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
 36   37   38   39   40 
Sistem Pendukung Keputusan berbasis Web dalam Pengangkatan Pekerja PKWT Menjadi Pekerja Tetap Chaniago, Audiya Ananda; Siddik, Mohd; Muhazir, Ahmad
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 2 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i2.29817

Abstract

The uncertainty of the status of certain time work agreement (PKWT) workers, the appointment of which is still done manually, takes a long time, thus causing certainty problems among workers at CV. Pelita Berjaya Bersama. This study aims to design a decision support system for the appointment of non-permanent workers to permanent workers using the Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW) methods. This research chose a structured and linear waterfall development model, ensuring each stage of development is completed sequentially with complete documentation. The system was developed by going through the stages of requirements gathering, architecture design (database, interface, AHP, and SAW algorithms), design realization (PHP code writing and MySQL database development), and testing using a black box to ensure the system functions properly. The result of our findings is a decision support system that can help companies in making decisions objectively and simplify the appointment process. The system produces output data in the form of ranking results from each alternative so that it is easy to understand. The use of AHP and SAW algorithms in the system is expected to increase accuracy in the selection process. The results of system testing using black box are declared successful starting from the login to logout process. With this system, the company can appoint PKWT workers to become permanent workers.
Optimasi Deteksi Objek pada Video dengan Kompresi Region of Interest menggunakan Model YOLOv8 Assagaf, Azhryl; Muhtadi, Muis
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 2 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i2.30007

Abstract

The demand for real-time object detection systems, such as those used in video surveillance and autonomous vehicles, drives the need for efficient data storage and transmission without compromising accuracy. One promising approach is Region of Interest (ROI)-based video compression, which preserves visual quality in important areas. This study aims to evaluate the impact of video compression on object detection accuracy using the YOLOv8 model through statistical analysis using Analysis of Variance (ANOVA), and to compare the effectiveness of uniform and ROI-based compression methods. Videos from the VIRAT Video Dataset were compressed using the Constant Rate Factor (CRF) parameter and evaluated based on mAP_50, mAP_50_95, and file size. ANOVA results indicate no statistically significant differences between the two methods. At CRF 50, file size can be reduced by over 60%, but mAP_50 accuracy drops below 50% due to quality degradation in non-ROI areas, which disrupts the spatial context required by the model. This study contributes by examining the compression tolerance limits of YOLOv8 and reveals that overall visual quality, rather than just object-focused quality, plays a crucial role in model performance. These findings have important implications for real-time applications such as CCTV and autonomous vehicles, where a balance between compression efficiency and detection accuracy is critical. Future studies may explore adaptive ROI approaches that consider dynamic object movement.
Analisis Sentimen Publik Program PPPK di Media Sosial X menggunakan Naïve Bayes dan SVM Sarumpaet, Lisyo Hileria; Suryono, Ryan Randy
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 2 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i2.30065

Abstract

Sentiment analysis of the Government Employee Program with Work Agreement (PPPK) is important to understand public perception and as a basis for policy evaluation. This study aims to analyze public sentiment towards the PPPK policy and evaluate the performance of the Support Vector Machine (SVM) and Naïve Bayes algorithms in classifying public opinion on social media X. This study is a quantitative study with a data mining approach. The stages begin with collecting data collection of 7,508 tweets and processed through the stages of preprocessing, labeling, feature extraction using TF-IDF, and classification with SVM and Naïve Bayes. Data balancing is done using the Synthetic Minority Oversampling Technique (SMOTE). Our findings show that SVM produces the highest accuracy of 95%, while Naïve Bayes reaches 87%. The application of SMOTE has been shown to improve the performance of both models, especially in recognizing negative sentiment. The advantage of SVM lies in its ability to optimally separate classes through maximum margin, which is effective for high-dimensional text data. Meanwhile, SMOTE plays an important role in balancing class distribution, thereby increasing accuracy, precision, and recall. These findings provide an important basis for policy makers to respond to public opinion more appropriately based on valid and representative data.
Real-time Petty Cash Monitoring: Inovasi Web untuk Pengelolaan Anggaran Operasional yang Efisien Ridwan, Witsqa Inayatussholiha; Sarah, Ira Siti
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 2 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i2.30103

Abstract

The petty cash management at PT PLN (Persero) UP3 Garut was previously conducted manually using Excel files, which were then uploaded to the SAP ERP system for financial reporting. This process lacked real-time monitoring and automated alerts when expenditures approached budget limits. This study aims to develop a web-based application to digitally monitor petty cash through weekly transaction recording, nominal validation, automated notifications, and real-time reporting. The application was developed using the waterfall model through stages of requirement analysis, UML-based system design, implementation using PHP and MySQL, and black box testing. The main result of this study is a web-based petty cash monitoring system with key features such as login, transaction input, budget validation, budget recap, and automatic notifications. Testing results showed that all functions operated properly without errors and improved weekly recording efficiency by over 70%. Data is stored locally using XAMPP to ensure financial information security. The developed application meets the unit’s internal needs and contributes to better transparency and budget control. However, the system is not yet integrated with SAP, making automated integration a future development goal.
IKN Public Opinion on TikTok Before and After Efficiency Policy: CNN-LSTM on Imbalanced Data Sufiya, Ikhwanus; Umam, Khotibul; Handayani, Maya Rini
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 2 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i2.30123

Abstract

Growing polarization in Ibu Kota Nusantara (IKN) stems from conventional sentiment analysis tools’ inability to decode TikTok’s contextual complexities, particularly multimodal sarcasm and vernacular-policy relationships (e.g., mangkrak for project cancellations). This study develops a policy-aware hybrid model (CNN-BiLSTM + Policy Knowledge Graph) to decode TikTok’s multimodal sarcasm and vernacular-policy links (e.g., mangkrak), enabling: youth sentiment quantification post-IKN’s 73.3% budget cuts, social criticism-socio-political reality mapping, and evidence-based interventions mitigating Global South strategic project polarization. Using the Knowledge Discovery in Databases framework, we analyzed 2,950 high-engagement TikTok comments (≥10 interactions) from verified accounts (@Polindo.id and @geraldvincentt) across two periods: pre-policy (June-August 2024) and post-policy (January-March 2025). Methodologically, slang normalization, stemming, and minority-class weighting (15×) preceded classification via a CNN-BiLSTM architecture integrated with Policy Knowledge Graphs. Results showed an 18.88% reduction in negative sentiment (83.2%-8.7%), model accuracy of 94.13% (AUC-PR 0.91), and strong correlations between vernacular terms (e.g., mandek [stagnation]) and policy outcomes (r = -0.89; p < 0.01), with investor asing mentions surging 463% post-policy. These validate deep learning-enabled social listening for real-time policy diagnostics, with implications for fiscal transparency dashboards, algorithmic bias mitigation, and context-driven policy communication prioritizing vulnerable groups in SDG infrastructure governance.
Analisis Sentimen Ulasan Game dengan KNN: Perbandingan Rating dan Kamus Sentimen Sunyaruri, Wisesa Sat; Ningrum, Novita Kurnia
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 2 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i2.30133

Abstract

The growth of the global gaming industry makes sentiment analysis of user reviews a crucial tool for understanding satisfaction and identifying technical issues. This study aims to evaluate three labelling methods (rating-based, Sentiwords_id, and InSet) for classifying the sentiment of Indonesian-language reviews for the game Zenless Zone Zero (ZZZ) using the K-Nearest Neighbor (KNN) algorithm. The study analyzes 4,282 reviews from the Google Play Store, which underwent a Data Preprocessing stage, including Null Handling, Cleaning, Case Folding, Tokenization, Stopword Removal, and Stemming. The KNN's performance for each labelling method was evaluated using accuracy, precision, recall, and F1-score metrics on 80:20 train-test split. The labelling results reveal different sentiment perceptions: the rating-based method tends toward positive, InSet toward negative, while Sentiwords_id is dominated by the positive and neutral classes. The KNN performance evaluation shows that rating-based labelling achieved the highest accuracy (72%), excelling on the positive class (86% recall) but performing poorly on the neutral class (9% recall). Conversely, the lexicon-based labelling methods (both 69% accuracy) have specific strengths: InSet in negative detection (81% recall) and Sentiwords_id in recognizing the neutral class (83% recall). Main challenges of this study include the lexicon's limitations in handling slang and game-specific terms, as well as the inconsistency between ratings and text. This study is expected to provide empirical evidence on performance trade-offs among automatic labelling methods to aid in identifying player satisfaction and advancing the quality of game development.
Analisis Sentimen Pinjaman Online: Studi Komparatif Algoritma Naïve Bayes, Decision Tree, dan KNN Miranda, Khyntia; Suryono, Ryan Randy
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 2 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i2.30142

Abstract

The development of online lending services in Indonesia has led to various responses from the public on social media, including complaints about billing methods and concerns about high interest rates. This study aims to compare the performance of Naive Bayes, Decision Tree, and K-Nearest Neighbors (KNN) algorithms. This type of research is quantitative, and the data used is 5,941 tweets through crawling techniques from X social media, followed by preprocessing, data labeling with a lexicon-based feature extraction using TF-IDF, and sentiment classification using the three algorithms. The evaluation stage uses a confusion matrix, which can calculate accuracy, precision, recall, and the F-1 score. The results show that the decision tree provides the most consistent performance with 69% accuracy due to its ability to recognize complex data patterns and understand relationships between features. Naive Bayes excels in negative sentiment classification with 68% accuracy, while KNN shows the lowest performance with 44% accuracy because it is not effective in handling high-dimensional text data. These results can be utilized by online loan service providers and regulators to build an accurate public opinion monitoring system in order to respond to issues of public concern and improve service quality on an ongoing basis.
Kinerja Naive Bayes dan SVM pada Data Survei Tidak Seimbang: Studi Klasifikasi Kepuasan Masyarakat Romadhoni, Mellynda Noor; Winarsih, Nurul Anisa Sri
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 2 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i2.30185

Abstract

The utilization of Public Satisfaction Survey (SKM) data has not been optimal, highlighting the need for an effective classification method to determine the level of public satisfaction. This study aims to classify satisfaction levels using the 2024 SKM data from the Regional Civil Service and Training Agency (BKPPD) of Grobogan Regency, employing Naive Bayes and Support Vector Machine (SVM) algorithms. This quantitative research uses nine service elements rated on a scale of 1 to 4 as features, with satisfaction level as the target variable. The dataset consists of 303 entries: 156 “very satisfied,” 115 “satisfied,” and 32 “dissatisfied.” Random oversampling was applied to address class imbalance. Model performance was evaluated using accuracy, precision, recall, and F1-score, both before and after oversampling. Results showed Naive Bayes achieved 96.72% accuracy, while SVM scored 95.08%. After oversampling, SVM accuracy significantly improved to 98.36%, while Naive Bayes slightly decreased to 95.08%. Precision, recall, and F1-scores also demonstrated strong performance across all classes. This study is expected to support the improvement of public service delivery at BKPPD Grobogan and similar institutions.
Aplikasi Pengawasan Kepatuhan Minum Obat Pasien Tuberkulosis (TBC) berbasis Mobile Erdian, Naufal; Rosika, Herliana; Widiartha, Ida Bagus Ketut
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 2 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i2.30231

Abstract

Based on WHO data from 2024, Indonesia ranks second after India as the country with the highest number of tuberculosis (TB) cases in the world. Low patient adherence to regular medication intake, caused by the weakness of manual monitoring systems by healthcare workers, remains a major challenge in TB control. This study aims to develop a mobile application to monitor tuberculosis treatment and improve patient medication adherence. The application was developed using a software engineering approach with the Personal Extreme Programming (PXP) method, involving stages of planning, design, implementation, and iterative testing. Data collection techniques included observation, structured interviews, and User Acceptance Testing (UAT) through an online Likert-scale-based questionnaire. The outcome of this study is a mobile-based application featuring medication reminders, daily consumption reporting, monitoring by healthcare workers, and the provision of educational information. Black-box testing results indicated that all application features functioned as intended, while UAT results showed that the application is user-friendly and supports healthcare workers in effectively monitoring patient treatment. These findings demonstrate that the application can serve as a digital solution to improve TB treatment adherence and strengthen medication monitoring systems, particularly in high-burden areas.
Sistem Penjadwalan dan Presensi Adaptif untuk Optimalisasi Manajemen Operasional di Rumah Sakit Sari, Gita Mailand; Widiartha, Ida Bagus Ketut; Rosika, Herliana
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 2 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i2.30267

Abstract

Mataram University Hospital faces problems in managing work schedules that are still manual using excel and are not integrated with the attendance system. This has an impact on operational management in hospitals that are less than optimal. This research aims to produce an adaptive scheduling and attendance system display that is integrated with the registration module in the Hospital Management Information System (HMIS). The method used to build the system is the User Centered Design (UCD) Method integrated with the Extreme Programming (XP) Method, which includes the stages of user identification, identification of user needs, design and testing iteratively. Data was collected through observation and interviews with the hospital and analyzed qualitatively. Our findings resulted in the appearance of a scheduling and attendance system that aims to optimize operational management in hospitals. Testing the system interface using Usability Testing Method, System Usability Scale (SUS) and User Experience Questionnaire (UEQ) to test the functionality and user experience conducted to 30 respondents consisting of staff and doctors. The SUS test results obtained a score of 76.41 and the “Good” category for all aspects of the UEQ test. Overall, the system has a good level of usability and user experience. This system successfully optimizes operational management in hospitals.

Page 38 of 44 | Total Record : 439

 36   37   38   39   40 

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