cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Muhammad Zamroni Uska
Contact Email
zamroniuska@gamil.com
Phone
-
Journal Mail Official
zamroniuska@gamil.com
Editorial Address
-
Location
Kab. lombok timur,
Nusa tenggara barat
INDONESIA
EDUMATIC: Jurnal Pendidikan Informatika
Published by Universitas Hamzanwadi
ISSN : -     EISSN : 25497472     DOI : 10.29408
Core Subject : Science, Education,
Computer Science & IT Education
EDUMATIC: Jurnal Pendidikan Informatika (e-ISSN: 2549-7472) adalah jurnal ilmiah bidang pendidikan informatika yang diterbitkan oleh Universitas Hamzanwadi dua kali setahun yaitu pada bulan Juni dan Desember. Adapun fokus dan skup jurnal ini adalah (1) Komputer dan Informatika dalam Pendidikan; (2) Model Pembelajaran dan Model TIK; (3) Pengembangan Media Pembelajaran Berbasis Teknologi Informatika; (4) Interaksi Manusia dan Komputer; (5) Sistem Informasi dan Teknologi Informasi.
Arjuna Subject : -
Articles 439 Documents
 40   41   42   43   44 
Logistic Regression and Naïve Bayes Comparison in Classifying Emotions on Indonesian X Social Media Rasyad, Gerald Shabran; Maharani, Warih
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 1 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i1.29120

Abstract

Emotions are integral to human interaction and decision-making, often expressed on social media platforms like X, which provides valuable data for sentiment analysis. However, analyzing texts from X poses challenges due to informal language, slang, and unique textual features. This study compares Logistic Regression and Naive Bayes in classifying emotions from Indonesian tweets, addressing gaps in prior research by exploring feature extraction methods, data split ratios, and hyperparameter tuning. Data were collected from 100 Telkom University students, resulting in 8,978 tweets labeled into four emotions: Happy, Sad, Angry, and Fear. After preprocessing, feature extraction methods TF-IDF and Bag of Words (BoW) were applied. Models were trained and tested on 10%, 20%, and 30% data splits, and performance was evaluated using accuracy, precision, recall, and F1-score. Hyperparameter tuning was conducted for Logistic Regression using GridSearch. Results showed Logistic Regression outperformed Naive Bayes, achieving 73.49% accuracy compared to 70.27%, with BoW yielding superior results over TF-IDF. The 20% data split provided the best balance for training and testing. This research demonstrates the effectiveness of Logistic Regression and highlights the importance of tailored feature extraction and parameter optimization for emotion classification in informal text datasets, particularly for Indonesian tweets.
Zebra Cross Violation Detection with YOLOv9: A Novel Approach for Traffic Regulation in Indonesia Kamil, Muhammad Najmi; Gamma Kosala
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 1 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i1.29244

Abstract

Technological advancements in zebra cross-violation detection are necessary to address traffic rule violations in Indonesia, especially zebra cross violations. The You Only Look Once (YOLO) algorithm has been effective for detecting objects in various situations. The objective of this research is to focus on detecting zebra cross violations using YOLOv9, the improved accuracy and efficiency from earlier versions of YOLO. Consisting of two models to detect violations of the zebra crossing. The first model, a segmentation model called YOLO, is used for zebra cross localization, while the second model, a pretrained YOLO, detects the vehicles. The results of these two models are used for calculations in considering violations by drivers. Two datasets were used in this research. One of the datasets has 1100 images of zebra crosses, while the other comprises 100 surveillance videos from CCTV in Yogyakarta, Indonesia, for testing. The findings from this study indicate that the approach enables effective and efficient detection and classification of zebra crossing violations with an accuracy of 93%. This research demonstrates the approach's enhanced ability to handle real-world scenarios with diverse camera angles and varying traffic conditions. Additionally, it underscores the potential for practical applications in automated traffic monitoring and enforcement.
Understanding Public Sentiments on the 2024 Presidential Election through BERT-Powered Analysis Setiawan, Abiyyu Daffa Haidar; Maharani, Warih
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 1 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i1.29267

Abstract

Social media platforms serve as dynamic communication across boundaries, with X serving as a platform for opinion exchange. This research examines public sentiment on the 2024 Indonesian Presidential Election to understand voter sentiments based on what happened during the pre-election. Using the Twitter API, 2,146 tweets were collected based on election-related keywords and hashtags, focusing on Indonesian-language tweets with direct opinions. The method that we use is data crawling using the Twitter API. Preprocessing steps included case folding (converting text to lowercase), cleansing (removing noise like URLs and emojis), tokenization, stemming (reducing words to base forms), and stop word removal (e.g., “yang,” “dan”). Slang was standardized with a custom dictionary to ensure consistency and accurate interpretation. Leveraging BERT for sentiment analysis, the model achieved 99% accuracy; results indicate that 93.1% of analyzed tweets expressed negative sentiment, highlighting public dissatisfaction about the 2024 presidential election. Hyperparameters are also tested to optimize model performances. With the best result accuracy in 99% using an 80:20 split ratio, with a batch size of 16 and a learning rate of 0.00001. This research underlines the importance of sentiment analysis in elections, demonstrating BERT’s capability to handle linguistic complexities and providing a methodological framework for analyzing social media data in political contexts.
Analisis Perbandingan Algoritma SVM, Random Forest dan Logistic Regression untuk Prediksi Stunting Balita Febriyanti, Nada Rizki; Kusrini, Kusrini; Hartanto, Anggit Dwi
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 1 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i1.29407

Abstract

The prevalence of stunting in Banjarmasin City in 2023 reached 26.5%, exceeding the WHO target (below 20%). Stunting impacts physical growth, cognitive development, and long-term economic productivity. The purpose of this study is to compare the performance of SVM, random forest, and logistic regression algorithms in classifying the stunting status of toddlers. The approach we use is comparative quantitative with machine learning methods for health data classification. Data totaling 2,231 under-five records were obtained from the Banjarmasin City Health Office. We used age, weight, height, and z-score information. Data preprocessing includes handling missing values, categorical data transformation, numerical data standardization, and feature selection. The dataset was divided into 70:30 and 80:20 ratios using stratified sampling with 5-fold cross-validation. Our results show that SVM is the best model, with accuracy 92%, precision 91%, recall 99%, F1-score 95%, and AUC 99%, followed by random forest (accuracy 91%, AUC 98%) and logistic regression (accuracy 92%, AUC 97%). SVM showed superior performance due to its ability to find the optimal hyperplane that maximally separates stunted and non-stunted classes, as well as its effectiveness in handling non-linear data through kernel tricks. SVM's good generalization ability on new data makes it a top choice as a predictive tool for stunting prevention in Banjarmasin City.
Penerapan E-Catalog Terintegrasi Chatbot Decision Tree untuk Optimalisasi Layanan Penjualan Puspitaningrum, Hapsari Retno; Subhiyakto, Egia Rosi
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 1 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i1.29435

Abstract

Digitalization drives business service optimization through more efficient and interactive systems. E-catalogs enable structured product information delivery but still have limitations in product search and customer interaction, which rely heavily on administrators. This study proposes the integration of a decision tree-based chatbot in a web-based e-catalog to enhance service efficiency and user experience. The prototyping method was implemented through four stages: requirement identification via interviews and observations, system design involving user interface and chatbot interaction flow, web-based implementation, and testing using Black-box Testing for functionality evaluation and the System Usability Scale (SUS) to measure user satisfaction. The results indicate that the system improves product search and ordering efficiency, with the chatbot providing automatic recommendations and reducing the workload of administrators. All system features functioned properly, and the SUS evaluation yielded an average score of 87.2. It is reflecting a high level of user satisfaction. The integration of chatbots in e-catalogs has proven to enhance service efficiency and customer interaction and can be applied to other SMEs facing challenges in digital promotion and order management.
Aplikasi Buku Pintar Ruang Angkasa sebagai Media Pembelajaran berbasis Augmented Reality Yusuf, Farid Maulana; Dijaya, Rohman; Rosid, Mochamad Alfan; Taurusta, Cindy
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 1 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i1.29491

Abstract

Space learning is still dominated by traditional books, but is often considered monotonous because it only presents static text and images. This study aims to develop a Smart Space Book based on Augmented Reality (AR) as an interactive learning media to present space concepts more visually and attractively. The method used is the Multimedia Development Life Cycle (MDLC), which consists of six stages: conceptualization, application design, material collection, application development, application testing, and application distribution. This application was developed using Unity 3D on Android devices with the marker-based tracking method, which displays 3D objects such as the Solar System, Galaxy, Stars, and Space equipped with interactive explanatory materials. Testing was carried out through black box testing to assess system functionality as well as usability tests and field studies at Aisyiyah Bustanul Athfal Kindergarten. The results of our findings are in the form of a Smart Space Book application based on Augmented Reality (AR), which can function well according to the design that has been determined and is applied as an interactive AR-based learning media. The test results of this application provide an alternative in presenting space material more visually and interactively and have the potential to be applied at various levels of education.
ChatGPT sebagai Alat Bantu dalam Penulisan Karya Ilmiah Mahasiswa: Analisis Keterlibatan dan Kreativitas Yasmine, Yuliana Sventy; Hikmawan, Rizki
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 1 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i1.29496

Abstract

The field of education is facing challenges due to the emergence of various artificial intelligence technologies, one of which is the latest version of ChatGPT-4o. This version is more human-like and feature-rich, making it an effective feedback tool for assisting in the preparation of scientific papers. However, its role raises concerns among educators, as it has the potential to affect student engagement and creativity. This study aims to analyze the impact of using ChatGPT on student engagement and creativity in writing scientific papers. A quantitative approach using a survey method was conducted with 170 students from the Information Technology Systems Education program at a state university in Indonesia. Data were collected through a 29-item questionnaire using a four-point Likert scale to minimize ambiguity in respondents' answers. The data were then analyzed using descriptive and inferential statistics. Based on the results of the coefficient of determination analysis, ChatGPT usage was found to influence writing engagement by 25.3% and creativity by 8.10%. In addition to providing corrections, ChatGPT also offers suggestions and generates new ideas, enhancing interactive participation in the writing process. However, it may reduce students' reliance on more valid feedback sources, potentially impacting engagement and decreasing creativity in writing. These findings highlight the importance of ethical awareness among students and the need for educational institutions to develop strategies to mitigate the negative effects of ChatGPT in scientific writing.
Perbandingan Naive Bayes dan Support Vector Machine dalam Klasifikasi Tingkat Kemiskinan di Indonesia Mukharyahya, Zulfa Alviandri; Astuti, Yani Parti; Cahyani, Okta Nur
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 1 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i1.29512

Abstract

Poverty in Indonesia is a complex issue influenced by various economic and socio-cultural factors. This study aims to compare the performance of Naïve Bayes and Support Vector Machine (SVM) in classifying poverty levels in Indonesia while also evaluating the effectiveness of random oversampling in addressing data imbalance. The dataset consists of 514 samples from various districts and cities in Indonesia, with 452 samples classified as "not poor" and 62 as "poor." After applying oversampling, the total number of samples increased to 730, with a balanced distribution (365 samples per class). The observed data include socio-economic indicators such as the percentage of the poor population, per capita expenditure, the Human Development Index, and the open unemployment rate. The study splits the data using an 80:20 ratio for training and testing. Experimental results show that SVM achieved a higher accuracy of 81% compared to naïve bayes, which reached 76%. Additionally, SVM demonstrated a more stable balance between precision and recall. On the other hand, the oversampling technique effectively improved the model’s ability to identify the minority class, particularly for Naïve Bayes, which was more responsive to data duplication. These findings highlight the role of machine learning in designing more effective social policies for poverty data management.
Analisis Perbandingan Sistem Pelaporan Kinerja Kementerian Dalam Negeri menggunakan Metode Usability Testing Ardaneswari, Awanda; Manongga, Daniel H.F; Sembiring, Irwan
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 1 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i1.29565

Abstract

Performance Evaluation System for Apparatus Positions (Sikerja) is a web-based application owned by the Ministry of Home Affairs (MoHA), used to assess and measure the performance of civil servants across all units within MoHA. Currently, MoHA uses two versions of the Sikerja application: the old Sikerja and the new Sikerja. The purpose of this study is to compare the usability levels between 2 systems using the usability testing method, focusing on the performance input menu. The study employs a quantitative descriptive approach using a survey method involving 100 respondents from the Directorate General of Regional Governance, with a questionnaire as the research instrument. The questionnaire items are derived from the five usability indicators based on Jacob Nielsen's framework. The results of the validity and reliability tests on the questionnaire confirmed that it is valid and reliable. Subsequently, a descriptive analysis was conducted for each usability indicator. The analysis results show that the learnability score of the old Sikerja system is 3.24 (high), efficiency is 2.18 (moderate), memorability is 2.98 (moderate), errors is 1.88 (low), and satisfaction is 3,17 (high). On the other hand, the new Sikerja system has a learnability score of 2,23 (moderate), efficiency of 2.07 (moderate), memorability of 1.02 (low), errors of 2.77 (moderate), and satisfaction of 2.54 (moderate). It can be concluded that the new Sikerja system requires workflow simplification, increased user training and socialization, and regular evaluation for employees. These recommendations are expected to improve the usability score of the new Sikerja system within the Ministry of Home Affairs.
Diagnosis Dini Penyakit Mata: Klasifikasi Citra Fundus Retina dengan Convolutional Neural Network VGG-16 Putri, Chana Amelinda; Rakasiwi, Sindhu
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 1 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i1.29571

Abstract

Retinal fundus image-based eye disease classification is important to support early diagnosis of vision disorders such as cataracts, glaucoma, and diabetic retinopathy. This study aims to diagnose early eye diseases with retinal fundus image classification using Convolutional Neural Network VGG-16. The model was developed to detect cataract, glaucoma, and diabetic retinopathy to support early diagnosis. The dataset used comes from Kaggle, including 4,217 retinal fundus images consisting of 1,038 cataract, 1,007 glaucoma, 1,098 diabetic retinopathy, and 1,074 normal images. The images were processed through normalization, augmentation, and resizing to 224×224 pixels, with the dataset divided in a ratio of 80:10:10 for training, validation, and testing. Results showed that the VGG-16 model with transfer learning achieved 88% accuracy, a 10% increase from the previous 75% in the CNN model without transfer learning. This model has the potential to be integrated in clinical decision support systems or mobile applications to improve the speed and accuracy of diagnosis. Limitations of the study include the limited dataset size and potential data bias that may affect the accuracy of the model in detecting eye diseases early, so future research is recommended to use larger and more diverse datasets, as well as explore other deep learning architectures to improve classification performance.

Page 42 of 44 | Total Record : 439

 40   41   42   43   44 

Filter by Year

2017 2026


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