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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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Optimalisasi Aplikasi Financial Tracker berbasis Mobile dengan Penerapan Design Pattern MVVM untuk Mengelola Keuangan Jaelani, Muhammad Naufal Hady Anshari; 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.v8i2.27709

Abstract

The use of an appropriate design pattern is crucial in application development to maintain code consistency and simplify task division. This research aims to optimize the development of a mobile-based financial tracker application by implementing the Model-View-ViewModel (MVVM) design pattern. MVVM was chosen because it separates business logic (Model), the interface (View), and presentation logic (ViewModel), making the code more structured, easier to test, and more maintainable. This study falls under the Research and Development (R&D) category, employing the waterfall model through the stages of analysis, design, implementation, testing, and maintenance. Data were collected through literature review and observation, and analyzed to evaluate the effectiveness of MVVM implementation. Our findings show that the mobile-based financial tracker application developed with MVVM design pattern successfully aids in financial management. Application testing results indicated significant performance improvements, with CPU efficiency at 74.8% on the RenderThread and 27.8% on the MainThread. This study contributes by demonstrating how MVVM improves responsiveness, simplifies real-time data synchronization, and enhances application flexibility and efficiency. These findings fill the gap in previous studies that have underexplored the technical aspects of financial application architecture.
Analisis Perbandingan Pearson Correlation dan Cosine Similarity pada Rekomendasi Musik berbasis Collaborative Filtering Yuniardini, Fatma; Widiyaningtyas, Triyanna
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.v8i2.27781

Abstract

Advances in digital technology have revolutionized the world of music, making access to various genres and musicians easier and unlimited, but users still have difficulty finding music that suits their tastes. This research aims to analyze and compare the performance of the pearson correlation and cosine similarity methods on personal music recommendations based on Collaborative Filtering, with a focus on Item-Based Filtering, measured using Mean Absolute Error (MAE) and Root Mean Squared Error  (RMSE). The dataset utilized comprises public metal music ratings from Amazon, sourced from Kaggle, totaling 19,065 samples. The k-Nearest Neighbors (KNN) algorithm was employed for recommendation prediction. The research steps included data collection, pre-processing to address missing values, duplicates, normalization, and outlier detection, followed by prediction using the KNN algorithm, and accuracy measurement using MAE and RMSE. Evaluation results indicated that Pearson Correlation produced an MAE of 0.066538 and an RMSE of 0.086698, while cosine similarity yielded an MAE of 0.066559 and an RMSE of 0.086709. These findings suggest that pearson correlation is more effective in capturing linear relationships within the rating data, leading to recommendations that are more relevant and aligned with user preferences. Pearson correlation considers the variability in each user's ratings, resulting in more accurate recommendations that align with individual rating patterns.
Perbandingan Cosine Similarity dan Mean Squared Difference dalam Rekomendasi Buku Fiksi berbasis Item Rosydah, Lucyta Qutsyaning; Widiyaningtyas, Triyanna
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.v8i2.27783

Abstract

The need for recommendations is increasingly crucial in the digital era, especially with the abundance of fiction book data from e-book platforms and digital libraries. This study aims to evaluate the effectiveness of item-based collaborative filtering using cosine similarity and Mean Squared Difference (MSD) metrics for book recommendations. The knowledge discovery in databases method was applied, encompassing data selection, pre-processing, transformation, data mining, and evaluation. The dataset includes 100,000 user ratings obtained from Kaggle's "Book Recommendation Dataset." Our findings show that the Mean Absolute Error for MSD is 0.152307, slightly better than cosine similarity at 0.152406. The Root Mean Squared Error for MSD is lower at 0.185551, compared to cosine similarity's 0.185636. However, Cosine Similarity is more efficient in processing time, with 0.50 seconds compared to 0.59 seconds for MSD. Understanding these metrics is crucial, as they reveal differences in accuracy and efficiency in book recommendation. The results indicate that MSD performs better in the accuracy of fiction book recommendations compared to cosine similarity, making it more suitable for applications prioritizing recommendation precision, while Cosine is more efficient for large data processing.
Peningkatan Akurasi Deteksi Dini Penyakit Parkinson melalui Pendekatan Ensemble Learning dan Seleksi Fitur Optimal Wulandari, Kang Andini; Nugraha, Adhitya; Luthfiarta, Ardytha; Nisa, Laila Rahmatin
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.v8i2.27788

Abstract

Early detection of Parkinson's disease (PD) is essential to enhance patient quality of life through timely intervention. This research aims to develop a predictive model using an ensemble learning approach and optimal feature selection. This experimental study employs three machine learning algorithms: random forest, XGBoost, and extra trees, optimized through hyperparameter tuning, feature selection techniques, and Kernel Principal Component Analysis (KPCA) for dimensionality reduction. The study utilizes the UCI Machine Learning Parkinson Dataset, which consists of 80 samples and 44 acoustic features extracted from patients' voices as they sustain the vowel sound "/a/" for five seconds. Results show that XGBoost achieved the highest accuracy at 88.93% after tuning and KPCA, followed by extra trees with 86.15%, and random forest with 85.47%. The application of KPCA successfully reduced data dimensionality without sacrificing accuracy, thereby improving modeling efficiency. These findings suggest that voice data holds significant potential for early PD detection and that selecting appropriate algorithms and dimensionality reduction techniques is crucial for optimizing data-driven diagnostic models.
Implementasi BERT dan Cosine Similarity untuk Rekomendasi Dosen Pembimbing berdasarkan Judul Tugas Akhir Sabilillah, Ferris Tita; Winarno, Sri; Abiyyi, Ryandhika Bintang
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.v8i2.27791

Abstract

Challenges in completing final projects, which often contribute to delays in student graduation, are frequently due to a mismatch between students' research topics and the expertise of their supervisors. Therefore, a method is needed to address this misalignment in the final project process. This study aims to implement a Bidirectional Encoder Representations from Transformers (BERT) model and cosine similarity to recommend supervisors based on students' final project titles. The research dataset includes 3,723 research titles collected through web scraping from Google Scholar and ResearchGate, representing the expertise of 63 lecturers in the Informatics Engineering Program at Universitas Dian Nuswantoro. Data processing includes preprocessing to generate embedding vectors from lecturers' research titles, which are then compared with students' final project titles. Our findings indicate that the developed recommendation model achieves an accuracy of 90% in identifying relevant supervisors based on topic alignment between students' final project titles and lecturers' areas of expertise, as reflected in their publications. This result can make a significant contribution to supporting students in completing their final projects more efficiently and improving the quality of academic supervision by facilitating more appropriate supervisor selection.
Analisis Metode Collaborative Filtering menggunakan KNN dan SVD++ untuk Rekomendasi Produk E-commerce Tokopedia Hazizah, Chalista Yulia; Widiyaningtyas, Triyanna
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.v8i2.27793

Abstract

The rapid development of internet technology has driven increased adoption of e-commerce, yet companies face challenges in enhancing users' shopping experiences. To assist users in finding products that match their preferences, relevant recommendation analysis is crucial. This research compares the effectiveness of K-Nearest Neighbors (KNN) and Singular Value Decomposition Plus Plus (SVD++) algorithms for e-commerce product recommendations using the Tokopedia Product Reviews dataset from Kaggle, which contains 40,893 reviews. The study includes data collection and preprocessing steps such as removing duplicates, replacing missing values with the average, and normalizing ratings. KNN and SVD++ are then applied to predict ratings using cosine similarity and factor matrices. Evaluation using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) shows that SVD++ outperforms KNN, achieving a lower MAE of 0.161176 and RMSE of 0.185252, compared to KNN's MAE of 0.163964 and RMSE of 0.197045. This indicates that SVD++ is more effective in delivering accuracy and capturing data complexity. The findings highlight the potential to enhance recommendation effectiveness in e-commerce, improving user satisfaction by efficiently matching products to preferences.
Inovasi Pembelajaran Cerita Anak: Pengembangan E-Komik Interaktif berbasis Multimedia Tahir, Arifin; Tahir, Muhlis
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.v8i2.27802

Abstract

An effective and fun learning process plays an important role in increasing students' interest in understanding culture, one of which is folklore, based on an initial survey at SDN Keleyan 1 the available learning media is less interesting and interactive, so that students' interest in folklore decreases by up to 60%. This research aims to develop e-comics as a learning medium for folklore for grade VI students. This type of research is development using the ADDIE model with the stages of Analysis, Design, Development, Implementation, and Evaluation. Data collection techniques include observation and questionnaires. Data analysis was carried out in a quantitative descriptive manner to assess the feasibility and effectiveness of the product. The results of this study are in the form of e-comics which show that e-comics are very feasible with a 100% feasibility percentage based on expert assessments. Student responses to small and large group trials reached 92.38% and 90.24%, with features such as dynamic illustrations, interactive quizzes, and engaging storylines. Students' comprehension improved by an average of 35% after using e-comics.  E-comics are effective and feasible to be used as a learning medium that can increase students' interest and understanding of folklore. This media makes a theoretical contribution to the development of culture-based digital learning and is ready to be used in the classroom or independently.
Si Pelabuhanna: Game Edukasi Pengenalan Buah-Buahan Mengandung Vitamin A menggunakan Metode Forward Chaining Kurniawan, Rizky Rizaldi; Widiyaningtyas, Triyanna
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.v8i2.27837

Abstract

Providing education in a fun way about fruits that contain vitamin A is important because one of the important benefits of vitamin A is for human vision. The purpose of this study to develop the game Si Pelabuhanna and apply the method of forward chaining to determine the eligibility of players to rise to level 2. Using the Game Development Life Cycle (GDLC) development method with stages used initiation, pre-production, production, testing, and release. Our findings are in the form of Si Pelabuhanna games that have a play menu, material, and information and apply the forward chaining method. The Si Pelabuhanna Game can be used in elementary school children's subjects where the material is about fruits containing vitamin A. Application of forward chaining method by specifying variables to be used to create rules. Rules are used to determine if a player is eligible to advance to level 2. Testing using simulation by testing one by one rule after being applied in the game. Based on the test obtained an accuracy value of 100%. This means that the forward chaining method is successfully applied to determine whether a player is eligible to rise to level 2 in a Si Pelabuhanna game.
Centing: Aplikasi Cegah Stunting Anak berbasis Android menggunakan TensorFlow Lite Abiyyi, Ryandhika Bintang; Subhiyakto, Egia Rosi; Sabilillah, Ferris Tita
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.v8i2.27875

Abstract

Stunting is a serious health problem that affects children's growth and development, especially in areas with limited access to early detection. This research aims to develop a TensorFlow Lite-based “CENTING” Android application to detect stunting risk quickly and accurately. The prototyping method is used with the stages of identifying user needs, making initial prototypes, testing, and refinement based on the feedback of health workers and parents, until the application is ready to be implemented optimally. The dataset contains 121,000 child growth data from public sources, with variables such as age, gender, height, and nutritional status to detect stunting traits early. The data was processed and split 80:20 for training and testing, resulting in a detection accuracy of 98%. The selection of TensorFlow Lite is based on its advantage in response speed on mobile devices. The results showed that the CENTING application functioned optimally with a user acceptance score of 89.5%. The app supports self-detection, prevention education, and offline access, relevant for network-limited areas. These findings accelerate stunting intervention efforts and support government programs in reducing stunting prevalence.
Integrasi Kamus Multibahasa pada Feed Forward Neural Network dan IndoBERT dalam Pengembangan Chatbot Mobile Pamungkas, Arba Adhy; Alam, Cecep Nurul; Atmadja, Aldy Rialdy; Juliansyah, Roby
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.v8i2.27886

Abstract

The development of digital technology drives the need for efficient and responsive communication services that support multilingual. This study aims to develop a chatbot that facilitates communication and operational tasks for users of the DigiTeam application by integrating a multilingual dictionary into the Feed Forward Neural Network (FFNN) model and IndoBERT. The research method used is CRISP-DM, a systematic approach in data exploration, preparation, modeling, and implementation. The DigiTeam application was developed using the Agile methodology to gradually enhance the features and functionalities of the application. The dataset consists of 456 patterns and 106 tags containing common and operational work-related questions. This study utilizes a multilingual dictionary with 309 words to improve the chatbot's context understanding and response accuracy to user queries. The test results show that integrating the multilingual dictionary into the FFNN and IndoBERT models yields an accuracy of 95.45% with balanced precision and recall, demonstrating the chatbot's ability to understand and respond to multilingual queries in real-time, while also improving operational efficiency and information access in the workplace.

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