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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 Penjualan Berbasis Web menggunakan Metode Supply Chain Managemen untuk Manajemen Persediaan Barang Lubis, Irgi Arianda; Maharani, Dewi; Dristyan, Febri
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.v8i2.27281

Abstract

The manual inventory management at Andriani Wholesale Store in Air Teluk Kiri Village causes instability and significant fluctuations in stock levels. This problem leads to financial losses, high storage costs, and the loss of customers. This research aims to create a web-based sales system for inventory management integrated with the supply chain management (SCM) method. The model we use in building this system is the waterfall model, with stages of analysis, design, development or implementation, and testing. The testing we conducted used black box testing to ensure that the application or system could perform the desired functions according to the specified requirements. The data collection methods we used were interviews and observations. The data was processed using the SCM concept. Our findings resulted in a web-based sales system applying the SCM concept. The testing results showed that the system could perform its functions well without errors. With this system, real-time inventory monitoring, improved planning, and better communication with suppliers at Andriani Wholesale Store are achieved.
Sistem Pendukung Keputusan dalam menentukan Guru Teladan menggunakan Metode Composite Performance Hidayanti, Sri; Mulyani, Neni; Ramadhani, Andrew
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.v8i2.27292

Abstract

The development of technology has had a significant impact on various aspects of human life, including education. Yayasan Mas Bustaanul Uluum, a high school, faces challenges in selecting exemplary teachers objectively and accurately. The process of selecting exemplary teachers has so far been subjective, with assessments based on attendance and years of service. This research aims to develop a decision support system (DSS) by applying the web-based Composite Performance Index (CPI) method. The system is built using the waterfall model, which consists of analysis, design, implementation, and testing. Data collection was conducted through observation and interviews. The aspects used to measure exemplary teachers include attendance, teaching experience, highest education level, and achievements. The data processed using the CPI method is used to determine exemplary teachers. Furthermore, the system built was tested using black box testing on each system component. Our findings resulted in a DSS integrated with the CPI method for determining exemplary teachers. The calculations within the system are also consistent with the manual calculations using the CPI method. Additionally, the system operates smoothly without errors. Therefore, this system can assist Yayasan Mas Bustaanul Uluum in selecting exemplary teachers in an objective manner.
Penerapan Metode Perbandingan Eksponensial pada Sistem Pendukung Keputusan untuk Menentukan Pegawai Terbaik Aritonang, Nurmala Plorensia Br; Helmia, Fauriatun; Rohminatin, Rohminatin
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.v8i2.27295

Abstract

The Department of Animal Husbandry and Health of Asahan Regency is a government agency that conducts education and health checks for animals. Employee assessment in this department is conducted to determine the quality of staff. However, the current assessment process is still manual using assessment sheets and remains subjective. The aim of this research is to develop a decision support system by applying the Exponential Comparison Method (ECM) to determine the best employees. We utilized the waterfall model to build this system, involving stages of analysis, design, implementation, and testing. The analysis stage was conducted to gather the necessary data for system development, processed using the ECM method. For the design stage, we presented flowcharts, data flow diagrams (DFD), and use case diagrams. In the implementation stage, we developed an application using the web programming language PHP with a MySQL database. System testing employed black box testing to assess the functionality of all system components. Our findings revealed Herman Sitepu as the top-ranking employee. The system, presented as a web application with multiple menus, performed well based on black box testing results. Consequently, this system can be used to evaluate employee performance at the Department of Animal Husbandry and Health of Asahan Regency.
Sistem Klasifikasi Strata Kelas Peserta Kursus berbasis web menggunakan algoritma K-Means Maulida, Vivi; Mulyani, Neni; Sibuea, Mustika Fitri Larasati
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.v8i2.27311

Abstract

The increasing number of participants in online courses has driven the development of effective systems to manage and classify their data. The objective of this research is to develop a web-based class strata classification system for course participants using the K-Means algorithm. This research is developmental in nature, employing the waterfall model. We implemented this model through the stages of analysis, design, implementation, and testing. The data used were course participants from the Lembaga Swadaya Training Centre from 2013 to 2024. The system testing we developed utilized the black box method. The K-Means algorithm was chosen for its ability to cluster data without supervision, which is suitable for processing large and heterogeneous data from course participants. The data analysis results show that there are 2 clusters of class strata data: elementary, university, and general (C1) and junior high and high school (C2). Furthermore, our findings also include a web-based classification system integrated with the K-Means algorithm. System testing also showed that the system functions as intended according to the design and requirements analysis. This system can assist relevant parties in making decisions for promoting the market share of course participants.
Media Pembelajaran Android Network Integrated pada Mata Pelajaran Informatika berbasis Mobile Lestari, Isnania; Permana, Ryan
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.v8i2.27378

Abstract

The integration between the world of education and technology today has created various types of innovations in learning. Android learning media integrated with the internet network is one example of the type of media that can be produced from innovation in learning. The purpose of this study is to produce android-based learning media integrated with the internet network. The type of research used in this development is Research and Development (R&D) with the ADDIE model which has five stages, namely Analysis, Design, Development, Implementation and Evaluation. The subjects in this study were divided into two, namely the development subjects consisting of 2 media validators and 2 material validators. Furthermore, the product trial subjects were 35 students of class VIII MTS Al-Mujtahid. The data collection technique used was a questionnaire. The data analysis used was quantitative descriptive analysis. The results of the material expert test obtained a score of 81.75%, including the very feasible category. The results of the media expert test were 82.8%, including the very feasible category. The response given by students after using this media was 84%, which means that the students' response was very good. This proves that the learning media developed has provided good results because it has fulfilled the aspects needed to produce good learning media.
Pemetaan Kasus DBD di Pulau Lombok menggunakan Regresi Binomial Negatif berbasis Geografis Ayundasari, Dita Septiana; Hastuti, Siti Hariati; Kertanah, Kertanah
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.v8i2.27460

Abstract

According to the Indonesia Health Profile Report 2022, NTB Province is among the 11 provinces with the highest incidence rate of dengue hemorrhagic fever (DHF). On Lombok Island, there were 2,074 cases with 4 deaths in 2022. DHF remains a serious threat in Lombok, so this study aims to map sub-districts based on significant factors for the spread of DHF in 54 sub-districts throughout Lombok Island. This study used quantitative analysis with one response variable, the number of DHF cases, and three predictor variables: the ratio of medical personnel (nurses) (X1), the percentage of proper sanitation facilities (healthy latrines) (X2) and the percentage of standard drinking water facilities (X3) in 54 sub-districts. Data were obtained from the Health Office throughout Lombok Island. Analysis techniques include descriptive analysis, GWNBR modeling, and significant variable mapping. The mapping results showed six groups of sub-districts with a combination of significant variables, which included variables X1, X2, and X3. The findings suggest the need for additional studies or prevention policies that are more focused on hygiene to reduce the risk of DHF spread. Related parties also need to be informed to take strategic steps based on these findings.
Klasifikasi Stunting pada Balita menggunakan Algortima Gradient Bossting Clasifier Azhari, Daffa Maulana; Hidajat, Moch Sjamsul
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.v8i2.27502

Abstract

Stunting is a significant public health problem, impacting the physical and cognitive growth and development of children under five. In Indonesia, stunting is a major issue caused by a lack of nutritional intake since birth, including in the city of Semarang. This study aims to compare the performance of K-Nearest Neighbor (KNN), Naïve Bayes, and Gradient Boosting Classifier algorithms in classifying stunting in toddlers, to find the best model. The data used is quantitative data from posyandu, consisting of 1288 samples with variables including Name, Gender, Age, Date of Birth, Parent's Name, Village, Rt, RW, weight, height, arm circumference, and Z-score. After data collection, a data preprocessing process is carried out to clean and prepare the data. The data was divided into training and test data with a ratio of 80:20, 70:30, and 60:40, which were then trained and tested using the three algorithms. The best model was further evaluated with K-Fold Cross Validation to assess the stability and generalizability of the predictions. Model evaluation uses accuracy, precision, recall and F1-Score metrics. The results showed that Gradient Boosting Classifier gave the best performance with 99.92% accuracy, 99.92% precision, 99.92% recall, and 99.92% F1-score. This study concludes that the Gradient Boosting Classifier is the most optimal model in the classification of stunting in toddlers, giving the best precision results.
Deteksi Dini Cacar Monyet menggunakan Convolutional Neural Network (CNN) dalam Aplikasi Mobile Triginandri, Rifqi; 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.v8i2.27625

Abstract

Monkeypox is a skin infection that has become a serious concern in Indonesia since the increase in cases in 2022. Diagnosis of monkeypox requires special expertise, laboratory tests, and clinical observations. Diagnosis generally uses PCR tests which are often not available in remote areas. This study aims to develop a deep learning-based mobile application for early detection of monkeypox through image classification of skin lesions. The CRISP-DM methodology is applied in developing this application, starting with collecting datasets from the Kaggle site consisting of 8,910 images and divided into 80% training groups, 10% validation, and 10% testing with augmentation techniques to improve model accuracy. The developed CNN model was implemented using Create ML on the iOS platform. The model evaluation uses several metrics such as accuracy, precision, recall, and F1 score, with the threshold being the highest probability of the model predicting model evaluation results show an accuracy of 81%, precision of 80.2%, recall of 76%, and F1 score of 0.78 for the test data. The resulting application allows rapid detection of monkeypox and is accessible to the wider community, thereby helping to reduce delays in diagnosis, especially in hard-to-reach areas. This study shows significant potential in supporting the health system in Indonesia through the application of artificial intelligence technology for infectious diseases.
Perbandingan Kinerja Model Prediksi Cuaca: Random Forest, Support Vector Regression, dan XGBoost Syahreza, Ahmad; Ningrum, Novita Kurnia; Syahrazy, Muhammad Anjas
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.v8i2.27640

Abstract

Accurate weather predictions are essential to mitigate the impacts of weather changes and support better planning in sectors such as agriculture, transportation, and tourism. Indonesia often faces unpredictable weather, such as sudden rains and long droughts, which can cause huge losses. This study aims to compare the performance of three machine learning algorithms Random Forest, Support Vector Regression (SVR), and XGBoost in predicting weather using meteorological data (minimum temperature, maximum temperature, rainfall, wind direction, average humidity) as well as IoT data totaling 1650 data per variable. The variables used in this study include minimum temperature, maximum temperature, rainfall, wind direction, and average humidity. Data analysis techniques were performed using three main evaluation metrics, namely Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (R²). The results showed that XGBoost gave the best performance with MAE 0.3744, MSE 0.2278, and R² 0.8183. Random Forest and SVR also produced good predictions, with MAE values of 0.3869 and 0.3820, MSE 0.2422 and 0.2524, and R² 0.8068 and 0.7987, respectively. The results show XGBoost is the best model for weather prediction, which can help improve accuracy in agricultural planning and weather-related disaster risk mitigation.
Optimalisasi Proses Digitalisasi UMKM melalui Aplikasi Marketplace berbasis Design Thinking Sari, Aprilisa Arum; Pramono, Pramono; Saputra, Ilham Trisatdika; Prakoso, Aprilrianto Dirhamdan
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.v8i2.27702

Abstract

Tourism villages in Boyolali district have enormous potential to boost economic growth, especially MSMEs. However, many MSMEs do not know digital applications. This research aims to optimize the digitalization process of Micro, Small, and Medium Enterprises (UMKM) through the development of a Design Thinking-based marketplace application. This research found the digital needs of UMKM through interviews and thorough analysis. The results show that effective and easily accessible digital products with interactive guidance features, automated inventory management, and easy-to-do market analysis are essential. Two prototypes each with seven main menus, were tested by 15 villagers, teenagers and general participants, using cognitive walkthrough, SEQ, A/B, and UEQ methods. Results showed that Prototype B was superior to Prototype A. In the Cognitive Walkthrough test, Prototype B showed a 100% success rate among teenagers and general participants. SEQ results indicated higher user comfort in Prototype B, with 84.375% of respondents giving the highest score of 5. A/B Testing showed higher interaction in Prototype B on various pages of the app, showing better efficiency. In UEQ aspects, Prototype B also performed better in perspicuity, stimulation, and novelty. These findings confirm that the Design Thinking approach is effective in developing user-friendly apps, helping to accelerate the digital transformation of UMKM in Indonesia.

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