cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
edukomputika@mail.unnes.ac.id
Phone
-
Journal Mail Official
edukomputika@mail.unnes.ac.id
Editorial Address
Sekaran, Kec. Gn. Pati, Kota Semarang, Jawa Tengah 50229
Location
Kota semarang,
Jawa tengah
INDONESIA
Edu Komputika Journal
Published by Universitas Negeri Semarang
ISSN : -     EISSN : 2599297X     DOI : https://doi.org/10.15294/edukom
Core Subject : Education,
Education
Edu Komputika Journal uses Open Journal Systems (OJS) for online journal management in submission, review, copyediting, and publication. Submitted manuscripts are written in English and should follow the style of the Edu Komputika Journal. Manuscripts are original research results, or theoretical/literature study results that have never been published in other journals or are not considered for publication elsewhere. The author should follow all the provisions and processes. Accepted papers will be available online and will be charged a publication fee.
Arjuna Subject : Ilmu Sosial - Pendidikan
Articles 24 Documents
 1   2   3 
Ecofy App UX Design using User-Centered Design Approach for Waste Management Baihaqi, Wiga Maulana; Liulinuha, Qurrota A'yun; Buana, Tresnanda Agsifa Cakra; Najmuddin, Faris Labib
Edu Komputika Journal Vol. 11 No. 1 (2024): Edu Komputika Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukom.v11i1.10927

Abstract

Indonesia faces a significant waste management challenge, particularly with plastic waste, which poses severe environmental risks and has low recycling rates. This study aims to address these challenges by designing and developing the Ecofy application using the User-Centered Design (UCD) approach. The focus is on creating a solution that not only meets user needs but also effectively facilitates the waste management process, making it more accessible and efficient for the general population. The UCD methodology was employed to ensure that the application’s features are intuitive, user-friendly, and tailored to the local waste bank model, which encourages recycling at the community level. The research involved multiple stages, including user needs assessment, iterative design, and usability testing, to refine the application. The findings demonstrate that the Ecofy application significantly enhances the effectiveness of waste management by offering practical tools for users to sort, manage, and recycle waste. Furthermore, the implementation of this application is projected to reduce the burden on Final Processing Sites (TPA), lower environmental pollution, and increase recycling rates, especially for non-biodegradable materials like plastic. The success of Ecofy could serve as a model for other regions facing similar waste management issues, contributing to broader sustainable development goals in Indonesia.
Decision Support System for Service Development Priority Selection at Pitcar Service Using the ELECTRE Method Nur Aela Dewi; Khairun Nisa Meiah Ngafidin
Edu Komputika Journal Vol. 11 No. 2 (2024): Edu Komputika Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukom.v11i2.10310

Abstract

The increasing competition in the automotive industry is pushing companies like Pitcar Service, a car workshop in South Purwokerto, to develop efficient and quality services. However, this workshop faces limitations in assets, materials, and capabilities. Therefore, Pitcar Service requires a Decision Support System (DSS) to assist in determining the priority for service development. The DSS helps identify and prioritize the most important services or those that have the greatest impact on business growth. By focusing on strategic development that provides significant added value, the company can achieve its strategic goals in line with the company's vision. The method used in this study is ELECTRE (Elimination and Choice Translation Reality), a suitable algorithm for designing a decision support system. This method allows pairwise comparisons between alternatives based on relevant criteria. The assessment is done by comparing each criterion individually to determine whether an alternative dominates other alternatives. With this method, the assessment of workshop services can be done objectively. The service alternatives considered include Workshop Service, Pick Up Service, Emergency Service, Home Service, Variations, and B2B Services. Meanwhile, the criteria used for the assessment include the number of requests, average income per job, tool usage, the number of mechanics, and processing time. The calculation results show that B2B Service is the main priority for service development with a dominant aggregation matrix result is 2.
Creation of a Virtual Laboratory for Collision Dynamics Educational Tool with Integrated Collision Algorithm Yusupa, Ade; Kalua, Aditya Lapu; Zalmi, Wahyuni Fithratul; Kurnia, Rahmi Putri; Tarigan, Victor
Edu Komputika Journal Vol. 11 No. 2 (2024): Edu Komputika Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukom.v11i2.10612

Abstract

Conventional physics laboratories often suffer from limitations in terms of equipment availability and safety, which hinders optimal learning of collision dynamics concepts. This research aims to develop a virtual laboratory based on collision algorithm to simulate perfect collision as an alternative solution in physics learning. The development uses the ADDIE model, which includes the stages of analysis, design, development, implementation, and evaluation. The collision algorithm was implemented using ActionScript 3, with interpolation allowing for more accurate collision detection at high speeds. The validation results show that the simulation is in line with the law of conservation of momentum and kinetic energy and is consistent with analytical solutions from MATLAB and Python. Functionality testing was conducted by 20 students, and the results showed that the use of this virtual laboratory significantly improved their concept understanding, with the average improvement ranging from 24% to 56%. Students also reported that this virtual laboratory is more interactive and interesting, thus increasing their learning motivation. The conclusion of this study is that the collision algorithm-based virtual laboratory is effective as a physics learning media and can be adopted more widely in technology-based education, especially to understand complex physics concepts more deeply.
Implementation of Equivalence Partitioning Techniques and ISO/IEC 25010 Functional Suitability Standards for Testing SCM Applications in Small-Scale Automotive Industries Ratwiyanti, Puji; Ismono, Ahlan; Dhiya Rahmani, Almas
Edu Komputika Journal Vol. 11 No. 2 (2024): Edu Komputika Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukom.v11i2.18109

Abstract

A Supply Chain Management (SCM) application prototype for PT XYZ has been developed. Before its release, the testing phase is crucial to minimize functional failures. This study aims to conduct comprehensive testing to verify that all functions operate as intended, deliver the expected results, and identify potential errors in the application. Unlike previous research, this study not only uses Equivalence Partitioning as a functional testing technique but also combines it with the ISO/IEC 25010 standard, which guides Functional Suitability and software quality more comprehensively. This approach addresses the limitations of Equivalence Partitioning, which typically focuses only on input class division without ensuring coverage of specific quality standards. Furthermore, integrating testing techniques with the internationally recognized ISO/IEC 25010 standard enhances the relevance and applicability of this research in software development for industries, particularly in supply chain information processing automation. By combining both methods, the testing process becomes more thorough and structured, as evidenced by the 254 test cases generated. Additionally, this study provides quantitative data in the form of valid (83%) and invalid (17%) test case percentages, which can serve as indicators of testing effectiveness and the prototype's quality.
Benchmarking YOLOv3 and SSD: A Performance Comparison for Multi-Object Detection Prasetyo, Septian Eko; Atmaja, Chandra; Ardian, Muhammad; Ardhiansyah, Alfian; Sudarni, Ajeng Rahma; Khaira, Mulil
Edu Komputika Journal Vol. 11 No. 2 (2024): Edu Komputika Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukom.v11i2.28005

Abstract

Multiple object detection remains a significant challenge in the field of computer vision. One of the key factors affecting detection performance is the feature extraction process, especially when objects are relatively small or positioned closely together. This study aims to compare the effectiveness of two popular object detection models, YOLO (You Only Look Once) and Single Shot MultiBox Detector (SSD), in detecting multiple objects within images. These models were selected due to their reported high accuracy and real-time processing capabilities, outperforming traditional methods such as the Hough Transform, Deformable Part-based Models (DPM), and conventional CNN architectures. The models were evaluated using a subset of the PASCAL VOC dataset, which includes object categories such as aircraft, faces, cars, and others, with a total of 1,447 annotated images used in training and testing. The evaluation metric used was mean Average Precision (mAP) to assess detection accuracy. Experimental results indicate that YOLO achieves a mAP of 82.01%, while SSD achieves 70.47%. These findings demonstrate that YOLO provides better performance in detecting multiple objects under the same conditions. Overall, this study confirms the advantages of YOLO in scenarios requiring fast and accurate multi-object detection, highlighting its potential for deployment in real-time applications such as autonomous vehicles, surveillance systems, and robotics. The main contribution of this study lies in providing a comparative performance benchmark between YOLO and SSD on a standard multi-object dataset to guide practical model selection in real-time computer vision tasks.
Ontology Engineering for Modeling National Student Achievements in Higher Education Sudarni, Ajeng Rahma; Prasetyo, Septian Eko; Ardhiansyah, Alfian; Khaira, Mulil
Edu Komputika Journal Vol. 11 No. 2 (2024): Edu Komputika Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukom.v11i2.28254

Abstract

The need for structured and semantically rich data in higher education underscores the role of ontology-based knowledge modeling. This study develops an ontology to represent national-level student achievements, covering key aspects such as institution, achievement field, category, year, level, and student status. Using a formal ontology engineering approach, the ontology was developed in Protégé and encoded in OWL. Evaluation involved technical validation and reasoning tests including class subsumption, consistency checking, instance classification, and rule-based inference to assess logical soundness and semantic correctness. Description Logic (DL) queries were also executed based on competency questions to evaluate the ontology’s ability to support semantic querying. The results demonstrate that the ontology effectively supports knowledge inference and structured data retrieval, offering strong potential for integration within semantic web environments. This provides a foundation for data interoperability and knowledge sharing across educational systems at the national level. Future work includes expanding the ontology to incorporate dynamic achievement updates and linking with external educational data sources.
Performance Evaluation of Machine Learning Models for Soil Fertility Classification Based on the Indian Soil Fertility Dataset Yoga Pristyanto; Ibrahim Aji Fajar Romadhon; Nugraha, Anggit Ferdita; Nurmasani, Atik; Wulandari, Irma Rofni
Edu Komputika Journal Vol. 12 No. 1 (2025): Edu Komputika Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukom.v12i1.10317

Abstract

Rice farming productivity worldwide has been declining due to improper soil management practices, including excessive chemical fertilizer use and irregular irrigation. The main challenge lies in accurately classifying soil fertility levels to support optimal land use and reduce resource waste, especially when dealing with imbalanced datasets. This study aims to compare the performance of single classifiers and ensemble classifiers in classifying soil fertility. The single classifiers used include K-Nearest Neighbor (KNN), Naive Bayes, Decision Tree, Support Vector Machine (SVM), and Artificial Neural Network (ANN), while the ensemble classifiers include Random Forest and XGBoost. The Indian Soil Fertility Dataset, obtained from Kaggle, contains 880 samples with 12 features and 1 output class. The research methodology involved data acquisition, preprocessing, data splitting, standardization, and classification, with performance evaluation conducted using a confusion matrix. The results show that ensemble classifiers, particularly Random Forest and XGBoost, outperform single classifiers in imbalanced datasets, achieving accuracy, precision, recall, and F1-score values exceeding 92%-95% across all split scenarios. The findings conclude that Random Forest and XGBoost can serve as reliable models for assisting farmers and agricultural experts in evaluating soil conditions, minimizing unnecessary fertilizer usage, and improving rice farming productivity globally.
Development of Service Satisfaction Instrument Application Based on 5 Stars Rating Using PHP, SQL, and Javascript on FT UNNES Website Asri, Sarwi; Ristanto, Riska Dami; Hidayat, Hanif; Mukti, Gandhi Satria; Irzhafran Ridho Pradana; You-he, Tian
Edu Komputika Journal Vol. 12 No. 1 (2025): Edu Komputika Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukom.v12i1.14009

Abstract

Service satisfaction instruments are tools used to measure the level of user satisfaction with services provided by an institution, company, or organization. Service satisfaction instruments can be in the form of forms, applications, interviews, and many more. Service satisfaction instruments in the form of website applications have more advantages than others. Applications can be designed to suit various user needs. The service satisfaction instrument application at the Faculty of Engineering, UNNES, was designed as a form of renewal of the instrument that previously used a form. The instrument application has many advantages over the previously used form. The service satisfaction instrument application can present data in real-time after the user inputs an assessment. The application design is designed to be as attractive as possible so that users can use and receive information from the application easily. The application was developed using the agile software development method. This method is used because it has many advantages such as good flexibility and adaptability.In addition, the program is designed with a good structure to facilitate the development and maintenance process. With an organized structure, the code becomes more modular, clearly separated between components, and follows the principles of clean code.
Enhancing Waste Classification with MobileNetV2: Adding a Plastic Sachets Class for Sustainable Management Pritama, Argiyan Dwi; Sandy Kusuma, Velizha; Baihaqi, Wiga Maulana; Subarkah, Pungkas
Edu Komputika Journal Vol. 12 No. 1 (2025): Edu Komputika Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukom.v12i1.18931

Abstract

The issue of waste management remains a critical concern due to its adverse impact on the environment. This research enhances a deep learning-based waste classification model by introducing a new class, namely plastic sachets, to broaden the classification scope and increase the model's relevance to waste types commonly found in the community. The dataset used is an extended version of a previous open-source dataset, comprising 2,968 images divided into seven classes. Data preprocessing steps include stratified data splitting, data augmentation to increase image diversity, and pixel normalization. The model adopts the MobileNetV2 architecture through a transfer learning approach, utilizing 2D Global Average Pooling and Dense layers with softmax activation for multi-class classification. Evaluation using precision, recall, and F1-score demonstrated strong performance, with an overall accuracy of 97%. While the model performs well across most classes, further improvement is needed for minority classes such as plastic sachets. This study highlights the promising potential of deep learning in supporting automated waste sorting to promote sustainable waste management practices in Indonesia.
A Comparison of Machine Learning and Deep Learning Methods for Temperatures Predictions on Java Island Handhayani, Teny; Hendryli, Janson; Pragantha, Jeanny; Wasino; Darius Andana Haris; Castello Purba, Andrew
Edu Komputika Journal Vol. 12 No. 1 (2025): Edu Komputika Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukom.v12i1.23812

Abstract

Climate change is a global long-term change in temperatures and weather. Climate change is a worldwide issue that requires proper handling to reduce the negative impact on humans and the environment. Analyzing historical data is beneficial for studying climate change. Machine learning and deep learning methods are useful tools for data analysis. The goal of this paper is to find the best model for forecasting temperatures, a case study in Java Island. Java Island is the most densely island and the central economy and business in Indonesia. Climate change research in Java Island is important for sustainability. It runs several algorithms i.e., Gradient Boosting, AdaBoost, XGBoost, CatBoost, Light GBM, Random Forest, Support Vector Regression, Extreme Learning Machine, Long Short-Term Memory, Gated Recurrent Unit, Bidirectional Long Short-Term Memory, and Bidirectional Gated Recurrent Unit. The experiment uses a historical daily time series of temperatures from 1 January 1990 to 31 December 2024. In general, the experimental results show that Gradient Boosting produces the highest average coefficient of determination R2 scores of 0.34 and the lowest Mean Absolute Error scores of 0.69. Long Short-Term Memory and Gated Recurrent Units are the deep learning models that also work well for forecasting. According to the experimental results, in some cases, machine learning models outperform deep learning models and vice versa.

Page 2 of 3 | Total Record : 24

 1   2   3 

Filter by Year

2024 2025


Reset
Filter By Issues
All Issue Vol. 12 No. 1 (2025): Edu Komputika Journal Vol. 11 No. 2 (2024): Edu Komputika Journal Vol. 11 No. 1 (2024): Edu Komputika Journal