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Journal of Computer System and Informatics (JoSYC)
Published by Forum Kerjasama Pendidikan TInggi
ISSN : 27147150     EISSN : 27148912     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Industrial & Manufacturing Engineering
Journal of Computer System and Informatics (JoSYC) covers the whole spectrum of Artificial Inteligent, Computer System, Informatics Technique which includes, but is not limited to: Soft Computing, Distributed Intelligent Systems, Database Management and Information Retrieval, Evolutionary computation and DNA/cellular/molecular computing, Fault detection, Green and Renewable Energy Systems, Human Interface, Human-Computer Interaction, Human Information Processing Hybrid and Distributed Algorithms, High Performance Computing, Information storage, Security, integrity, privacy and trust, Image and Speech Signal Processing, Knowledge Based Systems, Knowledge Networks, Multimedia and Applications, Networked Control Systems, Natural Language Processing Pattern Classification, Speech recognition and synthesis, Robotic Intelligence, Robustness Analysis, Social Intelligence, Ubiquitous, Grid and high performance computing, Virtual Reality in Engineering Applications Web and mobile Intelligence, Big Data
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 38 Documents
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Search results for , issue "Vol 5 No 4 (2024): August 2024" : 38 Documents clear
Pengenalan Pola untuk Identifikasi Jenis Kain Tenun Sibolga Menggunakan Metode Principal Component Analysis dan K-Nearest Neighbours Piliang, Dinara Sarvina; Sriani, Sriani
Journal of Computer System and Informatics (JoSYC) Vol 5 No 4 (2024): August 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i4.5727

Abstract

Sibolga woven fabric is one of Indonesia's traditional fabrics that has high artistic and cultural value. Sibolga woven fabric motifs are usually inspired by nature, such as flora, fauna, and local culture. Sibolga woven fabric and is famous for its unique and diverse motifs. Sibolga woven fabric motifs are usually inspired by nature, such as flora, fauna, and local culture. Manually classifying the types of Sibolga woven fabrics is a time-consuming process and requires special expertise. This causes the complexity of motifs and color variations found in Sibolga woven fabrics. Therefore, a system is needed that can classify the types of Sibolga woven fabrics automatically and accurately. The method used in this study is the feature extraction method, which is to extract new features from the initial data set. One of the feature extraction techniques that can be used is Principal Component Analysis (PCA). The use of PCA can be used to reduce the lower dimensions of data with very little risk of information loss. The study also uses KNN because this algorithm is used effectively to classify fabrics based on these key features, thereby reducing computational complexity and improving accuracy. The results of the classification of sibolga woven fabrics using the K-NN algorithm by utilizing the feature extraction process using PCA obtained an accuracy of 72%. It can be concluded that the classification of sibolga woven fabrics using an algorithm using the K-Nearest Neighbours (K-NN) algorithm can be done by extracting features using the PCA method (Pricipal Component Analysis).
Clustering Analysis of Bus Fares Trans Metro Deli Medan Using Mean Shif Clustering Method Rambe, Rinanda Putri; Zufria, Ilka
Journal of Computer System and Informatics (JoSYC) Vol 5 No 4 (2024): August 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i4.5728

Abstract

Medan City is the 3rd most populous city in Indonesia, according to data from the Central Statistics Agency, Medan has a population of 2.49 million in 2022, an increase from the previous 2.46 million in 2021. The increasing number of population inhabiting the city of Medan means that the need for transportation for the people of Medan is also increasing. Trans Metro Deli bus data can be grouped effectively using the mean shift algorithm based on several attributes, namely passenger category, payment method and fare. Each passenger group has different needs and ability to pay, which makes setting fair and efficient fares a challenge. Inappropriate pricing can lead to passenger dissatisfaction, reduce the number of public transportation users, and affect bus operators' revenue. Cluster technique is a well-known clustering technique, which aims to group data into clusters so that each cluster contains data that is as similar as possible. Mean shift belongs to the category of clustering algorithms with unsupervised learning that assigns data points to clusters iteratively by shifting the points towards the mode (mode is the highest density of data points in the region in the context of mean shift). Mean shift does not require determining the number of clusters in advance The attributes used in the clustering process, namely passenger category, payment method and fare can properly create a hyperplane between clusters, thus creating significant differences from each cluster, as evidenced by the silhouette score obtained by 0.64. By conducting this analysis, it is expected to find a more efficient and fair fare clustering pattern, and provide practical recommendations for management in setting fares that are more in line with passenger needs. In addition, this research also aims to evaluate the effectiveness of mean shift clustering in the context of transportation fare analysis.
K-Nearest Neighbor (KNN) Algorithm to Determine the Stock of Building Material Store Materials Safitri, Delilla; Fakhriza, Muhammad
Journal of Computer System and Informatics (JoSYC) Vol 5 No 4 (2024): August 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i4.5731

Abstract

In recent months, a lot of infrastructure has been built, resulting in a shortage of goods in the warehouse due to increased demand for consumer goods and some goods not being sold. Such was the case in January and February 2024 when Riko Jaya panglong experienced a shortage of sand and cement supplies, causing losses. This makes it difficult to predict the inventory of an item in the warehouse. Inventory of goods has great strategic importance for the company. This prediction is very useful in determining the amount of goods to be shipped in the following month. Therefore, companies must implement proactive inventory management. The K-Nearest Neighbor algorithm which looks at the ecluiden distance between old cases and is compared with new cases in an effort to recognize supervised data or data that already exists and has been recorded to help make decisions on the latest cases, this algorithm is very widely applied in other studies because this algorithm has very simple steps and logical reasoning processes by producing the right data and decisions. This data is processed to determine the classification of goods whether increasing or decreasing. And the K-NN algorithm with a value of k = 3 is used to predict stock items. The test results show that K-NN can provide accurate predictions by calculating the Euclidean distance between testing data and training data. The prediction accuracy obtained from the Confusion Matrix reached 100%, indicating the high reliability of this model. Implementation of the K-NN algorithm in RapidMiner with cross-validation technique resulted in a performance of 71.43% for decreasing classification and 67.57% for increasing classification, showing the efficiency of the algorithm in classifying stock data.
Penerapan Algoritma K-Means Clustering Dalam Pola Penjualan Beras Damayanti, Alvina; Putri, Raissa Amanda
Journal of Computer System and Informatics (JoSYC) Vol 5 No 4 (2024): August 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i4.5734

Abstract

This study uses K-Means Clustering to analyze rice sales patterns with the aim of identifying market segments based on purchasing characteristics. The data analyzed include sales volume, purchase frequency and price. The clustering results show several consumer groups with similar purchasing patterns, allowing producers and retailers to better adjust their marketing strategies. These findings provide useful insights to improve the effectiveness of promotional campaigns and meet market needs more efficiently. K-Means Clustering is one of the data analysis techniques that is often used to group objects based on similar attributes. Identify different market segmentation and purchasing patterns that may not be directly visible. This study aims to cluster rice sales data to reveal hidden patterns in sales transactions. By applying K-Means Clustering, this study identifies several consumer groups that have similar characteristics in terms of rice purchases. The results of this study provide insight into market segments that can be used for more effective marketing strategies and product personalization. These findings are expected to help rice producers and retailers design more targeted promotional campaigns and increase efficiency in meeting market needs.
Spatio-temporal Analysis through NDVI, NDBI, and SAVI Using Landsat 8/9 OLI Singgalen, Yerik Afrianto
Journal of Computer System and Informatics (JoSYC) Vol 5 No 4 (2024): August 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i4.5735

Abstract

This research underscores the significant role of remote sensing and spatio-temporal analysis in promoting sustainable tourism development on Kakara Island, North Halmahera. Applying NDVI, NDBI, and SAVI models provided valuable insights into vegetation health, urban expansion, and soil-adjusted indices from 2013 to 2024. NDBI values in 2013, 2018, and 2024 revealed changes in urban development with minimum values of -0.8837597, -0.8867515, and -0.7182528, respectively. NDVI values indicated improvements in vegetation health, with mid values increasing from 0.3804683 in 2013 to 0.8090699 in 2024. Similarly, SAVI values demonstrated better vegetation density, with maximum values rising from 0.3782764 in 2013 to 0.6022941 in 2024. These models effectively monitored environmental changes and informed sustainable land management practices. As tourism on Kakara Island grows, with visitor numbers increasing by 25% annually, a balanced approach is essential to preserve its natural and cultural heritage. Integrating remote sensing and spatio-temporal analysis is crucial for identifying areas under environmental stress and implementing sustainable practices to mitigate negative impacts. Future research should include additional models, such as the Enhanced Vegetation Index (EVI) and Normalized Burn Ratio (NBR), and integrate socio-economic data with environmental datasets for a more comprehensive understanding. This approach will foster sustainable development that benefits both the environment and the local community, ensuring the long-term resilience and viability of Kakara Island's tourism industry.
Decision Support System for Best Teacher Selection using the Multi-Objective Optimization on the Basic of Ratio Analysis (MOORA) Sudarsono, Bernadus Gunawan; Karim, Abdul
Journal of Computer System and Informatics (JoSYC) Vol 5 No 4 (2024): August 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i4.5736

Abstract

Teachers are one of the most important assets owned by companies in their efforts to maintain survival, develop, ability to compete and earn profits. The selection of the best teachers will produce valid and useful information for employee administrative decisions such as promotions, training, transfers including reward systems and other decisions. Decision Support System is a computerized system and is designed to increase the effectiveness in decision making to solve semi-structured and unstructured problems so that the decision making process can be of higher quality. This application that will be made is an application that is guided by the MOORA method. The calculation results using the MOORA method revealed that alternative A5 shows the best performance with a score of 1.246, while alternative A9 occupies the lowest position with a score of 0.546.
Spatial Data Processing for Mangrove Ecotourism Development: Spatio-temporal Analysis through NDVI, NDBI, and SAVI Using Landsat 8/9 OLI Singgalen, Yerik Afrianto
Journal of Computer System and Informatics (JoSYC) Vol 5 No 4 (2024): August 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i4.5740

Abstract

This study evaluates the ecological trends on Tagalaya Island by analyzing the NDBI, NDVI, and SAVI indices from 2013 to 2024. The NDBI data reveals a notable improvement in vegetation conditions over this period. In 2013, NDBI values ranged from -0.8818104 to -0.3152868, indicating poor vegetation health. Although there was a slight deterioration by 2018, with values ranging from -0.8922318 to -0.2858251, a significant recovery was observed by 2024, with values ranging from -0.7118425 to 0.027627. NDVI values also demonstrate positive changes, with 2013 values ranging from -0.340193 to 0.4773595 and increasing substantially by 2024 to a range of -0.2155555 to 0.9997522, reflecting enhanced vegetation coverage and health. Similarly, SAVI values show improvement, increasing from -0.1651871 to 0.3954751 in 2013 to -0.0731807 to 0.6464996 in 2024. These trends suggest that Tagalaya Island has experienced successful ecological recovery or effective conservation measures. Continued monitoring is essential to sustain and further these positive developments, ensuring ongoing environmental stability and health.
Klasifikasi Karakteristik Kepribadian Siswa Berdasarkan Tipologi Hippocrates-Galenus dengan Metode Decision Tree-C4.5 Suliman, Suliman; Kusumawati, Nilam; Sahrullah, Sahrullah
Journal of Computer System and Informatics (JoSYC) Vol 5 No 4 (2024): August 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i4.5753

Abstract

Problem behavior or personality in students this no simple problem, moreover _ problem behavior in students school medium above (SMA), where at the level of the is Step teenager going to mature, so change behavior will seen clear on yourself students. If behavior the no get proper handling and direction, behavior _ the will attached permanent on personality students. Then based on problem that, then will conducted implementation on test typology hippocrates-galenus with C4.5 decision tree method for knowing character personality students school medium top (high school). Based on the Hippocrates-Galenus Typology process with the Decision Tree C4.5 method, discussions and calculations obtained the percentage results and test results of training data have level score answer by 100% of the 100 data that was taken and resulted in a 20% error because of the training data no in accordance with testing system , then the tests carried out by the system use method Decision Tree C4.5 generates level success by 80% which is appropriate with training data.
Enhancing Tourism Digital Content Engagement through Sentiment and Toxicity Analysis: Application of Perspective, Vader, and TextBlob Models Singgalen, Yerik Afrianto
Journal of Computer System and Informatics (JoSYC) Vol 5 No 4 (2024): August 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i4.5757

Abstract

This research examines the engagement with tourism digital content for Sumba Island through sentiment and toxicity analysis. The study uses advanced models such as Perspective, Vader, and TextBlob to reveal an average toxicity score of 0.04066, indicating minimal harmful language. Sentiment classification shows a predominantly positive reception, with VADER identifying 81.69% positive, 12.96% neutral, and 5.35% negative sentiments. TextBlob analysis supports these findings, confirming the robustness of the sentiment evaluation. The research underscores the effectiveness of well-crafted digital content in promoting positive user engagement while maintaining low toxicity. The urgency of this research is emphasized by the increasing reliance on digital platforms for tourism marketing, where understanding audience perception is crucial for effective strategy development. The study employs the Digital Content Reviews and Analysis Framework, which ensures systematic data processing and comprehensive evaluation. This framework includes data cleansing, sentiment, toxicity scoring, and rigorous evaluation using multiple analytical models to enhance the reliability and applicability of the findings. Future recommendations include expanding the analysis to encompass visual content and non-English comments and incorporating advanced multimodal techniques to capture a holistic view of digital content engagement. Addressing these areas will further enrich the understanding and impact of tourism digital content, driving more effective and engaging marketing strategies in the competitive digital landscape.
Pemetaan Topik Tugas Akhir Program Studi Ilmu Komputer Menggunakan Algoritma Latent Dirichlet Allocation Dalimunthe, Roma Gabe; Putri, Raissa Amanda
Journal of Computer System and Informatics (JoSYC) Vol 5 No 4 (2024): August 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i4.5759

Abstract

This research focuses on mapping students' final assignment topics in the Computer Science Study Program at the North Sumatra State Islamic University (UINSU) using the Latent Dirichlet Allocation (LDA) algorithm. The background to this research stems from the need to understand research developments and trends in the collection of submitted final assignments, which can provide an overview of academic trends and developing research areas. However, manual clustering of these topics is often a challenge due to the large data volume and complexity of the content. The Latent Dirichlet Allocation (LDA) algorithm offers a solution with its ability to automatically identify hidden topic structures in text documents. The aim of this research is to reveal dominant themes and topic patterns that appear in students' final assignments, so as to provide deeper insight into the research focus area. The research methodology includes collecting data from various final projects, preprocessing the data to reduce noise and redundancy, and applying the LDA algorithm for topic extraction. The research results show that the LDA algorithm is effective in mapping the topics of students' final assignment titles at UINSU. By using 1000 iterations of the LDA process on 774 final assignment titles, it was found that the most optimal topic division was 7 topics with a coherence score of 0.4011. These topics are visualized through word clouds and word lists, which facilitate understanding and thematic interpretation. It is hoped that these conclusions will provide useful insights into student research trends, facilitate assessment of the quality and relevance of topics, and support the development of better academic curricula in higher education institutions.

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