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
 56   57   58   59   60 
Design of IoT-Based Tomato Plant Growth Monitoring System in The Yard Marcheriz, Isnan Nugraha; Fitriani, Endah
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.v8i2.12226

Abstract

The development of tomato plants to produce good fruit cannot be separated from environmental factors that affect their growth and development of tomato plants. These factors include soil moisture, soil pH, temperature, or the amount of light received by tomato plants. The need for water in tomato plants is also very important for their continued growth. Monitoring the development of tomato plants in home gardens based on IoT (Internet of Things) is a monitoring system that utilizes IoT technology to collect, transmit, and analyze data about tomato plants in real-time. In this system, sensors connected to the internet network will be installed on tomato plants to measure several parameters such as soil moisture, air temperature, light intensity, and soil nutrient / pH levels in plants. The collected data will be sent to an IoT platform that will be able to analyze the data. The results of the analysis will be used to make decisions regarding plant care, such as providing water or nutrients that the plants need to grow properly. With cameras to monitor the physical development of the plants, plant height, and fruit development. With this system, communities and farmers can grow tomato plants and can monitor and control plant conditions in real-time through smartphone applications. By utilizing IoT technology, monitoring the development of tomato plants becomes more efficient and accurate. Communities and farmers can take preventive measures to avoid plant disorders and diseases before it's too late, to increase the production and quality of crops.
Implementation of the K-Nearest Neighbor (kNN) Method to Determine Outstanding Student Classes Munazhif, Nanda Fahrezi; Yanris, Gomal Juni; Hasibuan, Mila Nirmala Sari
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.v8i2.12227

Abstract

Education being one factor supporting students / I to be able to increase their knowledge. Each student has their own potential that they have obtained in the world of education. Therefore, every school has created an education program that functions to increase the potential of high achieving students. The program is a flagship class program. What is meant by a superior class program is a process of selecting and classifying students to be placed in the classroom superior (grade student / I achievement). Therefore, this study aims to implement classification on student data using the KNearest Neighbor (kNN) algorithm. K-Nearest Neighbor (kNN) is a method used to classify data based on training data (data set). The data that the writer will use is student data of 60 student data. In this classification using the kNN method aims to classify data on students who are eligible to enter the superior class (class of outstanding students). The first step is the process of determining data requirements. Then cleaning or pre-processing and the next is to design a widget model of the kNN method on the orange application to carry out the data classification process. The test results using 60 student data using the KNN method and using the Confusion Matrix obtained an Accuracy value of 91.6%, then a Precision value of 89.2% and a Recall value of 92.5%. The conclusion is that this study succeeded in obtaining a method that the best and also get the best results for Classification of superior student classes.
Pet Care Information System at Darussalam Pet Shop Based on Android: Sistem Informasi Perawatan Hewan Peliharaan Pada Darussalam Pet Shop Berbasis Android Syafitri, Siti; Suendri, Suendri
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.v8i2.12230

Abstract

Modern technology has changed people's lifestyles, including how to raise pets. Pet is an animal kept at home or in a cage and exclusively cared for by its owner. Recently, more people owning pets. This had led to questions related to the hygiene and health of pets. An unkempt pet can cause problems for the owner, so numerous pet stores have emerged to help owners provide their animals with the care they require. However, many pet shops still use a manual system, which is considered ineffective for service, and utilizing information technology will make jobs easier while improving accuracy and information quality. Based on the problems described above, an application is required to assist and facilitate the pet shop itself and customers in caring for their pets. The research goal is to design and build suitable applications. In this journal, the researcher utilized both the Quantitative research methods and the Waterfall method for application development. For testing, the researcher used the Likert Scale. The Likert Scale calculation yielded a total score of 80.3 (Satisfied). Therefore, it can be concluded that the application is operating as intended and makes it easier to obtain pet-related information for user and manage schedules and incoming orders for administrators.
Sentiment Analysis Of Hotel Reviews On Tripadvisor With LSTM And ELECTRA Husein, Amir Mahmud; Livando, Nicholas; Andika, Andika; Chandra, William; Phan, Gary
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.v8i2.12234

Abstract

This study examines the importance of hotel review data analysis and the use of Natural Language Processing (NLP) technology in predicting hotel review sentiment. In this study, deep learning models such as Long Short-Term Memory (LSTM) and Efficiently Learning an Encoder that Classifies Token Replacements Accurately (ELECTRA) are used to predict hotel review sentiment in Indonesian. Hotel review data was obtained through a data scraping process with webscraper.io from the Tripadvisor website and a total of 977 hotel review data were obtained from Grand Mercure Maha Cipta Medan Angkasa. Before the sentiment prediction process is carried out, hotel review data must go through the text preprocessing stage to remove punctuation marks, capital letters, stopwords, and a lemmatizer process is carried out to facilitate further data processing. In addition, sentiments that were previously unbalanced need to be balanced through the undersampling process. The data that has been cleaned and balanced is then labeled as negative (0), neutral (1) and positive (2) sentiments. The test results show that the ELECTRA model produces better performance than the LSTM with an accuracy of 47% by ELECTRA and 30% by LSTM.
Sentiment Analysis On Twitter Posts About The Russia and Ukraine War With Long Short-Term Memory Simarmata, Allwin; Xu, Anthony; Tiffany; Phanie, Matthew Evan
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.v8i2.12235

Abstract

Sentiment analysis is one method for evaluating public opinion from the received text. In this study, we evaluate the performance of the LSTM model with Sastrawi in sentiment analysis in Indonesian using a Twitter dataset totaling 2537 data collected regarding the Russo-Ukrainian war. The purpose of this study is to determine the reliability of the LSTM model with Sastrawi in sentiment analysis in Indonesian and to evaluate the performance of the model with the collected Twitter dataset regarding the Russian-Ukrainian war. The method used in this study is data pre-processing, training and validation of the LSTM model with Literature, and model evaluation using the metrics of accuracy, precision, recall, and F1 score. In the dataset collected in this study, positive, neutral and negative sentiments were 54.7%, 35% and 10.2%. The results obtained from this study indicate that the LSTM model with Literature can provide good results in sentiment analysis with a prediction accuracy of 82%. The implication of the results of this study is that the LSTM model with Sastrawi can be used for sentiment analysis on Twitter and further research needs to be carried out with a wider and more diverse dataset, especially to produce even better accuracy.
IMPLEMENTATITON OF RANDOM FOREST ALGORTIHM ON SALES DATA TO PREDICT CHURN POTENTIAL IN SUZUYA SUPERMARKET PRODUCTS Dharma, Abdi; Christnatalis; Candra, Windy; Turnip, Josua Presen
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.v8i2.12243

Abstract

Concentration of sales that are focused on products that are in great demand and are popular is one of the supermarket sales techniques. Seasonal sales techniques like this sometimes have an impact that can be seen obviously by the imbalance in sales of existing products in supermarkets. Sales imbalance can be the initial cause for a product to lose interest and become a product that is eventually removed from store. With a classification model made to predict which products will be eliminated or churn, it can assist staff in distributing the sales of each product. The more products are churn due to lack of enthusiasts which can affect the overall sales of the supermarket. The purpose of this study is to assist staff in classifying potentially churn products. The classification model consists of 3 models with different algorithms and the results show that the application of the Random Forest algorithm is more effective for predicting data with 96% accuracy compared to 81% for the Logistic Regression algorithm and 46% for the Support Vector Machine algorithm.
Application Of The C4.5 Algorithm to Determine Security Guard Work Schedules Sintawati, Ita Dewi; Widiarina, Widiarina; Mariskhana, Kartika
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.v8i2.12247

Abstract

All agencies, companies, and public spaces must employ security guards to maintain security. The problem with the security guard's schedule is that there is an imbalance in disciplinary issues, poor performance because there is no seniority that is emulated so that the guard is not optimal which results in the problem of losing employee items, working time is not according to the rules and there is a vacancy in personnel due to personnel not coming to work, suing With the existence of a policy in the placement and distribution of the right employee work schedule, it is hoped that it can synergize all elements in the institution so that the quantity and quality of the security guard's work can increase and be completed on time. One of the techniques in data mining is classification. By applying classification techniques to security guard data and work schedules, the Decision Tree method and C4.5 algorithm are developed. The results of data processing form the root node of the gender tree as the root, that those who get schedule A are men while those who get schedule B with high school and junior level education are women, besides that they get schedule A. The accuracy of all classifications of the correct number is 61, 53%.
Analysis of Visitor Satisfaction Levels Using the K-Nearest Neighbor Method Violita, Putri; Yanris, Gomal Juni; Hasibuan, Mila Nirmala Sari
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.v8i2.12257

Abstract

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 Decision Tree Method for Determining Interest in Prospective Student College Maizura, Safrina; Sihombing, Volvo; Dar, Muhammad Halmi
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.v8i2.12258

Abstract

Education is learning science, skills that are carried out by a person or a group of people. The education level starts from Elementary School Education, Junior High School and High School. Apart from that, the highest level of education is college. Lectures are further education carried out by people to gain knowledge and degrees. In college education everyone can choose their respective majors, according to their wishes and desires. With college education, there will be many prospective students who will go to college. But the interest of prospective students to study varies, there are some prospective students who want to study in public and there are some who want to study privately. Therefore the author will make research about prospective students' interest in college. This study aims to see the college interest of prospective students. For this research a data classification of prospective students will be carried out using the Decision Tree method. For this research stage using the Decision Tree method, the first is data analysis, then data preprocessing, then the Decision Tree method design and finally data mining testing. The classification was carried out using the Decision Tree method using 65 prospective student data. From the results of the classification using the Decision Tree method, the results of the Classification of prospective students who are interested in studying are 46 prospective students. The classification results above show that many prospective students are interested in studying.
Analysis of the Naïve Bayes Method for Determining Social Assistance Eligibility Public Siregar, Adinda Pratiwi; Irmayani, Deci; Sari, Mila Nirmala
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.v8i2.12259

Abstract

Economic needs are community needs that are used to meet daily needs. Therefore, economic needs are very important for the life of every society. There is a gap in the economic needs of the community, the government created a social assistance program which is assistance provided to the community in the form of cash or non-cash. The help is made for welfare society from inequality, especially economic inequality. So researchers will carry out a data classification of people who are eligible for social assistance. The classification will be carried out using the Naïve Bayes method. The Naïve Bayes method is a simple classification method for calculating the probability of a combination of certain data. The data to be used by researchers is community data as much as 62 community data. research done by using the Naïve Bayes method aims to classify community data that is feasible to forget social assistance. The first stage of this classification is the process of collecting community data and determining community data that will be used as a filtered sample cleaned, furthermore preprocessing data and then designing the Naïve Bayes Algorithm model. The results of data classification using the Naïve Bayes method show that the number of people who are eligible for social assistance is 14 community data and people who are not eligible for social assistance are 48 community data. These results can be a reference for determining the eligibility of the community to receive social assistance.

Page 58 of 120 | Total Record : 1196

 56   57   58   59   60 

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