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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 443 Documents
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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.
Pengaruh Ekstraksi Fitur Tekstur Pada Hasil Klastering Data Citra Buah Menggunakan Metode K-Means Cluster Lusiana, Veronica; Al Amin, Imam Husni; Hartono, Budi
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.v5i4.5770

Abstract

Penelitian ini bertujuan untuk menganalisis pengaruh ekstraksi fitur tekstur menggunakan metode grey level co-occurence matrix (GLCM) dan local binary pattern (LBP) pada hasil klastering data citra. Ekstraksi fitur tekstur dilakukan pada data uji 30 citra buah matang dan citra buah busuk. Melalui percobaan diperoleh hasil metode ekstraksi fitur LBP dapat menaikan nilai fitur kontras dan menurunkan nilai fitur korelasi. Pada fitur energi, dengan atau tanpa LBP maka perbedaan nilai fitur ini tidak terlalu jauh. Metode GLCM dan LBP berpengaruh pada hasil klastering data citra menggunakan k-means clustering. Data uji tanpa ekstraksi tekstur LBP, diperoleh dua alternatif hasil. Alternatif pertama, anggota klaster 1 yaitu 24 data dan klaster 2 yaitu 6 data. Alternatif kedua, anggota klaster 1 yaitu 22 data dan klaster 2 yaitu 8 data. Pada data uji dengan ekstraksi tekstur LBP, diperoleh tiga alternatif hasil. Alternatif pertama, anggota klaster 1 yaitu 23 data dan klaster 2 yaitu 7 data. Alternatif kedua, anggota klaster 1 yaitu 17 data dan klaster 2 yaitu 13 data. Alternatif ketiga, anggota klaster 1 dan klaster 2 masing-masing 15 data.
Sistem Penunjang Keputusan Rekomendasi Pemilihan Baja Ringan Menggunakan Metode Weighted Product (WP) Auditya, Yonathan; Akbar, Mutaqin
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.v5i4.5773

Abstract

The use of light steel in building construction has increased significantly in recent years. Due to its advantages, such as high strength, corrosion resistance, and ease of installation and maintenance. However, the complexity of various types of light steel makes it difficult for ordinary consumers to choose, and even risks falling victim to fraud related to quality, price, weight, and A-Z level. Therefore, an effective decision support system is needed to help determine the quality of light steel according to their desired specifications. This research develops a decision support system using the Weighted Product method, which identifies the best light steel alternative based on the criteria of quality, price, weight, and A-Z level. The data sample used includes five light steel alternatives, namely BUKIT, GECO, SMS, TARIGAN, and TASO. The results of the application of this method show that the light steel type “GECO” was selected as the best alternative with a value of 0.341, making it the main recommendation in this study. With this system, it is hoped that users who are still unfamiliar with the world of the construction industry can make better and more precise decisions in the selection of light steel, there by reducing the risk of errors and potential fraud in the process of selecting light steel.

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