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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
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Implementation of Cloud Computing for SOS Application Back-End using Google Cloud Platform Firdonsyah, Arizona; Indah, Mahrunisa
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14590

Abstract

This research discusses the implementation of cloud computing on the Backend of the SOS application using the Google Cloud Platform. The background of the research is based on the high crime rate in Indonesia, especially theft cases which reached 3396 cases in the period August-September 2024. The purpose of the research is to develop an application that can help users in emergency situations by providing information on the location of the nearest police station within a maximum radius of 5 KM. The method used is Agile Kanban, which was chosen because of its flexible nature and emphasizes rapid response to change. The Backend implementation uses Google Cloud Platform services including Maps API (Places API, Geocoding API, and Distance Matrix API) for location features, and Google Firestore for data storage. The results of the research show that the implementation of cloud computing for the Backend of the ResQHub application successfully displays the location of the nearest police station from the user, but there are still obstacles in the integration of Firestore for storing user data and signup/login authentication. Further research will focus on frontend development for mobile implementation and completion of Firestore integration.
Development of RPG-Based Mathematics Educational Games with the Waterfall Method on Fraction Material for Elementary School Students Satriyo, Azis; Azis, Abdul; Saputri, Fiqih Hana; Ferawati, Ferawati
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14600

Abstract

Mathematics is one of the subjects that plays an important role in everyday life and in developing logical thinking and problem-solving skills. One of the topics that often poses a challenge for elementary school students is fraction numbers. Fractions are often considered an abstract and difficult concept to understand because they involve numerical representations that differ from whole numbers. This difficulty frequently leads to a lack of interest in learning mathematics, ultimately affecting students' academic performance. The data collection stages applied include interviews, observations, and the distribution of questionnaires. The development of this learning media follows the Waterfall model, which aims to design improvements to the existing system. The results of the User Acceptance Test reveal that this game received a user perception score of 90.5%, categorizing it as "very good," indicating that students find it both enjoyable and effective as a learning tool. This suggests that the game is not only engaging but also effective in helping students understand fraction concepts in a more interactive and enjoyable way. With the presence of story elements, challenges, and engaging game mechanics, students can learn in a more immersive manner compared to conventional methods. Therefore, this game is suitable for use as a mathematics learning tool, particularly in understanding fraction operations such as addition, subtraction, multiplication, and division.
Development of Augmented Reality-Based Learning Media for Solid Geometry for Elementary School Students Saputra, Ricky Aditya; Alfarizi, Muhammad; Saputri, Fiqih Hana; Ferawati, Ferawati
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14601

Abstract

AR-based media is expected to create a more interactive and engaging learning experience, enhance students’ understanding, and motivate them to learn independently and actively in the digital era. The data collection stages applied include testing, observations, and the distribution of questionnaires. The development of this learning media follows the MDLC model, which aims to design improvements to the existing system. The results of the blackbox testing, the "Bangun Ruang" application is proven to be valid and successfully used, with excellent results in the SUS test, where the Ease of Use score reached 86%, Efficiency 88%, Effectiveness 90%, and Satisfaction 87%. This indicates that the application has high levels of usability, efficiency, and effectiveness, while also providing a satisfying user experience. The application not only operates according to the designed specifications but also provides a positive user experience. Thus, it can be widely used, especially in educational environments, to help students understand geometric concepts in a more interactive and engaging way. Developers can continue maintaining and improving the application based on user feedback to ensure it remains optimal and aligned with users' needs in the future.
Enhancing Sentiment Analysis Accuracy Using SVM and Slang Word Normalization on YouTube Comments Saputra, Alfin Nur Aziz; Saputro, Rujianto Eko; Saputra, Dhanar Intan Surya
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14613

Abstract

Sentiment analysis is a crucial technique in understanding public opinion, particularly on social media platforms such as YouTube. However, the presence of informal language, including slang words, poses significant challenges to accurate sentiment classification. This study aims to enhance sentiment analysis by implementing a Support Vector Machine (SVM) classifier combined with SMOTEENN data balancing techniques to address class imbalance issues. The research collects 3,375 YouTube comments on the movie Pengabdi Setan 2: Communion using the YouTube Data API. The preprocessing steps include text cleaning, tokenization, stopwords removal, stemming, and slang word normalization using kamusalay.csv to ensure standardization of informal expressions. The extracted features are represented using TF-IDF, and sentiment labeling is performed using VADER. Experimental results show that the SVM model achieves an accuracy of 98%, but struggles with detecting negative sentiments, as indicated by lower recall (38%) and F1-score (53%) for the negative class. Although the application of SMOTEENN improves data balance, further refinements, such as adjusting classification thresholds and integrating deep learning techniques, are necessary to enhance sentiment detection, particularly for informal and emotionally nuanced language. This study contributes to improving sentiment analysis models by demonstrating the effectiveness of slang word normalization in handling non-standard language variations. Future work will explore more sophisticated language models to enhance accuracy in sentiment classification.
Application of Extreme Programming Methods in the Design and Building of the Nusantara Capital Sentiment Analysis System Said, Famidin; Kristomo, Domy; Andriyani, Widyastuti
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14617

Abstract

Information about the capital city of the archipelago (IKN) in the digital era serves as a platform for individuals to express views on development, policies, and socio-economic impacts. Such information often contains personal emotional expressions, categorized as negative, neutral, or positive sentiments. This study aims to design a sentiment analysis system to evaluate public opinions regarding IKN. The system utilizes Google NLP services, which offer sentiment measurement features for analyzed text, and web scraping techniques to automate data collection from online sources. The development process employs the Laravel framework and follows the Extreme Programming approach, which ensures work efficiency. Sentiment analysis is conducted using the Support Vector Machine (SVM) method, achieving an accuracy rate of 95%. The system is designed to be web-based, ensuring accessibility across devices, including smartphones and computers. The results demonstrate that this sentiment analysis system can help individuals, organizations, and governments gain deeper insights into public perspectives on IKN. Furthermore, it serves as a valuable tool for strategic decision-making and policy evaluation related to IKN development. Future research may explore expanding the data sources and integrating more advanced analytical techniques to improve system performance.
Sentiment Analysis of the Relocation of the National Capital on Social Media X Dewi, Yesi Ratna; Saraswati, Ni Wayan Sumartini; Monny, Maria Osmunda Eawea; Sarasvananda, Ida Bagus Gede; Andika, I Gede
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14622

Abstract

The relocation of the national capital is a national strategic development project that seeks input from the public. This research analyzes public sentiment towards the relocation of the capital city using the Lexicon SVM method with data from X social media. The analysis was conducted in two languages, namely Indonesian and English, to find out how public opinion on the relocation of Indonesia's capital city at the global level. The sentiment classification results show that in Indonesian, public sentiment tends to be balanced with a model accuracy of 86.79%, where 51.3% is positive sentiment and 48.7% is negative. Meanwhile, in English, positive sentiment is more dominant with a model accuracy of 89.64%, where 83.3% is positive sentiment and 16.7% is negative sentiment. Evaluation using confusion matrix shows that this model provides good results, with high precision, recall, and F1-score values. Visualization using WordCloud and frequency analysis of unigrams, bigrams, and trigrams showed that positive sentiments mostly discussed the development aspects and government policies, while negative sentiments highlighted the social and economic impacts of the relocation. In addition, further analysis shows that public sentiment fluctuates based on important government announcements and major events related to the project. These findings demonstrate the importance of monitoring public opinion over time to understand shifts in perception. This research provides insights to the government and policymakers in understanding public opinion regarding the relocation of the nation's capital. By understanding sentiment patterns, more appropriate policies can be designed to increase public acceptance of the project and address public concerns effectively.
Sentiment Analysis Using Grok AI as an Auto-Labeling Tool in The Text Processing Stage Agustin, Yoga Handoko; Kurniadi, Dede; Julianto, Indri Tri; B. Balilo Jr , Benedicto
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14632

Abstract

A critical aspect of Natural Language Processing (NLP) is text processing, where text labeling represents the most significant challenge due to its resource-intensive nature when conducted manually. At this stage, automatic labeling emerges as a more practical solution, particularly with the advent of Artificial Intelligence (AI), which offers tools to address this obstacle. Grok AI, equipped with a new feature operable on Platform X, provides a promising approach. This study aims to leverage the Grok AI feature on Platform X for automatic text labeling. The research methodology involves labeling text data obtained from a public dataset. To assess the quality of the labeling results, an evaluation method employing Naive Bayes classification modeling is applied. The findings reveal that Grok AI's performance closely approximates that of human labeling. The highest accuracy achieved by Grok AI is 51.71% using the k-Nearest Neighbors (k-NN) algorithm, approaching the human labeling accuracy of 60.52% with k-NN. Furthermore, Grok AI surpasses the performance of VADER labeling, which achieves an accuracy of only 49.49% with Naive Bayes. Consequently, the Grok AI feature on Platform X presents a viable alternative for the automatic labeling of text data.
Vehicle Type Classification and Detection System using YOLOv7-tiny Model on Single-Board Computer Nadziroh, Faridatun; Sa’adah, Nihayatus; Widyatra Sudibyo, Rahardita; Mahmudah, Haniah; Imam Rifai, Moch.
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14637

Abstract

Transportation is playing an important role for human civilization, for example transportations is being used as distributing goods and products. Therefore, the total numbers of vehicles as a part of transportation will continue to increase every year. But in Indonesia, the majority of its people is still using their personal transport rather than public transportation. This is supported by the data of total number of vehicles in Indonesia from 2018 – 2020, which is shows that personal transport is still dominant than public transportation. The causes of traffic jams is a result of various factors, such as the roads are not designed to accommodate the increasing number of vehicles, insufficient traffic signs, and poor traffic management. The road traffic data is one of the aspects that could reduce traffic jams. The process of collecting road traffic data which is still done manually has several shortcomings, such as it takes a long time and there may be errors due to human error. This research has a goal to create a vehicle type detection and classification system that have a good detection accuracy and detection speed that can be run on single-board computer devices. YOLOv7-tiny model that performs detection and classification using input from video on the NVIDIA Jetson Nano device gets a True Positive (TP) score of 96.58%, a False Positive (FP) score of 0.98%, and a False Negative (FN) score of 2.44%. YOLOv7-tiny on the NVIDIA Jetson Nano device can run with an average Frame per Second (FPS) of 6 FPS.
Implementation of Support Vector Machine Algorithm for Heart Disease Risk Identification Using Signal Electrocardiogram Shiddik, Fahriza; Bagas Andhika; Yennimar; Grisela Sangap Damayanti Saragih; Gabriella Br. Surbakti
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14642

Abstract

: In the medical world, one of the biggest contributors to death in the world is heart disease. Early detection of the risk of heart disease can increase the chances of recovery and reduce mortality. This research applies the Support Vector Machine (SVM) algorithm to identify the risk of heart disease using Electrocardiogram Signals. The ECG data used was taken from a public database that contained a record of information on the electrical activity of the heart of patients with various heart health conditions. The Support Vector Machine algorithm is applied to classify ECG signals into 2 main classes, namely normal conditions and at-risk conditions. Several methods in data processing, including data normalization and feature selection are used to improve the accuracy and success of the model. The results of the evaluation with this method resulted in accuracy, precision, recall and also F1-score showed that the modeling of this algorithm produced a fairly good classification, with an accuracy of more than 90% in the identification of heart disease risk. This study shows the potential use of this algorithm in automatically detecting the risk of heart disease based on ECG signals, which can be a tool in medical diagnosis. The results show that implementing the SVM strategi with the RBF kernel appears to be a very easy execution when compared to the direct part. An important component that affects the adequacy of an SVM strategy is the parameters of the section and the way the information is handled.
Automated Attendance System for Contract-Based Employees at Purwakarta Communication and Informatics Agency Komara, Mutiara Andayani; Salim, Asep Yusapra; Firdaus, Maulvi
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14648

Abstract

Attendance is a crucial aspect of administrative management in both companies and institutions. However, manual attendance systems have several drawbacks, including disorganized data, minimal data control leading to a high potential for data fraud, and vulnerability to data loss or damage. These limitations underscore the need for more efficient and reliable attendance management solutions. This research addresses the challenges of manual attendance tracking for non-ASN (Aparatur Sipil Negara) staff at the Communication and Informatics Agency (Dinas Komunikasi dan Informatika (DISKOMINFO)) of Purwakarta Regency by developing a user-friendly web-based attendance system. The system leverages the PHP programming language and MySQL database to efficiently record and manage attendance data. The Waterfall method is used as the development framework, ensuring a structured and systematic approach. The system incorporates features designed with Unified Modelling Language (UML) to simplify attendance recording for staff and administrators, including online check-in/check-out, real-time attendance tracking, and automated report generation. Evaluation results demonstrate that the system significantly improves attendance accuracy, reduces administrative burden, and enhances overall efficiency within the office. This research highlights the importance of embracing technology to modernize administrative processes and improve operational effectiveness in government organizations. The implementation of this technology was also tested for its effectiveness using black box testing and the usability scale system.

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