 8 No. 1 (2026): Volume 08, Issue 1, June 2026
Publisher : Faculty of Science and Technology, Universitas Sanata Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24071/0cjbc345

Abstract

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.
Using the Six Sigma DMAIC Approach to Improve Maintenance Practices of Ground-Service Mechanical Equipment within a Sub-Saharan African Airport Olanrewaju Samson Omisakin; Sunday Ayoola Oke; Adeyinka Oluwo; John Rajan; Swaminathan Jose
International Journal of Applied Sciences and Smart Technologies Vol. 8 No. 1 (2026): Volume 08, Issue 1, June 2026
Publisher : Faculty of Science and Technology, Universitas Sanata Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24071/4etrde73

Abstract

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 Cp/Cpk 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 Cp/Cpk 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.
A Sub-Saharan African Airport Mechanical Equipment Failure Assessment Using Joint FMECA-GRA Method Based On Technical Process Efficiency Olanrewaju Samson Omisakin; Sunday Ayoola Oke; Adeyinka Oluwo; John Rajan; Swaminathan Jose
International Journal of Applied Sciences and Smart Technologies Vol. 8 No. 1 (2026): Volume 08, Issue 1, June 2026
Publisher : Faculty of Science and Technology, Universitas Sanata Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24071/x3kf5y87

Abstract

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.
IoT Based Nozzle Actuation System Design for Automated Fish Feed Distribution Sentot Novianto; Larasati Putri; Amrullah Ibrahim; Tono Sukarnoto; Faisal Adinegoro; Supriyadi Supriyadi; Nanang Ruhiyat
International Journal of Applied Sciences and Smart Technologies Vol. 8 No. 1 (2026): Volume 08, Issue 1, June 2026
Publisher : Faculty of Science and Technology, Universitas Sanata Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24071/cs137362

Abstract

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.
Development of an Arduino-Based Water Rocket Launcher in Physics Experiments Larasati Putri; Fakhrizal Arsi; Kiar Vansa Febrianti; Sentot Novianto; Ika Wahyu Utami; Muhammad Najih; Sofia Debi Puspa; Muhammad Gilang Ramadhan; Harry Munandar
International Journal of Applied Sciences and Smart Technologies Vol. 8 No. 1 (2026): Volume 08, Issue 1, June 2026
Publisher : Faculty of Science and Technology, Universitas Sanata Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24071/5qcv4a36

Abstract

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.
Principal Component Analysis-Driven Feature Reduction for Predicting Coffee Quality Using a Machine Learning Approach Siti Yuliyanti; Heni Sulastri; Sakifah
International Journal of Applied Sciences and Smart Technologies Vol. 8 No. 1 (2026): Volume 08, Issue 1, June 2026
Publisher : Faculty of Science and Technology, Universitas Sanata Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24071/pmjkr989

Abstract

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.
Analysis of Key Features in PCOS Diagnosis Using Random Forest and XGBoost with SMOTE and SHAP Aulia Firdatunnisa; Eka Wahyu Hidayat; Siti Yuliyanti
International Journal of Applied Sciences and Smart Technologies Vol. 8 No. 1 (2026): Volume 08, Issue 1, June 2026
Publisher : Faculty of Science and Technology, Universitas Sanata Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24071/yw5wsj38

Abstract

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.

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