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<![CDATA[Exploring YOLOv8 Pretrain for Real-Time Detection of Indonesian Native Fish Species ]]>
<![CDATA[Hindarto, Djarot]]>
<![CDATA[ Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023 ]]>
Publisher : <![CDATA[Politeknik Ganesha Medan ]]>
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DOI: 10.33395/sinkron.v8i4.13100
<![CDATA[The main objective of this research is to determine the efficacy of the YOLO model in detecting native fish species found in Indonesia. Indonesia has a variety of maritime natural resources and shows significant diversity. This research utilizes the YOLO architecture, previously trained on several datasets, for fish detection in the environment in Indonesian waters. This dataset consists of various fish species native to Indonesia and was used to retrain the YOLO Pretrain model. The model was evaluated using test data that accurately represents Indonesian water conditions. Empirical findings show that the modified YOLO Pretrain model can accurately recognize these fish in real-time. After utilizing YOLO and Pre-Train with Ultralytics YOLO Version 8.0.196, the results show an accuracy of 92.3% for head detection, 86.9% for tail detection, and an overall detection accuracy of 89.6%. The fish image dataset, consisting of a total of 401 images, is categorized into three subsets: the training dataset, which consists of 255 images; the validation dataset, which includes 66 images; and the testing dataset, which contains 80 images. This research has great potential for application in fisheries monitoring, marine biology research, and marine environmental monitoring. A real-time fish detection system for the Identification and tracking of fish species is carried out by researchers and field workers. The findings of this research provide a valuable contribution to ongoing efforts aimed at conserving marine biodiversity and implementing more sustainable management practices in Indonesia.]]>
<![CDATA[Revolutionizing Sustainable Public Transportation: The Go-Bus Mobile App Journey With Design Thinking ]]>
<![CDATA[Saputro, Rujianto Eko]]>;
<![CDATA[Faturama, Rafi]]>;
<![CDATA[Sarmini]]>
<![CDATA[ Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2024): Articles Research Volume 8 Issue 1, January 2024 ]]>
Publisher : <![CDATA[Politeknik Ganesha Medan ]]>
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DOI: 10.33395/sinkron.v9i1.13106
<![CDATA[Bus Rapid Transit (BRT) has become a popular solution to address traffic congestion in Indonesia, including in Banyumas Regency. However, the supporting services provided by the BRT system still require improvement. This study focuses on designing the Go-Bus application, by integrating gamification elements to encourage the usage of Trans Banyumas. The Design Thinking method is used, encompassing the empathy, definition, ideation, prototype, and testing stages. This prototype undergoes User Satisfaction Testing and Single Ease Question (SEQ). the average score of 84.84% has been reached from the evaluation of 11 tasks by six respondents. Then, satisfaction score of 6.73 indicates Go-Bus as a user-friendly and satisfying application. This research aims to address challenges in motivating and altering user behavior to utilize public transportation. By incorporating gamification into the UI/UX design of the application, Go-Bus offers a solution that enhances user motivation, satisfaction, and encourages a shift towards public transportation usage]]>
<![CDATA[Information System Strategic Planning To Improve UINSU Medan Service Performance ]]>
<![CDATA[Ridwan, M]]>;
<![CDATA[Siregar, Saparuddin]]>
<![CDATA[ Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2024): Articles Research Volume 8 Issue 1, January 2024 ]]>
Publisher : <![CDATA[Politeknik Ganesha Medan ]]>
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DOI: 10.33395/sinkron.v9i1.13109
<![CDATA[Facing increasingly rapid technological developments, UINSU Medan's efforts must be supported by developing existing information systems to meet the needs of the community and community. Currently, even though it has implemented an Information System in its activities, UINSU Medan does not yet have an Information System plan for the next 5 (five) years (2023-2027). It is hoped that this SI strategic planning will be able to improve the performance of UINSU services related to the Tri Dharma of Higher Education. The method used in this research is a qualitative survey. Data collection was carried out by observation, interviews, and literature review. The research informants are the managers of the UINSU Information Technology and Database Center (PUSTIPADA), and employees involved in the Information Systems section as well as service users such as students and lecturers. The stages of this research use Anita Cassidy's approach which consists of the visioning phase, analysis phase, direction phase, and recommendation phase. The data obtained was then analyzed using Value Chain analysis, SWOT, Porte's Five Forces, and other supports which were then confirmed by Focus Group Discussion with competent parties. This research recommends an Information Systems roadmap for UINSU Medan consisting of 34 integrated applications to be developed within 5 (five) years from 2023-2027.]]>
<![CDATA[Improvement of Kernel SVM to Enhance Accuracy in Chronic Kidney Disease ]]>
<![CDATA[Wijaya, Ganda]]>
<![CDATA[ Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2024): Articles Research Volume 8 Issue 1, January 2024 ]]>
Publisher : <![CDATA[Politeknik Ganesha Medan ]]>
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DOI: 10.33395/sinkron.v9i1.13112
<![CDATA[Chronic Kidney Disease (CKD) is a highly serious health issue, affecting millions of people worldwide. Early diagnosis and accurate prediction of chronic kidney disease are key factors in successful treatment. One of the approaches used for diagnosing this disease is through machine learning algorithms, specifically the Support Vector Machine (SVM) method. By collecting CKD data that includes various clinical parameters, initial kernel selection as well as various kernels are tested. However, the accuracy of the SVM method can be further improved for better diagnosis. The objective of this research is to enhance accuracy, optimize parameters, and improve the SVM kernel by incorporating the Particle Swarm Optimization (PSO) algorithm. The results of this study indicate that the use of PSO method to improve SVM kernels can significantly enhance accuracy in CKD diagnosis compared to conventional SVM approaches, potentially aiding medical practitioners in early disease diagnosis and better CKD management, which in turn can improve patient prognosis and quality of life]]>
<![CDATA[Methods for Development Mobile Stunting Application: A Systematic Literature Review ]]>
<![CDATA[Karim, Hildamayanti]]>;
<![CDATA[Dhani Ariatmanto]]>
<![CDATA[ Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2024): Articles Research Volume 8 Issue 1, January 2024 ]]>
Publisher : <![CDATA[Politeknik Ganesha Medan ]]>
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DOI: 10.33395/sinkron.v9i1.13123
<![CDATA[Stunting is a growth disorder in children. Stunting is one of the indicators of failure to thrive in toddlers caused by a chronic lack of nutritional intake in the first 1,000 days of life, from a fetus to a child aged 23 months. Based on data from the Asian Development Bank (ADB), in 2022 the percentage of stunting prevalence occurring in children aged <5 years in Indonesia reached 31.8%. So Indonesia is ranked 10th in Southeast Asia. The object of this review is to review the current literature and help researchers to find out what methods have been used in making stunting prevention applications. In a systematic search of the literature using quality databases including SpringerLink, ScienceDirect, and IEEE Xplore. The paper included in this review is a stunting prevention application information system by describing the methods most often used by researchers in making the stunting prevention application information system. There were 41 results based on the exclusion of titles and abstracts, based on the introduction and exclusion of conclusions there were 35 results, so we included 12 results for the full-text exclusion in the final analysis. So that the popular method used by researchers in Android-based stunting applications is the prototype method. Compared to other methods, prototyping is more suitable for systems that are made based on user needs.]]>
<![CDATA[Evaluation of Accounting Information System Using Usability Testing Method and System Usability Scale ]]>
<![CDATA[Putri, Rozza Maudina Ayuwan]]>;
<![CDATA[Parwita, Wayan Gede Suka]]>;
<![CDATA[Handika, I Putu Susila]]>;
<![CDATA[Sudipa, I Gede Iwan]]>;
<![CDATA[Santika, Putu Praba]]>
<![CDATA[ Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2024): Articles Research Volume 8 Issue 1, January 2024 ]]>
Publisher : <![CDATA[Politeknik Ganesha Medan ]]>
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DOI: 10.33395/sinkron.v9i1.13129
<![CDATA[The computer-based accounting information system IBS Core has been used by Renon Pekraman Village Credit Institution since 2016 to facilitate all transaction processes. This study aims to evaluate the usefulness of the IBS Core accounting information system, determine the effectiveness, efficiency, and user satisfaction with the IBS Core accounting information system, and identify areas that need improvement in the IBS Core accounting information system.The method used in this study is Usability Testing using performance measurement and retrospective think aloud (RTA) as well as the System Usability Scale (SUS). The results of the study show that the IBS Core system has a high effectiveness score of 92.50%. The average time required for participants to complete the task scenario is 68.9 seconds, and users feel that the available content is clear and consistent. In addition, the average System Usability Scale (SUS) score is 86.125, where the results were above the standard average SUS score. The IBS Core System Score was ranked B with the adjective ratings "Excellent” Next, the acceptability ranges are included in the "Acceptable" category, and finally the net promote score (NPS) is included in the "Promoter" category, showing that the use of the IBS Core system gets a very good assessment from its users. This shows that the IBS Core system is highly appreciated and considered very useful by its users.]]>
<![CDATA[OPTIMIZATION NAÏVE BAYES ALGORITHM IN SENTIMENT ANALYSIS OF BUKALAPAK APP REVIEWS ]]>
<![CDATA[Akmali, Fajar]]>;
<![CDATA[Andi Dwi Riyanto]]>;
<![CDATA[Irma Darmayanti]]>
<![CDATA[ Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2024): Articles Research Volume 8 Issue 1, January 2024 ]]>
Publisher : <![CDATA[Politeknik Ganesha Medan ]]>
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DOI: 10.33395/sinkron.v9i1.13132
<![CDATA[Bukalapak application reviews on Google Play Store include useful information if processed correctly. The activity of analyzing application reviews is not enough to see the number of stars, it is necessary to see the entire contents of the review comments to be able to know the intent of the review. Sentiment analysis system is a system used to automatically analyze reviews. Review data is retrieved via the bukalapak application API and then classified using Naive Bayes Multinomial. A total of 1,000 reviews of bukalapak application users were collected to be used as dataset samples. The purpose of this research is to determine the accuracy level of sentiment analysis using the multinomial Naive Bayes algorithm. The stages of this research include, data collection, automatic labeling using python, pre-processing, sentiment classification, and evaluation. In the pre-processing stage there are 6 stages, namely Cleaning, Casefolding, Word Normalizer, Tokenizing, Stopword Removal and Stemming. TF-IDF (Term Frequency - Inverse Document Frequency) method is used for word weighting. The data will be grouped into two categories, namely negative and positive. The test results show an accuracy value of 90%, this result shows that the bukalapak application reviews tend to be negative. The research at this time only looks for accuracy values and provides an overview of the bulapak application to potential new users.]]>
<![CDATA[Sentiment Analysis by Using Naïve Bayes Classification and Support Vector Machine, Study Case Sea Bank ]]>
<![CDATA[Christian, Yefta]]>;
<![CDATA[Wibowo, Tony]]>;
<![CDATA[Lyawati, Mercy]]>
<![CDATA[ Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2024): Articles Research Volume 8 Issue 1, January 2024 ]]>
Publisher : <![CDATA[Politeknik Ganesha Medan ]]>
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DOI: 10.33395/sinkron.v9i1.13141
<![CDATA[Information technology is developing at a rapid pace, changing people's lives, particularly in the financial sector where customer demands are rising, and banks must innovate to convert from traditional to technological banking systems while also increasing competency and efficiency through improved services. Innovations in digital banking have arisen in Indonesia as a result of technical progress. SEA Bank is one such digital bank; it was established in Indonesia in 2021. An app that may be found on the Google Play Store is used for all transactions. However, there are instances when the application's performance falls short of users' expectations, which prompts some users to voice their dissatisfaction. In order to determine if the evaluations are either beneficial or detrimental, the author therefore carried out a sentiment analysis study on SEA Bank using the Naïve Bayes classification and Support Vector Machine techniques. This was then implemented on a website utilizing the Flask framework. In the experiments with 90% training data, 10% testing data, and k = 10, the results of this study demonstrated that the sentiment classification process using the SVM algorithm was the best classification algorithm for evaluating its accuracy, precision, and recall values of 93.99%, 94.60%, 98.87%, and an F1 score of 96.69%.]]>
<![CDATA[Application of Data Mining for Clustering Human Development Index Based on West Java Province 2017-2022 ]]>
<![CDATA[Widiarina, Widiarina]]>;
<![CDATA[Mariskhana, Kartika]]>;
<![CDATA[Sintawati , Ita Dewi]]>
<![CDATA[ Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2024): Articles Research Volume 8 Issue 1, January 2024 ]]>
Publisher : <![CDATA[Politeknik Ganesha Medan ]]>
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DOI: 10.33395/sinkron.v9i1.13148
<![CDATA[Human development is used as a parameter to see development from the human side. The Human Development Index (HDI) explains how people get sufficient income, adequate health and education. Geographically, Indonesia is an archipelagic country where each province is spread across various islands separated by sea. Making the disparity in human development between provinces relatively high. The gap that occurs is still a problem that must be resolved immediately, because the gap in the human development index can hamper the government's goal of equalizing human welfare in Indonesia. −One of the problems related to population that West Java Province still has to face is the problem of imbalance in population distribution. Incomplete population distribution causes problems with population density and population pressure in an area. This research uses data sources from the West Java Province Central Statistics Agency (BPS). The data used in this research is data from 2017-2022 which consists of 27 regencies and cities of West Java Province. Therefore, researchers utilized the K-Means algorithm in clustering 27 Regencies and Cities of West Java Province. The data will be processed by clustering into 3 clusters, namely the high population area level cluster, the medium population area level cluster and the low population area level cluster. This research classifies population density using Ms. software. Excel and RapidMiner. The iteration process took place 3 times so that the results obtained were 8 regencies and cities with high population area clusters (C0), 0 regencies and cities with medium population area clusters (C1) and 19 regencies and cities with low population area clusters (C1). C2).]]>
<![CDATA[OPTIMIZATION ACCURACY VALUE OF AGRICULTURAL LAND FERTILITY CLASSIFICATION USING SOFT VOTING METHOD ]]>
<![CDATA[Pradana, Khaliq]]>;
<![CDATA[Budiman, F]]>
<![CDATA[ Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2024): Articles Research Volume 8 Issue 1, January 2024 ]]>
Publisher : <![CDATA[Politeknik Ganesha Medan ]]>
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DOI: 10.33395/sinkron.v9i1.13159
<![CDATA[Soil fertility on an agricultural land is very influential with agricultural yields, where plants can grow well and fertile if nutrient intake is met. The purpose of this research is to improve the accuracy in predicting soil fertility by utilizing machine learning by combining two classification algorithms using soft voting methods in the classification of agricultural land fertility. In this research, one of the ensemble learning methods called soft voting is employed. Soft voting is used to enhance accuracy by optimizing the combination of algorithms based on the highest probability provided by each model. The Gaussian Naive Bayes algorithm is used to predict classes in the sample data based on the Gaussian distribution of numerical data, while the decision tree is utilized to predict classes by constructing a decision tree using soil content attributes for the classification of fertile or infertile soil. The use of the Gaussian Naive Bayes algorithm in identifying fertile and infertile soil based on existing attributes achieved an accuracy rate of 87.2%. The decision tree algorithm, based on decision tree modeling, helped identify important attributes for decision-making with an accuracy rate of 88.3%. The soft voting method played a crucial role in improving accuracy by combining both algorithms, resulting in an accuracy rate of 88.8%. Based on the accuracy results obtained, the use of soft voting optimization in predicting soil fertility has the highest accuracy because it combines the Gaussian naïve bayes algorithm and the decision tree algorithm.]]>
