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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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Sinau: Javanese Educational Games for Early Childhood as an Effort to Preserve Javanese Heritage Pratama, Nanda Zakir Shihab; Ratnasari, Chanifah Indah
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.v8i2.27889

Abstract

Preserving the Javanese language is a collective responsibility to safeguard Indonesia's cultural heritage. This study aims to develop Sinau, a Javanese language learning game for early childhood (ages 4–8), focusing on vocabulary and unggah-ungguh (Kromo and Ngoko) with correct pronunciation. Utilizing the Game Development Life Cycle (GDLC) and Guided Discovery methods, the game was designed to facilitate active learning both at home and school. GDLC encompassed pre-production (concept, teaching methods, and prototype development), production (game creation with Unity, incorporating educational content, visuals, and sound), and post-production phases. Testing involved 10 second-grade students and 2 teachers, with observations despite the game's primary target being younger children. Participants evaluated the game by playing it and completing a 10-question Likert-scale questionnaire. Usability was measured using the System Usability Scale (SUS), resulting in a score of 74.17, indicating an acceptable level with a good adjective rating. These results demonstrate that Sinau is well-received and effective for Javanese language learning. While the study did not directly test early childhood learning outcomes, the findings suggest the game's potential for educational use, with recommendations for improving user experience and content to enhance its effectiveness further.
Optimasi Klasifikasi Stunting Balita dengan Teknik Boosting pada Decision Tree Hastuti, Nanda Tri; Budiman, Fikri
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.v8i2.27913

Abstract

Malnutrition in the growth of small children is known as stunting. Currently, nutrition is still a serious problem that needs to be addressed, especially the nutrition of children under five. Considering the target prevalence rate (14%) in 2024 and how dangerous stunting is in Indonesia, this stunting problem needs to be addressed. The purpose of this research is to optimize the decision tree algorithm in stunting classification using boosting technique optimization. The boosting techniques used are AdaBoost, XGBoost, and Gradient Boosting methods. The boosting technique was chosen because it can improve classifier performance by combining multiple models that are learned sequentially, resulting in more effective predictions. This research uses infant data from Kaggle, which has a total of 10,000 data points, 8 attributes, and 2 classes. Based on the results of this study, decision tree optimization using the XGBoost method achieved the best results with accuracy of 83.8%, precision of 82%, recall of 83.8%, and F1-score of 81.2%, which shows great potential in improving the classification of stunted infants. The boosting technique is the best choice compared to other techniques. Based on the results of this study, the boosting technique can accurately predict and demonstrate a high level of precision in handling stunting classification.
TaniSidemen: Aplikasi Pengadaan dan Penjualan Bibit biji Tanaman berbasis Mobile Maulana, Muhammad Hizbi; Asriningtias, Yuli
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.v8i2.27937

Abstract

Procuring and selling seeds is an important part of sustainable agriculture. To facilitate this process, mobile applications are a promising solution. This research aims to develop a mobile application specifically designed for the procurement and sale of plant seedlings. The method used for the development of this software is Software Development Life Cycle (SDLC) with waterfall approach. The planning stage is carried out for system planning, the analysis stage is carried out to analyze the running system and collect data by conducting observations and interviews, the design stage is carried out to design use case diagrams, activity diagrams and user interfaces, the implementation stage uses Kotlin when the implementation process and the testing stage uses black box testing. The results of black box testing are the main functionality of applications such as procurement and sales menus that work properly. The result of this research is an application called TaniSidemen, which is a mobile-based plant seed procurement and sales application. The TaniSidemen application facilitates the procurement and sale of plant seeds, increases time and cost efficiency, and supports the digitization of the agricultural sector for industrial sustainability.
Penerapan Metode Convolutional Neural Network pada Sistem Klasifikasi Penyakit Tanaman Apel berdasarkan Citra Daun Pamungkas, Nicholas Bagus; Suhendar, Agus
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.v8i2.27958

Abstract

Apple leaf diseases can cause significant crop failure and impact the economy of farmers and the agricultural industry. With the increasing demand for quality apples, it is important to develop effective and efficient solutions to detect apple plant diseases early. This research aims to develop an automated system that can identify diseases in apple plants based on leaf images using the Convolutional Neural Network (CNN) model. This model was developed with the ResNet50V2 architecture to classify four leaf conditions: three types of common diseases and one healthy condition. This research applies the CNN model for leaf image processing and the Waterfall system development method. The stages start from needs analysis by collecting data to be processed by the cnn model, interface design of the classification system, program code implementation, and functionality testing using black-box testing. CNN model development includes the stages of collecting datasets sourced from Malang apple plantations as many as 150 images and Kaggle public datasets totalling 3,071 images, then image preprocessing, model development and training. Our research results produced an apple plant disease classification system by implementing the CNN model. Based on the results of testing the system and the model used, it shows that the CNN model applied in the system achieves a classification accuracy of 99.01%, and the functionality of the system built runs well.
Klasifikasi Kategori Produk untuk Manajemen Keuangan Remaja menggunakan Algoritma Long Short-Term Memory Sutrisno, Hendra; Winarsih, Nurul Anisa Sri
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.v8i2.27959

Abstract

Generation Z often faces difficulties in managing their finances due to impulsive spending habits and a lack of financial planning, which can lead to long-term issues such as overspending and minimal savings. This research aims to develop a category classification model that can be integrated into a financial tracking application to help young people manage their money more effectively. The main feature of the application is an automated system that classifies product names into expense categories such as food, transportation, and shopping using a Long Short-Term Memory (LSTM) model. LSTM was chosen for its ability to understand word sequences and text context, which is essential in product grouping. The dataset used consists of 4,499 product entries divided into three categories: 1,488 for food, 1,682 for transportation, and 1,329 for shopping. The model was trained using a supervised learning approach, with data split for training and testing. The model achieved 86% accuracy on both validation and test data, with additional metrics such as precision, recall, and F1-score indicating good performance. This study contributes by applying innovative preprocessing techniques and oversampling to address data imbalance, which is expected to enhance the model's accuracy in classifying expenses.
Inovasi Digital dalam Pemesanan Makanan: Aplikasi Mobile Android untuk Pemesanan Ayam Geprek Secara Online Nugraha, Rivaldi Kenny; Pramudwiatmoko, Arif
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.v8i2.27969

Abstract

The development of information technology has increased the popularity of online food ordering, but many small restaurants still use conventional methods, so Android Mobile-based ordering systems are an effective solution to increase efficiency and reduce errors. This research aims to produce a food ordering application, namely geprek chicken at Warung Keysha online based on Mobile Android. This application builds the system using a Research and Development (R&D) strategy in conjunction with the Rapid Application Development (RAD) model, which comprises three stages: needs planning, RAD design workshop, and implementation. Research and Development (R&D) approach to build the system. The program was tested using black box testing, which makes sure that every system function works as intended.. Fifty customers were the subject of the study to measure user reactions. questionnaires for data collection, and quantitative descriptive methods for data analysis. The findings showed that the developed application received a feasibility test result of 94% (very feasible).
Sistem Klasifikasi Kualitas Bunga Cengkeh Kering berbasis Website menggunakan Logika Fuzzy Metode Tsukamoto Fadhila, Arifa Farras; Avianto, Donny
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.v8i2.27983

Abstract

Indonesian cloves have strong competitiveness in the main market due to their economic benefits, such as being raw materials for kretek cigarettes, spices, and the perfume industry. However, high global competition demands improvements in product quality and consistency. The manual and subjective sorting of cloves often leads to inaccuracies and inconsistencies in quality, which can be detrimental to farmers, especially in smallholdings. The objective of our research is to develop a web-based system for classifying the quality of dried clove flowers using the Tsukamoto fuzzy logic method. The stages of system development using the waterfall method include system requirements analysis, architecture and interface design, website implementation with the Tsukamoto fuzzy method, and testing. The Tsukamoto fuzzy logic implementation method was chosen due to its ability to process uncertain data and produce consistent output. Our findings successfully produced a web-based system called 'Clove Tester', with an average sensitivity of 45.99% from sensitivity testing based on modifications to the membership function of condition and quality variables. These results indicate that the system has a good adaptability to variations in input data, making it suitable for application to data with a high level of uncertainty or ambiguity in this research.
Analisis Performa Model Random Forest dan CatBoost dengan Teknik SMOTE dalam Prediksi Risiko Diabetes Irfannandhy, Rony; Handoko, Lekso Budi; Ariyanto, Noval
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.v8i2.27990

Abstract

Diabetes mellitus (DM) is increasing in prevalence globally and is becoming a serious health problem. Early detection reduces long-term complications. The purpose of our research is to evaluate and compare the effectiveness of Random Forest (RF) and CatBoost models with SMOTE technique in predicting DM risk based on test data processed to produce comparative analysis performance of both models in the form of precission, recall, F1-Score and accuracy. Our research type is quantitative using methods that include EDA, transformation, dividing test and training data, implementation of RF and CatBoost methods with SMOTE and evaluation of model performance. The dataset from the platform (Kaggle) includes 768 individual health data consisting of eight independent variables of pregnancy, glucose, blood pressure, skin thickness, insulin, Body Mass Index (BMI), DM history, age as well as one target (outcome) variable of DM status. The SMOTE analysis technique was applied to balance the class distribution and improve the representation of the minority class, making the prediction model more accurate and stable. The findings of the SMOTE-RF model were 82% accuracy and SMOTE CatBoost 81% accuracy. Based on the feature importances analysis, the main variables affecting DM risk prediction of both models are glucose, BMI and age. Glucose variable is the main DM risk indicator used for prediction to be more efficient. The practical implication of improved machine learning early detection has the potential to support doctors' decision making more accurately to prevent more serious complications in diabetes mellitus.
Analisis Value Proposition dan Persepsi Pengguna Terhadap Sistem Informasi Laboratorium (LIS) di Rumah Sakit Cahyati, Ade Puput; Subhiyakto, Egia Rosi
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.v8i2.27997

Abstract

Patient data management information in the health sector is not only a technological consideration but also includes an evaluation to facilitate medical personnel to store patient data. Thus, the hospital, which is a forum for medical personnel, needs to consider the development of a new recording system. This study aims to analyze the value proportion and user perceptions of Laboratory Information System (LIS) for application development so that developers can offer features and designs that users need. Data collection techniques in this study used a questionnaire with a sample of 52 people. The data analysis technique used uses the UX Honeycomb method and the System Usability Scale to be able to analyze the LIS application. The results of this study indicate that the value proposition variable has a significant influence on the LIS application based on the UX Honeycomb indicator. The dominant indicators are useful and usable, while the indicators that need to be improved are findable and valuable. User perception has a significant influence on the LIS application; the average value of 85.14 means that it has a very usable influence and is easy to use. So that hospitals can switch to digital data and reduce physical documents.
Teknologi Blockchain berbasis Non Fungible Token sebagai Penghargaan Partisipasi Donor Darah Moleo, Alif Safa; Hasanuddin, Tasrif; Darwis, Herdianti; Harlinda, Harlinda
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.v8i2.28005

Abstract

Non-fungible tokens (NFTs) are a technological innovation that has been widely used to provide a form of digital reward. However, the application of NFTs in the social domain, especially in blood donation programs, has not been widely explored. This research aims to develop an NFT-based reward system using blockchain technology as an appreciation for blood donors. The system is designed and developed using the Ethereum test network due to its stability in decentralized applications. This research uses the research and development (R&D) method with the 4D model approach, which consists of the Define, Design, Develop, and Disseminate stages. In the Define stage, a needs analysis was conducted to determine the system specifications. The Design stage involves the design of a web-based system3 to support NFT management. In the Develop stage, the system was developed using the Ethereum testing network. The Disseminate stage includes system testing using the black box method to ensure that all key features, such as NFT claims and data transparency, function properly. The result of the research is an NFT-based blockchain application that allows blood donors to easily claim their NFTs as a form of digital recognition. The evaluation showed an acceptability score of 61.34%, indicating that this application is acceptable to the community and has the potential to increase blood donor motivation. The implementation of this system is expected to have a sustainable positive impact on increasing blood donor participation in the future.

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