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Application of the adaptive neuro-fuzzy inference system for prediction of the electrical energy production in Jakarta

10.11591/ijai.v14.i3.pp1790-1798
Yoga Tri Nugraha , Catra Indra Cahyadi , Rizkha Rida , Margie Subahagia Ningsih , Dewi Sholeha , Indra Roza
Jakarta, as a rapidly growing urban area, faces challenges in balancing energy demand with supply while addressing environmental concerns associated with traditional energy sources. Electrical energy production prediction in urban environments like Jakarta is crucial for effective energy management, ensuring stable supply, and promoting sustainable development. The prediction of electrical energy production in Jakarta is critical for ensuring stable and sustainable energy supply. This research proposed the application of the adaptive neuro-fuzzy inference system (ANFIS) as a predictive tool specifically tailored for Jakarta's energy production prediction context. The research methodology used in this study is the ANFIS. Five levels make up the architecture of the ANFIS model: output, normalization, defuzzification, rule evaluation, and fuzzification. The fuzzification layer converts input variables into linguistic terms using membership functions, while the rule evaluation layer calculates the activation strength of each rule based on the input values. The predicted results of Jakarta electrical energy production from 2023 to 2028 are 65,288 GWh and there is an annual increase of 5.25%. The error contained in ANFIS is with a root mean square error (RMSE) value of 0.0001058% and a mean absolute percentage error (MAPE) value of 0.00875%.
Volume: 14
Issue: 3
Page: 1790-1798
Publish at: 2025-06-01

Development of character extraction techniques to detect chicken gender based on egg shape

10.11591/ijeecs.v38.i3.pp1851-1861
Adil Setiawan , Yuhandri Yuhandri , Muhammad Tajuddin
This research investigates the differentiation of chicken sex based on egg shape images by developing an innovative eccentricity shape feature extraction method. The goal is to determine the sex of chickens before hatching, by identifying the sex of the egg prior to incubation. Images of eggs are captured using a smartphone camera, creating a dataset of 150 images each of male and female eggs, with expert assistance. The research aims to accurately identify male and female eggs, aiding breeders in sorting them. The research introduces a unique method to expand the eccentricity value range, enhancing the precision of egg shape analysis. Characteristic extraction results include: area = 1290194, eccentricity = 6.56, contrast = 0.03, correlation = 0.99, energy = 0.44, and homogeneity = 0.98, with a previous value of 0.72. For Feature Selection, the values obtained are: eccentricity = 0.901188049, Area = 0.73, Energy = 0.03, Contrast = 0.01, Homogeneity = 0.01, and Correlation = 0.01. These findings demonstrate significant improvements in differentiating chicken sex from egg images, showcasing the effectiveness of the newly developed eccentricity shape feature extraction method.
Volume: 38
Issue: 3
Page: 1851-1861
Publish at: 2025-06-01

Optimization of sales by applying e-commerce and digital marketing through social networks

10.11591/ijeecs.v38.i3.pp2079-2089
Misael Lazo-Amado , Paico-Campos Meyluz
Companies must have a strategy plan to satisfy their users and implement new methods to work with technology since people nowadays are more related to technology avoiding traditional sales and having virtual sales is why it has the objective of optimizing sales in companies by applying e-commerce and digital marketing through social networks. The methodology was carried out with Scrum, which has five stages (planning meeting, sprint backlog, daily meetings, sprint review, and retrospective review) that allows to comply with each established sprint showing as a result a functional project. As a result indicates the solution of each phase of the methodology getting the ecommerce system, with a validation by 7 experts specialized in (realism, integration, adaptability, technology, innovation, functionality, and usability) indicating a total of 93% showing a perfect state of the system and meets the satisfaction for the user and finally indicates the development of digital marketing by the social network Facebook showing a great improvement in their sales reaching up to triple their sales.
Volume: 38
Issue: 3
Page: 2079-2089
Publish at: 2025-06-01

Need analysis: development of a teaching module for enhancing higher-order thinking skills of primary school students

10.11591/ijere.v14i3.30335
Hamidah Mat , Toto Nusantara , Adi Atmoko , Yusuf Hanafi , Siti Salina Mustakim
This research identified a pressing need to create specialized teaching modules for electrical topics within the science curriculum that target students’ higher-order thinking skills (HOTS). Despite the recognized significance of HOTS in improving students’ educational achievements, science educators encounter obstacles when attempting to effectively teach these skills. To tackle this challenge, the study utilized a qualitative research methodology, conducting semi-structured interviews with six science teachers from diverse Malaysian schools. The primary objective was to pinpoint the necessity for developing instructional modules that enhance students’ HOTS in primary school science subjects. This study revealed four key themes arising from the needs assessment: the importance of HOTS knowledge, obstacles in teaching HOTS, effective teaching strategies, and the actual teaching of HOTS. This study underscores the critical need for enhanced professional development opportunities for teachers to effectively impart HOTS and stresses the importance of providing suitable teaching resources. By developing these tailored modules, students’ critical thinking and problem-solving skills can be nurtured, paving the way for their academic and professional success. Consequently, the study’s recommendations offer valuable insights for policymakers, educators, and researchers seeking to create impactful teaching modules that cater to students’ HOTS in primary school science subjects.
Volume: 14
Issue: 3
Page: 1643-1650
Publish at: 2025-06-01

Hybrid semantic model based on machine learning for sentiment classification of consumer reviews

10.11591/ijai.v14.i3.pp2001-2011
Palaniraj Rajidurai Parvathy , Nagarajan Mohankumar , Rajendran Shobiga , Gour Sundar Mitra Thakur , Mamatha Bandaru , Velusamy Sujatha , Shanmugam Sujatha
Digital information is regularly produced from a variety of sources, including social media and customer service reviews. For the purpose of increasing customer happiness, this written data must be processed to extract user comments. Consumers typically share comments and thoughts about consumable items, technological goods, and services supplied for payment in the modern period of consumerism with simple access to social networking globe. Each object has a plethora of remarks or thoughts that demand special attention due to their sentimental worth, especially in the written portions. The goal of the current project is to do sentiment prediction on the Amazon Electronics, Kindle, and Gift Card datasets. In order to predict sentiment and evaluate utilizing many executions evaluates admitting accuracy, recall, and F1-score, a hybrid soft voting ensemble method that combines lexical and ensemble methodologies is proposed in this study. In addition to calculating a subjectivity score and sentiment score, this study also suggests a non-interpretive sentiment class label that may be used to assess the sign of the evaluations applying suggested method for sentiment categorization. The effectiveness of our suggested ensemble model is examined using datasets from Amazon customer product reviews, and we found an improvement of 2-5% in accuracy compared to the current state-of-the-art ensemble method.
Volume: 14
Issue: 3
Page: 2001-2011
Publish at: 2025-06-01

Higher education instructors’ and students’ attitudes toward distance learning

10.11591/ijere.v14i3.29383
Yousef M. Arouri , Yousef M. Alshaboul , Diala A. Hamaidi , Asia Y. Alshaboul
This study aimed at exploring the attitudes of higher education instructors and university students regarding distant learning during the COVID-19 epidemic. It took place at a higher education institution of Jordan. Using a mixed method approach, the researchers developed a two-part questionnaire and a semi-structured interview. The questionnaire was distributed questionnaire to 167 instructors and 349 students from the University of Jordan (UJ). The findings showed that the participants have a moderately favorable attitude toward distant learning. Additionally, the findings revealed no statistically significant differences (α=.05) in the attitudes of UJ instructors and students toward distance learning during the COVID-19 pandemic attributedto the study variables. Furthermore, the interviews revealed several themes that the university instructors and students identified as influencing the general effectiveness of their distance learning experience, including access to online platforms and professional training, offering electronic equipment, and protecting the integrity of exams. The study recommends that higher education institutions reconsider the concept of distance learning, considering lessons acquired from the era of compulsory distance learning.
Volume: 14
Issue: 3
Page: 1949-1960
Publish at: 2025-06-01

Managing cooperative learning and digital competences in secondary education: a systematic review

10.11591/ijere.v14i3.30449
Virginia A. Samane-Cutipa , Juan Carlos Callacondo Velarde , Fabian Hugo Rucano Paucar , Fabiola Talavera-Mendoza
The COVID-19 pandemic led most schools to opt for distance education, resulting in challenges in the educational field. However, the increased use of digital technology prompted studies on strategies to help reduce the digital divide concerning two key 21st-century skills: cooperation and digital competencies. This article aims to analyze the study of cooperative learning in relation to the achievement of digital competencies in secondary education. It was developed through a systematic literature review (SLR) using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology, retrieving scientific information from the Web of Science (WoS) and ERIC databases, published from 2018 to 2024. The results and findings emphasize the existence of strategies aimed at improving teaching and learning, academic performance, and students’ communication and social skills through task management, the formation of cooperative teams, and conflict resolution with shared leadership. Additionally, it highlights the development of digital competencies such as information retrieval, digital interaction, virtual object creation, digital security, and responsible citizenship. The conclusions focus on using cooperative learning strategies to make the teacher’s role more efficient in interactive spaces.
Volume: 14
Issue: 3
Page: 2088-2098
Publish at: 2025-06-01

Speed control of induction motor using fuzzy logic based on internet of things

10.11591/ijape.v14.i2.pp488-497
Charles Ronald Harahap , F. X. Arinto Setyawan , Desi Budiati
The aim of this research was to propose an innovative method of controlling the speed of an induction motor (IM) using fuzzy logic, integrated with internet of things (IoT). To achieve this aim, fuzzy logic was used to increase the performance of IM in order to obtain stable speed and high system response even in the presence of disturbances. Moreover, fuzzy logic relied on rules that used linguistic variables, and its main advantage was simple yet highly accurate, enabling the system to be efficient for determining parameters compared to the time-consuming and inefficient trial-and-error method. In this research, IoT implementation used Blynk platform to control and monitor IM speed remotely. Additionally, the components used in this research included an inverter, gate driver, Arduino Mega 2560, and NodeMCU ESP8266. Pulse width modulation (PWM) was required to obtain rotational speed of the motor through MOSFET switching process. The gate driver amplified PWM signal from Arduino Mega 2560, allowing MOSFET to operate. As a result, IM achieved a stable speed, and the system response followed the reference using fuzzy logic. In addition to this process, the system could be controlled and monitored remotely. Finally, the control system was successful, and the results were presented to show the viability of the proposed method.
Volume: 14
Issue: 2
Page: 488-497
Publish at: 2025-06-01

Changing perceptions on menstrual practices in Southern Bangladesh: a cross-generational study

10.11591/ijphs.v14i2.22409
Jannatul Bakia Jeni , Al Jamal Mustafa Shindaini , Md. Tanvir Mahtab , Shantanu Kumar Saha
Menstruation, a natural biological process, remains deeply intertwined with women's lives but is surrounded by superstitions and stigmas, posing health risks. This study explores generational shifts in menstrual perceptions, focusing on women's knowledge, rituals, social stigmas, and the evolving sources of socialization. Using a phenomenological approach, interviews were conducted with women from 25 households, spanning three generations. Thematic analysis has been used to analyze the data. Findings revealed that while the first and second generations understood menstruation primarily as a sign of fertility and bodily maturity, the third generation recognized it as a hormonal process. Hygiene practices also varied, with older generations relying on cloths and holy water for pain relief, whereas younger women opted for sanitary pads and were more aware of the dangers of unhygienic practices. Additionally, the study highlighted prevalent misconceptions and stigmas among the first and second generations, while the third generation was more informed, open, and vocal about menstruation. The process of socialization around menstruation has also evolved; earlier generations mainly learned from their mothers, with little involvement from male family members. In contrast, the third generation gained knowledge from both parents and media, reflecting a significant shift in the cultural dialogue surrounding menstruation.
Volume: 14
Issue: 2
Page: 808-817
Publish at: 2025-06-01

Stress, stressors, and stress management practices among public-school teachers

10.11591/ijphs.v14i2.24869
Jonathan Lapuz Mañas , Roel Sanchez Ang
Stress manifests differently among individuals in various circumstances, stemming from multiple sources. Teachers, in particular, encounter many stressors from personal and work-related domains. This study examines the stress levels of elementary, junior high school, and senior high school public school teachers within Congressional District IV of the Division Office of Nueva Ecija, focusing on everyday life stressors. Additionally, it investigates the stress management practices they employ for coping. The personal metrics of these teachers were analyzed to ascertain their significant relationship with stress levels. Data were randomly collected from 273 respondents through a questionnaire developed by Villamayor. The study unveils that public-school teacher experience slight stress levels and utilize diverse stress management techniques to tackle these stressors. However, the personal metrics of respondents were found to have an insignificant relationship with their stress levels. Nevertheless, the findings of this study pave the way for developing a comprehensive stress management plan to assist public school teachers.
Volume: 14
Issue: 2
Page: 818-826
Publish at: 2025-06-01

Modeling English teachers’ intention to use ICT: technology acceptance and TPACK

10.11591/ijere.v14i3.30444
Li Cao , Mohamad Sattar Rasul , Marlissa Omar , Hutkemri Zulnaidi
Teachers’ acceptance of technology in the teaching setting is significantly influenced by their behavioral intention to utilize information and communication technology (ICT). A considerable amount of study has been done on the use of ICT in teaching English as a foreign language (EFL). Nevertheless, there exists a significant lack of deep studies among EFL teachers in Chinese vocational colleges. Drawing on the technology acceptance model (TAM) and technological pedagogical content knowledge (TPACK) theoretical frameworks, this current study aimed to ascertain whether EFL teachers’ TPACK levels could predict their intention to adopt ICT. A quantitative study was conducted with the participation of 440 EFL instructors from vocational schools in Shandong Province. The seven components met the scale’s validity and reliability requirements and the partial least squares (PLS) approach was utilized to describe the structural model and examine the relationships among significant components. The findings revealed that EFL teachers’ perceived usefulness (PU), perceived ease of use (PEU), and attitudes towards use (ATCU) significantly impacted their behavioral intention to use (BIU) ICT. Moreover, the TPACK framework exerted a substantial influence on their acceptance of ICT. The study’s findings may provide insights and resources for subsequent theoretical research and teaching approaches centered on enhancing the integration of technology in EFL education.
Volume: 14
Issue: 3
Page: 2314-2326
Publish at: 2025-06-01

Demographic determinants of patronage of medicine hawkers by commercial vehicle passengers in Ghana

10.11591/ijphs.v14i2.24606
Joy Ato Nyarko , Kofi Osei Akuoko , Jonathan Mensah Dapaah , Nana Yaa Serwaa Akuoko , Egwolo Perpetual Iyengunmwena
Medicine hawking is one of the major public health problems of the global south. This present study examined the demographic determinants of patronage of the services of medicine hawkers among commercial vehicle passengers in Kumasi, Ghana. A cross-sectional study was carried out from February 2022 to March 2022 at major bus terminals in Kumasi. Data were descriptively and inferentially analysed. The survey revealed that 55% of the respondents had bought medicines from medicine hawkers before. There was a significant relationship between having bought from a medicine hawker before and the intention to buy from them again in the future. Also, age, religion and education contributed significantly to patronising the services of medicine hawkers. We recommend that government intensifies its public health education on the implications of seeking health care services from these medicine hawkers.
Volume: 14
Issue: 2
Page: 912-918
Publish at: 2025-06-01

An optimized transfer learning-based approach for Crocidolomia pavonana larvae classification

10.11591/ijai.v14.i3.pp2270-2281
Risnawati Risnawati , Rodiah Rodiah , Sarifuddin Madenda , Diana Tri Susetianingtias
The increasing demand for mustard greens has driven farmers to continuously improve mustard greens cultivation. One of the challenges in mustard greens cultivation is the presence of insect pests. A significant pest in mustard greens is Crocidolomia pavonana (C. pavonana). C. pavonana damages plants by feeding on various parts, especially the leaves. The initial step in controlling them is insect pest monitoring. Monitoring aims to establish the control threshold. C. pavonana larvae have four instar stages: instar 1, 2, 3, and 4. Identification of the instar larval stages utilizes deep convolutional neural network (CNN) to classify C. Pavonana larvae on mustard greens using ResNet50V2 and DenseNet169 architectures optimized to enhance classification accuracy. The classification evaluation results show that both DenseNet169 and ResNet50V2 models achieve high accuracy, with DenseNet169 reaching the highest accuracy at 97.1%, while ResNet50V2 achieves an accuracy of 94.2%. The lower loss values on the test data compared to the validation data indicate that the deep learning models have successfully captured the patterns in C. pavonana images for classification. This classification process is expected to be one of the activities in monitoring the instar larvae to improve the accuracy of insecticide spraying and enhance mustard greens production.
Volume: 14
Issue: 3
Page: 2270-2281
Publish at: 2025-06-01

Ensemble model-based arrhythmia classification with local interpretable model-agnostic explanations

10.11591/ijai.v14.i3.pp2012-2025
Md. Rabiul Islam , Tapan Kumar Godder , Ahsan Ul-Ambia , Ferdib Al-Islam , Anindya Nag , Bulbul Ahamed , Nujhut Tanzim , Md. Estiak Ahmed
Arrhythmia can lead to heart failure, stroke, and sudden cardiac arrest. Prompt diagnosis of arrhythmia is crucial for appropriate treatment. This analysis utilized four databases. We utilized seven machine learning (ML) algorithms in our work. These algorithms include logistic regression (LR), decision tree (DT), extreme gradient boosting (XGB), K-nearest neighbors (KNN), naïve Bayes (NB), multilayer perceptron (MLP), AdaBoost, and a bagging ensemble of these approaches. In addition, we conducted an analysis on a stacking ensemble consisting of XGB and bagging XGB. This study examines various arrhythmia detection techniques using both a single base dataset and a composite dataset. The objective is to identify the optimal model for the combined dataset. This study aims to evaluate the efficacy of these models in accurately categorizing normal (N) and abnormal (A) heartbeats as binary classes. The empirical findings demonstrated that the stacking ensemble approach exhibited superior accuracy when used with the combined dataset. Arrhythmia classification models rely on this as a crucial component. The binary classification achieved an accuracy of 98.61%, a recall of 97.66%, and a precision of 97.77%. Subsequently, the local interpretable model-agnostic explanations (LIME) technique is employed to assess the prediction capability of the model.
Volume: 14
Issue: 3
Page: 2012-2025
Publish at: 2025-06-01

Factors associated with medication adherence in diabetes patients during the COVID-19 pandemic

10.11591/ijphs.v14i2.25672
Tung Do Dinh , Linh Thuy Thi Phan , Van Thuy Thi Pham , Huong Thi Lien Nguyen , Thao Thi Bich Cao , Thao Thi Nguyen , Son Tu Nguyen , Dua Thi Nguyen , Duy Huu Nguyen , Linh Phuong Nguyen , Toan Quoc Tran , Xuan Nguyen Thanh
Identifying the factors affecting diabetes medication adherence is an important step in establishing interventions to improve prescription compliance and help patients manage their disease effectively and successfully. A cross-sectional study of patients with type 2 diabetes (T2D) at Saint Paul Hospital, Hanoi, Vietnam, used a structured questionnaire. Of the 250 patients, 60% (150) were female. The median age was 67.5 years, and the median duration of diabetes was 9.6 years. The mean medication adherence report scale (MARS-5) score was 23.1±3.1. The mean fear of COVID-19 (FCoVID-19) score was 16.8±6.3. The mean self-efficacy for appropriate medication uses scale (SEAMS) score indicating the patient’s confidence in taking medication correctly was 31.1±5.6. The mean medication literacy measure (MLM), which assesses the patient’s knowledge of diabetes medications, was 8.3±4.9, with 27.2% of patients having high levels. The relationships between adherence to medication and comorbidities, knowledge about medication, and psychological effects of diabetes were statistically significant according to multivariable linear regression. The study showed that intervention programs that focus on factors affecting adherence can be effective at improving patient health outcomes.
Volume: 14
Issue: 2
Page: 1096-1108
Publish at: 2025-06-01
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