cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Fahrur Rozi
Contact Email
joeict.ubhi@gmail.com
Phone
+6285646149638
Journal Mail Official
joeict.ubhi@gmail.com
Editorial Address
Jl. Mayor Sujadi No.7, Manggisan, Plosokandang, Kec. Kedungwaru, Kabupaten Tulungagung, Jawa Timur 66229
Location
Kab. tulungagung,
Jawa timur
INDONESIA
Journal of Education and ICT
Published by Universitas Bhinneka PGRI
ISSN : 2459993X     EISSN : 29873215     DOI : https://dx.doi.org/10.29100/joeict.v9i1.7958
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management
This journal encompasses original research articles, review articles, and short communications, including: Pendidikan Teknologi Informasi Information System Artificial Intelligence AI & Expert systems Database Systems Computing Languages & Algorithms Computer Networks & Communications Computer Architecture
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 5 Documents
 1 
Search results for , issue "vol 8, no 2 (2024)" : 5 Documents clear
AN SVM-BASED APPROACH FOR DETECTING DATA DEFINITION LANGUAGE OPERATIONS IN INDONESIAN NATURAL LANGUAGE Yayak Kartika Sari; Fahrur Rozi; Agung Prasetya
JoEICT (Jurnal of Education And ICT) Vol 8, No 2 (2024)
Publisher : STKIP PGRI TULUNGAGUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/joeict.v8i2.1649

Abstract

Text-to-SQL is an approach that enables users to interact with data-bases using natural language, eliminating the need to understand SQL syntax. However, most existing approaches translate input sentences directly into final SQL queries without explicitly identifying the type of SQL operation involved. This may obscure the distinction between structural and manipulative commands and increase the risk of execut-ing unintended or destructive queries. This study proposes separating the identification of SQL operation types—specifically Data Definition Language (DDL) commands—as a standalone classification task using the Support Vector Machine (SVM) algorithm. Indonesian-language sentences are preprocessed through tokenization, stopword removal, and stemming, then transformed into feature vectors using TF-IDF with unigram and bigram representations. Experiments were conducted on a dataset of 800 Indonesian sentences covering four DDL operations: CREATE, ALTER, DROP, and TRUNCATE. The results show that the proposed SVM model achieved an average accuracy of 93.05%, out-performing baseline models such as Naive Bayes and Random Forest. These findings indicate that early identification of SQL operation types can enhance the accuracy, efficiency, and safety of Text-to-SQL sys-tems. This work also highlights the importance of developing NLP ap-proaches tailored for the Indonesian language in the context of data-base querying.
GAUSSIAN NAIVE BAYES FOR EARLY DIABETES PREDICTION: A COMPREHENSIVE EVALUATION OF CLASSIFICATION PERFORMANCE ACROSS VARYING TRAINING TEST PROPORTIONS Fahrur Rozi; Bian Dwi Pamungkas; Vertika Panggayuh; Feraldy Satria Putra
JoEICT (Jurnal of Education And ICT) Vol 8, No 2 (2024)
Publisher : STKIP PGRI TULUNGAGUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/joeict.v8i2.9282

Abstract

Diabetes mellitus is a chronic metabolic disorder whose global prevalence continues to rise at an alarming rate, imposing substantial burdens on public health systems worldwide. Early and accurate prediction of diabetes risk is critical to enable timely clinical intervention and to mitigate life-threatening complications such as cardiovascular disease, nephropathy, and retinopathy. Machine learning algorithms have emerged as promising tools for automated diabetes risk classification; however, the comparative stability of probabilistic classifiers across varying data-partitioning strategies remains insufficiently studied. This study presents a systematic evaluation of the Gaussian Naive Bayes (GNB) algorithm for binary diabetes prediction using a publicly available dataset sourced from Kaggle (n = 768 instances; 9 clinical and demographic features). The experimental protocol includes a standard 75:25 training–test split and a sensitivity analysis spanning ten training–test ratio configurations (10%–100%). Under the canonical split, the GNB model attained an overall accuracy of 78.89%, with precision of 64.29%, recall of 67.74%, and F1-score of 66.00%. Cross-partition sensitivity analysis demonstrated that classification accuracy remained relatively stable in the range of 71–76% across all ratio configurations, with the 50% training proportion yielding the most balanced performance (accuracy = 76.30%, F1-score = 67.20%). These findings confirm that GNB constitutes a computationally efficient and interpretable baseline for diabetes screening, while simultaneously revealing limitations in sensitivity that motivate the integration of feature selection and ensemble learning strategies in future research. The novelty of this work lies in its structured cross-partition stability analysis, which provides empirically grounded guidance for dataset splitting decisions in small-scale clinical prediction tasks.
IDENTIFYING ARITHMETIC OPERATION IN MATH WORD PROBLEM BASED ON RECURSIVE NEURAL NETWORK AND SUPPORT VECTOR MACHINE Agung Prasetya
JoEICT (Jurnal of Education And ICT) Vol 8, No 2 (2024)
Publisher : STKIP PGRI TULUNGAGUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/joeict.v8i2.7421

Abstract

Math word problems act as a test bed to design an intelligent system. An approach is needed to identify arithmetic operations including addition, substraction, multiplication and division. Template-based approaches have addressed this problem. However, the template-based approach is less efficient because it requires the process of building a template repository that have to cover a wide variety of story implied by math words. The template-based approach is potentially sub-optimal when solving story problems that have not been covered yet by templates. The proposed approach resolves this by using Recursive Neural Network and Support Vector Machine. Recursive neural network is used as an encoder that can generate semantic vectors of math word problems. Then, this vector becomes as an input for a Support Vector Machine-based classifier. Tests were conducted on a dataset collected manually from Kemdikbud’ electronic school books. The results showed that the proposed approach does not require the formation of templates, thereby reducing human involvement. In addition, the use of Recursive Neural Network reduces feature engineering making it more efficient. Experimental results by applying k-fold cross validation show that the proposed approach has an accuracy of 81% and a precision of 66%
DEVELOPMENT OF A WEB-BASED INVENTORY INFORMATION SYSTEM FOR A UNIVERSITY ROBOTICS LABORATORY: AN AGILE DEVELOPMENT APPROACH WITH ISO 25010 EVALUATION Bian Dwi Pamungkas; Muhammad Alifi Ferdiansyah; Abdul Haris Indrakusuma; Vertika Panggayuh
JoEICT (Jurnal of Education And ICT) Vol 8, No 2 (2024)
Publisher : STKIP PGRI TULUNGAGUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/joeict.v8i2.9280

Abstract

Effective management of laboratory assets is a critical operational challenge in higher education institutions, particularly in engineering and robotics facilities where equipment is diverse, high-value, and subject to frequent use. Manual inventory practices remain prevalent in many university laboratories, leading to data inaccuracies, untracked loans, and difficulty in accountability. This study presents the development and evaluation of a web-based inventory information system for the Robotics Laboratory of Universitas Bhinneka PGRI. The system was developed using the Agile Development methodology, encompassing six iterative phases: Planning, Implementation, Testing, Documentation, Deployment, and Maintenance. The technology stack consisted of PHP with the Laravel 9 framework for the backend and Bootstrap 5 for the frontend, supported by a MySQL relational database. The system supports three role-based user levels — Administrator, Manager/Staff, and Member — and incorporates features for real-time item registration, loan management, return tracking, transaction approval workflows, and exportable reporting. System quality was evaluated using the ISO/IEC 25010 standard, specifically the Functional Suitability and Compatibility quality characteristics. Expert validation yielded scores of 99.16% and 100% for Functional Suitability and Compatibility, respectively, both classified as 'Very Feasible.' Usability was assessed using the System Usability Scale (SUS) with small-group (n=10, SUS=94.16) and large-group (n=30, SUS=93.50) tests, both falling within the 'Excellent' category. The findings demonstrate that the developed system substantially addresses the limitations of manual inventory management in robotics laboratory environments and provides a replicable model for similar institutional contexts.
DEVELOPMENT AND FEASIBILITY EVALUATION OF AN ANDROID-BASED ELECTRONIC MODULE FOR MATHEMATICS LEARNING IN JUNIOR SECONDARY EDUCATION Abdul Haris Indrakusuma; Vertika Panggayuh; Fahrur Rozi; Richo Prastianto
JoEICT (Jurnal of Education And ICT) Vol 8, No 2 (2024)
Publisher : STKIP PGRI TULUNGAGUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/joeict.v8i2.9286

Abstract

The integration of mobile technology into formal education has generated substantial interest in the development of Android-based learning media, particularly in subjects that students traditionally find challenging. This study addresses the limited availability of interactive, portable learning tools for eighth-grade mathematics specifically on the topic of three-dimensional flat-sided geometric solids at SMP Negeri 6 Tulungagung, where mathematics consistently recorded the lowest average scores among twelve subjects. The primary objective was to develop and evaluate the feasibility of an Android-based electronic module (e-module) built with Construct 2, following the four-stage 4D (Define, Design, Develop, Disseminate) research and development model. The e-module was systematically validated through two media experts, one content expert, and field-tested with 74 students across small-group and large-group trials. Validation and user-response data were collected via structured questionnaires and scored against a five-tier eligibility rubric. Results indicate that media expert validation yielded scores of 84% and 99%, respectively; content expert validation returned 90.9%; and student user trials recorded 92% and 94.5% for small and large groups, respectively. The overall mean feasibility score was 92.1%, falling within the Very Eligible category. These findings confirm that the developed e-module meets the pedagogical, technical, and usability standards required for deployment in classroom and independent learning contexts. The study contributes a replicable development framework for mobile-based mathematics learning media in resource-constrained secondary school environments and highlights directions for future work, including cross-platform deployment and integration of adaptive assessment features.

Page 1 of 1 | Total Record : 5

 1 

Filter by Year

2024 2024


Reset
Filter By Issues
All Issue Vol 10, No 1 (2026) Vol 9, No 2 (2025) Vol 9, No 1 (2025) Vol 8, No 2 (2024) Vol 8, No 1 (2024) Vol 7, No 2 (2023) Vol 7, No 1 (2023) Vol 6, No 2 (2022) Vol 6, No 1 (2022) Vol 5, No 2 (2021) Vol 5, No 1 (2021) Vol 4, No 2 (2020) Vol 3, No 2 (2019) Vol 3, No 1 (2019) Vol 2, No 1 (2018) Vol 1, No 1 (2017) More Issue