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TENS device for cervical pain during teleworking controlled remotely by mobile application

10.11591/ijres.v14.i1.pp60-68
Ricardo Yauri , Juan Balvin , Renzo Lobo
Monitoring cervical muscle pain during teleworking, exacerbated by the COVID-19 pandemic and increased remote work, highlights electrotherapy as a crucial physical therapy tool to mitigate muscle pain and promote tissue recovery, addressing ergonomic and occupational health problems that affect the well-being of remote workers. The research proposes to design a transcutaneous electrical nerve stimulation (TENS) device to monitor cervical muscle pain during teleworking, addressing the urgent need for technological solutions to mitigate this problem and improve the quality of life of teleworkers through data acquisition and processing, hardware development, implementation device monitoring, and evaluation software. For this, a TENS device was designed with a graphical interface to treat muscle pain in the neck of teachers who do remote work, dividing it into four stages: signal acquisition and generation, Bluetooth communication with an Android device, signal conditioning, and amplification and protection, following a development scheme that includes circuit design in Proteus and the creation of a mobile application in App Inventor. In conclusion, it was obtained that the power supplies have an average error of less than 1%, indicating good general performance and confirming the consistency and optimal performance of the proposed therapies.
Volume: 14
Issue: 1
Page: 60-68
Publish at: 2025-03-01

Optimizing renewable energy potential in island areas based on analytical hierarchy process

10.11591/ijaas.v14.i1.pp218-226
Arnawan Hasibuan , Muhammad Sayuti , Widyana Verawaty Siregar , Ferdy Hidayatullah , Muhammad Daud , Azmi Azmi , Azman Azman , Rizky Almunadiansyah , Fahrian Roid
The decreasing availability of fossil fuels and their contribution to environmental degradation and emissions of harmful gases has encouraged a shift to renewable energy sources. This change aims to produce electricity without using fossil fuels. Banyak Islands, a new tourist destination in Aceh Singkil, Indonesia, is experiencing an increase in visitors, increasing electricity demand currently supplied by diesel power plants. Therefore, this research aims to examine the possibilities of wind and solar power plants and assess their energy output. The four factors used to determine the potential of renewable energy are technology, economics, environment, and social politics, with 13 sub-criteria. Analytical hierarchy process techniques prioritize potential alternative energy sources such as wind and solar power facilities. Research findings show that environmental criteria have a higher value (0.3455) than financial criteria (0.2861), next is technological criteria (0.1734), then socio-political criteria (0.1603). Based on the results of the requirements and sub-requirements, wind power generation is the most effective and efficient alternative energy used in the Banyak Islands region (0.549), followed by solar power generation (0.451). This data shows that the area is prone to medium to high wind speeds, specifically in geographic locations facing the Indian Ocean which is located offshore.
Volume: 14
Issue: 1
Page: 218-226
Publish at: 2025-03-01

Investigation on OMYA with single superstrate layer

10.11591/ijeecs.v37.i3.pp2087-2095
Kamelia Quzwain , Oki Ardiansyah , Levy Olivia Nur , Sevierda Raniprima , Alyani Ismail
The need for wireless communication technology is increasing rapidly. Many people are already using internet data services offered by providers or using wireless fidelity (Wi-Fi) services. One of the things that must be considered in Wi-Fi technology is that it requires antenna characteristics that have a relatively small shape and light mass. This paper aimed to improve gain and to analyze the performance of octagon microstrip Yagi antenna (OMYA) with a single superstrate layer. The antenna was designed, simulated, and measured. The experimental result presents that this concept is capable to produce a gain of 11.80 dB, a return loss of -23.24 dB, and the bandwidth is up to 800 MHz with total dimensions of 70×75 mm.
Volume: 37
Issue: 3
Page: 2087-2095
Publish at: 2025-03-01

Internet of things based seasonal auto regression integrated moving average model for hydroponic water quality prediction

10.11591/ijaas.v14.i1.pp123-131
April Firman Daru , Susanto Susanto , Whisnumurti Adhiwibowo , Alauddin Maulana Hirzan
Technological progress significantly impacts agriculture, with the rapid expansion of industrial and residential areas leading to a scarcity of agricultural land. Modern farming techniques like hydroponics have emerged as a solution, allowing plant growth with water as a medium. Real-time monitoring of water quality is crucial for hydroponic systems. Lettuce (Lactuca sativa) is particularly compatible with hydroponics due to its short growth cycle and nutritional value. Key factors for successful cultivation include maintaining pH, temperature, and nutrient levels within optimal ranges. To address water quality monitoring complexities, internet of things (IoT) technology offers a promising solution. IoT devices autonomously gather environmental metrics such as temperature, pH, humidity, and nutrient concentrations. This study integrates an IoT-driven hydroponic water quality monitoring system using the seasonal auto-regressive integrated moving average (SARIMA) algorithm and the ESP32 microcontroller. This approach allows real-time water quality management, enhancing lettuce cultivation efficiency and productivity. The proposed model achieved 98.6% accuracy, effectively predicting water quality.
Volume: 14
Issue: 1
Page: 123-131
Publish at: 2025-03-01

Kinetic study of biogas production from anaerobically digested rice straw

10.11591/ijaas.v14.i1.pp247-254
Maninder Kaur , Sandeep Dhundhara
Rising concern about environmental protection has demanded prompt researchers’ attention towards alternative renewable energy sources. Thus, biofuel production with biodegradation of crop straws through anaerobic digestion has attracted the attention of the scientific community. However, the lignocellulosic nature of rice straw poses resistance to its disintegration through anaerobic digestion. Aiming to optimize the concentration of sodium hydroxide pretreatment of rice straw for efficient biogas production this study was conducted. For this purpose, the pretreatment was done on rice straw with different concentrations of sodium hydroxide at about 25 °C temperature for 24 hours before subjecting it to anaerobic digestion for biogas production. The 6% sodium hydroxide pretreated rice straw was observed to be resulting in the highest cumulative biogas production which was found to be 56.3% higher than untreated rice straw. In the kinetic study of biogas production, 6% NaOH pretreated rice straw shows the highest biogas production potential at the highest rate of 15.8496 ml/day with a minimum lag period of 0.6758. The experimental study and kinetic study results represent that 6% NaOH pretreated rice straw has the highest biogas production.
Volume: 14
Issue: 1
Page: 247-254
Publish at: 2025-03-01

Main eco-properties of hazelnut (Corylus avellana L.) on the Sheki-Zagatala economic region

10.11591/ijaas.v14.i1.pp77-85
Lala Bunyatova Novruz , Gunay Mammadova Israphil , Turkan Hasanova Allahverdi , Aida Gahramanova Yarish , Sevil Akhundova Maharram , Gulnara Alakbarli Yashar
The aim of the study is to determine leaf nutrients, the number of ecological-trophic groups of microorganisms and the structure of microbial communities, microbial biomass ratios, and soil parameters in the upper soil horizons in the territory of the Sheki-Zagatala economic region from five representative hazelnut gardens located in the hazelnut producing villages of Katex, Darvazbina, Boyuktala (Balakan region), and Car, Galal (Zagatala region). Has been reported that soils with slightly acidic reactions are ideal for hazelnut cultivation. Hazelnut trees in Azerbaijan have sustainable and nutritious characteristics. They grow naturally without using any chemical fertilizers and pesticides. The soil fertility of the orchards is a key factor affecting the yield and quality of nuts. The number of microorganisms at 0-15 cm in spring was 10.059·103, 7.786·103 bacterias, 3.009·103 ray fungi, and 73·103 other microscopic fungi. In moderately eroded areas, the total number of microorganisms in spring was 8.927·103, bacterias 5.895·103, ray fungi 1.874·103, and other microscopic fungi 68 103. In non-eroded fertile soils, the number of microorganisms at a depth of 0-15 cm in autumn was 8.020·103 bacteria, 5.246·103 bacteria, 1.789·103 radiant fungi, and 5.8·103 microscopic fungi. The nitrogen (N), phosphorus (P), and potassium (K) contents of tree leaves in each variant ranged from 0.49-1.07, 0.16-0.36, and 0.58-1.49%, respectively, with average values of 0.73, 0.21, and 1.03%.
Volume: 14
Issue: 1
Page: 77-85
Publish at: 2025-03-01

Application of quantum annealing solvers along with machine learning algorithms to identify online deception

10.11591/ijeecs.v37.i3.pp1936-1944
Surya Prasada Rao Borra , Bhargavi Peddi Reddy , Baba Venkata Nageswara Prasad Paruchuri , Rachakulla Sai Venkata Ramana , Onteru Srinivas , Lakshmi Rathod
The rising frequency of online transactions has heightened the potential of online fraud, posing significant concerns for consumers, organizations, and financial institutions. Conventional fraud detection systems frequently inadequately handle the dynamic and shifting characteristics of fraudulent activity. The increasing menace of online fraud requires novel strategies to improve the effectiveness of fraud detection systems. This study has developed and implemented a detection framework utilizing a quantum machine learning (QML) technique that integrates support vector machines (SVM) with quantum annealing solvers. We assessed its detection performance by comparing the QML application's efficacy against twelve distinct ML techniques. This study examines the integration of classical ML algorithms with quantum annealing solutions as an innovative approach to enhance online fraud detection. This study examines the possible integration of ML and quantum computing to tackle the rising issues of fraudulent activities in online transactions, as existing solutions are inadequate. This work seeks to illustrate the viability and efficacy of using these technologies, including quantum annealing to enhance the intricate decision-making processes involved in fraud detection. We offer insights on the performance, speed, and adaptability of the integrated model, highlighting its potential to transform online fraud detection and enhance cyber security measures.
Volume: 37
Issue: 3
Page: 1936-1944
Publish at: 2025-03-01

BanSpEmo: a Bangla audio dataset for speech emotion recognition and its baseline evaluation

10.11591/ijeecs.v37.i3.pp2044-2057
Babe Sultana , Md Gulzar Hussain , Mahmuda Rahman
Speech interfaces provide a natural and comfortable way for humans to communicate with machines. Recognizing emotions from acoustic signals is essential in audio and speech processing. Detection of emotion in speech is critical to the next generation of human-computer interaction (HCI) fields. However, a lack of large-scale datasets has hampered the progress of relevant research. In this study, we prepare BANSpEmo, a demanding Bangla speech emotion dataset consisting of 792 audio recordings totaling more than 1 hour and 23 minutes. The recordings feature 22 native speakers and each speaker uttered two sets of sentences representing six emotions: disgust, happiness, anger, sadness, surprise, and fear. The dataset consists of 12 Bangla sentences, each expressed in these six emotions. Furthermore, a series of investigations are carried out to assess the baseline performance of the support vector machine (SVM), logistic regression (LR), and multinomial Naive Bayes models on the BANSpEmo dataset presented in this study. The studies found that SVM performed best on this dataset, with an accuracy of 87.18%.
Volume: 37
Issue: 3
Page: 2044-2057
Publish at: 2025-03-01

Technological readiness and business performance: the mediating effect of social media marketing

10.11591/ijaas.v14.i1.pp143-150
Sabri Shaker Ashoor Bin-Obaidellah , Noor Fadhiha Mokhtar , Safiek Mokhlis , Nur Aishah Awi
Understanding employee readiness towards adopting online marketing platforms is paramount in today’s digital economy, as it directly influences the effectiveness and competitiveness of businesses. This study investigates the relationship between employee technological readiness (ETR) and the business performance of micro and small enterprises (MSEs) in developing economies, focusing on data from Yemen. Drawing on the resource-based view theory, the study examined how adopting social media marketing (SMM) mediates this relationship. Data was collected from 362 managers/owners of MSEs in Yemen. The relationship between ETR, SMM, and business performance was examined using partial least squares structural equation modeling. The results show that ETR significantly influences SMM adoption, which impacts the business performance of MSEs. In addition, the study reveals that the relationship between ETR and business performance is partially mediated by SMM adoption. This finding highlights the critical role of employees’ readiness for technological advancements in facilitating effective SMM adoption, thereby contributing to the sustainability and success of MSEs.
Volume: 14
Issue: 1
Page: 143-150
Publish at: 2025-03-01

Development of antioxidative edible film from red dragon fruit peel extract with the addition of CMC and soy protein isolate

10.11591/ijaas.v14.i1.pp164-174
Triana Lindriati , Sih Yuwanti , Asmak Afriliana , Aji Sukoco , Ivan Rivaldy Budianto , Wafiq Azizah , Umrotus Shofiyatul Fadhiyah
The red dragon fruit peels (RDFP) have a high content of pectin and total phenolic compounds. This research studied the development of RDFP be an antioxidative edible film. The RDFP was extracted by microwave to obtain high pectin and polyphenol content, and then the red dragon fruit peel extract (RDFPE) was used as a based material. The RDFPE was added with 5% (w/v) of carboxymethyl cellulose (CMC) and 10% (w/v) of soy protein isolate (SPI) to increase their tensile strength. The result showed that RDFPE potential to develop as an antioxidative edible film. There are different effects of CMC and SPI. The addition of CMC had a positive effect on total polyphenol and antioxidant properties but SPI had a negative effect. Against the peroxide number of peanut oils, all RDFPE films can inhibit. The effect of CMC and SPI on physical and mechanical properties were increasing thickness, and tensile strength decreasing transparency, solubility, also elongation. The FTIR showed a difference in macromolecule interaction between RDFPE with CMC and SPI. The interaction between RDFPE with CMC occurred with pectin while SPI interacted both with pectin and polyphenol. Thus, macromolecule interaction affected on physical, mechanical, and antioxidative properties of RDFPE edible films, and revealed that CMC was more suitable to add to RDFPE edible film.
Volume: 14
Issue: 1
Page: 164-174
Publish at: 2025-03-01

Fabrication of hydrogenated amorphous silicon-based solar cells using RF-PECVD

10.11591/ijape.v14.i1.pp173-179
Soni Prayogi , Wahyu Kunto Wibowo
Thin-film solar cells made of hydrogenated amorphous silicon have succeeded in crystallization technologies as a less expensive alternative because of their straightforward design, sparse material requirements, low processing temperatures, and cheap manufacturing costs. A multi-chamber plasma-accelerated chemical vapor deposition apparatus driven by radio frequency was used to create the intrinsic and extrinsic layers of the a-Si: H solar cell. Multi-chamber allows us to upgrade each layer of the gadget utilizing a distinct space, preventing cross-contamination throughout the procedure. To enhance cell conversion efficiency, a thorough analysis has been conducted in this work to evaluate the manufacturing process and comprehend the link between process factors and property dependency. Our findings demonstrate an amorphous Si: H solar cell with a maximum cell efficiency of 6.52%, Voc 880 mV, Isc 11.33 mA/cm2, and FF 65%. We think that a modeling method followed by manufacturing can further enhance the performance of a-Si: H-based solar cell devices.
Volume: 14
Issue: 1
Page: 173-179
Publish at: 2025-03-01

Earthquake epicenter prediction from the Java-Bali radon gas telemonitoring station using machine learning

10.11591/ijaas.v14.i1.pp39-45
Christophorus Arga Putranto , Sunarno Sunarno , Faridah Faridah , Thomas Oka Pratama
Predicting the location of earthquake epicenters is a critical aspect of earthquake forecasting, as it complements efforts to determine the time and magnitude of seismic events. This research addresses the challenge posed by the uncertainty in epicenter locations, particularly along the extensive plate faults of Indo-Australia and Eurasia. In these regions, effective earthquake prediction is compromised without accurate epicenter information, impeding mitigation strategies and complicating disaster impact estimation. The primary objective of this study is to devise an algorithm for forecasting earthquake epicenter locations by harnessing variations in radon gas concentrations on southern Java Island, Indonesia, as a predictive precursor. Using a supervised machine learning approach, this study integrates radon gas concentration data to predict the distance between a radon gas telemonitoring station and the impending earthquake epicenter. Three distinct machine learning algorithms were evaluated using data from six Java-Bali radon gas telemonitoring stations within an early warning system. The random forest algorithm emerged as the most effective, yielding an average root mean square error of 453.10 kilometers. The findings of this research significantly contribute to earthquake risk mitigation efforts. This work enhances our capability to anticipate seismic events, and more effective disaster preparedness and response strategies in earthquake-prone regions.
Volume: 14
Issue: 1
Page: 39-45
Publish at: 2025-03-01

Automated adversarial detection in mobile apps using API calls and permissions

10.11591/ijeecs.v37.i3.pp1672-1681
Sanjaikanth E Vadakkethil Somanathan Pillai , Rohith Vallabhaneni , Srinivas A Vaddadi , Santosh Reddy Addula , Bhuvanesh Ananthan
Android mobile phones’ growing popularity has led to developers creating more malicious apps, which can be included in third-party arcades as protected applications. Detecting these malware applications is challenging due to time-consuming and high-cost techniques. This study proposes a robust deep learning (DL) model for detecting adversarial third-party apps using adaptive feature learning. The strategy involves preprocessing raw apk files, extracting permission behavioral features, and using the proposed spatial dropout-assisted convolutional autoencoder (SD_ConvAE) model to determine if the app is benign or malignant. The approach is simulated using a Python tool and assessed using various measures like accuracy, recall, weighted F-score (W-FS), false discovery rate (FDR), and kappa coefficient. The overall accuracies achieved by the developed techniques are about 99.6% and 99% for detecting benign and malignant apps, respectively.
Volume: 37
Issue: 3
Page: 1672-1681
Publish at: 2025-03-01

Machine learning models in renewable energy forecasting: a systematic literature review

10.11591/ijeecs.v37.i3.pp1874-1886
Mohamed Yassine Rhafes , Omar Moussaoui , Maria Simona Raboaca
During the past years, the convergence of machine learning (ML) technologies with renewable energy sectors has become a significant key area of innovation as a key area of innovation, enhancing the efficiency and predictability of sustainable energy sources. ML algorithms, adept at handling complex data, have become essential in forecasting energy outputs from variable sources like solar and wind. This integration has led to the development of smarter, more adaptive grid systems, capable of efficiently managing the variability of renewable energy sources. This review paper focuses on several key areas: firstly, it provides a summary of related work, specifically focusing on ML in the renewable energy field. Secondly, it delves into ML models and evaluation metrics used for solar and wind energy forecasting. Thirdly, it analyzes 21 studies published from 2019 to 2023, primarily centered on solar energy (60%) and wind energy (40%), with an emphasis on various forecasting horizons, highlighting the results of the ML algorithms used and the performance metrics to evaluate their effectiveness. Finally, it identifies gaps and opportunities in this field. The state-of-the-art review and its findings can offer a solid foundation for future research initiatives.
Volume: 37
Issue: 3
Page: 1874-1886
Publish at: 2025-03-01

Assessing fingerprinting and machine learning approaches for wireless indoor localization

10.11591/ijeecs.v37.i3.pp2021-2031
Azkario Rizky Pratama , Muhammad Evan Anindya Wahyuaji , Muhammad Fadhil Nur Hidayat , Bimo Sunarfri Hantono , Nur Abdillah Siddiq
This paper presents a comparative analysis of fingerprinting and machine learning techniques for bluetooth low energy (BLE)-based localization. Two fingerprinting algorithms, namely fingerprint feature extraction (FPFE) and Bayesian estimation (BE), along with various machine learning approaches including support vector regression (SVR), ensemble learning, and instance-based learning, are investigated. The selection of techniques depends on the availability of training data or the fingerprint database, explored in both ideal scenario and real-world scenario. In ideal scenario where the system administrator can collect fingerprint data through users’ devices, FPFE emerges as the preferred algorithm, achieving superior performance with a mean error of 0.50 m. In the context of real-world scenario, where data collection from multiple devices is limited, the system administrator may gather fingerprint data for localization using one or a few specific devices. Our experiments reveal that when there is a scarcity of fingerprint data, BE and SVR exhibit acceptable performance, reaching a mean error of 1.785 m and 1.965 m, respectively.
Volume: 37
Issue: 3
Page: 2021-2031
Publish at: 2025-03-01
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