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Dahlan Abdullah
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International Journal of Engineering, Science and Information Technology
Published by Universitas Malikussaleh
ISSN : -     EISSN : 27752674     DOI : -
Core Subject : Science, Education, Social, Engineering,
Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation
The journal covers all aspects of applied engineering, applied Science and information technology, that is: Engineering: Energy Mechanical Engineering Computing and Artificial Intelligence Applied Biosciences and Bioengineering Environmental and Sustainable Science and Technology Quantum Science and Technology Applied Physics Earth Sciences and Geography Civil Engineering Electrical, Electronics and Communications Engineering Robotics and Automation Marine Engineering Aerospace Science and Engineering Architecture Chemical & Process Structural, Geological & Mining Engineering Industrial Mechanical & Materials Science: Bioscience & Biotechnology Chemistry Food Technology Applied Biosciences and Bioengineering Environmental Health Science Mathematics Statistics Applied Physics Biology Pharmaceutical Science Information Technology: Artificial Intelligence Computer Science Computer Network Data Mining Web Language Programming E-Learning & Multimedia Information System Internet & Mobile Computing Database Data Warehouse Big Data Machine Learning Operating System Algorithm Computer Architecture Computer Security Embedded system Coud Computing Internet of Thing Robotics Computer Hardware Information System Geographical Information System Virtual Reality, Augmented Reality Multimedia Computer Vision Computer Graphics Pattern & Speech Recognition Image processing ICT interaction with society, ICT application in social science, ICT as a social research tool, ICT in education
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 659 Documents
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Analysis of the Efficiency and Performance Effectiveness of Srikandi Application Using the UTAUT Model and Delone & Mclean Wawan Syahputra; Dahlan Abdullah; Nurdin Nurdin; Muhammad Daud; Taufiq Taufiq
International Journal of Engineering, Science and Information Technology Vol 6, No 1 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i1.1806

Abstract

The development of information technology has encouraged the government to carry out digital transformation in administrative governance, one of which is through the implementation of the SRIKANDI Application (Integrated Dynamic Archive Information System). This application is designed to support the management of electronic archives and correspondence integrated across government agencies. This study aims to analyze the efficiency and effectiveness of the SRIKANDI Application in supporting government administration, focusing on service speed, documentation accuracy, and resource efficiency. The method used in this study is a mixed methods approach with a sequential explanatory design. Quantitative data were collected by distributing questionnaires to employees who used the application to assess perceptions of efficiency and effectiveness. Furthermore, qualitative data were obtained through in-depth interviews and document analysis to delve into the quantitative findings and explore contextual factors that influence application implementation. Data analysis is carried out in stages, starting with descriptive and inferential statistical analyses for quantitative data and with thematic analysis for qualitative data. This research is expected to contribute to the development of an electronic government system and serve as a reference for evaluation and policymaking related to bureaucratic digitalization. In addition, the results of this study are also expected to strengthen the literature on the effectiveness of government information systems and provide an empirical picture of the practice of implementing the SRIKANDI Application in government agencies.
Improving TDS Sensor Accuracy in an IoT-Based Fertigation Prototype Using Polynomial Regression Calibration and Interpolation Arif Harjanto; Happy Nugroho; Aprilia Amrina Ainurrosyidah; Aji Ery Burhandenny
International Journal of Engineering, Science and Information Technology Vol 6, No 1 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i1.1776

Abstract

Accurate measurement of Total Dissolved Solids (TDS) is a critical requirement for hydroponic nutrient management, especially in automated fertigation systems that adjust nutrient concentrations based on plant age. However, non-industrial TDS sensors often exhibit significant fluctuations and non-linear errors, leading to unreliable nutrient dosing. This study proposes a calibration approach using second-order polynomial regression combined with interpolation to improve the accuracy of TDS measurements in an IoT-based fertigation prototype for lettuce hydroponics. The calibration was performed using reference TDS solutions and a digital TDS meter to ensure accurate measurements. The results show that the Mean Absolute Percentage Error (MAPE) decreased from 24.771% to 7.5768% during calibration, demonstrating a significant improvement in measurement accuracy. The calibrated sensor readings fall within the acceptable range for hydroponic nutrient control (±10%). This method provides a low-cost, reliable alternative for improving sensor accuracy in IoT fertigation systems.
Empowering Students in Maggot Cultivation at the Jamiyyatul Mubtadi Cibayawak Islamic Boarding School Syafitri Jumianto; Lusi Anindia Rahmawati; Asep Maksum; Musoffa Musoffa; Aris Machmud
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.1807

Abstract

Organic waste management remains a critical global challenge, particularly in developing countries where improper handling contributes to environmental degradation and public health risks. In Indonesia, the dominance of organic waste in landfills presents an opportunity for circular economy practices. This study aims to examine the effectiveness of maggot (Black Soldier Fly/BSF) cultivation as a community-based empowerment strategy to enhance waste management, improve student nutrition, and strengthen economic resilience within an Islamic boarding school environment. This research employed a participatory community-based approach involving 300 students at the Jamiyyatul Mubtadi Cibayawak Islamic Boarding School. The intervention consisted of five main stages: preparation and coordination; socialisation and team formation; technical and managerial training; implementation of appropriate technology; and continuous mentoring and evaluation. Organic waste generated from the school kitchen (approximately 200 kg/day) was partially processed (30–50 kg/day) into maggot biomass, which was subsequently utilised as feed for catfish cultivation. Data were collected through observation, production records, and pre- and post-intervention assessments. The findings demonstrate significant improvements across multiple dimensions. Maggot production increased from approximately 0.5 kg/day to 1.6 kg/day following enhanced waste utilisation and the introduction of supporting technologies such as drying and pelletizing machines. Student participation expanded from 5 to 20 active members in the santripreneur group. Nutritional outcomes also improved, as indicated by increased frequency of fish consumption from once to twice per week and a rise in nutritional knowledge scores from 56% to 82%. Additionally, maggot-based feed production improved in quality, achieving a longer shelf life (up to 3 months) and greater efficiency in aquaculture practices, with catfish survival rates exceeding 80%. The study highlights that integrating maggot cultivation with waste management and aquaculture can effectively support a sustainable circular economy model in educational institutions. Beyond environmental benefits, this approach contributes to improved food security, reduced feed costs (up to 40%), and the development of entrepreneurial skills among students. Despite initial constraints in technology and management capacity, targeted training and infrastructure support proved essential in optimising outcomes. This model demonstrates strong potential for scalability and replication in similar community-based settings.
Finite Element Analysis of Strength Degradation and Interface Behavior Controlling Bund Wall Stability in an Ex-Mining Pit Revia Oktaviani; Tommy Trides; Albertus Juvensius Pontus; Afdal Jamil Tanjung; Haviluddin Haviluddin
International Journal of Engineering, Science and Information Technology Vol 6, No 1 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i1.1786

Abstract

This study presents a finite element analysis of the stability and interface behavior of a bund wall constructed under tropical mining conditions at the Banda Mine, East Kalimantan, Indonesia. The research aims to evaluate the effects of strength degradation and geometric interfaces on the overall stability of the embankment system, which was designed to retain overburden material and mitigate slope failure under combined rainfall and seismic loading. The bund wall, with an overall height of 75 m and a base width of 130 m, was constructed using compacted sandy loam sourced from site overburden. Laboratory testing determined the geotechnical parameters of the materials, including soft clay, sandy loam, and siltstone foundation. Finite element modeling was performed on six critical cross sections to simulate progressive strength reduction representing 0%, 25%, and 50% cohesion loss. The calculated factors of safety ranged from 1.87 to 2.55 along the slope, 3.40 to 5.81 at the bund wall base, and 7.79 to 10.27 within the foundation, confirming overall structural stability under all modeled scenarios. However, local reductions in stability were observed near the interface zones and the central section of the wall at depths exceeding 25 m. These findings highlight the influence of interface geometry and strength degradation on stability performance and emphasize the need for improved compaction and continuous stress monitoring during operation
Reconnecting with Nature in the Built Environment: The Roles of Biophilic and Biomimetic Urban Design Awal Prasetyo; Hendro Prabowo; Wahyu Prakosa; Destri Maya Rani
International Journal of Engineering, Science and Information Technology Vol 6, No 2 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i2.1790

Abstract

Integrating nature into urban design has become a crucial approach for creating healthy, sustainable, and livable cities. This paper explores the multidimensional benefits and distinct roles of two transformative frameworks: Biophilic Design and Biomimetics. While both are rooted in nature, they offer complementary pathways. Biophilic Design focuses on human well-being, systematically integrating natural elements, light, and materials into the built environment to strengthen the innate human-nature connection, thereby improving mental and physical health, reducing stress, and enhancing social cohesion. In contrast, Biomimetics is a technical approach that solves complex urban challenges by emulating nature's models, principles, and ecological systems. It leads to innovative, efficient, and sustainable solutions, such as bio-inspired ventilation systems and materials that enhance urban resilience. The paper argues that moving beyond superficial greening, the synergistic application of Biophilic (human-centered) and Biomimetic (performance-centered) strategies enables a fundamental transformation towards regenerative, resilient, and dignified human settlements capable of addressing the pressing challenges of global urbanization and climate change.
Transforming Real-Time Payment Infrastructure with Cloud-Native Architecture Priyatham Nagaiya Seenu Naidu
International Journal of Engineering, Science and Information Technology Vol 6, No 3 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i3.1823

Abstract

The global financial services sector is undergoing a profound transformation driven by the rapid adoption of digital payment technologies, increasing customer expectations for real-time transactions, and the growing demand for scalable and secure financial infrastructures. Traditional monolithic payment systems often struggle to meet these requirements due to limitations in scalability, operational flexibility, fault tolerance, and integration capabilities. Although cloud-native technologies have emerged as a promising solution for modern payment ecosystems, the scholarly literature has not adequately examined how cloud-native architectural patterns address enterprise integration, performance scalability, resilience, and regulatory compliance challenges in large-scale payment environments. This article presents a systematic analysis of cloud-native payment infrastructure by synthesizing architectural patterns, performance benchmarks, and industry case studies from both developed and emerging digital payment ecosystems. The findings demonstrate that event-driven cloud-native architectures can deliver substantial performance improvements, including a 94% reduction in transaction processing latency from 2,400 milliseconds to 128 milliseconds, near real-time fraud detection with response times of approximately 0.05 seconds, and fraud identification performance achieving 99% recall and 83% precision through hybrid deep learning models. Furthermore, production-scale validation is evidenced by successful implementations such as India’s Unified Payments Interface (UPI), which processes over 10 billion transactions monthly, and Brazil’s PIX platform, capable of handling up to 200,000 transactions per second. Security and compliance analyses reveal that immutable event-sourcing mechanisms, combined with comprehensive audit trails and encryption strategies, facilitate adherence to major regulatory frameworks, including PCI-DSS, GDPR, and anti-money laundering requirements. However, significant implementation challenges remain, particularly concerning legacy system integration, cross-jurisdictional regulatory fragmentation, organizational transformation, and shortages of cloud-native engineering expertise. Based on these findings, practical implementation guidelines emphasize microservice-based architectures, end-to-end event-driven processing, and zero-trust security principles. Future research directions include post-quantum cryptographic migration strategies, central bank digital currency interoperability standards, sustainable payment infrastructure design, and the application of artificial intelligence for autonomous financial operations and risk management.
Workforce Unit Abstraction for Governing Hybrid Human and Artificial Intelligence Operations Gopal Yuvaraj
International Journal of Engineering, Science and Information Technology Vol 6, No 2 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i2.1811

Abstract

Enterprise service organizations increasingly deploy artificial intelligence agents alongside human workers. Yet, incumbent workforce management frameworks remain anchored to a purely human labor model, rendering AI agents invisible to capacity planning, performance attribution, and governance enforcement. This article addresses that conceptual gap through a design science research methodology, introducing three constructs as reusable primitives for hybrid workforce platform design. The Workforce Unit Abstraction defines a unified seven-attribute operational schema applicable to both human workers and AI agents, enabling consistent representation across planning, scheduling, and governance systems. The Hybrid Capacity Model extends demand-to-supply planning across heterogeneous workforce pools, resolving a multi-objective allocation problem that simultaneously optimizes cost, quality, and risk constraints. Governance-bound autonomy constrains AI Workforce Unit actions within a five-level, policy-enforced autonomy ladder supported by six mandatory governance controls. Together, these constructs provide a coherent reference model that closes the documented gaps in hybrid workforce management, including scheduling inefficiencies of up to 28%, attribution failures in 68% of organizations, and governance ambiguity responsible for 61% of hybrid workflow failures. The framework establishes a principled vocabulary for designing enterprise service platforms that manage human and artificial intelligence labor responsibly, transparently, and at scale.
Semantic Condensation of High-Cardinality Time Series for LLM-Driven Observability Akila Balasubramanian
International Journal of Engineering, Science and Information Technology Vol 6, No 2 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i2.1820

Abstract

Modern cloud observability platforms generate high-cardinality time-series data comprising thousands of distinct metric streams differentiated by service, endpoint, region, infrastructure component, and operational context. While this telemetry provides valuable insights into system behavior, its volume and complexity are inherently mismatched with the reasoning constraints of Large Language Models (LLMs), which operate within finite context windows and token budgets. When raw telemetry is passed directly to LLM-based observability assistants, four recurring failure modes significantly degrade analytical quality: token explosion that saturates context windows before signal-rich entries are reached, lossy truncation that discards diagnostically critical streams under arbitrary cardinality limits, incoherent analytical narratives arising from contradictory statistical summaries, and resolution gaps that obscure trend evolution, anomaly propagation, and change-point detection. These limitations reduce the effectiveness of LLM-assisted troubleshooting and hinder the practical adoption of AI-driven observability systems in large-scale cloud environments. To address these challenges, this paper introduces Semantic Condensation, a token-aware transformation framework designed to convert large-scale time-series telemetry into structured, semantically consistent summaries optimized for LLM-based reasoning. The proposed approach integrates vectorized statistical pre-analysis to efficiently identify anomalous behaviors, multi-signal importance scoring to rank Time Series Identifiers (TSIDs) according to diagnostic relevance, behavior-aware trend classification to capture temporal dynamics, and consistency enforcement mechanisms to prevent contradictory interpretations across generated summaries. Furthermore, an adaptive token-budget allocation strategy dynamically distributes descriptive detail based on diagnostic importance, ensuring that the most critical telemetry receives greater representational fidelity while maintaining strict token efficiency.Experimental evaluation conducted on high-cardinality observability workloads demonstrates that Semantic Condensation can process more than 10,000 time-series identifiers within production-grade latency requirements. Results show substantial reductions in token consumption compared with raw telemetry transmission, while simultaneously improving downstream LLM reasoning accuracy, anomaly interpretation, root-cause investigation, and troubleshooting effectiveness. These findings indicate that Semantic Condensation provides a scalable and practical foundation for next-generation AI-assisted cloud observability and operational intelligence systems.
Sustainable Exploration of Peat Water for Drinking Water Resilience in the Remote Community of Muara Enggelam Ansahar Ansahar; Irkhamiawan Ma’ruf; Bondansari Bondansari; Sherly Trifena Karundeng
International Journal of Engineering, Science and Information Technology Vol 6, No 2 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i2.1748

Abstract

Peat water is an abundant raw water source in tropical peatlands; however, its use for drinking water is constrained by its acidic pH, high colour, elevated levels of natural organic matter (NOM), and microbial contamination, all of which increase the risk of carcinogenic disinfection byproducts when chlorinated. In Muara Enggelam Village, Kutai Kartanegara Regency, communities rely heavily on peat water and incur high costs to obtain clean water from neighbouring areas. This study aimed to design a laboratory-validated hybrid water treatment system capable of producing potable water from peat sources in remote communities. Water samples were collected from upstream, downstream, and an existing community treatment unit and analysed for physical, chemical, and microbiological parameters. Raw peat water exhibited acidic pH (5.11–5.66), high colour (176–235 TCU), turbidity (21.8–85.9 NTU), elevated iron concentrations (0.2–0.4 mg/L), and severe microbial contamination. The existing community system effectively reduced turbidity and iron, but further reduced pH, leaving residual colour and detectable bacteria. Based on laboratory results and process evaluation, a four-stage hybrid system is proposed, consisting of pH neutralisation with quicklime, controlled coagulation–flocculation, multistage filtration using quartz sand, activated carbon, and ion-exchange resin, followed by ultraviolet disinfection to prevent disinfection byproduct formation. System sustainability is supported through a village-owned enterprise management model and community willingness to pay of IDR 50,000–150,000 per month. The proposed system demonstrates that peat water can be transformed into a safe, resilient drinking water source for remote tropical communities through an integrated technical and institutional approach.
Analysis of Informatics Engineering Students’ Dependency Level on the Use of ChatGPT Using the Support Vector Machine Method Aswita Indah Luthfiana Hasibuan; Dahlan Abdullah; Kurniawati Kurniawati
International Journal of Engineering, Science and Information Technology Vol 6, No 2 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i2.1813

Abstract

This study aims to analyze the level of dependency among Informatics Engineering students on the use of ChatGPT in academic activities using the Support Vector Machine (SVM) method. The research data were collected through questionnaires distributed to students of the Informatics Engineering Study Program at Universitas Malikussaleh from the 2022–2025 cohorts, involving a total of 400 respondents. The research indicators included usage intensity, duration of use, purpose of use, perceived effectiveness, and dependency behavior toward ChatGPT. The collected data underwent several preprocessing stages. including missing value checking. label transformation. data normalization using Min-Max Scaling. and dataset splitting into 80% training data and 20% testing data. The classification process was performed using the Support Vector Machine (SVM) algorithm with a linear kernel. The experimental results showed that the proposed SVM model successfully classified student dependency levels into three categories, namely low, medium, and high, achieving an accuracy of 95%, which indicates excellent classification performance. The findings also revealed that usage frequency, duration of use, and the utilization of ChatGPT for academic assignments and programming activities were the most influential factors affecting student dependency. Furthermore, a web-based system was developed using Python Flask and SQLite to facilitate data processing, model training, and visualization of classification results. The system testing results demonstrated that all implemented features functioned properly according to the specified requirements.

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