cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Irpan Adiputra pardosi
Contact Email
irpan@mikroskil.ac.id
Phone
+6282251583783
Journal Mail Official
sinkron@polgan.ac.id
Editorial Address
Jl. Veteran No. 194 Pasar VI Manunggal,
Location
Kota medan,
Sumatera utara
INDONESIA
Sinkron : Jurnal dan Penelitian Teknik Informatika
Published by Politeknik Ganesha Medan
ISSN : 2541044X     EISSN : 25412019     DOI : 10.33395/sinkron.v8i3.12656
Core Subject : Science,
Computer Science & IT
Scope of SinkrOns Scientific Discussion 1. Machine Learning 2. Cryptography 3. Steganography 4. Digital Image Processing 5. Networking 6. Security 7. Algorithm and Programming 8. Computer Vision 9. Troubleshooting 10. Internet and E-Commerce 11. Artificial Intelligence 12. Data Mining 13. Artificial Neural Network 14. Fuzzy Logic 15. Robotic
Arjuna Subject : Ilmu Komputer (semua kategori) - Visi Komputer dan Pengenalan Pola
Articles 1,196 Documents
 54   55   56   57   58 
COMPARISON OF K-N EAREST NEIGHBOR AND NAÏVE BAYES ALGORITHMS FOR PREDICTION OF APTIKOM MEMBERSHIP ACTIVITY EXTENSION IN 2023 Fauzia, Fathia Alisha; Adjani, Kannisa; Juliane, Christina
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.12081

Abstract

So far APTIKOM as the Informatics and Computer Higher Education Association has provided many opportunities for registered members to participate in discussions on the development of science among fellow association members, access to various professional experts, as well as technical and non-technical guidelines in the field of education. With the various opportunities above, it is hoped that all members will support the activities of each member who has joined or has just joined so that a good association can be created. This study aims to find out about the problems that occur in APTIKOM, namely members who have registered as members but rarely renew their membership which results in data accumulation in APTIKOM. This research method uses the k-nn and naïve Bayes algorithms by using data sets from 2012 to 2022. The dataset used is APTIKOM member data and has 5 attributes namely name, gender, last education, institution and validation secret. To calculate the research test using a rapid miner. The purpose of this study is to predict whether in the following year there will be a membership renewal process for all APTIKOM members who have been recorded from 2012 to 2022. Furthermore, the results of this study have a different level of accuracy. Where for k-nn the resulting accuracy is 94.00% and for the result of naïve Bayes is 91.35%.
Detect Fake Reviews Using Random Forest and Support Vector Machine Hadi, Zulpan; Utami, Ema; Ariatmanto, Dhani
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.12090

Abstract

With the rapid development of e-commerce, which makes it possible to buy and sell products and services online, customers are increasingly using these online shop sites to fulfill their needs. After purchase, customers write reviews about their personal experiences, feelings and emotions. Reviews of a product are the main source of information for customers to make decisions to buy or not a product. However, reviews that should be one piece of information that can be trusted by customers can actually be manipulated by the owner of the seller. Where sellers can spam reviews to increase their product ratings or bring down their competitors. Therefore this study discusses detecting fake reviews on productreviews on Tokopedia. Where the method used is the distribution post tagging feature to perform detection. By using the post tagging feature method the distribution got 856 fake reviews and 4478 genuine reviews. In the fake reviews, there were 628 reviews written with the aim of increasing product sales or brand names from store owners, while there were 228 reviews aimed at dropping their competitors or competitors. Furthermore, the classification is carried out using the random forest algorithm model and the support vector machine. By dividing the dataset for training data by 80% while 20% for data testing. Here it is known that the support vector machine gets much higher accuracy than the random forest. The support vector machine gets an accuracy of 98% while the random forest gets an accuracy of 60%
Scrum Framework Implementation of Fish Mobile Auction Module in Pasar Iwak Marketplace Ayuningsih, Dewi Putri; Dewi, Ika Novita; Rohmani, Asih
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.12096

Abstract

Lelang Ikan mobile application is an online auction in the marketplace platform of Pasar Iwak based on Android platform. Scrum framework is applied and consists of determining the product backlog, creating sprint planning and sprint backlogs, and conducting sprint reviews and sprint retrospectives. The product backlog resulted 14 backlog items based on the results of system and user requirements for user auctioneers. Sprint planning and sprint backlog are divided into four sprints, namely front-end and back-end development, system integration process and system implementation. Sprint reviews are carried out by implementing two types of testing, namely blackbox testing and user acceptance testing (UAT). Blackbox testing emphasizes testing application functions or features, while UAT is applied to measure the level of user acceptance. The results of blackbox testing showed that the features provided by the application are in accordance with the predetermined requirements. Whereas UAT showed the result of 66.8%, which means that the application is in the appropriate category and can be accepted by users. The application development process ends at the sprint retrospective stage which is a suggestion or feedback after the application testing. The suggestions obtained are in the form of adding tracking features, payment features with payment gateways, and application development with the iOS platform.
Optic Disc Detection on Retina Image using Extreme Learning Machine Wibawa, Helmie Arif; Sutikno, Sutikno; Sasongko, Priyo Sidik
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.12123

Abstract

Optic disk detection on retina image has become one of many initial steps in evaluation of Diabetic Macular Edema (DME) severity. As much as early the step is, the result of the step is extremely essential. This article discusses the optic disk detection on retina image based on the color histogram value. The detection is done by using color histogram value which is taken from window sliding process with the size of 50x50 pixels. First, the candidates of optic disc were detected using Extreme Learning Machine towards the histogram value. Then the optic disc was selected form the candidates of optic which has highest average intensity. 4 retina image datasets were employed in the evaluation, including Drions dataset, DRIVE dataset, DiaretDB1 dataset, and Messidor dataset. The result of evaluation then validated by medical expert. The model outcome reaches the accuracy as much as 85,39 % for DiaretDB1 dataset, 95% for DRIVE dataset, 98,18% for Drions and 99% for Messidor dataset.
Effect Effect of Gradient Descent With Momentum Backpropagation Training Function in Detecting Alphabet Letters Alkhairi, Putrama; Batubara, Ela Roza; Rosnelly, Rika; Wanayaumini, W; Tambunan, Heru Satria
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 1 (2023): Articles Research Volume 7 Issue 1, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i1.12183

Abstract

The research uses the Momentum Backpropagation Neural Network method to recognize characters from a letter image. But before that, the letter image will be converted into a binary image. The binary image is then segmented to isolate the characters to be recognized. Finally, the dimension of the segmented image will be reduced using Haar Wavelet. One of the weaknesses of computer systems compared to humans is recognizing character patterns if not using supporting methods. Artificial Neural Network (ANN) is a method or concept that takes the human nervous system. In ANN, there are several methods used to train computers that are made, training is used to increase the accuracy or ability of computers to recognize patterns. One of the ANN algorithms used to train and detect an image is backpropagation. With the Artificial Neural Network (ANN) method, the algorithm can produce a system that can recognize the character pattern of handwritten letters well which can make it easier for humans to recognize patterns from letters that are difficult to read due to various error factors seen by humans. The results of the testing process using the Backpropagation algorithm reached 100% with a total of 90 trained data. The test results of the test data reached 100% of the 90 test data.
Web-Based Village Fund Assistance Distribution Information System Using the Quota Based Method Damanik, Elfira Shenita; Suendri S
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.12208

Abstract

The development of information technology has greatly influenced several aspects of human life in the implementation of daily activities. One of the benefits of the development of information technology is that it can help local governments provide information regarding the distribution of financial assistance through monitoring. Village fund assistance is one of the government programs aimed at economic recovery for people affected by the pandemic. Currently, assistance from village funds is often not supported by good governance in every village. Particularly in Tanah Merah Village, aid arrangements were still carried out by recording manually, so sometimes data input errors occurred which resulted in inaccurate data. Therefore, we need a computerized system that can overcome the limitations and problems that occur. In making this system using the Waterfall method, the stages are needs analysis, application design, system design, testing, and system maintenance. The tools used to design this system are UML (Unified Modeling Language), which consists of Use Case Diagrams, Activity Diagrams, and Class Diagrams. The results of this study are an information system for distributing village fund assistance at the Tanah Merah Village office. The application of this information system for distributing village fund assistance can help provide alternative solutions for the village government to problems in the data collection process so that the implementation process can achieve its objectives properly.
Implementation Of K-Nearest Neighbor Algorithm With SMOTE For Hotel Reviews Sentiment Analysis Gazali Mahmud, Firman; Iman Hermanto, Teguh; Maruf Nugroho, Imam
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.12214

Abstract

Indonesia has considerable tourism development potential, this phenomenon is in accordance with the number of foreign tourist visits to Indonesia from January to September 2022 recorded by Badan Pusat Statistik many as 2,397,181 visitors. This research focuses on super-priority destinations in Labuan Bajo, East Nusa Tenggara, based on the government's plan that the focus of developing this destination is to increase hotel development to meet the need for an additional 2,000 hotel rooms. Thus, the available hotel rooms are still limited. Then for need to choose a hotel based on the November 2021 survey by the Populix website, 76% of 1,012 respondents chose to book hotels online with the majority using the Traveloka website. However, making decisions in choosing hotels using the reviews feature in the Traveloka website still raises various problems, such as biased information and even the rating values ​​given do not match the reviews submitted. So that users to know what becomes the perception of positive and negative ratings, it is necessary to do in-depth research on satisfaction factors to find out positive and negative sentiments of hotel visitors. This study uses the k-nearest neighbor algorithm with SMOTE on the research objects of the three most popular hotels in Labuan Bajo. Data testing uses a value of k = 3 so that it produces an accuracy value of 87.71% - 93.47% with a maximum error tolerance of 12.29%. In addition, the performance of accuracy results is validated by the appropriate AUC value, namely the good classification category.
Comparison of Accuracy in Detecting Tomato Leaf Disease with GoogleNet VS EfficientNetB3 Saputra, Adi Dwifana; Hindarto, Djarot; Rahman, Ben; Santoso, Handri
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.12218

Abstract

Tomato diseases vary greatly, one of which is tomato leaf disease. Some variants of leaf diseases include late blight, septoria leaf, yellow leaf curl virus, bacteria, mosaic virus, leaf fungus, two-spotted spider mite, and powdery mildew. By knowing the disease on tomato leaves, you can find medicine for the disease. So that it can increase the production of tomatoes with good quality and a lot of quantity. The problem that often occurs is that farmers cannot determine the disease in plants, they try to find suitable herbal medicines for their plants. After being given the drug, many plants actually died due to the pesticides given to the tomato plants. This is detrimental to tomato farmers. This problem is caused by incorrect disease detection. Therefore, this study aims to solve the problem of disease detection in tomato plants, in a more specific case, namely tomato leaves. Detection in this study uses a deep learning algorithm that uses a Convolutional Neural Network, specifically GoogleNet and EfficientNetB3. The dataset used comes from kaggle and google image. Both data sets have been pre-processed to match the data set class. Image preprocessing is performed to produce appropriate image datasets and improve performance accuracy. The dataset is trained to get the model. The training using GoogleNet resulted in an accuracy of 98.10%, loss of 0.0602 and using EfficientNetB3 resulted in an accuracy of 99.94%, loss: 0.1966.
Improved Accuracy In Data Mining Decision Tree Classification Using Adaptive Boosting (Adaboost) Riansyah, Muhammad; Suwilo, Saib; Zarlis, Muhammad
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.12055

Abstract

The Decision Tree algorithm is a data mining method algorithm that is often applied as a solution to a problem for a classification. The Decision Tree C5.0 algorithm has several weaknesses, including: the C5.0 algorithm and several other decision tree methods are often biased towards modeling whose features have many levels, some problems for the model can occur such as over-fit or under-fit challenges, big changes to decision logic can result in small changes to data training, C5.0 can experience modeling inconvenience, data imbalance causes low accuracy in C5.0 algorithm. The boosting algorithm is an iterative algorithm that gives different weights to the distribution of training data in each iteration. Each iteration of boosting adds weight to examples of misclassification and decreases weight to examples of correct classification, thereby effectively changing the distribution of the training data. One example of a boosting algorithm is adaboost. The purpose of this research is to improve the performance of the Decision Tree C5.0 classification method using adaptive boosting (adaboost) to predict hepatitis disease using the Confusion matrix. Tests that have been carried out with the Confusion Matrix use the Hepatitis dataset in the Decision Tree C5.0 classification which has an accuracy rate of 80.58% with a classification error rate of 19.15%. Whereas in the Decision Tree C5.0 classification Adaboost has a higher accuracy rate of 82.98%, a classification error rate of 17.02%. This difference is caused by the adaboost algorithm, because the adaboost algorithm is able to change a weak classifier into a strong classifier by increasing the weight of the observations, and adaboost is also able to reduce the classifier error rate.
Development of Android-based Edutainment game on Numerical Ability Rahanra, Nicodemus; Destari , Dina; Cakranegara, Pandu Adi; Andriyana, Erni; Ellyawati , Noor; Pratiwi, Vidya
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.12057

Abstract

Learning mathematic cannot be separated from the mathematical symbol and a numerical skill, which is required as ability to perform calculation and related matters with the numbers. This research is aimed to improve the numerical ability of students. The method used was based on Borg and call, which consisted of ten main stages involved. The first research result is the validation of android-based mobile games edutainment, that is, the average value of the three experts is 78.33% with valid criteria. Then the average validation results of the android mobile game test questions based on edutainment is 77.77% with valid criteria. The second is the value of simplicity, seen from the value of the questionnaire which was filled in by all fresh graduates which was accumulated so that a percentage of 85.84% was obtained with very practical criteria. Furthermore, the effectiveness is seen from the increase in scores workmanship of fresh graduate pretest and posttest which is calculated by the formula T test results obtained by increasing the pretest posttest score then ability numerical increase so that it is categorized as effective. Thus it is concluded that the development of android mobile games is based on edutainment on numerical ability is categorized valid, practical, and effective for used

Page 56 of 120 | Total Record : 1196

 54   55   56   57   58 

Filter by Year

2016 2025


Reset
Filter By Issues
All Issue Vol. 9 No. 4 (2025): Articles Research October 2025 Vol. 9 No. 3 (2025): Article Research July 2025 Vol. 9 No. 2 (2025): Research Articles April 2025 Vol. 9 No. 1 (2025): Research Article, January 2025 Vol. 8 No. 4 (2024): Article Research Volume 8 Issue 4, October 2024 Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024 Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024 Vol. 8 No. 1 (2024): Articles Research Volume 8 Issue 1, January 2024 Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023 Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023 Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023 Vol. 7 No. 1 (2023): Articles Research Volume 7 Issue 1, 2023 Vol. 6 No. 4 (2022): Article Research: Volume 6 Number 4, October 2022 Vol. 6 No. 3 (2022): Article Research Volume 6 Number 3, July 2022 Vol. 6 No. 2 (2022): Articles Research Volume 6 Issue 2, April 2022 Vol. 6 No. 1 (2021): Article Research Volume 6 Issue 1: January 2021 Vol. 5 No. 2 (2021): Article Research Volume 5 Number 2, April 2021 Vol. 5 No. 2B (2021): Article Research October 2021 Vol 4 No 2 (2020): SinkrOn Volume 4 Number 2, April 2020 Vol. 5 No. 1 (2020): Article Research, October 2020 Vol. 4 No. 1 (2019): SinkrOn Volume 4 Number 1, October 2019 Vol. 3 No. 2 (2019): SinkrOn Volume 3 Number 2, April 2019 Vol 3 No 2 (2019): SinkrOn Volume 3 Number 2, April 2019 Vol. 3 No. 1 (2018): SinkrOn Volume 3 Nomor 1, Periode Oktober 2018 Vol 3 No 1 (2018): SinkrOn Volume 3 Nomor 1, Periode Oktober 2018 Vol. 2 No. 2 (2018): SinkrOn Volume 2 Nomor 2 April 2018 Vol. 2 No. 1 (2017): SinkrOn Volume 2 Nomor 1 Oktober 2017 Vol. 1 No. 2 (2017): SinkrOn Volume 1 Nomor 2 April 2017 Vol. 1 No. 1 (2016): SinkrOn Oktober Volume 1 Edisi 1 Tahun 2016 More Issue