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Zakat Fitrah Application based on Web Framework using Waterfall Method Karsan Friyansyah; Gomal Juni Yanris; Rahma Muti’ah
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2022): Articles Research Volume 7 Issue 2, April 2022
Publisher : Politeknik Ganesha Medan

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

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.

Oil Palm Fruit Ripeness Detection using Deep Learning Suci Ashari; Gomal Juni Yanris; Iwan Purnama
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2022): Articles Research Volume 7 Issue 2, April 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i2.11420

To detect the maturity level of oil palm Fresh Fruit Bunches (FFB) generally seen from the loose fruit that fell to the ground. This method is always used when harvesting swit coconuts. Even though this method is not always valid, because many factors cause the fruit to fall from the bunch. The manual harvesting process can result in the quality of palm oil being not optimal. For this reason, technology is needed that can ensure the maturity level of oil palm FFB. This study aims to detect the maturity of oil palm FFB based on digital images by applying a deep learning algorithm so that the maturity level can be classified into three categories, namely: raw, ripe, and rotten. The deep learning algorithm was chosen because there have been many studies that have proven its high level of accuracy. This research method starts from; preparation of data, designing architectural models and convolutional neural network parameters, testing models, testing images, and analyzing results. From the results of the study, it was found that the convolutional neural network algorithm can be applied to detect the maturity level of oil palm FFB with an accuracy value of 92% for test data, and 76% for model testing.

Development of Corporate Digital Archives in the Industrial Age 4.0 Ria Rizki Faujiah; Gomal Juni Yanris; Rahmadani Pane
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2022): Articles Research Volume 7 Issue 2, April 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i2.11422

CV. Ziefa Karya is a company in Labuhanbatu which is engaged in construction contractors who have experience in working on national projects. CV. Ziefa Karya has used computer applications in daily data processing. However, archive storage and management is still done manually. The current filing system can be said to be still less efficient and effective because the incoming and outgoing mail data is often not in accordance with the actual incoming and outgoing mail data. For that we need a solution to overcome this problem. This study aims to build an information system that can be used to manage digital-based archives in CV. Zief Karya. The information system built is web-based. System development using the waterfall model. This research has produced an archive management information system on CV. Ziefa Karya web-based. The result of this research is a website-based mail filing system on CV. Ziefa Karya which has various uses such as data collection of incoming and outgoing letters, details of incoming and outgoing letters, classification of types of letters, disposition of incoming letters, reports of incoming and outgoing letters, and printing of reports of incoming and outgoing letters, as well as printing disposition sheets. Records management information system on CV. Ziefa Karya is able to provide convenience for admins and users in managing digital-based archives. To build a web-based archive information system on CV. Ziefa Karya, development must be carried out by applying the stages in the Waterfall method.

Design and Build Expert System Application for Diagnosing Facial Skin Disease based on Android Sarinawati Sarinawati; Gomal Juni Yanris; Rahma Muti’ah
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2022): Articles Research Volume 7 Issue 2, April 2022
Publisher : Politeknik Ganesha Medan

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

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.

A Mobile-based Expert System for Disease Diagnosis Child using Best-First Search Algorithm Nurwahyuni Hasan; Gomal Juni Yanris; Elysa Rohayani Hasibuan
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2022): Articles Research Volume 7 Issue 2, April 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i2.11426

Currently, many parents want their children to be free from disease. Although this cannot be fully expected. Problems that often occur to parents are when their child is sick, lack of knowledge and limited sources of information about the disease that causes parents to leave their children without first aid. In other conditions, in areas that are far from the doctor's practice, the need for information on disease management is very necessary. Based on the problems that have been described previously, this expert system was created to assist parents in understanding the symptoms of skin diseases that occur in children. In the development of this expert system using the Best-First Search (BFS) algorithm as an inference engine. In this expert system application the user can choose the symptoms of the disease in children, then the output produced is the conclusion of the disease. From the test results based on Blackbox, it was found that 100% functionality runs according to the list of system requirements. After this research was completed, it was concluded that to design an expert system in detecting childhood diseases, starting from conducting interviews, followed by system design, the next process was implementing the system, then testing by experts for compatibility with the data that had been obtained.

SVM and Naïve Bayes Algorithm Comparison for User Sentiment Analysis on Twitter Rahmat Syahputra; Gomal Juni Yanris; Deci Irmayani
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2022): Articles Research Volume 7 Issue 2, April 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i2.11430

With the emergence of the Peduli Protect application, which is used by the government to monitor the spread of Covid-19 in Indonesia, it turns out to be reaping the pros and cons of public opinion on Twitter. From this phenomenon, a research was conducted by mapping the sentiment analysis of twitter users towards the Peduli Protect application. This study aims to compare two classification algorithms that are included in the supervised learning category. The two algorithms are Support Vector Machine (SVM) and Naïve Bayes. The two algorithms are implemented in analyzing the sentiment analysis of twitter user reviews on the Peduli Protect application. The dataset used in this research is tweets of twitter users with a total of 4,782 tweets. Then, compared to how much accuracy and processing time required of the two algorithms. The stages of the method in this research are: collecting data from user tweets with a crawling technique, preprocessing text, weighting words using the TF-IDF method, classification using the SVM and Naïve Bayes algorithm, k-fols cross validation test, and drawing conclusions. The results showed that the accuracy of the SMV algorithm with the k-fold test method was 86% and the split 8020 technique resulted in an accuracy of 79%. Meanwhile, the Naïve Bayes algorithm produces an accuracy of 85% with k-fold, and an accuracy of 80% with a split 8020. From these results it can be concluded that both algorithms have the same level of accuracy, only different in processing time, where Naïve Bayes algorithm is faster with time required 0.0094 seconds.

Applying genetic algorithm for optimization income value Ayu Febri Siagian; Gomal Juni Yanris; Sahat Parulian Sitorus
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2022): Articles Research Volume 7 Issue 2, April 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i2.11431

In this digital era, the use of information technology and internet technology cannot be separated from digital services. Starting from product promotion media, recording customer data, determining the amount of revenue from product sales, and optimizing the value of revenue. Sales of digital service products owned by PT. XYZ needs to be evaluated to find out which products are most in demand by customers from each product offering that has been made. Therefore we need a system to calculate revenue from the number of customers who use the product for further promotion. The object of this research focuses on optimizing the value of income at PT. XYZ of the products they market, the results of the object will be used as an evaluation to determine a new strategy in carrying out promotions for products that are less attractive to customers. The data used in this study is customer data for January 2017-December 2021. The method used in this study uses a genetic algorithm to determine the optimization of the revenue value. For the optimization results, the genetic algorithm went well, because it resulted in a smaller comparison of error values compared to values that were not optimized. The error value in January 2019 with a non-optimized value was 35,498.8 and the optimized value got an error value of 32,364.9. The results of this study are used as a sales evaluation to increase promotions on digital services that are less attractive to customers. In addition, the results of the application of this genetic algorithm method can provide a better solution to increase income in the next period.

Analysis of Visitor Satisfaction Levels Using the K-Nearest Neighbor Method Putri Violita; Gomal Juni Yanris; Mila Nirmala Sari Hasibuan
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2023): Research Article, Volume 8 Issue 2 April, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.12257

Visitors are people who come to a place, entertainment, shopping, and tourism. Visitors are one of the important factors for the progress and development of a place. With visitors, an entertainment, tourism and shopping area can progress and develop. Therefore researchers will make a study of the level of visitor satisfaction. This research aims to improve the quality of an entertainment venue, shopping and increase the quantity of visitors. This research was conducted using the K-Nearest Neighbor method. The K-Nearest Neighbor method is a classification method based on training data (dataset). The data used by researchers is 45 visitor data. The classification carried out using the K-Nearest Neighbor method aims to classify data of satisfied visitors and dissatisfied visitors at an entertainment or tourism place. In using the K-Nearest Neighbor method, the first stage is selecting sample data, the data to be selected, then preprocessing, then designing the widget with the K-Nearest Neighbor method and finally testing data mining using the K-Nearest Neighbor method. The K-Nearest Neighbor Method. This visitor data was obtained by researchers through a questionnaire and the results of the questionnaire that 41 visitors were satisfied. After classifying visitor data using the K-Nearest Neighbor method, the classification results were 41 satisfied visitors. The conclusion is that many visitors are satisfied.

Analysis of the SVM Method to Determine the Level of Online Shopping Satisfaction in the Community Arini Mawaddah; Muhammad Halmi Dar; Gomal Juni Yanris
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2023): Research Article, Volume 8 Issue 2 April, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.12261

Online shopping is an activity of buying goods done online (virtual). This online shopping process is done because it doesn't waste a lot of time. With online shopping, it is very easy for people. Just need open mobile phone view and select the desired item and then order goods and goods will be delivered to the house. But online shopping sometimes also has drawbacks which are one of the reasons people don't want to shop online, such as long shipping times, expensive shipping costs. Therefore a study was made about the level of public satisfaction in online shopping. Researchers will make a data classification about the level of public satisfaction in online shopping using the SVM method. This study aims to see the level of public satisfaction with online shopping, many or nope satisfied people when shopping online. The first step is to collect data that will be used in the data mining process. After that, data preprocessing will be carried out planning the design of the SVM method and finally the prediction process to get Classification results. Then the classification results obtained using the SVM method in data mining show that 34 people are satisfied with online shopping (for a representation result of 59.65%), 23 people are dissatisfied with online shopping (for a representation result of 40.35%). These results state that there are still many people who are satisfied with shopping online and there are some people who are dissatisfied with online shopping

Implementation of Data Mining for Data Classification of Visitor Satisfaction Levels Hubban Arfi Pratama; Gomal Juni Yanris; Mila Nirmala Sari Hasibuan
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2023): Article Research Volume 8 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12674

An amusement park is a location or place that can provide a special attraction to the public. This is because in amusement parks there is lots of entertainment provided. But not all amusement parks are liked by visitors, usually because the location is still not good enough. Therefore the authors make a study of the level of visitor satisfaction. This research was made so that the writer can determine whether or not the number of visitors is satisfied at the amusement park. To conduct this research, the authors used 2 methods with a classification model in data mining. The methods used are the K-Nearest Neighbor (kNN) method and the Naïve Bayes method. Study this is done using 100 visitor data. The classification results obtained from both methods give the same results. The results obtained were 77 satisfied visitor data at amusement parks and 23 dissatisfied visitors at amusement parks. The result of the two methods used is that many visitors are satisfied with the amusement park. The accuracy results obtained are also very good. This means that these two methods are very suitable to be used as a method with a classification model. The conclusion is that the amusement park has beauty and a great location that can give attraction to visitors. With this research it can be a reference that the K-Nearest Neighbor (kNN) method and the Naïve Bayes method are very suitable for carrying out a data classification.

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