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31,456 Article Results

Hybrid geostatistical and machine learning for gold grade estimation

10.11591/csit.v7i3.p394-403
Tanyaradzwa Miriam Mtetwa , Monika Gondo
The evaluation of ore grade is a basic part of the process of mine planning, production scheduling and resource evaluation. The nugget effect and irregular mineralisation that occurs in some greenstone belt deposits on the Zimbabwean Archean causes problems in estimating accurately. The traditional geostatistical models like Ordinary Kriging (OK) produce smoothed estimates which have systematic underestimation in high-grade areas and overestimation in low-grade areas, thus affecting the deposit selectivity. In addition, machine learning (ML) techniques can capture the more complex nonlinear grade relationships, but they are unable to model spatial continuity and can be unrealistic in predicting grades. The hybrid OK-Gradient Boosting (GB) model was designed and evaluated with the drill-hole data of a gold mine in Zimbabwe. The spatial baseline was produced using OK and GB was trained to correct the systematic residual errors. The hybrid model was found to have the maximum overall explanatory power (R²=0.8795), competitive prediction accuracy and increased spatial realism. A spatial prediction map and extraction priority zone classification was created to aid operational mine planning.
Volume: 7
Issue: 3
Page: 394-403
Publish at: 2026-11-01

Risk-integrated contractor allocation in Zimbabwe’s timber value chain

10.11591/csit.v7i3.p337-345
Tavengwa Norman , Brian Mupini
In Zimbabwe, the commercial forestry industry is reliant on a large proportion of outsourced harvest and milling contractors whereas existing enterprise resource planning (ERP) systems record completed transactions instead of predicting contractor failure before assigning activities. We introduce the dynamic resource allocation framework (DRAF), a risk-integrated decision-support framework that supplements the probability of contractor failure derived from XGBoost to a non-dominated sorting genetic algorithm II (NSGA-II) multi-objective optimizer. It divides contractor-block-mill combinations, minimizes cost and expected delay, and maximizes risk-adjusted timber recovery and operational reliability. We created the solution on an 828,789-record virtual ERP dataset and calibrated it to Manicaland forestry conditions and tested with statistical, heuristic and risk-free optimization baselines. Extreme gradient boosting (XGBoost) obtained a holdout receiver operating characteristic area under the curve (ROC-AUC) of 0.965, recall of 0.999, and F1 of 0.867, showing an improvement of 0.365 over logistic regression. The optimizer developed 64 complete Pareto solutions to balanced and high-recovery scenarios and uncovered a constraint-feasibility boundary for a more conservative low-risk scenario. The satisfaction score of the 30-practitioner stakeholder assessment was 4.19 out of 5.0. The results demonstrate that embedding predictive risk into an optimization objective can optimize forestry allocation decisions and suggest that some real ERP validation is required before such measures will be broadly implemented.
Volume: 7
Issue: 3
Page: 337-345
Publish at: 2026-11-01

Convolutional neural network and long short-term memory forecasting and variational autoencoder anomaly detection in 4G cellular networks

10.11591/csit.v7i3.p256-270
Ruvarashe C. Hove , Eng Mainford Mutandavari
Spectrum monitoring in cellular networks with limited resources remains predominantly reactive because capacity planning and failure detection occur after network congestion, while spectrum monitoring data are rarely available for reproducible research. Existing studies address short-term traffic forecasting and anomaly detection as separate tasks and largely depend on densely sampled operator telemetry data that are inaccessible to academia and regulators in emerging markets. This study integrates both tasks into a unified deep-learning pipeline for a 4G cellular environment. The Zimbabwe spectrum dataset contains 315,247 hourly measurements collected from 13 cell sites, three operators, and five frequency bands, annotated with four operator-defined anomaly classes representing 2.03% of all measurements. A hybrid one-dimensional (1-D) convolutional neural network-long short-term memory (CNN–LSTM) model uses a 72-hour traffic window to forecast the next six hours of aggregate network traffic, while a variational autoencoder (VAE) trained exclusively on normal records detects anomalies when reconstruction error exceeds the 99th-percentile validation threshold. On the held-out test set, the model achieved a mean absolute error (MAE) of 158.97 GB, root mean square error (RMSE) of 230.41 GB, and mean absolute percentage error (MAPE) of 52.93%, outperforming a seasonal-naive baseline (MAE=216.43 GB, MAPE=66.77%, p0.001, Diebold-Mariano test). The study contributes a publicly available localized synthetic dataset, a reproducible end-to-end forecasting and anomaly detection baseline, and a per-class anomaly analysis.
Volume: 7
Issue: 3
Page: 256-270
Publish at: 2026-11-01

Developing communicative and intercultural competence through integrating art in language teaching

10.11591/ijere.v15i5.39794
Zhanel Mangazina , Shynar Tazhibayeva , Magira Moldabayeva , Ardak Nurgaliyeva , Aliya Zhakupova
The article explores the development of communicative and intercultural communicative competencies in language education with the help of the integration of artistic practices. In the context of the sociocultural theory of language education, the development of communicative competence is an operational language construct, while the development of intercultural communicative competence is a meta-construct that regulates language use. The integration of art is understood not as a supplementary teaching tool, but as a pedagogical mediator that transforms learning processes through experiential engagement, symbolic interpretation, emotional involvement, and reflective dialogue. The article is based on a mixed-methods approach that incorporates a quasi-experiment, control group, and experimental group of students. The results of the study suggest that there is a correlation between the art-based instruction and an increase in communicative activity, intercultural competence, and students’ own reflective capacity. However, the results are seen as indicative rather than causal. The results of the study support the pedagogical value of instruction that is oriented towards art as a means of creating a culturally rich and dialogic learning environment. However, the results also highlight the need for future research into how different art forms influence the development of competence.
Volume: 15
Issue: 5
Page: 4614-4624
Publish at: 2026-10-01

Opening the black box of students’ economic behavior in higher education: do formal and informal education really matter?

10.11591/ijere.v15i5.39135
Mohamad Arief Rafsanjani , Waspodo Tjipto Subroto , Luqman Hakim , Handri Dian Wahyudi , Prattana Srisuk
Poor economic behavior and low levels of economic literacy (EL) among university students remain persistent concerns in developing countries, where young adults must navigate increasingly complex economic decisions. Previous studies often conceptualize education as a single construct or examine formal education and family-based learning separately, leaving the psychological mechanisms linking these educational experiences to economic behavior insufficiently understood. Drawing on social cognitive theory (SCT), this study examines the influence of instructional quality as a form of formal education and parental economic socialization as a form of informal education on students’ economic behavior, while investigating the mediating roles of EL and self-efficacy (SE). Data were collected from 291 undergraduate students from faculties of economics and business in East Java, Indonesia, and analyzed using partial least squares structural equation modeling (PLS-SEM). The results show that instructional quality and parental economic socialization significantly influence students’ economic behavior both directly and indirectly through EL and SE. These findings indicate that students’ economic behavior is shaped not only by educational environments but also by cognitive and psychological processes. The study highlights the importance of strengthening collaboration between universities and families to foster EL, SE, and responsible economic decision-making among students.
Volume: 15
Issue: 5
Page: 3820-3831
Publish at: 2026-10-01

Interprofessional attitudes, empathy, communication skills, and patient safety competency in traditional Korean medicine and nursing students

10.11591/ijere.v15i5.39879
Mikyoung Lee , Hunpyeong Park
Patient safety competency is an essential part of health professions education and may be influenced by learners’ interprofessional attitudes, empathy, and communication skills. This study investigated the relationships among these variables and identified factors influencing patient safety competency among traditional Korean medicine and nursing students. A cross-sectional correlational study was conducted among 238 students at a university in South Korea, consisting of 150 nursing students and 88 traditional Korean medicine students. Data were collected using questionnaires assessing interprofessional attitudes, empathy, communication skills, and patient safety competency. Data were analyzed using descriptive statistics, t-tests, analysis of variance (ANOVA), Pearson’s correlation coefficients, and multiple regression analysis. Patient safety competency varied by academic year, patient safety education (PSE) experience, college life satisfaction, and major satisfaction. Students with clinical practicum (CP) experience demonstrated significantly higher interprofessional attitudes and patient safety competency than those without practicum experience. Patient safety competency was positively associated with interprofessional attitudes, empathy, and communication skills. Furthermore, PSE experience, major satisfaction, interprofessional attitudes, empathy, and communication skills were significant predictors of patient safety competency, with interprofessional attitudes identified as the strongest predictor. Patient safety competency among traditional Korean medicine and nursing students was influenced more by modifiable educational and interpersonal factors than by fixed background characteristics. These findings support the development of interprofessional, communication-centered, and empathy-informed patient safety curricula to enhance collaborative readiness and safe practice prior to entry into clinical settings.
Volume: 15
Issue: 5
Page: 4220-4231
Publish at: 2026-10-01

Evaluating the effectiveness, structure, and influencing factors of legal awareness education activities in higher education institutions

10.11591/ijere.v15i5.39776
Hiep Ho Duc , Long Nguyen Trong , Van Vu Hong
This study evaluates the effectiveness, structure, and influencing factors of legal awareness education activities in higher education institutions in Vietnam. Using a convergent mixed-methods design, the research surveyed 375 administrators, lecturers, and students and conducted 15 semi-structured interviews to integrate statistical evidence with contextual interpretation. The results show that structural components were rated fairly high, but experiential methods and updated legal content remained weaker than lecturer competence and content relevance. Legal attitude received the highest outcome score, whereas lawful behavior, particularly in cyberspace, was less consistent. Reliability, exploratory factor analysis (EFA), and confirmatory factor analysis (CFA) results confirmed acceptable measurement quality, and regression analysis explained 46.2% of the variance in legal awareness outcomes. Educational content and methods were the strongest predictors, followed by environment and educational actors. The study contributes a systemic evaluation framework and highlights the awareness-attitude-behavior gap in digital legal education.
Volume: 15
Issue: 5
Page: 3667-3676
Publish at: 2026-10-01

Transformational leadership and crisis resilience in K-12 schools: a review

10.11591/ijere.v15i5.40085
Rayner Robert Kenneth , Aida Hanim A. Hamid , Mohamed Yusoff Mohd Nor
Global disruptions, particularly the COVID-19 pandemic, have intensified the need to understand how school leadership supports crisis response and organizational resilience. Although transformational leadership (TL) is widely associated with positive educational outcomes, it is role in crisis contexts remains fragmented and requires clearer synthesis. This systematic literature review examined the relationship between TL, crisis response, and organizational resilience in K–12 schools. Guided by the PRISMA framework, searches were conducted in Scopus, Web of Science, and ERIC. A total of 251 records were identified, of which 21 empirical studies met the final inclusion criteria. Methodological quality was assessed using the mixed methods appraisal tool (2018), and the findings were synthesized thematically. The review found that TL supports crisis response by strengthening direction, trust, communication, teacher commitment, and collective mobilization. However, it is influence is neither direct nor sufficient on its own. Rather, TL contributes to crisis resilience through psychological, organizational, and digital pathways, including teacher self-efficacy, emotional well-being, collective efficacy, organizational learning, relational coordination, and digital adaptation. The findings further indicate that leadership effectiveness is shaped by contextual conditions, including digital infrastructure, workload, organizational support, policy flexibility, and socio-cultural context. This review contributes a critical, mechanism-based explanation of how and under what conditions TL supports crisis resilience in K–12 schools.
Volume: 15
Issue: 5
Page: 3892-3911
Publish at: 2026-10-01

Teacher readiness and blended learning implementation in Chinese vocational colleges: a quantitative survey study

10.11591/ijere.v15i5.38761
Ding Ge , Mohamad Zuber Abd Majid
Teacher readiness remains a key constraint on blended learning adoption in vocational education, yet limited research has examined how multiple readiness dimensions jointly shape implementation among vocational economics educators in China. This study investigated how technological literacy (TL), attitudes toward technology (AT), educational service quality (ES), and knowledge to create content (KC) predict teacher readiness for blended learning (RL), and how readiness influences actual practice among 390 economics teachers in vocational colleges in Hunan Province, China. A quantitative cross-sectional survey design was employed. Path analysis showed that all four antecedent variables significantly predicted readiness (R²=37.0%), while readiness significantly predicted blended learning practice (β=0.322, p
Volume: 15
Issue: 5
Page: 4080-4092
Publish at: 2026-10-01

Web traffic optimization and institutional branding in higher education

10.11591/ijere.v15i5.40342
Mohammad Syahidul Haq , Ahmad Abdullah Zawawi , Muhamad Sholeh , Mufarrihul Hazin , Muhammad Ivan Dwi Saputra
Digital visibility has become an important component of institutional branding in higher education, yet limited empirical evidence explains how faculty-level website optimization contributes to brand image formation. This study evaluates web traffic optimization as a strategic capability using the context, input, process, output, and outcome (CIPOO) framework. An evaluative quantitative design was applied at Universitas Negeri Surabaya, involving 341 respondents consisting of 11 faculty website administrators and 330 website users. Data were collected through a five-point Likert-scale questionnaire and analyzed using descriptive evaluation and exploratory Pearson correlation based on aggregated faculty-level scores. The results show that context and outcome achieved the highest evaluation scores, indicating strong institutional alignment and positive branding outcomes, while process showed greater variability across faculties. Correlation analysis revealed a very strong significant positive association between output and outcome, whereas the input–process and process–output relationships were positive but not statistically significant. The novelty of this study lies in integrating faculty-level web traffic optimization, digital governance, and institutional branding within a single CIPOO-based evaluation model. These findings suggest that universities should strengthen website governance, digital capacity, and traffic monitoring to support institutional brand image.
Volume: 15
Issue: 5
Page: 3882-3891
Publish at: 2026-10-01

Futures thinking framework: relevance to veterinary students

10.11591/ijere.v15i5.39434
Adrian P. Ybañez , Naomi A. Bajao , Siegfried D. Mendoza , Marlou C. Godinez , Dawn I. Calibo-Senit , Ryan O. Tayco , Vernadyn A. Morillo , Eloiza May S. Galon
The futures thinking framework (FTF) is increasingly recognized as an educational approach that helps learners anticipate change and prepare for long-term challenges, but evidence from Philippine veterinary education remains limited. This cross-sectional study examined how selected Filipino veterinary students perceived the relevance of FTF to career preparedness, organizational learning, and professional adaptability. Following an introductory lecture at the 2024 National Congress of Veterinary Medicine Students, an online survey was administered that included demographic items, 18 five-point perception statements (Cronbach’s α: 0.95), and three open-ended questions. Of 204 responses from 12 veterinary schools, 195 were retained after data cleaning. Data were analyzed using descriptive statistics, Kruskal-Wallis tests, exploratory factor analysis (EFA), internal consistency analysis, and thematic analysis. Most respondents were female (61.5%) and in the 5th–6th year (51.8%). The mean perception score was 4.04/5.00, indicating generally favorable views, particularly regarding eagerness to learn more about FTF and the relevance of student organizations to future professional life. After Bonferroni adjustment, only school differences in perceived administrative support for national organization activities remained significant (p=0.0012). The EFA identified three related domains with good-to-excellent reliability (α=0.833–0.939). Qualitative findings revealed concerns about educator/workforce and resource gaps, and organizational suggestions emphasizing planning and mentorship.
Volume: 15
Issue: 5
Page: 4122-4135
Publish at: 2026-10-01

Need-supportive instructional leadership, basic psychological needs and teacher professional development in higher education: a multilevel study from Kazakhstan

10.11591/ijere.v15i5.39804
Anara Zhakisheva , Murshida Nurgaliyeva , Bakhyt Galiyeva , Maiya Kayumova , Samal Antikeyeva
Quality of teaching in higher education depends on faculty development and well-being. Drawing on self-determination theory (SDT), this study examined how academic managers’ instructional leadership, operationalized as a management-cycle quality (MCQ) index, relates to faculty outcomes via basic psychological need satisfaction, with organizational culture as context. A cross-sectional study was conducted at Toraighyrov University with 100 staff across 18 departments (50% female; M_age=39.8). Measures included MCQ coding, basic psychological need satisfaction and frustration scale (BPNSFS), Utrecht Work Engagement Scale (UWES-9), Copenhagen Burnout Inventory (CBI), Teachers’ Sense of Efficacy Scale (TSES-12), and trait emotional intelligence (EI). Analyses involved reliability (ω≈.78–.92), confirmatory factor analysis (CFA), inter-rater intraclass correlation coefficient (ICC) (.76–.84), multilevel regression, and bootstrap mediation. Need satisfaction predicted higher engagement (β=.44) and lower burnout (β=−.22), while need frustration predicted higher burnout (β=.39). MCQ independently related to engagement (β=.21) and burnout (β=−.18). Teacher self-efficacy was associated with need satisfaction (β=.31) and trait EI (β=.24). Models explained substantial variance (R² up to .41). Mediation analyses showed that need satisfaction partially mediated MCQ effects on engagement, burnout, and self-efficacy. Findings indicate that need-supportive managerial behavior is a practical lever for improving motivation and well-being, with organizational culture shaping implementation. Limitations include cross-sectional design and modest sample size.
Volume: 15
Issue: 5
Page: 4400-4411
Publish at: 2026-10-01

Constrained finite-control-set predictive control of an H-bridge direct current motor drive

10.12928/telkomnika.v24i5.27909
Bouiri; University of Tindouf Abdesselam , Kendzi; University Center Salhi Ahmed Naama Mohammed , Boughazi; University of Bechar Othmane , Drauoi; University of Tindouf Abdelghani
This paper presents a constrained finite-control-set model predictive control (FCS-MPC) scheme for an H-bridge converter-fed direct current (DC) motor drive, aimed at fast dynamic response and low steady-state armature-current ripple. By selecting the converter switching state directly at each sampling instant, the method removes the separate pulse-width modulation (PWM) stage used in conventional cascaded proportional-integral (PI) control and embeds the armature-current limit directly in the cost function. When the predicted armature current exceeds the predefined limit, a penalty term drives the optimizer to choose the sw itching state that returns the current within safe bounds while preserving speed tracking. The controller is evaluated in simulation under fixed-speed reference tracking, variable-speed reference tracking, and a step load-torque disturbance. The results show fast settling, low steady-state speed error, and bounded current during transients; under the idealized simulation conditions the steady-state armature-current total harmonic distortion (THD) is 0.21 %, indicating low current ripple for the studied operating points. The present study is limited to simulation; experimental validation and a quantitative comparison against conventional PI-PWM control are identified as future work.
Volume: 24
Issue: 5
Page: 1816-1826
Publish at: 2026-10-01

Development model of remote sensing data management for long-term storage using big data

10.12928/telkomnika.v24i5.27639
Riyan; National Research and Innovation Agency Mahendra Saputra , Agnes; National Research and Innovation Agency Sondita Payani , Ogi; National Research and Innovation Agency Gumelar
The growing volume of remote sensing data requires efficient approaches for long-term storage and data management. The National Remote Sensing Data Bank (BDPJN) stores multi-resolution satellite imagery acquired since 1993 to support disaster monitoring, climate studies, natural resource management, and other Earth observation applications. However, managing continuously growing archives remains challenging due to manual migration processes, inefficient storage utilization, and limited scalability. This study proposes an integrated remote sensing data management model that combines hierarchical storage management (HSM) with Apache Spark based parallel processing to support policy-driven data migration and long term storage optimization. The proposed model enables data migration across multiple storage tiers based on data characteristics and storage policies while improving migration efficiency through distributed processing. Experimental evaluation was conducted using Landsat-8, SPOT 6, SPOT-7, and Pleiades datasets under both 1 Gbps and 10 Gbps network environments. The results demonstrate migration performance improvements ranging from 80.14% to 91.04% compared with the existing manual migration system. These findings indicate that integrating HSM with Spark-based parallel processing can reduce migration bottlenecks, improve storage utilization, and provide a scalable and cost-effective approach for managing large-scale remote sensing archives while supporting long-term data governance and accessibility.
Volume: 24
Issue: 5
Page: 1535-1549
Publish at: 2026-10-01

TPACK-based AI integration in Kazakh language teacher education: evidence from a controlled pedagogical experiment

10.11591/ijere.v15i5.39655
Baimukhambetova Kuralay , Kalibek Ybyraimzhanov , Bakirova Sholpan
Pre-service teacher education in Kazakhstan faces a persistent gap between digital infrastructure investment and teachers’ practical ability to integrate artificial intelligence (AI) and digital tools into Kazakh language instruction. This study examined whether a technological, pedagogical, and content knowledge (TPACK)-aligned specialist course could simultaneously improve TPACK among pre-service primary school teachers. A quasi-experimental pretest–posttest control-group design was employed with 123 third-year pre-service teachers (experimental: n=62; control: n=61) at two universities in Shymkent, Kazakhstan. The Dubovitskaya diagnostics of educational motivation orientation scale and a researcher-developed digital competence survey were used to assess outcomes. Between-group differences were analyzed using the independent-samples student’s t-test (α=0.05), with effect sizes reported via Cohen’s d. The intervention produced statistically significant, large-effect improvements in professional motivation (t=6.84, p
Volume: 15
Issue: 5
Page: 4412-4421
Publish at: 2026-10-01
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