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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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Sistem Identifikasi Kualitas Biji Kopi Robusta berbasis Image Processing dengan Support Vector Machine Gusmaliza, Debi; Aminah, Siti
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 2 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i2.28008

Abstract

Pagar Alam is a region producing robusta coffee, a superior coffee variety in Indonesia with a strong taste and high caffeine quality. However, the selection process for robusta coffee beans in Pagar Alam is still traditional. It needs to be more consistent, impacting product quality, causing economic losses, and damaging the region's reputation as a producer of quality robusta coffee. Therefore, innovation is needed in the coffee bean selection process to improve the quality and competitiveness of robusta coffee from Pagar Alam. This study aims to build an image processing-based identification system for the quality of Pagar Alam robusta coffee beans. Identification is made by extracting visual features of coffee beans, including colour, shape, and size. Implementation of the Software Life Development Cycle (SDLC) through the stages of analysis, design, implementation, testing, and maintenance as a method of system development and identification process using a Support Vector Machine (SVM) with a kernel radial basis function (RBF) to extract visual features such as colour, shape, and size of coffee beans. In the system feasibility test, a percentage of 80% was obtained, a dataset of 170 data with a division ratio of 80:20, accuracy reached 91.17%, precision 100%, recall 91.17%, and F1-score 94.79%. These findings show great potential in improving the efficiency of coffee bean selection and the quality of Pagar Alam robusta coffee bean products by utilizing the support vector machine (SVM) algorithm.
Algoritma SARIMA sebagai Pendukung Strategi Peramalan HPS dalam Persaingan Tender di LPSE Indonesia Fernando, Daud; Syawanodya, Indira; Muhammad, Raditya
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 2 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i2.28009

Abstract

Tender in Indonesia's Electronic Procurement Service (LPSE) is the procurement of goods/services in the form of public facilities and managed by the provider with the lowest estimated price (HPS) value during the reverse auction process. The fluctuating value of HPS and the tight competition of competitors make winning for providers increasingly difficult and competitive. The purpose of this research is to create a forecasting model of the HPS value of tenders in LPSE Indonesia using the Seasonal Autoregressive Integrated Moving Average (SARIMA) algorithm. This type of research is experimental research to determine the order of the best SARIMA model. The research variables used are tender publication date and HPS value as much as 747,098 tender data from historical data from web scraping of the LPSE website with a withdrawal date range of January 7, 2013 to November 30, 2022. The data analysis technique uses data exploration analysis to determine the characteristics of the data distribution and then the implementation of the SARIMA forecasting algorithm. The results of this study show that the SARIMA((5,1,1),(4,1,1,7)) model is the optimal model with an evaluation value of mean absolute percentage error (MAPE) percentage error value of 33.56% which in relation to LPSE can provide a reasonable forecasting value. The results of forecasting for the next 30 days show that the distribution of HPS values is in the range of 680 million - 700 million rupiah in the period December 2022.
Machine Learning untuk Deteksi Stres Pelajar: Perceptron sebagai Model Klasifikasi Efektif untuk Intervensi Dini Zahrah, Febrina Nabila; Muljono, Muljono
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 2 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i2.28011

Abstract

Stress is a serious challenge for students that can negatively impact physical health, mental well-being, and academic performance. However, accurate and effective stress detection approaches to support early intervention are still limited. This study aims to evaluate machine learning models for detecting student stress levels with optimal accuracy to facilitate early intervention. The research employs a quantitative approach using a dataset containing 1,100 student samples from Nepal, encompassing 20 stress-related features from psychological, social, academic, environmental, and physiological aspects. Data were collected via a self-report questionnaire, processed with StandardScaler scaling, and analyzed using 10-fold cross-validation. The models tested include Perceptron, Gradient Boosting Trees Classifier (GBTC), Naive Bayes (NB), Logistic Regression (LR), and AdaBoost. The results show that Perceptron performed the best with an accuracy of 97.27%, followed by NB (95.45%), GBTC (94.54%), LR (94.54%), and AdaBoost (93.63%). Perceptron, with its advantage in linearity and evaluation through 10-fold cross-validation, shows great potential as an effective classification model for student stress detection, which can accelerate early intervention and enhance student well-being and learning environments.
Sistem Cerdas Deteksi Status Gizi Anak melalui Eksplorasi Algoritma C.45 dan Forward Feature Selection Arif, Alfis; Gusmaliza, Debi
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 2 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i2.28014

Abstract

The problem of high rates of child malnutrition remains in Pagar Alam City. The lack of understanding of nutritional interventions and the limited ability of Posyandu cadres to conduct accurate nutritional assessments are the main factors. This situation makes it difficult for the community to monitor the nutritional status of children and provide appropriate nutritional intake. This research aims to create an intelligent system for detecting children's nutritional status through the exploration of C.45 algorithm and Forward Feature Selection in Pagar Alam City. This system is designed to detect children's nutritional status and provide recommendations for appropriate nutritional intake based on detection results with variables of children's weight and height. The data used amounted to 7519 data obtained from the Pagar Alam City Health Office. The model we use to build this system is waterfall with stages of planning, analysis, design, development, testing and implementation. Then the method we apply to this system is CRISP-DM and the C.45 algorithm and Forward Feature Selection technique. Our results are in the form of an intelligent system for detecting children's nutritional status, with the results of system testing using test data and training data showing 100% accuracy. In addition, black box testing also proves that the system works well as expected.
Analisis Sentimen Publik di Twitter Pasca Debat Kelima Pilpres 2024 dengan Naive Bayes Zharifa, Anjana Haya Atha; Ujianto, Erik Iman Heri
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 2 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i2.28048

Abstract

The presidential election in Indonesia is a frequently discussed topic on social media, especially Twitter. This platform provides a space for people to express their views on presidential candidates and election issues, making it suitable as a data source for this study. This study aims to analyze public sentiment towards presidential election news on Twitter using the Naïve Bayes Classifier method. Data was taken from Twitter for the period 5–13 February 2024 with a total of 2,561 comments. The research process includes data collection, preprocessing, data labeling, and model training and testing. Naïve Bayes was chosen because it is efficient in text classification and has several variants for model experiments. Sentiment is classified into three main categories, namely positive, negative, and neutral. The results showed that negative comments dominated (41%), followed by positive (37.3%) and neutral (21.7%). The Multi Naïve Bayes Classifier model provided the highest accuracy (81%), followed by Bernoulli Naïve Bayes (80%) and Gaussian Naïve Bayes (76%). This difference in accuracy is influenced by the model's sensitivity to data characteristics, such as the number of features and sentiment distribution. This research has the potential to help campaign teams understand the issues that trigger negative responses and support policy makers in designing more effective political communication strategies.
Peningkatan Akurasi Prediksi Curah Hujan menggunakan Gradient Boosting dan CatBoost dengan Pendekatan Voting Classifier Fudhlatina, Dina; Budiman, Fikri
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.v9i1.28988

Abstract

Accurate rainfall prediction is essential for agriculture, disaster mitigation, and water resource management, especially in the face of climate change impacts. This research aims to improve the accuracy of rainfall prediction using gradient boosting and CatBoost with a voting classifier approach. The data used in this study amounted to 1,461 based on weather data from BMKG Semarang City (2020-2023). The data was analyzed using the Gradient Boosting and CatBoost algorithms with a voting classifier framework. The input features include temperature (Tn, Tx, Tavg), humidity (RH_avg), rainfall (RR), length of irradiation (ss), wind speed (ff_x, ff_avg), and wind direction (ddd_x). The GridSearchCV technique was used for hyperparameter optimization. The model predicts based on rainfall intensity categories, namely no rain, light rain, moderate rain, heavy rain, and extreme rain. The results showed that the model with optimization and ensemble approach achieved 87.89% accuracy, 0.88 precision, 0.88 recall, 0.88 f1-score, and 0.8486 cohen's kappa. Meanwhile, gradient boosting and CatBoost individually produced 75.99% and 85.68% accuracy. With these data input features, the model is able to predict extreme rainfall categories that match the actual data. This research is an important contribution to the development of early weather warning systems, disaster mitigation, and climate management.
Penerapan Convolutional Neural Network dengan ResNet-50 untuk Klasifikasi Penyakit Kulit Wajah Efektif Khani, Nadia Ifti; 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.v9i1.29572

Abstract

Skin diseases have a high prevalence in Indonesia, reaching 12.95% of the population, so early detection is an important step in handling them. This research aims to implement deep learning based on Convolutional Neural Network (CNN) with ResNet-50 architecture to improve the accuracy of facial skin disease classification through medical images. The data used comes from the Augmented Skin Conditions (Kaggle) dataset with a total of 2,394 images, which are processed through preprocessing, augmentation, and division of training and testing data with a ratio of 80%: 20%. The augmentation process resulted in image variations, but vertical distortions were found due to zooming settings and possible shearing effects. The model achieved an accuracy of 94%, higher than the previous study on pneumonia classification using ResNet-50, which obtained an accuracy of 86% and was affected by data imbalance and similarity of visual features between classes. These results show that ResNet-50 can overcome the vanishing gradient problem and extract complex features from medical images optimally. With this performance, this model can be applied in artificial intelligence systems to assist medical personnel in the early detection of skin diseases quickly, accurately, and efficiently.
Perbandingan Algoritma Random Forest, XGBoost, dan Logistic Regression untuk Prediksi Risiko Kekambuhan Kanker Tiroid Ais, Salma Rihadatul; Sanjaya, Ucta Pradema
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.v9i1.29664

Abstract

Thyroid cancer, although relatively rare (0.85-2.5% of all cancer cases), is of serious concern due to its higher prevalence in women and challenges in diagnosis due to limitations of conventional methods such as fine-needle aspiration biopsy and ultrasound. This study aims to predict the risk of thyroid cancer recurrence by applying random forest, XGBoost, and logistic regression methods. Classifying the recurrence of thyroid cancer using 14 dataset variables obtained from Ken Saras Hospital, which amounted to 2000 datasets. The data will be classified using 3 method models and evaluated using a confusion matrix to find the best accuracy evaluation value. Based on the evaluation results, logistic regression gets an accuracy value of 83%, and random forest and XGBoost get an accuracy of 82%. Our findings prove that machine learning approaches can serve as an effective clinical decision support system in improving diagnosis efficiency and facilitating timely medical interventions. The implementation of this in clinical practice still requires integration with comprehensive medical considerations and supervision of healthcare professionals to ensure safety. The results contribute to the development of more reliable and efficient thyroid cancer diagnostic tools.
Sistem Pendukung Keputusan berbasis Web untuk Evaluasi Kinerja Pelayanan Kantor Camat Terbaik di Indonesia Faturrohim, Aulia; Siddik, Mohd; Azmi, Sri Rezeki Maulina
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.v9i1.29767

Abstract

Excellent service is a service provided by the government to meet the needs of the community according to applicable regulations. Low quality of service can reduce public trust in the government. This study aims to build a web-based decision support system to assess the performance of the best sub-district offices objectively and transparently. The system development uses a waterfall model, which consists of stages of analysis, design, implementation, and testing. The analysis is carried out by collecting system requirements and determining seven assessment criteria: facilities and infrastructure, accountability, administration, performance, innovation and creativity, information systems, and preservation of customs and culture. At the design stage, use case diagrams and flowcharts are created, while implementation is carried out by building a web-based system that applies the SAW method. Testing is carried out using the black-box method to ensure the system runs well without errors. The results of our findings are in the form of a decision support system that identifies Kisaran Timur District as the district with the best performance, with a score of 0.896. The test results prove that the system functions optimally, transparently, and accurately. With this system, objectivity and transparency in assessing the performance of sub-district offices can be increased. In addition, this system helps the government in identifying superior sub-district offices and those that need improvement in order to improve the quality of public services.
Syshunt: Game Quiz Mobile untuk Pengenalan Perangkat Keras Komputer menggunakan Successive Approximation Model Suryadila, Lusi; Ismanto, Edi; Novalia, Melly; Syahfutra, Wandi
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.v9i1.29773

Abstract

Game-based learning media is an example of ongoing technological advances in education. This media is becoming increasingly popular as an innovative solution in education. This research aims to develop and test the feasibility of mobile-based quiz games as learning media for computer hardware introduction using the Successive Approximation Model (SAM). This research is a kind of development using the SAM model. The three main stages of the SAM model consist of the preparation stage, the iterative design stage, and the iterative development stage. The data collection technique in this study used a questionnaire. Meanwhile, the data analysis technique used descriptive quantitative. The result of our findings is a mobile-based quiz game as a learning medium for computer hardware introduction. The results of media and material expert validation show that this game has a media feasibility of 87.14% and material feasibility of 86%. However, the results of the student practicality test were slightly lower with a score of 79.88%, which may be influenced by limitations in the interface features or the time needed for students to adapt to the game mechanics. Nevertheless, the game proved to be effective in understanding computer hardware and is more interactive and fun compared to conventional learning. With a more engaging learning experience, this game can be a creative alternative that supports the teaching and learning process, overcoming the problems of traditional learning that is often monotonous.

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