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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 80 Documents
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Search results for , issue "Vol 5, No 1 (2025)" : 80 Documents clear
Predicting Electricity Consumption in Aceh Province Using the Markov Chain Monte Carlo Method Gavinda, Virza; Nurdin, Nurdin; Fajriana, Fajriana
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.678

Abstract

Electricity is essential to nearly every aspect of modern life, from industrial sectors to household needs. In Aceh Province, the demand for electricity has consistently increased along with economic growth, urbanization, and population expansion. Various studies indicate that rising electricity consumption is closely linked to economic growth and industrialization. This study uses the Markov Chain Monte Carlo (MCMC) method with the Metropolis-Hastings algorithm to predict electricity consumption in Aceh Province. The research addresses the significant increase in electricity consumption driven by economic growth and urbanization in the region. Electricity consumption data from January 2018 to December 2022 was utilized as the basis for modeling. The results indicate a 32.4% increase in electricity consumption over the past five years. The predictive model achieved high accuracy with a Mean Absolute Percentage Error (MAPE) of 2.41%, demonstrating its reliability in forecasting future electricity needs. Projections through 2030 show a continuous increase, reaching 482 GWh by the end of the period. These findings are expected to support decision-making in sustainable energy planning and providing adequate electricity infrastructure in Aceh. This study highlights the effectiveness of the Me-tropolis-Hastings algorithm in handling complex data with high variability, providing valuable insights for long-term energy planning
Comparison of Bored Pile Capacity Based on Analytical Design and Pile Load Test – A Case Study Putri, Karina Meilawati Eka; Fatikasari, Aulia Dewi; Wibisana, Hendrata
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.659

Abstract

This paper presents a comparative study of bored pile ultimate capacity based on analytical design and field tests. The object of this analysis is the bored pile foundation of the Sei Alalak Bridge in Banjarmasin, Indonesia. The analytical design of pile ultimate capacity was carried out using the empirical methods provided by Reese and O'Neill (1988) and Meyerhof (1976). The calculation of pile ultimate capacity using the empirical method is based on SPT data from four boreholes representing soil data in the abutment, tower, and counterweight zones. Two pile load tests were used to validate the analytical design: pile driving analysis (PDA) and the bi-axial load test Osterberg Cell (O-Cell). The pile ultimate capacity from the empirical method is then compared to field tests regarding pile shaft resistance and end-bearing capacity. The analysis results indicate that the empirical methods tend to underestimate the pile's ultimate capacity by 30–60%. The results reveal that the Reese and O'neill (1988) empirical method generates a significantly lower pile ultimate capacity than Meyerhof (1976). This indicates that the Meyerhof (1976) method gives a closer result of pile ultimate capacity than the field test. On the contrary, the Reese and O'Neill (1988) method is more consistent with the PDA test results. As a result, in this study, the Reese and O'Neill (1988) method is preferred over the Meyerhof (1976) method for predicting the ultimate capacity of a bored pile since it has been demonstrated to be more reliable in estimating the pile's ultimate capacity.
Implementation of Data Mining for Nutrition Clustering of Toddlers at Posyandu Using K-Means Algorithm Kamilah, Muna; Abdullah, Dahlan; Suwanda, Rizki
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.718

Abstract

This study aims to determine the nutritional status of toddlers in the Peulimbang sub-district using a clustering method that can group toddlers based on their dietary indicators, such as gender (jk), age (u), height (tb), weight (bb), and upper arm circumference (Lila). By using data analysis techniques such as K-Means clustering, this study successfully identified several groups of toddlers with different nutritional statuses, ranging from malnutrition to good nutrition to obesity nutritional status. The data for this study were taken directly from the Peulimbang Health Center, and as many as 765 toddler data were collected from 16 villages in the Peulimbang area. The programming language used in this study is PHP, which functions in web development and is often used to process data sent via web formula, interact with databases, and manage user sessions. Based on the results of clustering with the K-Means method using Euclidean Distance as a measurement between points, toddler data has been grouped into three Clusters, namely C1, C2, and C3. Cluster C1 covers 22.81% of 147 malnourished toddlers, C2 covers 48% of 323 well-nourished toddlers, and C3 covers 29.19% of 205 obese toddlers. Based on the clustering results, improving nutrition education programs for parents is essential, especially in areas with poor or lacking nutritional status. This program can include counseling on the importance of balanced nutrition, nutritious cooking methods, and choosing the right Food for toddlers. Thus, this study is expected to contribute to improving toddlers' nutritional status and overall public health in the Peulimbang District area.
Student Literacy Through Library Visits and Gemini AI Programs at SD Negeri Potrobangsan 2 Yarmini, Yarmini; Supriadi, Didi; Purnami, Sri
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.631

Abstract

People often say that literacy is the foundation for a person's lifelong education. Improving students' literacy skills is one of the main focuses in today's education world. Literacy, which includes reading, writing, and understanding information well, is an essential foundation in the learning process at all levels of education. Good literacy skills help students succeed academically and equip them with the critical and analytical thinking skills needed in everyday life. We need to consider literacy development because it is a fundamental ability that every individual must possess to navigate life in the future. This study aims to develop a strategy to improve the literacy of fifth-grade students at SD Negeri Potrobangsan 2 through a combination of library visits and the Gemini AI program. We expect library visits to cultivate students' interest in reading physical books, while the Gemini AI program serves as an interactive digital literacy medium to enhance students' learning experiences. This research approach uses a qualitative method with observation, in-depth interviews, and focus group discussions (FGD) as the primary instruments for data collection. The study results indicate that integrating traditional literacy learning with digital technology can improve students' literacy skills holistically. Students showed increased reading comprehension, higher involvement in literacy activities, and mastery of digital literacy skills. Implementing this strategy also increased motivation among students to read and utilize the library optimally. Therefore, elementary schools can apply this strategy as an effective literacy learning model.
Clustering Agricultural Productivity by Type and Results Using K-Medoids Method in Districts North Aceh Zahara, Mutia; Fuadi, Wahyu; Meiyanti, Rini
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.699

Abstract

This research aims to develop a web-based application that can cluster sub-districts in North Aceh District based on the type and yield of agricultural productivity, focusing on increasing the ease of visualization and data analysis by users. The method applied in this research is K-Medoids, a clustering technique used to group sub-districts based on high, medium, and low harvest levels. The application will use data from the North Aceh District Agriculture Office, covering 2021 to 2023, including various food crops such as rice, corn, peanuts, green beans, cassava, sweet potatoes, and soybeans. This research will analyze the sub-district name, type of agriculture, year of production, planting area, and harvest area to identify clusters of sub-districts with similar agricultural yield patterns. The system is developed using the PHP programming language to facilitate implementation and data access by stakeholders. As an evaluation tool for clustering results, the Davies-Bouldin Index (DBI) is used to measure the quality of clustering results. The results of this study are expected to provide insights into agricultural productivity in North Aceh District and assist policymakers in designing more effective strategies to increase agricultural yields, especially in low-yielding sub-districts. In addition, this application also provides an interactive platform for users to analyze agrarian data quickly and efficiently.
Comparison of Rigid Pavement Planning Using PD T-14-2003 and NAASRA 1987 Methods in Industrial Areas Krisdiyanto, Aris; Dewi, Kemmala; Azis, Faruk
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.613

Abstract

As vital infrastructure, roads are a means of transportation access and function as a distribution route for goods and services. Road pavement plays a crucial role in highway construction, necessitating proper planning according to Indonesian standards and planning criteria to ensure smooth land transportation and provide comfort and safety for its users. This study aims to provide information on road conditions, drainage dimensions, and construction costs. Planning is crucial in every road construction design, particularly for rigid pavement, based on survey data collected from the road section. Numerous methods exist for calculating the design value of rigid pavement. We use the Pd T-14-2003 method and the NAASRA 1987 method (National Association of Australian State Road Authorities) to plan rigid pavement thickness in the industrial area of PT. Bukit Muria Jaya Kudus. The Pd T-14-2003 method yielded a JSKN of 4x107 and a CBR of 35% for the subgrade, resulting in a plate thickness of 150 mm. While the Naasra 1987 planning method achieved a JSKN of 6x107 and a CBR of 35% for the base soil, a plate thickness of 160 mm was obtained with a value of K = 80 kPa/mm. The reinforcement planning using the Pd T-14-2003 and Naasra 1987 methods involves longitudinal reinforcement D10 mm at a distance of 150 mm and transverse reinforcement D10 mm at 250 mm. The drainage dimensions at the cross-section location are width x height (1 m x 1 m), and the guard's height is 0.2 m.
Comparative Analysis of K-Means and K-Medoids to Determine Study Programs Salamah, Salamah; Abdullah, Dahlan; Nurdin, Nurdin
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.673

Abstract

Education is the main foundation for the advancement of civilization. A high level of education in society is directly proportional to the progress of that civilization. Higher education plays an important role in shaping quality human resources and contributing to community and national development. In today’s era of information and technology, data processing and analysis are key to understanding the development of study programs in higher education institutions. Clustering techniques are used to identify patterns and relationships in large and complex datasets, which are crucial in determining study programs at educational institutions. This research compares two popular clustering methods, K-Means and K-Medoids to determine study programs. The data used consists of odd semester grades of 87 students in the third-years of high school with 5 variables. The information of clusters is based on the minimum academic criteria of 18 study programs representing 7 faculties in Malikussaleh University and grouped into 5 clusters. The evaluation of clusters is conducted using the Davies-Bouldin Index (DBI). The result of the study indicate that K-Means algorithm has 5 clusters with cluster members of 31, 5, 13, 26 and 17, and a DBI value of 1,19010. Meanwhile, the K-Medoids algorithm has 5 clusters with cluster members of 33, 15, 17, 17 and 5, and a DBI value of 1,27833. Based on the DBI value, the K-Means algorithm demonstrates better cluster quality compared to the K-Medoids algorithm.
Performance Analysis of SVM and Linear Regression for Predicting Tourist Visits in North Sumatera Ginting, Andriyan; Nurdin, Nurdin; Agusniar, Cut
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.667

Abstract

Indonesia, an archipelago rich in cultural diversity, historical heritage, and stunning natural scenery, offers an extraordinary travel experience to visitors who make this country their vacation destination. Tourism in Indonesia plays an essential role in the domestic economy, contributing to Gross Domestic Product. With its abundant natural and cultural resources, North Sumatra has long been recognized as an attractive destination for foreign tourists. However, the tourism sector faces significant challenges related to fluctuations in the number of visits, mainly due to the impact of the COVID-19 pandemic, which has disrupted global travel patterns and caused considerable uncertainty in tourism forecasting. Therefore, predicting the number of tourist visits becomes crucial for effectively planning and managing tourist destinations. This research aims to compare the performance of two forecasting algorithms, SVM and linear regression, in predicting foreign tourist visits in North Sumatra using historical data from 2019 to 2023. The dataset was subjected to a preprocessing phase to ensure data cleanliness and consistency, focusing on key variables such as seasonal trends, external factors, and market dynamics. Both models were evaluated based on two commonly used accuracy metrics, MAPE and RMSE, to assess how well the models could predict actual tourist arrivals. The results of the study indicate that Linear Regression outperforms SVM in terms of prediction accuracy, with a MAPE of 42.40% and an RMSE of 6735.6, compared to SVM with a MAPE of 46.65% and an RMSE of 8020.42. These findings provide valuable insights for local government authorities and tourism industry stakeholders to enhance destination planning, resource allocation, and strategies to attract more foreign tourists in the post-pandemic era.
The Role of Job Authority and Employee Empowerment on Employee Commitment in the Public Service Sector Djunaedi, Djunaedi; Kusnadi, Iwan Henri; Apramilda, Riesna; Osman, Isnawati; Setyawati, Kiki; Handayani, Sri
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.749

Abstract

Team member commitment is at the core of organizational performance and durability, especially in the public sector. The study seeks to supplement existing knowledge by examining the effects of job authority and job empowerment on team member commitment and its impact on motivation, job satisfaction, and loyalty to the organization. This was obtained through a mixed-method approach, drawing together quantitative survey data with qualitative in-depth interviews with public workers. The findings indicate that the greater the level of authority, the more responsibility there is, and the more freedom there is for decision-making, the greater the commitment to the organization. Knowing how to do the work properly and having the proper skills and self-confidence were also essential for team member commitment. Employees at the lower organizational levels reported having feelings of encouragement and interest in the company, as well as integration into the strategic objectives of the company or enterprise. These results indicate the need for public service organisations to adopt a sustainable employee commitment culture focusing on authority and empowerment. Such issues need to be examined as time passes in future studies. The findings emphasize the importance of job authority and empowerment as determinants of team member engagement in the public sector compared to team member engagement in the private sector: assistive medical devices and many other organizations.
Identification of Papaya Ripeness Using the Support Vector Machine Algorithm Maito, Rizki Minta; Qamal, Mukti; Fajriana, Fajriana
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.710

Abstract

Papaya is a tropical fruit that is commonly consumed and found in Indonesia. The ripeness level of papaya is typically assessed based on its colour. However, farmers and consumers often make mistakes identifying the fruit's ripeness. This research aims to design an application capable of determining the ripeness level of papaya based on colour images using Red, Green, Blue (RGB) and Hue, Saturation, Value (HSV) features and applying the Support Vector Machine (SVM) algorithm for ripeness classification. The dataset consists of images of California papayas, with 150 samples. The outcome of this study is a digital image application that can classify papaya ripeness into three categories: raw, half-ripe, and fully ripe. The evaluation used 80% of the data for training and 20% for testing. The results show an accuracy of 80%. With this relatively high level of accuracy, it can be concluded that the SVM algorithm is reliable for classifying papaya ripeness levels of Papayas.

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