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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 582 Documents
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Cotton Disease Prediction Using Deep Transfer Learning: Comparative Analysis of Resnet50, VGG16 and Inceptionv3 Models Gupta, Sandeep; Hamid, Abu Bakar Abdul; Nyamasvisva, Tadiwa Elisha; Jain, Vishal; Tyagi, Nitin; Mun, NG Khai; Ather, Danish
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.1116

Abstract

Cotton is among the most critical crops in the world textile industry, but it is highly susceptible to a vast array of infections that have a tremendous impact on output and fiber quality. Traditional cotton disease diagnosis is mostly based on manual inspection by farmers and experts and is time consuming, labor intensive and inaccurate due to similarity of symptoms. The high rate at which artificial intelligence, especially computer vision and deep learning (DL), have advanced has provided effective alternatives to auto-detecting plant diseases. As a subdivision of the DL approach, transfer learning allows adapting existing convolutional neural networks to the agricultural domain using smaller datasets to guarantee higher performance. This work introduces comparative analysis of three popular deep transfer learning (DTL) models ResNet50, VGG16, and InceptionV3 that are used in the classification of cotton leaf diseases. The training, validation, and testing were performed on a dataset of 1,991 labelled images that included four categories of normal and diseased cotton leaves and plants. All models were optimized and assessed with standard measures, such as validation and test accuracy. The experimental results show that InceptionV3 had the highest accuracy of 95.28, VGG16 had 85.85, and ResNet50 had the lowest accuracy of 69.81. The high accuracy of InceptionV3 is also a testament to its ability in the extraction of multi-scale features, and the trade-off between accuracy and computational efficiency. The results affirm the feasibility of DTL frameworks to revolutionize precision agriculture by facilitating diagnosis of cotton diseases in a timely and reliable manner. This development can help in ensuring that farming activities are sustainable, pesticides are used efficiently and the economy does not suffer economic losses and helps in ensuring that productivity and environmental protection are maintained in cotton farming.
A Dual-Microcontroller IoT Platform for Integrated Flood and Air Quality Monitoring: Performance and Integration Challenges Syam, Rafiuddin; Maruddani, Baso; Pramono, Eko Kuncoro; Kartika, Irma Ratna; Irsyad, Daffa Ihsanullah
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.v5i4.1539

Abstract

Jakarta faces escalating environmental challenges, including heightened flood risk and deteriorating air quality, driven by rising rainfall intensity and increasing pollution levels. Conventional monitoring systems for these hazards often operate in isolation, lacking the integration, realtime capability, and accessibility offered by modern Internet of Things (IoT) technology. To address this gap, this study designed and developed a unified, dual-microcontroller IoT platform for the simultaneous and integrated monitoring of flood potential and air pollution. The research followed an Experimental Development methodology, involving systematic hardware design, firmware development, system integration, and rigorous performance testing. The prototype hardware architecture strategically separates data acquisition and network communication by utilizing an Arduino Uno for data acquisition and an ESP32 microcontroller for network communication, respectively. The system incorporates an HC-SR04 ultrasonic sensor for water level detection, a DHT22 sensor for temperature and humidity measurement, and an MQ-135 gas sensor for assessing air quality. Data is displayed locally on a 20×4 LCD and transmitted to a cloud server. A critical finding from the integration phase was a 2.3% data loss rate attributable to serial communication instability between the microcontrollers, highlighting a significant challenge in multi-processor IoT architectures and underscoring the necessity for robust inter-processor protocols. During a comprehensive 24-hour endurance test with measurements taken at ten-minute intervals, the system demonstrated high accuracy in individual sensor readings. It successfully transmitted 97.7% of the data in realtime to a web application built on the Firebase platform. The study concludes that while the integrated dual-microcontroller approach is highly viable for holistic environmental monitoring, future iterations must prioritize enhanced communication reliability through hardware flow control and error-checking mechanisms to achieve the robustness required for mission-critical deployments.
Futuristic Design Based on Sustainable Culture and Creative Economy: Material Technology Innovation in Commercial Buildings Pranajaya, I Kadek; Mahadipta, Ngurah Gede Dwi; Nutrisia Dewi, Ni Made Emmi; Wijaya, Made Eka
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.v5i4.1371

Abstract

 The integration of futuristic design with sustainable culture and the creative economy offers a transformative paradigm in the development of commercial architecture. This study examines the Dekkson Knowledge Shop as a case study to explore how material technology innovation can address environmental challenges while reinforcing cultural identity and strengthening market competitiveness. Employing a qualitative descriptive method, the research utilizes field observations, in-depth interviews, and documentation analysis to investigate the application of high-performance materials and their interpretation within architectural narratives. The findings highlight that the use of eco-friendly composite panels, fiber-cement boards, and adaptive lighting systems not only enhances energy efficiency and durability but also embeds Balinese cultural values within a futuristic aesthetic framework. A key novelty of this research lies in positioning material technology as a narrative medium that connects modern innovation with cultural sustainability, rather than perceiving it solely as a structural element. This integration enriches user experience, strengthens brand identity, and supports the creative economy by transforming architectural design into a cultural and economic asset. Furthermore, the study proposes a replicable design model for future commercial projects that harmonizes sustainable material innovation with local narratives. The model provides both theoretical contributions to architectural discourse and practical strategies for sustainable design practices applicable in a global context. Through this approach, the research underscores the importance of balancing technological advancement with cultural preservation, thereby establishing commercial architecture as a medium for sustainable innovation, cultural continuity, and economic resilience.
Vision Transformer-Based Multi-Head Self-Attention for Early Recognition and Classification of Paddy Leaf Diseases in Rice Fields Palaniappan, M; Saravanan, A
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.v5i4.1410

Abstract

Rice has become an essential food source for a large portion of the world's population, greatly enhancing global food security. One of the fundamental staple crops, paddy, is especially susceptible to diseases primarily caused by bacteria and viruses. The source of the rice blast, Magnaporthe oryzae, poses a severe danger to the world's rice supply, mainly in South India. Both yield and quality are at risk due to the continuous threat of different diseases. However, a few diseases can drastically lower crop yields and quality, making agricultural productivity extremely vulnerable. Therefore, it is crucial to detect diseases at an early stage to effectively manage these risks. Scalable and effective solutions are required because conventional approaches are laborious, expensive, and frequently inaccessible to smallholder farmers. Data-driven strategies like machine learning (ML) and deep learning (DL), can assist in addressing these issues and increasing agricultural sustainability and crop yield. This study presents a new Vision Transformer-based hyperparameter optimization approach for the classification and detection of paddy leaf diseases in rice crops field (VTMHSA-RCPRF). The VTMHSA-RCPRF model comprises data preprocessing, ViT multi-head self-attention-based feature extraction, MLP-based Focal Loss for classification and detection, and Population-Based Training (PBT) as hyperparameter tuning. A wide range of experiments have been carried out to exhibit the promising performance of the VTMHSA-RCPRF method. The simulation outcomes highlighted that the VTH-RCPFRF approach reaches better performance over its recent approaches in terms of distinct measures.
Entrepreneurial Intention of MSME Actors in Indonesia: An Empirical Study on the Influence of Entrepreneurship Learning and the Moderating Role of Subjective Norms Gunawan, Indra; Indradewa, Rhian; Kustiawan, Unggul
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.v5i4.1484

Abstract

Entrepreneurship is one of the strategic pillars in driving national economic growth, particularly through the role of Micro, Small, and Medium Enterprises (MSMEs), which contribute more than 60% of Indonesia’s Gross Domestic Product (GDP). This study aims to analyse the factors affecting entrepreneurial intention among MSME actors in Indonesia who have participated in entrepreneurship training. Specifically, the study examines the influence of entrepreneurial motivation, market orientation, entrepreneurial orientation, entrepreneurial learning, entrepreneurial attitude, and entrepreneurial self-efficacy on entrepreneurial intention, as well as testing the role of subjective norms as a moderating variable. Using a quantitative approach and the Partial Least Squares Structural Equation Modelling (PLS-SEM) method, data were collected from 380 MSME respondents across the Greater Jakarta area. The findings reveal that entrepreneurial learning significantly mediates the relationship between market orientation and entrepreneurial intention, as well as between entrepreneurial motivation and entrepreneurial intention. Meanwhile, subjective norms were found to moderate the relationship between entrepreneurial attitude and entrepreneurial intention, but not the relationship between entrepreneurial self-efficacy and intention. These findings contribute theoretically to the understanding of the cross-path relationships between psychological and contextual variables in shaping entrepreneurial intention. In practical terms, entrepreneurship training should be designed to strengthen active learning and foster social norms that support entrepreneurial intention.
Evaluation of the Quality and Safety of Smoked Fish Produced Using a Modified Efhilink Smoking Cabinet With Different Bio-Smoke Sources Joesidawati, Marita Ika; Suwarsih, Suwarsih; Sriwulan, Sriwulan
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.v5i4.1476

Abstract

Traditional fish smoking methods often raise significant concerns regarding product safety, quality inconsistency, and environmental pollution. This study aimed to evaluate a modified Efhilink smoking cabinet designed to address these issues by utilizing agricultural waste, specifically corn cobs and coconut shells, as bio-smoke sources for producing high-quality, safe smoked fish compliant with the Indonesian National Standard (SNI 2725:2013). Three fish species (mackerel tuna, Euthynnus affinis; flying fish, Cypselurus spp.; and ray, Dasyatis spp.) were processed using the modified cabinet and a traditional cabinet (control) and subsequently analyzed for sensory properties, proximate composition, histamine, TVB-N, pH, total phenolic content, and various contaminants (microbiological, heavy metals, chemical residues, and polycyclic aromatic hydrocarbons (PAH4)). The results demonstrated that all smoked fish samples from the modified cabinet met all critical parameters of the national standard. Coconut shell smoke generally yielded superior products, characterized by higher acceptability in aroma and taste, a greater infusion of phenolic compounds (up to 0.334 mg/kg), and significantly lower levels of PAH4 contaminants compared to the traditional control. All samples from the modified cabinet exhibited histamine levels well below the 100 mg/kg safety limit (12.36–19.37 mg/kg), total plate counts within the permissible range (10 to 3.6x10? CFU/g), and a complete absence of detectable pathogens (E. coli, Salmonella spp., S. aureus, V. cholerae) or hazardous chemical residues (chloramphenicol, malachite green, nitrofuran); heavy metal contaminants were also found at levels far below the maximum allowable limits. The modified cabinet significantly outperformed the traditional method in reducing PAH4 contamination. The technology not only enhances food safety but also promotes sustainable practices by converting agricultural waste into value-added products. In conclusion, the modified Efhilink cabinet, using either corn cob or coconut shell bio-smoke, effectively produces safe, high-quality smoked fish that complies with stringent food safety standards, with coconut shells demonstrating superior performance as a smoke source by enhancing sensory attributes and bioactive compound content while minimizing hazardous contaminants.
Investigating the Energy Cost for n Wireless Sensor Network using IoT by Implementing RMP Algorithm K, Kishore Kumar; Srinivasulu, G.
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.v5i4.1243

Abstract

Internet of Things (IoT) sensor networks frequently see energy savings since the nodes in the network are powered by their own finite batteries. While data processing uses a lot less energy than data transmission in IoT sensor nodes is expensive energy-intensive. Over the last few years, wireless sensor systems based on IoT has witnessed an evolutionary breakthrough across several industries  various sectors. The Internet of Things, or IoT, is a network that allows physical items, machinery, sensors, and other devices to communicate with one another without the need for human intervention. The WSN (Wireless Sensor Network) is a central component of the IoT, which has proliferated into several different applications in real-time. Nowadays, the critical and non-critical applications of the IoT and WSNs affect nearly every part of our daily life. WSN nodes are usually small, battery-powered machines. Therefore, Energy-efficient data aggregation techniques that prolong the network's lifespan are crucial. Reducing data transmission is the primary goal of many energy-saving techniques and concepts. As a result, significant energy savings can be achieved in IoT sensor networks by reducing data transfers. The proliferation of IoT-based Wireless Sensor Network has triggered a paradigm shift in the business, necessitating the use of dependable and efficient routing techniques. A compression-based data reduction (CBDR) method that operates at the level of IoT sensor nodes was proposed in this study. To recover the data at the sink or BS end, we suggest using a Randomised Matching Pursuit algorithm. Additionally, beneficial is the use of CLH and relay routing.
Designing and Validating an Instrument to Assess Home Literacy Environment in Early Childhood: A Confirmatory Factor Analysis Oktaviani, Maya; Elmanora, Elmanora; Silitonga, Mirdat; Mashabi, Nurlaila A; Muchtar, Eka Nur Pebriyanti; Marjan, Lu'lu' Wal
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.v5i4.1540

Abstract

Strengthening literacy from preschool age impacts children's social, emotional, and critical thinking development. This activity aligns with the Sustainable Development Goals, particularly SDG 4, which targets quality and inclusive education for all children. In early literacy development in preschool-aged children, the environment closest to them plays a significant role: the family and school. Therefore, this study aims to develop a Home Literacy Environment (HLE) instrument for preschool-aged children using Confirmatory Factor Analysis (CFA). The study employs a research and development methodology, specifically the 4D model (Define, Design, Develop, Disseminate), to produce a standardized measurement tool. Validation procedures were conducted in three stages: construct validation by three experts, content validation by 14 panellists, and empirical testing involving 165 families with children aged 5–6 years in the Greater Jakarta area. Data were analyzed using CFA to examine factor structure and construct validity. Results indicated that 20 items across the three core dimensions demonstrated adequate factor loadings and significant t-values, with high construct reliability and variance extracted, confirming their validity. Nine indicators of goodness of fit met the criteria. Overall, the model was deemed sufficiently fit and suitable for further interpretation. This study supports the broad applicability of the HLE as a valid measure of the literacy environment created at home for preschool children. By providing a validated HLE instrument, educators, researchers, and policymakers are equipped to assess and enhance the literacy support provided at home. This result enables targeted interventions and informed decision-making to strengthen early learning foundations and promote inclusive, equitable education from the earliest years.
Analysis of the Influence of Knowledge Management, Digital Adoption, and Technology-Based Training on Organizational Performance Widiantoro, Didik; Judijanto, Loso; Suhartono, Suhartono; Pramono, Susatyo Adhi; Dewa, Dominica Maria Ratna Tungga; Februati, Bernadin Maria Noenoek
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.v5i4.1222

Abstract

The manufacturing industry in Indonesia faces significant challenges in the era of digitalization and technological transformation. To remain competitive, companies need to implement strategies that support improved organizational performance. This study aims to analyze the influence of knowledge management, digital adoption, and technology-based training on organizational performance. The study was conducted using a quantitative approach with a survey method of 130 employees from six manufacturing companies in Indonesia. Data were collected through closed-ended questionnaires and analyzed using multiple linear regression. The results of the analysis indicate that all independent variables significantly influence organizational performance. Knowledge management is the most dominant factor with a beta coefficient value of 0.438, followed by digital adoption (0.386) and technology-based training (0.342). The Adjusted R² value of 0.675 indicates that the three variables explain 67.5% of the variation in organizational performance. Furthermore, all proposed hypotheses are accepted based on significance values below 0.05. This study confirms the findings of previous studies, which demonstrate the importance of knowledge and technology management in driving organizational competitiveness. These findings provide practical recommendations for manufacturing company management to integrate knowledge management systems, accelerate technology adoption, and increase the effectiveness of technology-based training to improve organizational performance sustainably.
Smart Stego: A Web Application for Hiding Secret Data in Images with LSB and CNN Suryawan, I Gede Totok; Sudarma, Made; Putra, I Ketut Gede Darma; Sudana, Anak Agung Kompiang Oka
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.v5i4.1008

Abstract

This study develops a web-based steganography model to insert the identity of artisans in the form of palmprint images into the image of gringsing ikat woven cloth as a medium for ownership authentication. The method used in the insertion process combines a Convolutional Neural Network and the Least Significant Bit. In contrast, extracting or re-introducing palmprint images from stego images is carried out using a CNN-based classification model. This system was tested with two scenarios; in the first scenario, one palmprint image was inserted into 26 different cloth motifs, while in the second scenario, one cloth motif was inserted into 99 different palmprint images. The test results showed that the system produced consistent confidence values for all cloth motifs in the first scenario. In contrast, in the second scenario, the system achieved an average confidence of 93.5% and a recognition accuracy of 87%. The developed application has proven to be efficient with a reduction in stego image size of up to 66% while maintaining the quality of the stego image, as well as a speedy average execution time of 0.15 seconds for insertion and 0.09 seconds for extraction. These findings prove that the developed steganography model can effectively insert and re-recognize identity images (palmprints) in woven cloth images and has the potential to be applied as an image-based craft product ownership verification system.

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