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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 434 Documents
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Evaluasi Kinerja Metode Peningkatan Kontras (CLAHE & HE) pada Klasifikasi Ras Kucing menggunakan VGG16 Juslan, Wulandari; Muhammad, Alva Hendi
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.v9i1.29578

Abstract

Cat breed classification is challenging in image processing due to complex visual variations from crossbreeding, which affect care requirements. This study evaluates the effectiveness of Contrast Limited Adaptive Histogram Equalization (CLAHE) and Histogram Equalization (HE) in cat breed classification using a VGG16-based Convolutional Neural Network (CNN). The dataset consists of 4,656 cat images from six breeds, processed with CLAHE and HE for contrast enhancement before training. It is divided into 70% for training, 15% for validation, and 15% for testing. The model is trained for 10 epochs using the Adam optimizer, a 0.0001 learning rate, and batch sizes of 16, 32, and 64. Evaluation using accuracy, precision, recall, and F1-score shows that CLAHE achieves the highest accuracy (99.39%), surpassing HE (99.17%) by 3.29%. CLAHE is more effective in preserving local details, improving precision (78.67%), recall (78.33%), and F1-score (78%). The highest performance is in the Sphinx breed (F1-score 92%), while the lowest is in American Shorthair (F1-score 72%). A high standard deviation indicates classification variations across breeds, but CLAHE consistently improves model accuracy. These findings suggest that CLAHE is more effective than HE in enhancing cat breed classification and offers a more efficient solution than adopting a complex model architecture.
Sistem Informasi Pengelolaan Stok Bahan Baku Roti secara Real-Time berbasis Web Wulandari, Gilang Ayu; Riadi, Aditya Akbar; Susanto, Arief
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.v9i1.29605

Abstract

Technological developments can improve efficiency in business management, including in UMKM Anna Bakery Guwo Tlogowungu which still uses manual recording in managing raw material stock, which can hinder the production process. This has the potential to cause errors in recording, data loss, financial losses, delays in stock information, and obstacles in decision-making. The purpose of this research is to design and develop an information system for managing bread raw material stock to increase productivity and operational efficiency. The system was developed using the waterfall method, which includes the stages of needs analysis, design, implementation, verification, and maintenance, with data collected through interviews and observations. System testing through the black box to ensure that the system functions in accordance with the provisions set. This system is built with PHP and MySQL and is equipped with authentication features to differentiate user access rights to maintain data security. The results of the study show that this system is able to accelerate stock recording by up to 70% compared to manual methods, reduce recording errors, and present reports accurately and in real time, and support flexible data updates at Anna Bakery.
Analisis Performa Metode Extreme Learning Machine dan Multiple Linear Regression dalam Prediksi Produksi Gula Ananta, Ahmadi Yuli; Ariyanto, Rudy; Rozi, Imam Fahrur; Arianto, Rakhmat
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.v9i1.29626

Abstract

Sugar is a crucial commodity in Indonesia, with demand increasing annually. Variations in sugar production require accurate prediction strategies for industrial planning. This study aims to analyze the performance of the Extreme Learning Machine (ELM) and Multiple Linear Regression (MLR) methods in predicting sugar production. This research employs a quantitative experimental approach, with sugar production data during the 2020-2023 milling period as the research subject. Data collection techniques involve observation and documentation, while data analysis techniques utilize Mean Absolute Percentage Error (MAPE) and 10-Fold Cross-Validation to measure model accuracy. The results indicate that ELM has a lower error rate (MAPE 16.06%) compared to MLR (MAPE 27.90%), making it more effective in capturing complex sugar production patterns. Implementing this model in a web-based system also enables more efficient production monitoring. The ELM method proves to be superior in predicting sugar production and can be integrated into industrial systems to support data-driven decision-making. Future research can explore other predictive models, such as deep learning, and consider external factors like weather and soil conditions to enhance accuracy.
Prediksi Penjualan Tanaman Hias menggunakan Regresi Linier Berganda dengan Perbandingan Eliminasi Gauss dan Cramer Bagaswara, Dwiky Ihza; Astuti, Yani Parti
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.v9i1.29627

Abstract

Sales prediction is a crucial element in the ornamental plant business to support inventory planning and marketing strategies. Our research aims to compare the Gauss and Cramer elimination methods (determinant matrix) in multiple linear regression to assess the accuracy of sales prediction. Gauss elimination is effective for systems of large size, while the Cramer method is more consistent in handling systems of linear equations that have correlated variables. The dataset used consists of 212 data points, including unit price as the dependent variable and stock, quantity sold, and total revenue as the independent variables. The accuracy was compared using Mean Absolute Percentage Error (MAPE) due to its ability to measure the error relative to the true value. Our findings show that the Cramer method has a MAPE of 21%, which is lower than Gauss elimination with a MAPE of 40%, making it more accurate in sales prediction. With a more precise method, business owners can optimize inventory management, set prices more efficiently, and devise data-driven marketing strategies. Our results also provide insights for other sectors that use predictive analytics to improve business decision-making.
Pemantauan pH Produk Skincare berbasis IoT: Solusi untuk Keamanan Konsumen Pieters, Luntungan Stephen
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.v9i1.29644

Abstract

Skincare products play an important role in skin health, but there is often a discrepancy between the product content and the information on the label, which can harm consumers. This research aims to develop an IoT-based pH monitoring system with a digital pH sensor and UV-Vis spectroscopy to detect changes in pH in real-time, provide more accurate data, and improve efficiency in monitoring the quality of skincare products. The method used is a development approach with a prototype model, which integrates an IoT system with a digital pH sensor and UV-Vis spectroscopy. The system is designed to measure the pH of skincare products based on the principle that each compound absorbs light at a specific wavelength. The tested product samples consist of five types of skincare facial cleansers, toners, serums, moisturizers, and sunscreens that are illuminated with UV-Vis light to measure absorbance values. The pH data obtained was statistically analyzed to calculate the pH deviation compared to the value on the packaging label. Results showed the system was able to detect real-time pH changes with an average deviation below 0.2 for most products, although serums showed larger deviations. The system improves transparency and quality control, helping manufacturers ensure products meet safety standards and giving consumers confidence in choosing safe products.
Sentiment Analysis on Indonesian National Football Team Naturalization using KNN and SVM Adharani, Salza Kartika; Kacung, Slamet; Vitianingsih, Anik Vega
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.v9i1.29653

Abstract

The naturalization of football players in Indonesia is largely viewed positively, with supporters highlighting its benefits for team performance, international competitiveness, and player development. While PSSI endorses naturalization to strengthen the national team, Liga Indonesia Baru (PT LIB) imposes limits to maintain fairness. The purpose of this research is to examine public sentiment toward the naturalization of Indonesian football players by analysing discussions on X and YouTube. This research analyses public sentiment toward the naturalization of Indonesian football players using a data and text mining approach based on 3,267 comments from X and YouTube between 2022 and 2024. The research process includes data collection, preprocessing, TFIDF, data labeling, and model training and evaluation. Two machine learning models, KNN and SVM, are implemented for classification, with SVM outperforming KNN in accuracy. Our results show that KNN achieved 76.71% accuracy (precision: 52%, recall: 56%, F1-score: 53%), while SVM RBF outperformed with 86.51% accuracy (precision: 59%, recall: 42%, F1-score: 26%). SMOTE and GridSearch effectively address the class imbalance and optimize model performance. Public sentiment is predominantly positive, highlighting enhanced team performance and global recognition. These insights assist PSSI and policymakers in making informed decisions regarding fairness, discrimination, and the governance of Indonesian football.
Sistem Informasi Kepegawaian dan Penggajian Karyawan berbasis Web dengan Fitur Selfie dan Pemantauan Lokasi Kirana, Dinar Mersasi; Riyadi, Aditya Akbar; Susanto, Arief
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.v9i1.29662

Abstract

Manual management of personnel and payroll data at Anna Bakery Pati faces obstacles in attendance accuracy, data processing efficiency, and transparency in payroll. This study aims to develop a web-based personnel and payroll information system with the main features of attendance validation using selfie photos and GPS, as well as automatic integration between attendance recording and salary calculation. The development method used is waterfall, which includes the stages of analysis, design, implementation, testing, and maintenance. Data were collected through observation, interviews with company owners, and user surveys using questionnaires. System testing uses the black box method to ensure system functionality and data analysis is carried out based on the results of user surveys. The results of our findings are a web-based personnel and payroll information system equipped with attendance validation features using GPS and selfies, automatic salary calculations, and integrated employee permit and leave management. In addition, the system provides a reporting feature to facilitate access to attendance and salary data. The test results show that the system can function well according to needs and is well received by the owner and employees of Anna Bakery. This system increases transparency in attendance recording, reduces the risk of fraud in attendance, and speeds up the payroll administration process.  With this system, companies can optimize HR management more effectively.
Optimasi Klasifikasi Sentimen Ulasan Game Berbahasa Indonesia: IndoBERT dan SMOTE untuk Menangani Ketidakseimbangan Kelas A'la, Fiddin Yusfida
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.v9i1.29666

Abstract

The increased use of gaming apps on platforms like the Google Play Store has signaled the importance of user reviews as a source of app quality evaluation. However, sentiment analysis of Indonesian-language reviews faces challenges due to the peculiarities of language structure, emotional expressions, and the use of slang and specialized terms in game reviews. This study aims to classify reviews into three sentiment classes: positive, negative, and neutral, using the IndoBERT-base-uncased model. The type of research used is experimental by comparing the performance of the model using original and synthetic datasets. The total original dataset collected was 998 reviews. The k_neighbors SMOTE parameter used is 5. The IndoBERT-base-uncased epoch parameter is 10, with a batch value per device and a batch for evaluation of 16. Configuration variable warmup_steps is 500 with L2 weight_decay regularization at 0.01. Evaluation results after SMOTE implementation: the precision score increased from 0.44 to 0.45, and the F1-score from 0.46 to 0.47. However, the recall score did not increase. The evaluation results show that the model has variable performance between classes with an initial accuracy of 69.,5%. Data imbalance is a major challenge, especially in minority classes such as class 1 (neutral), which cannot be predicted by the model. The SMOTE technique successfully improved data balance and increased accuracy to 72.5%, as well as improving metrics such as precision, recall, and F1-score overall.
Analisis Sentimen Ulasan Co-Pilot Google Play dengan SVM, Neural Network, dan Decision Tree Najibulloh, Imam Kharits; Tahyudin, Imam; Saputra, Dhanar Intan Surya
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.v9i1.29673

Abstract

Sentiment analysis is a technique used to understand user opinions through product or service reviews. The purpose of this research is to compare three classification methods, namely Support Vector Machine (SVM), Neural Network (NN), and Decision Tree (DT) in analyzing the sentiment of users of the Indonesian-language Microsoft Co-Pilot application taken from the Google Play Store. The dataset consists of 20,000 reviews, which first went through preprocessing such as normalization, tokenization, stopwords removal, and stemming. The three methods we used in this study have a Multilayer Perceptron (MLP) architecture with three hidden layers and a ReLU activation function, as well as a dropout regularization technique to avoid overfitting. Model evaluation was conducted using accuracy, precision, recall, and F1-score, with the results showing that NN achieved the highest accuracy of 95.5%, followed by SVM with 95.4% and DT with 92.1%. The advantage of the NN method lies in its ability to recognize more complex patterns in Indonesian, especially in handling informal text and code-mixing. This research contributes to the development of Artificial Intelligence (AI)-based applications by providing insights into the effectiveness of classification methods in Indonesian sentiment analysis, which is important for improving service quality and the development of NLP technology in Indonesia. The practical implications of this research can be used in the development of AI-based applications that are more responsive to user sentiment.
DiabTrack: Sistem Prediksi Dini Diabetes Melitus Tipe 2 berbasis Web menggunakan Algoritma K-Nearest Neighbors Pangestu, Aditya Gilang; Winarno, Sri; Nugraha, Adhitya; Muttaqin, Almas Najiib Imam
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.v9i1.29691

Abstract

Type 2 diabetes mellitus is a chronic disease that is often not detected early enough, increasing the risk of serious complications. Based on this, early detection of this disease is very important to reduce its negative impact. This research aims to develop the DiabTrack system, a web-based prediction system using the K-Nearest Neighbors (KNN) algorithm. This type of research is development research using the Rapid Application Development (RAD) model, including the requirements planning, design workshop, and implementation stages. The dataset used comes from Kaggle, containing 53,000 samples and 8 features. The model is trained using the KNN algorithm and the SMOTE technique to balance the data. Evaluation results show that the KNN model achieves an accuracy of 99.17%, a recall of 100%, and an F1-score of 94%, making it the chosen algorithm for the DiabTrack website. Additionally, Black Box testing results indicate that all features in the DiabTrack system function as expected, helping the public monitor their health conditions while serving as an initial analysis tool for medical professionals.

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