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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika International Journal Of Science, Technology & Management (IJSTM) Jurnal Bina Bangsa Ekonomika Jurnal Pengabdian Masyarakat Gemilang (JPMG)
Muti’ah, Rahma
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Journal : Sinkron : Jurnal dan Penelitian Teknik Informatika

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Zakat Fitrah Application based on Web Framework using Waterfall Method Friyansyah, Karsan; Yanris, Gomal Juni; Muti’ah, Rahma
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 2 (2022): Articles Research Volume 6 Issue 2, April 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i2.11412

Abstract

Zakat Fitrah is an obligation for every Muslim. In the management of zakat fitrah, the Amil Zakat Agency has a very vital function, namely managing the receipt and distribution of zakat to groups of people who are entitled to receive it (Mustahik). If the amil zakat is negligent and not careful in managing zakat, then the distribution of zakat is not right on target. The Buyung Rahimah Rantauprapat Mosque has the Amil Zakat Fitrah Agency, but in the management process it still uses the manual method so that the zakat management process takes a long time and the data stored is inaccurate. To solve these problems, an orderly, neat and good recording system is needed. This study aims to create an application for the management of Zakat Fitrah at the Buyung Rahimah Rantauprapat Mosque based on the Web Framework. The application development method uses the Waterfall model which divides into four stages, namely: analysis, design, program code generation, and system testing. This research has produced a zakat fitrah application with the main menus, namely: login menu, amil agency, types of zakat, zakat payment, zakat distribution, user management. This application also manages to display zakat fitrah data and zakat fitrah distribution history data. With the application of a web-based framework, making this application user friendly, making it easier for the Amil Agency to manage zakat fitrah. A web framework with the MVC concept and a complete library makes zakat fitrah data management accurate and fast.
Comparative analysis of software defined network performance and conventional based on latency parameters Harahap, Raja Pahlevi; Irmayani, Deci; Muti’ah, Rahma
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 2 (2022): Articles Research Volume 6 Issue 2, April 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i2.11424

Abstract

Software Defined Network (SDN) offers convenience in managing network devices by simply setting the software control plane so as to reduce the complexity of network configuration. However, the ease of management offered is not always accompanied by an increase in network performance when compared to conventional network architectures. This study will perform a performance comparison analysis between networks with SDN architecture (OpenFlow) and conventional architecture. The research methods applied are: topology implementation on Mininet, network simulation and data collection, data analysis, and comparative analysis. Network performance testing is carried out based on latency parameters that are simulated in one subnet using the Mininet emulator. From the results of the latency test, it is found that the average latency on the SDN network is 0.119ms, while the average latency on the conventional network is 0.09588ms. Based on these results, it can be concluded that the network topology that has better performance based on latency parameters is a conventional network topology with an average latency value of 0.09588ms.
Design and Build Expert System Application for Diagnosing Facial Skin Disease based on Android Sarinawati, Sarinawati; Yanris, Gomal Juni; Muti’ah, Rahma
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 2 (2022): Articles Research Volume 6 Issue 2, April 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i2.11425

Abstract

Today, skin health is the main focus for women and men who want healthy, well-groomed and clean facial skin. The use of chemicals and other external materials allows potential disease for facial skin health. Diseases caused on the face are quite diverse from small to medium scale. The problem that often occurs in the community is the lack of knowledge and limited sources of information about facial skin health causing people to tend to let the disease happen. Based on the problems that have been described previously, this expert system was created to help the public understand the symptoms of facial skin diseases experienced and solutions to overcome these diseases. In the development of this expert system using the forward chaining method as the inference engine and the certainty factor method to determine the diagnostic confidence value. In designing this expert system application the user can choose the symptoms of facial skin diseases, then the output produced is the level of confidence, the possibility of the disease experienced and an explanation of the solution for the initial treatment of the facial skin disease. From the test results based on the Black Box, the results obtained 100% functionality runs according to the list of system requirements. In the accuracy test, a very good accuracy value is obtained, which is 100% of the 10 existing sample data.
Development of Expert System Application to Detect Chicken Disease using the Forward Chaining Method Ramadan, Ahmad; Harahap, Syaiful Zuhri; Muti’ah, Rahma
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12843

Abstract

Chicken is the most widely kept and consumed poultry in Indonesia. Due to the large population of poultry, a variety of diseases have also emerged, from minor diseases to diseases that can kill chickens and infect humans. As a result of these diseases, there are implications for the losses suffered by chicken farmers. Most farmers find it very difficult to identify chicken diseases due to their lack of knowledge. On the other hand, expecting treatment from a veterinarian or expert is very limited and expensive. Therefore, a system is needed that can easily help chicken farmers detect diseases in their pet chickens. This research aims to build an expert system to detect chicken diseases by applying the forward chaining method. The expert system is implemented as a web-based application. The research stages start with identifying problems, collecting data, creating a knowledge base and production rules, building applications, and getting results. The results showed that the forward chaining method provides convenience in detecting chicken diseases. This is proven by only selecting the symptoms of the disease that appear, and then the application will provide conclusions regarding what type of disease is being suffered by chickens. In addition, this application also provides information related to ways of handling and control that can be done to overcome the chicken disease. Hopefully, the results of this research can facilitate chicken farmers in identifying and handling diseases effectively and efficiently.
Prediction of Student Graduation Rates using the Artificial Neural Network Backpropagation Method Ariani, Yayuk; Masrizal, Masrizal; Muti’ah, Rahma
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13659

Abstract

This student graduation rate research focuses on analyzing academic performance with the main aim of identifying and distinguishing between students who graduate on time and those who graduate late. The application of data mining techniques in this research uses the neural network method, which is expected to offer deeper insight into the factors that influence students' graduation times. In this study, the neural network method was used to classify graduation data from 150 students. The results of this analysis were very encouraging, with 149 students identified as graduating on time and one student graduating late. The level of accuracy achieved in this classification is 98%, which shows the effectiveness of the neural network method in processing and analyzing academic data. These results confirm that neural networks are a powerful and reliable tool for predictive tasks like this. The successful use of neural networks in this study also proves their potential in broader educational applications, particularly in optimizing educational and intervention strategies. By understanding the characteristics of students who graduate on time versus those who graduate late, educators and administrators can design more effective programs to support student success. This is important not only to improve graduation statistics, but also to improve the overall educational experience for students.
Comparative Analysis of Machine Learning Algorithm Performance in Predicting Stunting in Toddlers Syahfitri, Nur’aini Indah; Juledi, Angga Putra; Muti’ah, Rahma
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.13698

Abstract

Stunting is a condition where the growth of children and toddlers is stunted, which causes children to be shorter than they should be. In the long term, stunting can reduce reproductive health, study concentration, and work productivity, thereby causing significant state losses. The prevalence of stunting in Indonesia, which is still above 20 percent, shows that there are still chronic nutritional problems among toddlers. To prevent this from happening, identification as early as possible can be done using machine learning for predictions. The aim of this research is to conduct a comparative analysis of the performance of machine learning algorithms for predicting stunting in toddlers. Random Forest, K-Nearest Neighbors, and Extreme Gradient Boosting are the algorithms that are compared for their performance. The performance of each algorithm is measured using evaluation matrices such as accuracy, precision, recall, and f1-score. The research method starts with data collection, data preprocessing, data splitting, application of machine learning algorithms, evaluation of algorithm performance, and comparison of results. The performance evaluation matrix measurement results show that Random Forest has an accuracy of 99.95%, precision of 99.89%, recall of 99.94%, and f1-score of 99.91%. K-Nearest Neighbors has an accuracy of 99.93%, precision of 99.87%, recall of 99.88%, and f1-score of 99.88%. Meanwhile, Extreme Gradient Boosting has an accuracy of 99.36%, precision of 98.86%, recall of 98.95%, and f1-score of 98.90%. From the results of all performance evaluation matrices, it can be concluded that the random forest algorithm is the best algorithm for predicting stunting in toddlers.
Implementation of the K-Means Method for Clustering Regency/City in North Sumatra based on Poverty Indicators Wardani, Syafira Eka; Harahap, Syaiful Zuhri; Muti’ah, Rahma
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.13720

Abstract

Poverty has many negative effects on people's lives, such as difficulty meeting basic needs, limited access to adequate health and education services, and limited economic opportunities. North Sumatra faces significant poverty problems as one of the largest provinces in Indonesia. This requires special attention and a thorough investigation. Reducing poverty is a very important issue for the government of North Sumatra Province. Poverty-alleviation strategies can no longer be applied uniformly. Instead, it is necessary to consider all the factors that cause poverty in each region. This means that the approach that must be given to each regency or city based on its poverty level must be adjusted. To overcome this problem, clustering must be carried out to identify areas with different levels of welfare. The aim of this research is to cluster regencies and cities in North Sumatra Province using the K-means method based on poverty indicator variables. This research only uses three poverty indicators: gross regional domestic product, human development index, and unemployment rate. The optimal number of clusters is determined based on the results of the silhouette coefficient. The research method begins with dataset collection, exploratory data analysis, data preprocessing, and k-means clustering. The value k = 6 produces a silhouette coefficient of 0.4135. This research produced six regency/city clusters. Cluster 1 consists of 11 regencies and 1 city; cluster 2 consists of 1 regency and 2 cities; cluster 3 consists of 4 regencies; cluster 4 consists of 7 regencies; cluster 5 consists of 4 cities; and cluster 6 consists of 2 regencies and 1 city. The variables gross regional domestic product, human development index, and unemployment rate have a big influence on the cluster results. This will enable the government to adopt policies to tackle poverty quickly and effectively.
Data Mining Clustering Analysis of Child Growth and Development Using the K-Means Method Nurjannah, Eka; Nasution, Marnis; Muti’ah, Rahma
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.13817

Abstract

This research aims to group children based on their growth and development characteristics. This method helps identify groups of children with normal growth and development, early signs of growth and development problems, and serious growth and development problems. The stages used in this research follow the Knowledge Discovery in Database (KDD) process, which consists of data selection, data pre-processing, data transformation, data mining, and evaluation or interpretation of results. By applying the K-Means method, this research aims to provide a clearer and more detailed picture of the distribution of children's growth and development problems and assist in decision making for more appropriate interventions. The K-Means method in data mining was used to group 102 sample data into three clusters based on children's growth and development characteristics. The results of this analysis show that 38 samples fall into Cluster 1 (C1), 36 samples into Cluster 2 (C2), and 28 samples into Cluster 3 (C3). Evaluation of clustering results is carried out using Box Plot and Scatter Plot. Box Plot shows a clear distribution of data for each cluster, ensuring that the data grouping corresponds to statistical evaluation. Cluster C1 is toddlers with normal growth and development. Cluster C2 shows early signs of growth and development problems. Cluster C3 indicates serious growth and development problems
Co-Authors Anora, Ayu Ariani, Yayuk Fitria Rahma, Indah Friyansyah, Karsan Harahap, Raja Pahlevi Irmayani, Deci Juledi, Angga Putra Nasution, Marnis Nazliah, Rahmi Nurjannah, Eka Ramadan, Ahmad Ray Silaen, Belinda Ritonga, Mulkan Sarinawati, Sarinawati Suharni Simamora, Siti Syahfitri, Nur’aini Indah Syaiful Zuhri Harahap Wardani, Syafira Eka Yanris, Gomal Juni Zahara Saragih, Siti