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Advanced materials for crosstalk and power optimization in TSV-enabled 3D ICs

10.11591/ijict.v15i3.pp1143-1153
Tappeta Chinna Sanjeeva Rayudu , Merrin Prasanna Nagadasari
The continued scaling of semiconductor devices has exposed the limitations of traditional two-dimensional (2D) integrated circuit architectures. To address performance bottlenecks and interconnect constraints, the industry is increasingly adopting three-dimensional (3D) integration technologies. through-silicon vias (TSVs) are a fundamental enabler of this advancement, facilitating vertical signal transmission between stacked silicon layers. Despite their benefits, TSVs face critical challenges related to crosstalk, power dissipation, and signal delay issues that are especially pronounced in dense via arrays. This research explores the use of multi-walled carbon nanotube (MWCNT) based TSVs insulated with different dielectric liners, including silicon dioxide (SiO₂), PPC, polyimide, and benzocyclobutene (BCB). HSPICE simulations are used to evaluate crosstalk noise, power dissipation, power delay product (PDP), and energy delay product (EDP) across varying TSV pitches. Among the materials studied, BCB demonstrates the most promising results. Specifically, MWCNT TSVs with BCB at a 10,000 μm pitch achieve up to 58% reduction in functional crosstalk, 75% in dynamic crosstalk, 78% in power dissipation, and a 52% improvement in PDP compared to single-walled CNT (SWCNT) based TSVs. These findings confirm the suitability of combining MWCNT cores with low-k BCB liners for enhancing performance, energy efficiency, and signal reliability in advanced 3D integrated circuits.
Volume: 15
Issue: 3
Page: 1143-1153
Publish at: 2026-09-01

Design and evaluation of a simple load balancing prototype using the round robin algorithm in local networks

10.12928/telkomnika.v24i4.27560
Muh. Fahmi; Universitas Sulawesi Barat Rustan , Wawan; Universitas Sulawesi Barat Firgiawan , Wiwi; Universitas Sulawesi Barat Nopiana
Load balancing plays a crucial role in ensuring efficient workload distribution and maintaining stable performance in web service systems. This study presents the design and experimental evaluation of a round robin–based load balancing system implemented in a multi-client local area network (LAN) environment using NGINX as a centralized controller. The system consists of three physical machines, comprising one load balancer and two backend servers hosting identical web applications. Multiple clients generate simultaneous hypertext transfer protocol (HTTP) requests, which are distributed alternately to the backend servers using the default round robin mechanism provided by NGINX. Experimental evaluation was conducted under three workload scenarios of 50, 100, and 200 concurrent requests. The results show that the round robin algorithm consistently distributes requests evenly between the backend servers. The average response time increased from approximately 110 ms at 50 requests to 165 ms at 100 requests and 290 ms at 200 requests, indicating stable performance under light to moderate load conditions. These findings demonstrate that the proposed system is lightweight, modular, and easy to deploy in resource limited environments. The implementation is particularly suitable for campus-scale networks and small institutional settings, serving as a practical platform for local server deployment, academic applications, and experimental learning in networking and distributed systems.
Volume: 24
Issue: 4
Page: 1121-1130
Publish at: 2026-08-01

GenAI as an IoT programming assistant: a case study on automated debugging for air quality monitoring systems

10.12928/telkomnika.v24i4.27707
Steven; Pradita University Imanel Bawole , Handri; Pradita University Santoso
The rapid expansion of internet of things (IoT) technology has necessitated the development of user-friendly programming solutions for non–experts. While generative artificial intelligence (GenAI) offers the potential to democratize code development, its ability to assist in the intricate task of automated debugging, particularly regarding hardware integration remains a critical area of research. A design research approach was employed, employing a structured four – phase workflow: error analysis, diagnostic execution through prompting, iterative solution analysis, and functional verification. The methodology was applied to an experimental case study involving an air quality (AQ) monitoring system. The study tested the artificial intelligence (AI)’s capacity to debug C++ code intended for the Arduino integrated development environment (IDE). Gemini AI successfully identified and resolved three critical logic errors arising from mismanaged MQ135 calibration variables, incorrect loop sequencing, and data desynchronization between the organic light emitting diode (OLED) display and the internal status logic. GenAI proved effective as a programming assistant for resolving bugs in IoT applications. However, effective debugging still depends on well-structured prompts and a basic understanding of the underlying IoT hardware.
Volume: 24
Issue: 4
Page: 1278-1286
Publish at: 2026-08-01

Decentralized multi-agent orchestration for legacy order-to cash optimization

10.12928/telkomnika.v24i4.27807
Rahul Kumar; University of Connecticut Thatikonda , Sucharitha; Point Park University Donepudi
Legacy enterprise resource planning (ERP) systems serve as the operational backbone of global commerce but often create bottlenecks due to their rigid, monolithic design. As organizations incorporate artificial intelligence (AI), these outdated systems struggle to support high-speed, parallel workflows, creating a significant integration challenge. This paper introduces a non intrusive modernization approach that overlays a decentralized multi-agent system (MAS) onto existing infrastructure without requiring invasive code changes. By developing a digital twin of the order-to-cash (O2C) process, we train autonomous agents through multi-agent reinforcement learning (MARL) to manage credit validation, inventory allocation, and fulfillment. We adapt the centralized training, decentralized execution (CTDE) framework to meet O2C constraints, enabling agents to learn globally optimal strategies while operating independently. Simulation results show that this architecture surpasses rule-based robotic process automation (RPA) baselines, increasing total throughput by 6.9% over a monolithic setup, though at a 6.3% error rate due to aggressive allocation policies. These results indicate that decentralized agent-based orchestration provides a scalable approach for modernizing legacy ERPs, offering increased agility without the risks associated with platform replacement.
Volume: 24
Issue: 4
Page: 1216-1223
Publish at: 2026-08-01

Classification of P300 event-related potentials using SNN, CNN and LSTM deep learning models

10.12928/telkomnika.v24i4.27659
Ahlaam; Bright Star University M. Saed , Ibtihal; College of Electrical and Electronics Technology Fawzi Elshami , Ali; University of Benghazi I. Elgayar
Accurate classification of P300 event-related potentials remains challenging due to the complex, non-stationary, and low signal-to-noise characteristics of electroencephalography (EEG) signals in brain-computer interface (BCI) systems. P300-based devices, such as the P300 speller, enable communication for patients with severe motor impairments, including those with locked-in syndrome; however, reliable brain signal classification is still a critical limitation. This study presents a comparative evaluation of deep learning models, including convolutional neural networks (CNN), long short-term memory (LSTM) networks, and spiking neural networks (SNN), for P300 signal classification. SNNs represent a biologically inspired paradigm that models the discrete, time-dependent behavior of neural spiking activity and offers advantages in terms of energy efficiency and hardware implementability. Experimental results demonstrate that CNN achieved the highest average classification accuracy (81.04%), followed closely by SNN (80.94%) and LSTM (80.60%). Although CNN slightly outperformed the other models, SNNs showed comparable accuracy while requiring fewer training samples and offering potential benefits for low power and real-time BCI systems. These findings highlight the trade-offs between classification performance and computational efficiency and underline the promise of SNNs as an efficient alternative for P300-based BCI applications.
Volume: 24
Issue: 4
Page: 1294-1306
Publish at: 2026-08-01

Integration of public electric vehicle charging stations into a single gateway platform for net zero emissions goals

10.12928/telkomnika.v24i4.27786
Fajar; STMIK AMIK Bandung Sidiq Arrizal , Shafira; STMIK AMIK Bandung Febriani
The rapid adoption of electric vehicles (EVs) in Indonesia necessitates robust charging infrastructure. However, a major challenge is the highly fragmented network of public EV charging stations (SPKLU). Various operators utilize disparate protocols, resulting in isolated data silos and user range anxiety. This study proposes a nationally scalable application programming interface (API)-based gateway for cross-operator SPKLU integration. The system uses an event-driven internet of things (IoT) push mechanism. A standardized JavaScript object notation (JSON) API ensures interoperability and normalizes heterogeneous SPKLU data into a unified national dashboard. Pilot testing across 20 SPKLU locations in 10 major Indonesian cities validated cross-regional network reliability. The results demonstrate successful real-time status synchronization with a low margin of error (MoE) of 4.65%. To mitigate minor discrepancies caused by temporary network latency, a user interface (UI)-level timestamp transparency feature is proposed. Furthermore, by utilizing dynamic, region specific grid emission factors, the system accurately recorded an aggregated electricity consumption of approximately 3.5 million kWh in June 2025. This translates to an estimated 2.7 million kgCO₂eq in carbon emissions. Ultimately, this centralized platform eliminates information asymmetry for users. It also lays a crucial foundation for future smart grid integration, artificial intelligence (AI)-based demand forecasting, and Indonesia’s net zero emission (NZE) targets.
Volume: 24
Issue: 4
Page: 1113-1120
Publish at: 2026-08-01

Decision-tree-based machine learning for detecting coffee agroforestry using SPOT-7

10.12928/telkomnika.v24i4.27747
I Made; IPB University Khrisna Yoga Devandra , I Nengah; IPB University Surati Jaya , Tatang; IPB University Tiryana
This study develops a decision-tree-based machine-learning (ML) approach to identify coffee agroforestry plants using SPOT-7 satellite imagery. The algorithm was developed by examining the combination of image indices derived from SPOT-7 and biophysical variables. Detection using spectral variables is often hampered by spectral similarity between vegetation cover classes. This study found that a ML method that combines spectral and biophysical variables can significantly improve overall accuracy, from 60.4% (using conventional spectral variables alone) to 94% (using integrated spectral-biophysical variables). For detecting and identifying agroforestry coffee classes typically found under tree canopies, the addition of the “land cover” variable published by the Ministry of Environment and Forestry contributes significantly to the classification of agroforestry coffee. Important variables identified in this model are normalized difference vegetation index (NDVI), visible difference vegetation index (VDVI), normalized red-green vegetation index (NRGI), elevation, and land cover.
Volume: 24
Issue: 4
Page: 1307-1319
Publish at: 2026-08-01

NLP-driven hate speech detection on TikTok: a case study from UIN Sunan Ampel Surabaya

10.12928/telkomnika.v24i4.27419
Achmad; UIN Sunan Ampel Surabaya Teguh Wibowo , Aris; UIN Sunan Ampel Surabaya Fanani , Mujib; UIN Sunan Ampel Surabaya Ridwan , Bramasta; UIN Sunan Ampel Surabaya Kurnia Aji
This study examines hate speech detection in TikTok comments using natural language processing (NLP) techniques within the student community of UIN Sunan Ampel Surabaya. A dataset of 10,000 comments associated with the hashtag #PBAKUINSA2023 was analyzed using a lexicon-based sentiment analysis approach implemented through the TextBlob library, combined with Indonesian text preprocessing techniques, including tokenization, normalization, stopword removal, and stemming using the Sastrawi library. The results indicate that the proposed approach achieved an accuracy of 0.85, with precision of 0.88, recall of 0.83, and an F1-score of 0.854. Most comments were classified as neutral, while 31.8% were positive, and only a small proportion were negative. These findings suggest that discussions related to campus activities tend to be neutral or supportive. However, the findings also reveal that sentiment polarity does not always directly correspond to hate speech, as certain harmful expressions may appear neutral in lexicon-based analysis. This limitation highlights the need for more context-aware approaches. Overall, the proposed method provides an efficient solution for monitoring online discourse in academic environments.
Volume: 24
Issue: 4
Page: 1157-1167
Publish at: 2026-08-01

Phono-syntactic error pattern analysis in impromptu speaking among English learners: a content analysis

10.11591/ijere.v15i4.39044
Angelie V. Temario , Harold John U. Mamites , April Jane G. Sales , Marcelina S. Deiparine
Second language learners, particularly Filipino learners of English, often experience linguistic difficulties in producing accurate phonological and syntactic forms, especially during spontaneous speaking tasks. This study examines the dominant phono-syntactic error patterns found in impromptu speaking performances and explores their pedagogical implications using Corder’s error analysis framework. Employing a qualitative descriptive research design, the study analyzed recorded impromptu speaking performances of 36 Bachelor of Arts in English Language (BAEL) students. The analysis revealed that in the phonological aspect, segmental errors were more prevalent than suprasegmental errors, with vowel substitution emerging as the most frequent error type, accounting for 22 errors (57.1%). In the syntactic aspect, misformation was identified as the most dominant error category, with 164 errors (54.88%). These findings suggest that while learners demonstrate emerging grammatical awareness, they still encounter difficulties in maintaining phonological and syntactic accuracy during spontaneous speech production. The study highlights the importance of integrating pronunciation and grammar instruction within communicative speaking activities. Furthermore, impromptu speaking tasks serve as an effective diagnostic tool for identifying learners’ linguistic challenges and guiding instructional strategies aimed at improving spoken language proficiency.
Volume: 15
Issue: 4
Page: 3646-3657
Publish at: 2026-08-01

Bridging ethics and performance in engineering education through predictive learning analytics

10.11591/ijere.v15i4.38767
Hamza Abu Owida , Areen Arabiat
This literature review examines the opportunities, implementation challenges, ethical implications, and emerging directions of predictive learning analytics (PLA) in engineering education. Using a structured review of the literature, the study synthesizes evidence from several publications with emphasis on studies examining risk prediction, personalized support, curricular improvement, interpretability, fairness, and intervention design. The review shows that PLA can improve early identification of at-risk students, support adaptive learning pathways, and inform data-driven refinements in engineering curricula; however, its impact depends on data quality, model transparency, institutional capacity, and the availability of timely human support. The analysis further indicates that the most consequential barriers are fragmented data ecosystems, the difficulty of translating predictions into effective interventions, and unresolved ethical concerns related to privacy, bias, consent, and student agency. The article contributes to educational research by offering an integrated synthesis that connects technical development with pedagogical evaluation and ethical governance in engineering education. It concludes by proposing that future PLA adoption should align predictive modeling with explainable artificial intelligence, learning-theory-informed intervention design, and institution-level implementation strategies. Publications were selected for relevance to PLA in engineering education and then synthesized narratively across opportunities, challenges, ethics, and future directions.
Volume: 15
Issue: 4
Page: 2973-2982
Publish at: 2026-08-01

Religious literary literacy in pesantren: a phenomenological study of moral and spiritual learning

10.11591/ijere.v15i4.39013
Lamri Lamri , Else Liliani , Sri Harti Widyastuti
This study explores how santri (student) in pesantren (Islamic boarding school) education experience and interpret religious literary practices as a medium for moral and spiritual learning and conceptualizes these experiences into a religious literary literacy (RLL) model. Using a qualitative interpretative phenomenological approach through interpretative phenomenological analysis (IPA), the study examines santri’s lived experiences and meaning-making processes. Data were generated through in-depth semi-structured interviews with eight santri from four pesantren in East Java and Yogyakarta, Indonesia, supported by analysis of student literary works and pesantren literacy archives. The analysis involved iterative stages of close reading, coding, thematization, and hermeneutic interpretation. The findings reveal three interconnected dimensions of RLL: i) literature as spiritual and moral reflection; ii) transformative selfhood and moral consciousness developed through reflective reading and writing; and iii) pesantren as a pedagogical ecosystem integrating faith, ethics, and creative expression. These dimensions form the RLL model, which conceptualizes literary practice as a cyclical process of devotion, reflection, and self-transformation. The study contributes theoretically by extending the concept of religious literacy beyond textual competence toward experiential and interpretative moral formation. Practically, the model offers a conceptual framework for designing reflective literacy programs in Islamic education that support character development and address contemporary moral challenges among youth.
Volume: 15
Issue: 4
Page: 2806-2813
Publish at: 2026-08-01

Reframing teacher evaluation in higher education: a three-pillar framework from Assam

10.11591/ijere.v15i4.40024
Arabinda Rajkhowa , Munmi Dutta
Teacher evaluation shapes the quality of classroom instruction and, through it, student learning outcomes; yet in Indian higher education the dominant single-source model, student feedback channeled through the internal quality assurance cell (IQAC), is widely critiqued as ritualistic and developmentally inert. To the authors’ knowledge, no prior study has integrated student feedback, structured self-evaluation, and peer review into a coherent operational framework for regional Global South contexts. Drawing on primary data collected between 2009 and 2025 from approximately 200 undergraduate arts and science students, principally at North Lakhimpur College (now North Lakhimpur University) and other institutions across Assam, this study employs a qualitative-descriptive design with thematic analysis and triangulation of open-ended questionnaires and semi-structured interviews. Two contributions emerge. First, it yields a culturally grounded fivefold taxonomy of good teaching from Assamese student articulations, including culturally distinctive expectations: the teacher’s public moral role in the community and the obligation of intellectual life beyond the syllabus, that standardized student evaluation of teaching (SET) instruments routinely miss. Second, it proposes a developmental three-pillar framework integrating reformed student feedback, disciplined teacher self-evaluation, and structured peer review to restore the formative function of evaluation and improve student learning outcomes. Both the taxonomy and the framework are scalable across comparable institutions in the Global South.
Volume: 15
Issue: 4
Page: 2840-2851
Publish at: 2026-08-01

Tutor feedback, simulation-based learning, and AI-aware practice in MSc electrical engineering module: a case study

10.11591/ijere.v15i4.38929
Emad Al-Mahdawi , Nkaepe Olaniyi
Engineering programs routinely collect tutor evaluation data for quality assurance, yet the evidence is often not translated into a transparent, reproducible module enhancement plan that can be audited, monitored, and reported as scholarly work. This paper proposes a tutor feedback-to-enhancement (TFE) framework that transforms a standard tutor feedback worksheet into: i) a coded evidence base; ii) a descriptive closed-item profile; and iii) an evidence-to-action matrix (EAM) that links observed strengths and gaps to targeted interventions and measurable indicators. The framework is demonstrated through a single-module case study (an introductory MSc electrical power engineering systems (EPES) module) using one completed tutor feedback sheet containing closed ratings and open comments. The closed items show uniformly positive evaluations (7/12 items rated excellent and 5/12 rated good; no satisfactory/unsatisfactory responses; one item not applicable). The open-text evidence highlights simulation as a core learning scaffold, the importance of equitable access to laptops and e-learning resources, and a specific curriculum enhancement need for a dedicated lecture on photovoltaic (PV) design and battery energy storage systems (BESS). The main contribution is a practical, low-cost method that operationalizes routine tutor feedback into an auditable enhancement pathway, including an explicit artificial intelligence-aware (AI-aware) practice component that emphasizes verification and engineering judgment.
Volume: 15
Issue: 4
Page: 3567-3575
Publish at: 2026-08-01

Artificial intelligence usage in the teaching-learning process: perception and challenges

10.11591/ijere.v15i4.38964
Ishani Basak , Benny Thomas , Shinto Thomas
Artificial intelligence (AI) has advanced in the post-pandemic era and is unavoidable in teaching and learning. Teachers’ perceptions, as the primary gatekeepers, are essential for ensuring quality education and inclusive classrooms, with AI as a collaborator. While some teachers resist these technological shifts, others are actively adapting an AI-assisted teaching approach. We conducted this study to understand the reasons for teachers’ resistance (challenges and difficulties) and how they perceive the use of AI in the teaching, learning, and assessment process, because the first step in effective incorporation is having a favorable attitude towards it. Hence, this study explored the perceptions of 15 secondary private school teachers, selected through purposive sampling, regarding the incorporation of AI into teaching, learning, and assessment processes, as well as the challenges they faced. The researchers developed an in-depth interview schedule and conducted interviews to understand participants’ perceptions and challenges. The data is analyzed following the thematic analysis steps by Braun and Clarke. Thematic analysis revealed that teachers demonstrated a positive understanding towards the pedagogical relevance of AI, rather than merely having a favorable perception. Furthermore, teachers predominantly viewed AI as an additional tool to enhance the effectiveness of knowledge transactions and instructional design. The challenges include infrastructure accessibility and professional training; time management for preparation, skill updating, and fulfilling varied teaching and other responsibilities; the inability to verify the accuracy of information; and parental mindset. This study offers insights for developing AI-aided teacher training and relevant curricula for schools.
Volume: 15
Issue: 4
Page: 3124-3140
Publish at: 2026-08-01

Social support for hearing parents raising deaf children in Kazakhstan: challenges and solutions

10.11591/ijere.v15i4.38135
Zhansaya Janpeisova , Assel Sarsenova , Karlygash Yessenamanova , Kulmarash Kashkinbayeva
In Kazakhstan, social support for families with hearing-impaired children remains a pressing issue requiring further research and improvement. The aim of this study is to assess the social support provided to families of hearing parents with children who have hearing impairments in Kazakhstan. The study involved 176 families from Kazakhstan, in which the parents are hearing, and the children have hearing loss. This study employed a convergent mixed-methods approach, combining the simultaneous collection and analysis of qualitative (interviews) and quantitative (surveys) data with the subsequent integration of the findings to gain a more comprehensive understanding of the situation. The results showed that 89% of respondents were aware of existing social assistance programs; financial and medical support were considered particularly important, while psychological support was deemed insufficient. Socioeconomic and geographic factors significantly influenced access to services. The data also showed that income level and type of settlement significantly influence families’ access to social services and awareness of support programs, while the age of parents and children has less influence, although the needs of children of different ages vary. The practical significance of the study lies in identifying problems within the social assistance system, such as low information accessibility, long waiting times, inadequate support, and service quality fluctuations. The obtained data can be used to improve social support mechanisms and raise awareness among families with children who have hearing impairments. These findings can be used to improve social support mechanisms and raise awareness of this issue among families with hearing-impaired children.
Volume: 15
Issue: 4
Page: 2931-2945
Publish at: 2026-08-01
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