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International Journal of Information Engineering and Science
Published by Asosiasi Riset Teknik Elektro Dan Informatika Indonesia
ISSN : 30481902     EISSN : 30481953     DOI : 10.62951
Core Subject : Engineering,
Engineering
The scope of the this Journal covers the fields of Information Engineering and Science. This journal is a means of publication and a place to share research and development work in the field of technology
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 27 Documents
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Detection of Sugarcane Plant Diseases Based on Leaf Image Using Convolutional Neural Network Method Arfian Hendro Priyono; Ema Utami; Dhani Ariatmanto
International Journal of Information Engineering and Science Vol. 2 No. 2 (2025): May : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v2i2.252

Abstract

As the primary raw material for sugar and ethanol production, sugarcane is a highly significant plantation commodity. However, its relatively long growing period of approximately one year makes it more susceptible to diseases. Machine learning technology has been applied in the identification of sugarcane leaves, including through pre-processing methods and the development of disease classification models using Convolutional Neural Network (CNN) and Support Vector Machine (SVM) approaches. However, these methods exhibit limitations in terms of accuracy. Therefore, improving identification accuracy using VGG-16 is essential. The objective of this study is to enhance the accuracy of sugarcane leaf disease identification by utilizing VGG-16. The dataset consists of  2,521 sugarcane leaf images categorized into five classes. The results of this study indicate an accuracy improvement from 97.78% to 99.14%, reflecting an increase of 1.36%
Hybrid CNN GRU Framework for Early Detection and Adaptive Mitigation of DDoS Attacks in SDN using Image Based Traffic Analysis Danang Danang; Indra Ava Dianta; Agustinus Budi Santoso; Siti Kholifah
International Journal of Information Engineering and Science Vol. 2 No. 3 (2025): August : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v2i2.292

Abstract

The threat of Distributed Denial of Service (DDoS) is increasing develop along with increasing use of the Internet of Things (IoT) and Software-Defined Networking (SDN) architecture . Although SDN provides convenience in management network , properties its centralized control make it prone to to flooding attacks that can paralyze controller performance . Detection method conventional , such as approach statistics and machine learning, still own limitations in matter accuracy , high false positive rate , and dependence on extracted features manually . To overcome problem said , research This propose a hybrid deep learning based DDoS detection and mitigation model that combines Convolutional Neural Network (CNN) to extraction feature spatial from RGB and Gated Recurrent Unit (GRU) images for understand temporal correlation between traffic data network . System tested through network test-bed Mininet based with Ryu/Floodlight controller, using simulation DDoS attacks (Hping3, LOIC) and normal traffic (video streaming, HTTP server). Traffic data cross recorded in PCAP format, processed become RGB image measuring 200×200 pixels, and labeled based on type traffic . Evaluation results with metric accuracy , precision, recall, F1-score, and MCC show that the CNN–GRU model has performance more superior compared to baseline approaches such as CNN-only, GRU-only, as well as classical ML methods such as SVM and Random Forest. In addition , the system capable apply mitigation adaptive through automatic flow rule creation on edge switches. Findings This confirm that effective deep learning- based spatial -temporal hybrid approach in increase detection early and response DDoS attacks on SDN networks adaptive and real-time.  
Changes in the Physical and Mechanical Properties of Clay Soil Due to Stabilization with Lime Ferly Indra Putra; Kiagus Ahmad Roni; Sri Martini
International Journal of Information Engineering and Science Vol. 2 No. 2 (2025): May : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v2i2.295

Abstract

Clay soil stabilization is a crucial process to enhance the soil's bearing capacity and stability, making it more suitable for construction purposes. Stabilizing clay soils improves their mechanical properties, reduces swelling, and increases their load-bearing capacity, which is essential for the foundation of various structures. This study aims to investigate the effect of lime (CaO) addition and curing time on the physical properties of clay soil, particularly focusing on unconfined compressive strength (qu) and overall soil stability. The experimental methodology involved applying different percentages of lime content (ranging from 3% to 7%) and varying curing times (7, 14, and 28 days). The soil samples were tested for their unconfined compressive strength after each combination of lime content and curing duration. The results indicated that the addition of 5% lime (CaO) and curing for 14 days led to a significant improvement in the unconfined compressive strength by 153.3%, compared to the untreated clay soil. Furthermore, increasing the curing time beyond 14 days did not show substantial improvements in strength, suggesting that 14 days is the optimal curing period for this combination. The study also highlighted that the lime treatment not only enhanced the mechanical properties but also reduced the plasticity of the clay, making it more stable and easier to handle during construction. Based on these findings, it can be concluded that the appropriate combination of lime content and curing time plays a significant role in improving the stability of clay soils. This research provides valuable insights into optimizing soil stabilization techniques, offering an effective solution for enhancing soil properties for engineering applications
Grouping of Toddler Nutritional Status Based on Anthropometric Data in Pekan Kuala Village Using the K-Means Clustering Method Dita Mawarni; Relita Buaton; Kristina Annatasia
International Journal of Information Engineering and Science Vol. 2 No. 3 (2025): August : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v2i3.300

Abstract

Nutritional issues among toddlers continue to be a pressing public health challenge in Indonesia, including in Kelurahan Pekan Kuala, where although anthropometric data have been systematically collected through the e-PPGBM application, they have not been thoroughly explored in terms of clustering patterns that may provide deeper insights. This study seeks to classify toddler nutritional status by applying the K-Means Clustering method to anthropometric indicators such as age, weight, height, and weight-to-height index. A dataset consisting of 648 entries recorded between January and March 2025 was processed using MATLAB R2014b with cluster variations set at 5, 7, and 9. The analysis revealed that the majority of toddlers were categorized as having good nutritional status, while a portion of the sample was identified as undernourished and some at risk of overnutrition, indicating the diverse nutritional challenges faced by this community. Furthermore, testing the variance across cluster configurations demonstrated that the 9-cluster model yielded the lowest variance score of 0.20, thereby representing the most optimal solution since it produced more homogeneous, balanced, and stable clusters compared to other configurations. These outcomes highlight the importance of data-driven approaches in public health planning, as the clustering results not only provide a clearer picture of nutritional distribution among toddlers but also serve as a foundation for more evidence-based and targeted intervention strategies. By offering a more granular understanding of nutritional variations, this research is expected to support local health authorities in developing customized nutrition programs, allocating resources more effectively, and ultimately improving child health outcomes in Kelurahan Pekan Kuala and similar communities across Indonesia, where malnutrition and overnutrition risks continue to coexist.
Addition of Plastic Mixture (LDPE) for the Development of Alternative Mixtures in Concrete Blocks Abdullah, Abdullah; Erna Yuliwati; Eka Sri Yusmartini
International Journal of Information Engineering and Science Vol. 2 No. 3 (2025): August : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v2i3.301

Abstract

This study investigates the potential of Low-Density Polyethylene (LDPE) plastic waste as a partial substitute for sand in concrete block mixtures, focusing on its effects on compressive strength and water absorption. LDPE is a non-biodegradable plastic waste that poses significant environmental challenges. Its incorporation into construction materials offers a promising solution to reduce pollution while enhancing the performance of building components. The research employed LDPE substitution levels of 10%, 15%, 20%, 25%, and 30% by weight of sand, compared against conventional concrete blocks without LDPE. Experimental results revealed that the highest compressive strength was achieved with a 15% LDPE mixture, reaching 80.762 kg/cm² at 28 days of curing—an increase of approximately 40.8% compared to normal blocks, which recorded 57.359 kg/cm². LDPE additions up to 20% maintained favorable strength characteristics, while higher proportions (25% and 30%) led to a decline in mechanical performance. In terms of water absorption, the inclusion of LDPE demonstrated a decreasing trend, attributed to the hydrophobic nature of plastic, which enhances moisture resistance in the concrete blocks. These findings suggest that a 15% LDPE substitution represents an optimal formulation for producing eco-friendly concrete blocks with improved strength and reduced water absorption. The study highlights the dual benefits of waste management and material innovation, aligning with sustainable development goals. By repurposing plastic waste into construction applications, this approach not only mitigates environmental impact but also contributes to the advancement of green building technologies. Further research is recommended to explore long-term durability, thermal properties, and scalability of LDPE-based concrete products in real-world construction settings.
Exploring the Integration of Information Systems and Security Challenges in Afghanistan’s Current Market Sayed Zakariya Habib; Mohammad Ali Fahimi; Mir Mohammad Naim Sadat
International Journal of Information Engineering and Science Vol. 2 No. 4 (2025): November : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v2i4.323

Abstract

This study aims to investigate the integration of information systems and the associated security challenges within Afghanistan's current market, emphasizing the complex relationship between technological innovation, governance stability, and institutional readiness. Using the Delphi method, the study engaged experts from academia, government, and the private sector to identify key barriers and enablers shaping Afghanistan's digital transformation. Findings reveal that the country's progress in adopting information systems is hindered by fragmented policies, weak cybersecurity awareness, infrastructure limitations, and dependency on donor-funded projects. Despite growing recognition of the importance of digitalization, Afghanistan's institutional fragility continues to impede coordinated implementation and sustainable innovation. Comparative insights with other emerging markets highlight that long-term investment in digital literacy, regulatory coherence, and private sector engagement are critical to overcoming these barriers. The study highlights the importance of adopting a hybrid developmental model that harmonizes local institutional realities with internationally recognized technological standards, fostering adaptability and resilience within Afghanistan's volatile environment. It advances existing understanding by demonstrating how governance reform, human capital enhancement, and cybersecurity integration function as mutually reinforcing components of the nation's digital transformation. Sustainable progress depends on establishing a unified national vision that bridges technology, education, and governance, thereby reinforcing market integrity and institutional stability amid persistent security and economic uncertainty.
Customer Data Management Analysis for Customer Segmentation Using K-Means Clustering Method Andre Leto; Reza Aminullah; Ani Dijah Rahajoe
International Journal of Information Engineering and Science Vol. 2 No. 4 (2025): November : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v2i4.345

Abstract

This study aims to examine customer segmentation through K-Means clustering from a customer data management perspective, emphasizing the interpretive value of analytical results rather than solely their computational outcomes. The research addresses a critical issue in contemporary data-driven organizations, where customer analytics is often reduced to technical modeling without sufficient translation into managerial insights. To respond to this gap, the study adopts a qualitative interpretive approach embedded within a quantitative clustering process, positioning clustering as part of a broader information management cycle. The empirical analysis is based on the Mall Customers Dataset obtained from Kaggle, consisting of 200 customer records with numerical attributes representing age, annual income, and spending score. Quantitative processing using K-Means clustering was employed to identify customer segments, while qualitative interpretation was applied to analyze the managerial meaning of each cluster. Data interpretation was supported by analytical documentation, visualization outputs, and reflective analysis of cluster characteristics. The findings reveal four distinct customer segments with different behavioral and economic profiles, each carrying specific strategic implications for customer relationship management and marketing decision-making. The study demonstrates that the primary value of clustering lies not merely in segment formation, but in its ability to transform raw customer data into actionable managerial knowledge. In conclusion, this research contributes to customer analytics literature by integrating data mining techniques with qualitative interpretation, offering a more human-centered and decision-oriented framework for customer data management. Future research is encouraged to extend this approach using organizational case studies or participatory decision-making contexts.

Page 3 of 3 | Total Record : 27

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