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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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Pengembangan Video Pembelajaran Teknik Vokal sebagai Media Audio Visual Interaktif Alhafizi, Wahyu Maulana; Hafiz, Alwan
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.v9i2.31273

Abstract

Learning vocal techniques requires an understanding of physical, technical, and musical aspects, but many students have difficulty mastering diaphragmatic breathing, articulation, resonance, and tone control due to the limitations of media that are only oral or text-based. Therefore, teachers need more flexible and interesting learning media. This research aims to develop vocal technique learning videos as interactive audio-visual media. This research uses the Research and Development (R&D) method with the ADDIE development model which consists of analysis, design, development, implementation, and evaluation stages. The research subjects were 11th grade students of SMA N 1 Masbagik. The results of our research are in the form of vocal technique learning videos as interactive audio-visual media. The results of testing by media experts show that this media is very feasible (85.75%) and material media experts give an assessment that this media is also very feasible (86.2%). Meanwhile, the results of user responses to this media are very high (85.6%). These results indicate that the learning media is effective and very feasible as a medium for learning vocal techniques.
Aplikasi Chatbot Interaktif Pembelajaran Bahasa Pemrograman PHP dengan Algoritma NLP berbasis BERT Waleska, Rangga Febrio; Asnal, Hadi; Rahmiati, Rahmiati; Gunadi, Gunadi
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.v9i2.31427

Abstract

The digitalization of education facilitates access to information; however, beginners still face challenges in understanding programming languages such as PHP. This study aims to develop a chatbot based on Natural Language Processing (NLP) using the Sentence-BERT model (all-MiniLM-L6-v2) to understand user questions in natural language contextually. The research follows a prototyping development method, consisting of several stages: needs identification to determine relevant features for users; interface design to create an intuitive and user-friendly layout; web-based system implementation to realize system functions; and testing using the black-box method to ensure each feature works as specified, along with usability evaluation using the System Usability Scale (SUS) to assess user comfort and ease of use. The result is a chatbot application capable of matching user questions with a Q&A database using semantic similarity. All testing scenarios ran as expected. The SUS evaluation yielded a score of 89.58, indicating a very high level of user satisfaction. This research demonstrates that the integration of NLP and BERT can enhance the effectiveness and convenience of independent programming learning and has the potential to be applied to other educational platforms.
Studi Eksperimen Keamanan Jaringan Wi-Fi Kampus: Analisis Kerentanan terhadap Serangan Evil Twin dan Deauthentication Asiana, Kia Putri; Huwae, Raphael Bianco; Jatmika, Andy Hidayat
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.v9i2.31822

Abstract

The increasing use of Wi-Fi in higher education also brings security risks, such as Evil Twin Attacks that trick users into connecting to fake access points. This study aims to assess the vulnerability of Universitas Mataram’s Wi-Fi network to such attacks using a multihop model and to propose technical improvements. An ethical penetration testing method was applied using a NodeMCU ESP8266 with Deauther firmware, tested across 13 campus locations. Observed variables included the number of connected devices, user interaction with phishing pages, deauthentication success, and captured credentials. The results reveal that five out of 13 locations (38.46%) were vulnerable, where users were redirected to fake SSIDs and entered credentials, even though most deauthentication attempts failed. These findings highlight that attack success depends not only on deauthentication but also on firmware variation and AP configuration. The study implies the need for network security audits, firmware standardization, stronger authentication with full encryption, and enhanced user awareness to reduce phishing risks.
Segmentasi Pelanggan Menggunakan Kerangka LRFMV dan Algoritma K-Means untuk Optimalisasi Strategi Pemasaran Wawagalang, A. Nolly Sandra; Syahrullah, Syahrullah; Ardiyansyah, Rizka; Angreni, Dwi Shinta; Pratama, Septiano Anggun; Nugraha, Deny Wiria
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.v9i2.31025

Abstract

In this competitive digital era, customer behavior is key to maintaining loyalty and increasing profitability. This study aims to implement customer segmentation using the Length, Recency, Frequency, Monetary, Volume (LRFMV) approach and the K-Means algorithm to identify customer behavior characteristics and determine high-value segments. The combination of these five dimensions has rarely been used in previous studies, thus providing a new contribution to data-based customer behavior analysis. This study adopts an exploratory descriptive quantitative approach. The data used consists of 2,098 transactions from 452 customers, sourced from a public GitHub dataset. The data analysis process includes preprocessing, determining LRFMV values, and segmentation using K-Means Clustering. The Silhouette Coefficient is used to evaluate cluster quality and determine the optimal number of clusters. The results show that the best configuration is obtained at k=5 with a Silhouette value of 0.842. The findings show five customer segments with different characteristics and Customer Lifetime Value (CLV) values. Clusters 0 and 2 are categorized as Loyal Customers (L↑R↓F↑M↑V↑) with the highest CLV. Clusters 3 and 1 are Inactive New Customers (L↓R↑F↓M↓V↓) with low contribution. Cluster 4 consists of Inactive Customers (L↓R↓F↓M↓V↓), indicating overall inactivity. These segmentation results are used to develop more targeted strategies, such as loyalty programs or reactivation campaigns, to optimize marketing strategies based on customer value.
Sistem Peramalan Kelahiran, Kematian dan Kemiskinan berbasis Website dengan Metode Arima Munirah, Zahrahtun; Widiartha, Ida Bagus Ketut; Murpratiwi, Santi Ika
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.v9i1.28423

Abstract

Population data management in Wawonduru Village is currently not running optimally due to a lack of supporting technology, where data management is still done manually by visiting house to house. With the increasing population every year, a system is needed that can manage data efficiently to monitor population fluctuations. This research aims to develop a web-based birth, death and poverty forecasting system to predict population trends and plan appropriate policies in the future in Wawonduru Village. The system was developed using the Personal Extreme Programming (PXP) model and forecasting using the Autoregressive Integrated Moving Average (ARIMA) method. ARIMA was chosen because it is able to accurately predict data with changing patterns and seasonality based on historical patterns. The results show that the developed system not only runs well without errors but also functions as an integrated birth, death and poverty forecasting system. Forecasting using ARIMA resulted in MAPE values of 38.16% for births, 40.67% for deaths, and 44.4% for poverty. It is within the range between 20-50%, so it is feasible to use. Blackbox testing also states that the system has run well and meets user needs both in terms of convenience. So that this system can be used by the Wawonduru Village command to do forecasting to assist in forecasting.
Performa Logistic Regression dan Naive Bayes dalam Klasifikasi Berita Hoax di Indonesia Cahyani, Okta Nur; 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.v9i1.28987

Abstract

The spread of false information has become a major challenge in Indonesian society, with 2,484 cases recorded in 2022. This highlights the importance of developing a system that can effectively identify and filter out fake news. This research aims to develop a more accurate fake news detection model by applying logistic regression, which is optimized by grid search and oversampling to overcome data imbalance. The main focus of this research is to improve the performance of the model in detecting fake news on unbalanced datasets. The dataset used is the Indonesian Fake News dataset, which consists of 4,231 entries with two categories: valid (3,465 entries) and hoax (766 entries). Preprocessing steps include stemming, stopword removal, and text normalization using TF-IDF. Random oversampling was applied to balance the data between hoax and valid classes, and parameter optimization was performed using grid search to improve model performance. The results show that the optimized logistic regression achieved the highest accuracy of 93%, surpassing naive bayes, which achieved 86% accuracy. These findings suggest that the developed fake news detection model can be used to improve the social media news monitoring system, and increase digital literacy among Indonesians.
Metode Frame Difference untuk Deteksi Gerakan Tidur Bayi berbasis Computer Vision Ariyandi, Haffas Zikri; Muhtadi, Muis; Andreanto, Dodik Dwi
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.v9i1.29004

Abstract

Monitoring a baby's sleep is a critical task for parents, especially when balancing household responsibilities. This study combines the MobileNet-SSD object detection model with the Frame Difference method to analyze sleep movements based on motion thresholds. The system's performance was evaluated by calculating accuracy, precision, recall, and latency, implemented on both laptop and Raspberry Pi devices, and tested using 720p and 480p resolution videos. Results showed accuracy of 82%, precision of 81%, and recall of 92% at 720p, and accuracy of 77%, precision of 80%, and recall of 86% at 480p. However, the Raspberry Pi exhibited a latency of 400ms, 10 times higher than the laptop's 41.28ms latency. Compared to optical flow, this method offers ease of use, and lower computational complexity. The results of this study highlight the impact of resolution on motion detection accuracy, where higher-resolution videos yield more optimal performance. Limitations under low-light conditions suggest potential improvements using deep learning techniques like YOLO and Mediapipe to detect eye conditions. This research contributes to the development of computer vision where the frame differential and object detection methods are proven to provide a fairly high level of accuracy in detecting movement.
Analisis Sentimen berbasis Deep Learning Terhadap Kesetaraan Gender di Bidang STEM: Perspektif dan Implikasinya Mariam, Siti; Nurhaida, Ida
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.v9i1.29071

Abstract

Women's participation in Science, Technology, Engineering, and Mathematics (STEM) is still low due to discrimination, gender stereotypes, and lack of access to equal career opportunities. This research analyzes public sentiment about gender equality in STEM fields using the Knowledge Discovery in Database (KDD) approach with the Long Short-Term Memory (LSTM) algorithm. The data consists of 1,200 tweets (2018-2024) collected through web crawling and processed using KDD techniques such as preprocessing, transformation, data mining and evaluation. The resulting LSTM model showed 86.25% accuracy, 88.18% precision, 82.20% recall, and 85.00% F1-score. Sentiment analysis showed support and appreciation for women in STEM (positive sentiment) and criticism of gender discrimination and stereotypes (negative sentiment). This study faced challenges in the form of data imbalance and the model's difficulty in understanding the Indonesian context. Our findings confirm the importance of policies that support gender equality and inclusive work environments. This research is expected to improve people's perception of gender equality and increase the representation of women in STEM fields, especially in Indonesia.
Comparative Analysis of Naive Bayes and SVM for Improved Emotion Classification on Social Media Pratama, Rio Ferdinand Putra; Maharani, Warih
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.v9i1.29087

Abstract

identifying emotions such as happy, angry, sad, and fear. However, Indonesian text processing faces challenges due to language complexity and slang. This research aims to compare Naive Bayes and SVM models, focusing on evaluating the impact of preprocessing, feature extraction, and parameter optimization to improve emotion classification. The dataset was collected from API X using crawling techniques and manually annotated by six annotators. The training process used full and half preprocessing datasets with TF-IDF, BoW, and Word2Vec feature extraction. Naive Bayes and SVM models were evaluated using accuracy, precision, recall, and F1 score. Our results show that full preprocessing improves accuracy, with TF-IDF + BoW achieving 78.01% with SVM and outperforming Naïve Bayes at 75.53%. The results classify emotions into four classes: happy, sad, angry, and fear. This study demonstrates the value of preprocessing and feature selection to deal with slang and complexity in Indonesian texts. These results provide insights for developing optimal emotion classification models and offer applications in sentiment analysis, social media monitoring, and mental health detection.
Aplikasi Artificial intelligence untuk Klasifikasi Lengkungan Kaki: Solusi berbasis Radiografi Haris, Abdul; Nurhaida, Ida
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.v9i1.29098

Abstract

Identifying foot arch types is crucial for maintaining health and comfort. Flat foot arches can cause pain and discomfort, potentially interfering with activities such as sports. This research aims to develop an Artificial intelligence (AI)-based application to detect normal and flat foot arch types through X-ray images. The YOLOv8 model with bounding box is converted to TensorFlow Lite format to be integrated into a mobile platform through Android Studio. The application uses a waterfall model without maintenance, starting from the analysis of x-ray dataset needs, development and testing of the YOLOv8 model, conversion to TensorFlow Lite, design, black box testing, and application on Android devices. This application can only identify x-ray photos of the soles of the feet looking right and left. Confusion matrix application testing with 150 epochs shows performance with recall 86.2%, precision 77.1%, accuracy 83.3%, mAP50 94.9%, and mAP50-95 76.2%. Black box testing on mobile devices using datasets augmented with 45° horizontal shear and 90° rotation resulted in maximum identification accuracy compared to traditional methods such as the wet foot test. Traditional methods print the soles of the feet with an identification process that requires precision of the patient's standing position. This app detects flatfoot early, improving comfort in daily activities and sports.

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