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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 : -
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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 81 Documents
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Search results for , issue "Vol 5, No 3 (2025)" : 81 Documents clear
Comprehensive Risk Analysis in Cargo Transportation by Land: Implications for Safety and Operational Efficiency Soimun, Ahmad; Gilang Rupaka, Anggun Prima; Widiangga Gautama, Nengah; Ariesthada Sadri, Putu Diva; Darma Yanti, Ni luh; Rizky Navianti, Dynes; Yuda Novianto, Hendra
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.v5i3.949

Abstract

Land transportation is a key pillar in the freight transportation system that is often faced with various operational risk events. This study aims to identify risk agents and develop mitigation strategies for transporting goods through land transportation. The House of Risk (HOR) method identifies risks, determines priority risk agents, and formulates effective mitigation strategies. Based on observations and questionnaire analysis, 14 risk events caused by 21 risk agents were identified, of which 12 agents were categorized as priority and nine as non-priority. Delays in issuing delivery notes or bills of lading (A4) were identified as the risk agent with the highest priority index of 3954.71. Of the 16 risk-handling strategies formulated, periodic inspection and maintenance of vehicles (PA2) has the highest Effectiveness to Difficulty (ETD) value of 63755.2. The results of this study provide a systematic framework for risk management in land transportation to improve the effectiveness and reliability of freight operations.
Implementation of Intrusion Detection System Using Snort and Log Visualization Using ELK Stack Robbani, Fatih Dien; Haryatmi, Emy; Riyadi, Tri Agus; Supono, Riza Adrianti; Bima Kurniawan, Ary; Rosdiana, Rosdiana
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.v5i3.901

Abstract

Cyber threats like malware, ransomware, and DDoS attacks demand fast and integrated detection systems. Traditional network monitoring tools often struggle to identify complex real-time attack patterns. This study evaluates the integration of Snort, an Intrusion Detection System (IDS), with the ELK Stack (Elasticsearch, Logstash, Kibana) to detect and visualize cyberattacks effectively. The system was tested against three attack scenarios: a Windows ping flood, port scanning using Zenmap, and SSH brute force attacks via Nmap Scripting Engine (NSE). Wireshark was employed as a supporting tool to monitor raw network traffic. The results indicate that Snort detected all simulated attacks in real time, and the ELK Stack efficiently processed and visualized the alert data. However, limitations in Kibana's dashboard refresh rate slightly hindered real-time monitoring capabilities. Overall, the integration of Snort and the ELK Stack proves effective for network threat detection and analysis, with room for future improvements in visualization performance and automated response mechanisms.
Exploring the Synergy: User Stories in Agile Software Development Binti Mustaffa, Siti Nur Fathin Najwa; Bin Sallim, Jamaludin; Binti Mohamed, Rozlina
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.v5i3.1161

Abstract

User Stories are commonly used artifacts to capture user requirements in Agile Software Development (ASD). They are short, semi-structured statements that describe requirements. Natural Language Processing (NLP) techniques can be advantageous for research on user stories. This paper investigates User Stories and NLP about their applications, critically examines existing research approaches related to NLP in user stories, presents the challenges and suggested future work. Relevant papers were obtained from well-recognized digital libraries and scientific databases, including ScienceDirect, Scopus, SpringerLink, and IEEE Xplore. Inclusion and exclusion criteria were applied to filter search results and obtain comprehensive findings. The search results identified 1175 papers published between 2014 until 2024. After applying the inclusion/exclusion criteria, 35 primary studies discussing NLP techniques in user stories were selected. The purposes of these studies vary, encompassing defect discovery, software artifact generation, key abstraction identification in user stories, and linking models and user stories. NLP can assist system analysts in managing user stories. Implementing NLP in user stories offers numerous opportunities and challenges. Exploring NLP techniques and employing rigorous evaluation methods are necessary for high-quality research. As with general NLP research, understanding the context of sentences remains a challenge.
Advancing the Measurement of MOOCs Software Quality: Validation of Assessment Tools Using the I-CVI Expert Framework Praseptiawan, Mugi; Che Pee, Ahmad Naim; Zakaria, Mohd Hafiz; Noertjahyana, Agustinus
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.v5i3.911

Abstract

The growing use of MOOCs in the post-pandemic era, particularly in developing countries, requires the availability of valid assessment tools to ensure software quality that meets users' needs. However, several tools are still being used without a proper content validation process, which risks producing biased and unrepresentative data. This study aims to evaluate the validity of the content of an assessment instrument designed to measure the dimension of software quality on the Massive Open Online Courses (MOOC) platform, particularly in the context of the increased adoption of online learning post-pandemic in developing countries. The instrument comprises 27 statement items representing ten quality software factors: functionality, usability, reliability, performance, security, maintainability, portability, compatibility, support, and integration. The validation was carried out by involving seven experts in information systems and digital learning. The method used is an item-level content validity index (I-CVI) based on a descriptive quantitative approach, with each item being assessed using a 5-point Likert scale. An item is declared valid if it obtains an I-CVI score of ? 0.79. The analysis showed that 21 items were valid; three needed to be revised at the I-CVI value between 0.70–0.78, and 3 invalid items at the I-CVI value 0.70. The functionality, usability, support, and integration quality factors had the highest levels of validity, while the safety and support dimensions showed a higher degree of divergence in the expert assessment. These findings highlight the need for content validation to ensure MOOC indicators are accurate and relevant. The study showed the need for advanced validation tests involving real users and other validation methods, such as Aiken V or the fuzzy analysis hierarchy process (FAHP) to enhance the reliability and practical relevance of the tools developed.
Expert System For Detecting Soil Fertility Levels for Oil Palm Cultivation Using the Fuzzy Tsukamoto Yanti, Winda; Yunizar, Zara; Afrillia, Yesy
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.v5i3.884

Abstract

Soil fertility is one of the critical factors that affect the productivity of oil palm plants. Inappropriate soil fertility levels can cause suboptimal plant growth and even crop failure. Low public knowledge about soil fertility is also a significant factor. This research aims to build an expert system that can detect the soil fertility level for oil palm plants using the fuzzy Tsukamoto method. This system uses three main parameters as a reference: soil acidity (pH), soil moisture, and soil texture. The fuzzy Tsukamoto method was chosen because it can handle uncertain data and provide more flexible results. The system was developed web-based using the PHP programming language and MySQL database, and tested on 49 soil data points from the Agricultural Extension Center of Matangkuli District, North Aceh Regency. The system successfully detected soil fertility levels accurately and consistently. Tests were conducted on 49 soil sample data from various villages in Matangkuli District, North Aceh Regency, where soil fertility in the Low category was found in 43 villages with a percentage of 84%, soil fertility in the Medium category was found in 6 villages with a rate of 16% and soil fertility in the High category was not found in any town of Matangkuli District with a percentage of 0% with valid fertility classification results and by expert judgment. With this system, farmers and agricultural extension workers can be helped to make the right decisions regarding the feasibility of land for planting oil palm plants.
The Impact of Government Cooking Oil Subsidies on Social Welfare: Elasticity-Based Multimarket Approach Fatimah, Fatimah; A. Gani, Subhan; Bahri, Syamsul; Meutia, Sri; Syukriah, Syukriah
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.v5i3.967

Abstract

This paper explores the influence of the Indonesian government's palm cooking oil subsidy policies on the welfare of communities in Aceh Province. The Policy aims to stabilise prices, to support producers, and to increase access to affordable cooking oil for consumers. However, empirical evidence shows that the market price frequently exceeds the government-determined ceiling price, which raises concerns about the Policy's effectiveness. To evaluate the actual impact of the subsidy, this study employs a multimarket analytical model in conjunction with an interest-maximising function approach to identify the optimal subsidy level that maximises social welfare. The analysis includes palm and coconut cooking oil as interrelated commodities, with supply and demand modelled using Cobb-Douglas functions. The study focuses on Elasticity as a key determinant in understanding the effectiveness of the subsidy, given that inelastic behaviour in supply and demand significantly affects the price gap between market equilibrium and subsidy-induced outcomes. Simulation results reveal that the optimal subsidy rates are 16% for palm oil and 26% for coconut oil. Furthermore, sensitivity analysis across four scenarios shows that lower Elasticity necessitates higher subsidies, while higher Elasticity can reduce the required subsidy without diminishing welfare gains. The results indicate that producers enjoy most of the welfare gains, although consumers also benefit from lower market prices. Therefore, a well-calibrated subsidy policy, based on empirical elasticity values and multimarket interactions, can lead to a more balanced welfare distribution. The findings underscore the importance of data-driven policy formulation to enhance efficiency and equity in government subsidy programs. Ultimately, this research recommends that future subsidy frameworks integrate elasticity parameters and economic modelling to ensure affordability and sustainability in essential commodity markets.
Retrieval Augmented Generation-Based Chatbot for Prospective and Current University Students Hartono, Luluk Setiawati; Setiawan, Esther Irawati; Singh, Vrijraj
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.v5i3.951

Abstract

Universities utilize chatbots as assistants for users, especially prospective and current students, to access information and answer questions with relevant answers. This study introduces a new approach to an open-source model-based QA system using Gemma2-2b-it by combining Retrieval Augmented Generation (RAG) and Fine-tuning (FT) techniques. Previously, some studies have focused on only one approach, but this study will combine and compare both methods separately. Raw conversation data from WhatsApp, the main university website, and university PDF documents are used. The Retrieval Augmented Generation Assessment (RAGAS) framework will be used to evaluate the performance of the RAG model. In contrast, precision, recall, and similarity are used to assess the comparative performance of RAG and fine-tuning. The results of the RAGAS show that RAG using the base model is better than RAG using a fine-tuned model, which has 0.78 faithfulness, 0.64 answer relevancy, 0.81 context precision, and 0.68 context recall, so the overall RAGAS Score is 0.72. The comparison of precision and recall of fine-tuning are higher than those of using RAG, but the similarity score is not much different. Furthermore, the potential improvement for RAG of this study can be increased by adding a reranking process in the retrieved context, and fine-tuning of the embedding model can also be added to increase the retrieval process's performance. In addition, further experiments on various datasets and the challenge of overfitting in fine-tuning must be overcome so that the model can also perform better generalization.
Swarm Intelligence-Based Performance Optimization for Wireless Sensor Networks for Hole Detection Padmapriya, T; Jadhav, Chaya; Nyayadhish, Renuka; Kumar, Neeraj; Kaliappan, P
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.v5i3.1127

Abstract

Extensive research into maintaining coverage over time has been spurred by the growing need for wireless sensor networks to monitor certain regions.  Coverage gaps brought on either haphazard node placement or failures pose the biggest threat to this objective.  In order to identify and fix coverage gaps, this study suggests an algorithm based on swarm intelligence.  Using both local and relative information, the swarm of agents navigates a potential field toward the nearest hole and activates in reaction to holes found.  In order to spread out effectively and speed up healing, the agents quantize their perceptions and approach holes from various angles. The need for wireless sensor networks to monitor certain areas has grown, leading to many studies on maintaining coverage over time. Random node deployment or failures create coverage gaps, which pose the biggest threat to this objective.  A swarm intelligence-based approach is proposed in this paper to identify and fix coverage deficiencies. Even with Their encouraging performance and operational quality, WSNs are susceptible to various security threats. The security of WSNs is seriously threatened by sinkhole attacks, one of these. In this research, a detection strategy against sinkhole attacks is proposed and developed using the Swarm Intelligence (SI) optimization algorithm. MATLAB has been used to implement the proposed work, and comprehensive Models have been run to assess its effectiveness in terms of energy consumption, packet overhead, convergence speed, detection accuracy, and detection time. The findings demonstrate that the mechanism we have suggested is effective and reliable in identifying sinkhole attacks with a high rate of detection accuracy.
Smart Cropping Pattern: A Systematic Study of Sustainable Agriculture Optimization Model Mushthofa, Mushthofa; Suripin, Suripin; Wulandari, Dyah Ari; Qomaruddin, Mochammad
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.v5i3.1132

Abstract

Irrigated agriculture accounts for more than 40% of global food production despite covering only about 20% of the world's agricultural land. However, climate change, water constraints, and multisectoral pressures on natural resources demand greater efficiency in the management of agricultural systems. One key strategy is determining optimal cropping patterns under conditions of water and land constraints. This study aims to review mathematical approaches, especially Linear Programming (LP)-based optimization models, in developing efficient and sustainable cropping pattern strategies. This study was conducted through a systematic literature review of 185 scientific articles from the Scopus and ScienceDirect databases in the period 2014–2025. The analysis was carried out using the PRISMA method and visualization of research trends through VOS viewer software. The results of the review indicate that optimization models, especially Linear Programs, have been widely used to develop data-based land and water allocation strategies, considering agronomic, economic, and environmental aspects. The increasing number of publications in the last decade reflects the urgency of this theme and the shift towards quantitative-based decision-making in agricultural systems. This study provides a conceptual and applicative basis for the development of sustainable planting strategies that are adaptive to environmental changes.
Emerging Carbon Capture Technologies in the Palm Oil Industry: A Review of Bioenergy and Carbon Capture Storage Approaches Judijanto, Loso
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.v5i3.1170

Abstract

The accelerating impacts of climate change have heightened global interest in technologies that reduce greenhouse gas emissions from high-emission sectors such as agriculture and agri-processing. The palm oil sector is notably both a significant emitter and a promising avenue for decarbonization efforts, particularly through the integration of bioenergy systems and carbon capture technologies. This study aims to explore the current state of technological development in carbon capture and storage (CCS) and bioenergy applications within the palm oil industry and to identify the major challenges and opportunities that shape their implementation between 2021 and 2025. This investigation employs a qualitative design through the SLR method, structured in accordance with the PRISMA framework for transparency and rigor in literature synthesis. Data were collected from the ScienceDirect database using a refined combination of Boolean search terms. A total of 1,088 articles were initially identified and screened through a multistage filtration process that included relevance checks, publication period constraints, research article type, and open-access availability. This process resulted in 36 articles that met all inclusion criteria and were analyzed further. Data were synthesized through thematic analysis to classify technological pathways, assess implementation trends, and evaluate technical, economic, and policy-related barriers. Findings reveal that while bioenergy from palm oil residues is widely adopted, CCS deployment remains minimal due to cost, infrastructure, and regulatory limitations. The study concludes that targeted policy support and innovation are essential to scaling up carbon management in this sector. Future research should prioritize pilot demonstrations and interdisciplinary assessments of CCS integration feasibility.

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