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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
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Evaluating User Experience of SITASI System using HEART Metrics and Importance-Performance Analysis (IPA) Harmaniola, Elsa; Ahsyar, Tengku Khairil; Syaifullah, Syaifullah; Anofrizen, Anofrizen; Marsal, Arif
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.v9i2.30691

Abstract

SITASI is a web-based system developed to help students manage references and citations for their final projects. Despite its importance, no comprehensive evaluation of the system’s user experience (UX) had been conducted. This study aims to evaluate the UX of SITASI used by students of the Information Systems Study Programme at UIN Suska Riau. Using a quantitative survey method, data were collected from 72 active students from the 2018-2021 cohorts through an online questionnaire. The evaluation was based on the five HEART Metrics dimensions: Happiness, Engagement, Adoption, Retention, and Task Success. Respondents were selected through random sampling using the Slovin formula. Data were analysed using SPSS version 30 for validity, reliability, and Importance-Performance Analysis (IPA). The HEART results indicated high usability levels across all dimensions, with Happiness and Retention scoring highest (73%) and Engagement lowest (70%). IPA showed an average suitability of 92%, with four indicators placed in Quadrant I, signifying urgent areas for improvement. These results serve as a foundation for actionable recommendations to enhance system satisfaction, improve performance, and develop a more user-centered and adaptive SITASI platform.
Performance Evaluation of Naïve Bayes and SVM in Sentiment Analysis of Illegal Parking Attendants Saputra, Sandra; Paradise, Paradise; Nugraha, Novanda Alim Setya
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.v9i2.30714

Abstract

The increase in the number of vehicles in Indonesia has led to high demand for parking spaces, which has triggered the emergence of illegal parking attendants. This phenomenon has elicited various public responses, particularly on social media platform X. This study analyzes public sentiment toward the presence of illegal parking attendants by comparing the performance of the Naïve Bayes and Support Vector Machine (SVM) algorithms. The data used consists of 1,484 Indonesian-language tweets collected via crawling techniques. The pre-processing stage included data cleaning, case folding, word normalization, tokenization, stopword removal, and stemming. The data was then labeled with positive or negative sentiment using the InSet (Indonesia Sentiment Lexicon) approach and manually validated, before being divided into training and testing datasets. Feature extraction was performed using the TF-IDF method before being applied to the classification model. The evaluation results show that the SVM algorithm with a linear kernel approach produces the highest accuracy of 82%, outperforming Naïve Bayes: Gaussian 56%, Multinomial 74%, and Bernoulli 77%. These results are expected to contribute to the formulation of more organized and transparent parking policies, as well as demonstrate the importance of sentiment analysis as a tool to support data-driven decision making.
Sistem Pendukung Keputusan dalam Menentukan Jurusan Siswa di Sekolah Menengah menggunakan Metode Fuzzy Tahani Lubis, Alvin Sany; Sriani, Sriani
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.v9i2.30722

Abstract

The major selection process at MAS Plus Al Ulum, Medan, is still conducted manually, making it prone to subjectivity, time-consuming, and potentially misaligned with students’ potential. Our research aims to develop a web-based decision support system for major recommendations using the Fuzzy Tahani method. This development research follows the Waterfall model, encompassing requirements analysis, design, implementation, and testing. Requirements were gathered through observation and interviews, followed by designing data processing logic and implementing a web-based system. Testing was performed using the black-box method on features such as login, homepage, student data input, fuzzification process, recommendation results, front page, dark mode, and logout. The analysis technique employed fuzzification and fuzzy queries to calculate students’ suitability for Science, Social Studies, or Religious Studies based on academic scores, interests, talents, and learning styles. The study involved 151 tenth-grade students. The resulting system automatically recommends Science, Social Studies, or Religious Studies based on the aforementioned criteria. Analysis of the 151 students’ data showed 72 students suited for Social Studies, 40 for Science, and 39 for Religious Studies. Testing confirmed all features functioned correctly without errors. This research provides a foundation for schools to create a more targeted, time-efficient, and student-aligned major selection strategy.
Model Hybird Fuzzy Logic dan Deep Learning untuk Prediksi Harga Saham Muhidin, Asep; Rilvani, Elkin; Naya, Candra
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.v9i2.30890

Abstract

Stock price prediction is a major challenge in the financial sector due to nonlinear factors and data uncertainty. This study aims to develop a predictive model by integrating fuzzy logic into deep learning algorithms to improve accuracy and robustness against noise. This is a quantitative experimental study using 1,000 daily historical stock price data of BBCA (Bank Central Asia), collected via web scraping from public sources. The data were analyzed using three types of neural networks: Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU), both before and after fuzzy integration. Fuzzification was applied to the price data to generate linguistic features, which were added as input to the neural network models. The models were evaluated using Train Cost, Test Cost, and the number of epochs, and a t-test was conducted to assess the statistical significance of performance differences. Our findings show that the LSTM model with fuzzy input achieved the best performance, with a Train Cost of 0.0002 and a Test Cost of 0.0052, and demonstrated superior capability in handling long-term dependencies. In contrast, RNN and GRU models showed decreased accuracy after fuzzy integration. The combining fuzzy and LSTM model shows promise for broader applications in time-series forecasting under uncertainty.
Aplikasi Deteksi Otomatis Hukum Tajwid Utama pada Ayat Al-Qur’an menggunakan YOLOv8 Arfiansah, Arfiansah; Nasucha, Mohammad
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.v9i2.30978

Abstract

Tajwid learning faces challenges in visually recognizing recitation rules from Arabic script, thus requiring an interactive and accurate digital medium. This study aims to develop a web-based application to automatically detect seven core tajwid rules using YOLOv8. This research follows a Research and Development approach adopting the ADDIE model, which consists of five systematic stages: analysis, design, development, implementation, and evaluation. The YOLOv8 model was trained using 200 annotated images of Qur’anic verses, with a data split of 70% for training, 20% for validation, and 10% for testing. Data augmentation was applied through rotation, flipping, and brightness adjustment, with training facilitated using Roboflow. Our main finding is an interactive web application capable of automatically detecting seven tajwid rules from Qur’anic verse images. The application allows users to upload images, which are then analyzed and displayed with colored bounding boxes and interactive captions. Testing results showed accurate and responsive detection performance, achieving a mAP@50 of 89.88% with high accuracy across several tajwid classes. These findings highlight the potential of Artificial Intelligence (AI) to support more interactive, independent, and adaptive tajwid learning, while also promoting the digitization of Islamic manuscripts.
Enhanced Data Security in Video Media using RSA-LSB Hybrid Technique Putra, Nafis Artaruna; Sidiq, Muhammad Fajar; Amrulloh, Arif
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.v9i2.31030

Abstract

Securing structured documents in dynamic digital media such as video remains a challenge, especially under threats like unauthorized access, format conversion, and compression. This research aims to build a secure and hidden digital document insertion system into video media using a hybrid approach between RSA cryptography algorithm and Least Significant Bit (LSB) steganography. This research developed a steganography system using the prototype method to embed complex documents into video media securely. It uses Python with libraries like OpenCV and PyCryptodome, embedding data into the blue channel's LSB bit to maintain visual quality. The system was tested using various video samples and documents to ensure error-free embedding and 100% accurate extraction, with no file corruption. Robustness against compression and format conversion was also evaluated using metrics like PSNR, SSIM, and BER. The study successfully created a secure system for embedding complex digital documents into video media. Evaluation confirmed high visual quality (PSNR 45.3-63.2 dB), 100% data recovery, and resilience to post-processing, a significant advance over methods that handle only simple payloads.
Klasifikasi Sentimen Ulasan E-Wallet menggunakan TF-IDF dan Random Forest dengan Penyeimbangan Data SMOTE Syaputa, Muhammad Rioardian; Arifin, Muhammad; Fithri, Diana Laily
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.v9i2.31084

Abstract

The increasing use of e-wallets in Indonesia highlights the need to understand user perceptions automatically and efficiently. One valuable data source is user reviews from the Google Play Store. This study aims to classify sentiment toward three major e-wallets, such as GoPay, OVO, and DANA to support service improvement. A quantitative approach is used with a machine learning-based classification method. A total of 30,000 reviews (10,000 per application) were collected using the google-play-scraper library. The data were processed through several stages: preprocessing (labeling, stopword removal, tokenization, and stemming), feature extraction using TF-IDF, data balancing with SMOTE, and classification with the Random Forest algorithm. Our findings show that the combination of Random Forest and SMOTE significantly improves model performance. Accuracy reached 90% (GoPay), 90% (OVO), and 87% (DANA). Precision, recall, and weighted f1-score were 90%, 89%, and 89% for GoPay; 90%, 90%, and 90% for OVO; and 88%, 88%, and 88% for DANA. WordCloud visualizations further support the findings by highlighting dominant words in each sentiment, such as “good,” “help,” and “lost.” Overall, the integration of TF-IDF, SMOTE, and Random Forest is proven effective and reliable for sentiment classification across the three e-wallet platforms.
Integrasi Sistem Pakar Forward Chaining dan Decision Tree untuk Deteksi Hama berbasis WhatsApp Iskhak, Zaki Maulana; Darmanto, Eko; Muzid, Syafiul
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.v9i2.31189

Abstract

The Indonesian agricultural sector faces challenges in early detection of land disturbances such as pests, diseases, floods, and droughts. Low technology adoption and digital literacy among farmers lead to slow responses to early symptoms. The research aims to develop a web-based decision support system that integrates the decision tree method, forward chaining inference, and sending automatic classification results via WhatsApp. The research used a Waterfall model, encompassing needs analysis, design, implementation, testing, and maintenance. The system is built based on 24 input symptoms that generate five classification categories. This application allows farmers to enter land condition data and receive classification results directly through the system and WhatsApp notifications. Testing demonstrated 80% accuracy compared to expert diagnoses, with a 95% message delivery success rate and an average response time of under five seconds. These results demonstrate that a rules-based approach combined with real-time communication can improve the speed and effectiveness of decision-making at the farmer level. This solution has the potential to be applied in other regions as part of accelerating the digital transformation of agriculture.
Sistem Klasifikasi Kematangan Apel Fuji berdasarkan Warna menggunakan KNN untuk Sortasi Otomatis Maula, Ahmad Inzul; Triyanto, Wiwit Agus; Setiaji, Pratomo
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.v9i2.31243

Abstract

Manual fruit sorting typically relies on workers' visual observation to assess ripeness. This assessment is heavily influenced by individual experience and lighting conditions, often leading to inaccuracies. Furthermore, manual methods are time-consuming, increase the risk of misclassification, and reduce operational efficiency. Our research aims to develop a color-based Fuji apple ripeness classification application using the K-Nearest Neighbor algorithm that combines RGB and HSV features. Our research is developmental research using the Waterfall model, consisting of requirements analysis, design, implementation, testing, and maintenance. We used 240 fuji apple images sourced from images taken in the Kudus area. Our findings are an automatic classification application capable of classifying apple images into three ripeness levels: unripe, semi-ripe, and ripe. The evaluation results showed an accuracy of 93.75% with balanced precision, recall, and f1-score across all classes, confirming the system's stable performance without any indication of bias. Testing results using the black-box method in three scenarios opening the application, uploading an image, and reclassifying proved that all features performed as expected. The implication is that this application is ready for use in camera-based sorting in horticultural production lines and can be developed for other fruit classifications, supporting widespread post-harvest digitalization.
Sistem Informasi Pengelolaan Persediaan berbasis Safety Stock pada Industri Konveksi Seragam Polisi Farhan, Faris Ahmad; Setiawan, Raden Rhoedy; Irawan, Yudie
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.v9i2.31248

Abstract

Manual stock management often leads to various problems, such as delayed information, recording errors, and low operational efficiency, highlighting the need for a more integrated and efficient inventory information system. This study aims to develop a web-based inventory management information system that can automatically implement the safety stock and reorder point methods. The system is designed to determine the minimum inventory level, calculate reorder timing, and provide alerts when stock reaches the minimum threshold to support more accurate restocking decisions. This is applied research that uses the Waterfall model to develop a web-based inventory system for a garment manufacturing business. The stages include needs analysis through observation and interviews, system design using flowcharts and use case diagrams, implementation using PHP and MySQL, and testing using the black-box method. Our findings resulted in an information system equipped with safety stock and reorder point features to automatically determine the minimum stock level and reorder timing. The test results showed that all features functioned properly, increasing data entry speed, improving data accuracy, and supporting better restocking decisions. This system can be used by small businesses that require efficient, real-time, and structured inventory management.

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