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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
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.
Prototype Design for Education and Heritage Tourism through Rapid Application Development Pinia, Nyoman Agus Perdanaputra; Gintu, Agung Rimayanto; Wabiser, Yan Dirk; 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.v5i4.5776

Abstract

This research explores the development of a prototype for the Sangiran Information System, utilizing the Rapid Application Development (RAD) framework to meet the specific needs of researchers, destination managers, and tourists. The study emphasizes the importance of user-centric design, facilitated by iterative refinement, which ensures the system effectively supports data management and access related to the Sangiran heritage site. The coding results from content analysis were instrumental in shaping the system, particularly in digital technology integration, educational roles, museum management, and tourism impact. Despite these advancements, the research identifies a critical limitation: the lack of integration with the museum's internal systems and databases. This gap highlights the necessity for further development to achieve a more cohesive and comprehensive information system. The findings underscore the significant progress made in enhancing the educational and management functions of the Sangiran site while also pointing to the need for ongoing improvements to fully support heritage preservation and tourism objectives.
Election Hoax Detection on X using CNN with TF-RF and TF-IDF Weighting Features Adelia, Dila; Astuti, Widi; Lhaksmana, Kemas Muslim
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.v5i4.5778

Abstract

X social media is a microblogging platform for sharing brief thoughts and trends. It has become a focal point for expressing political views. The increased political engagement on X social media has facilitated the swift and extensive sharing of ideas. Still, it also brings the risk of spreading false information and hoaxes that can manipulate public opinion. Preventing fake news on social media is crucial because it can influence election outcomes and social stability. For example, X social media has been used during elections to spread hoaxes, such as false claims of vote tampering or misleading information about candidate qualifications. This study implements a Convolutional Neural Network (CNN) due to its advantages in recognizing complex patterns and achieving high performance in tasks like classification. The dataset used in this study consists of 2,670 tweets. The dataset is divided into three subsets: 60% for training, 20% for testing, and 20% for validation. It also uses Term Frequency Relevance Frequency (TF-RF) and Term Frequency Inverse Document Frequency (TF-IDF) weighting features to improve accuracy in detecting fake news. This study compares the TF-RF and TF-IDF weighting features using the CNN classification method on the topic of the 2024 election. The testing results indicate that both TF-RF and TF-IDF achieved similar overall performance, with TF-RF slightly excelling in recall and F1-score. At the same time, TF-IDF showed a marginally higher precision.
Sistem Pendukung Keputusan Pemilihan Konsentrasi Mata Kuliah dengan Metode MOORA Sihombing, Daniel Oktodeli
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.v5i4.5780

Abstract

This study was conducted to provide decision recommendations in choosing a concentration of courses for students based on their academic performance. The problem in this research is how students can choose a course concentration for themselves based on the academic performance they have previously achieved. The selection of this concentration is calculated based on the prerequisite courses that have been previously determined for each concentration of courses using the MOORA (Multi-Objective Optimization on the Basis Of Ratio Analysis) method. Each prerequisite course is a criterion that is given a weighting using the Rank Sum weighting technique. The alternatives used in this study were 32 alternatives, all of which were students who had completed all prerequisite courses and would choose a concentration of courses in the coming semester. The results of the implementation of the MOORA method in a decision support system with Rank Sum weighting for each criterion obtained results of 18 students recommended in the Emerging Technology concentration and 14 students recommended in the Software Engineering concentration according to their academic performance. The results of the comparison between decision recommendations with the MOORA method and the questionnaire of concentration choices according to student interests showed a level of conformity of 59.38%. Thus, the decision support system with the MOORA method can provide recommendations for choosing a concentration of courses based on previously achieved academic performance.
Pemilihan Siswa Berprestasi Menggunakan Analisis Metode SAW, WASPAS, dan WP Prayogo, M. Ari; Subastian, Eko; Annafi, Naufal
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.v5i4.5792

Abstract

The problem in this research is that the selection process for achievement students often faces obstacles, including data processing and the risk of human error. With this research, it can be used as very good input for selecting achievement students because it uses the Simple Additive Weighting calculation method analysis or commonly called SAW, Weighted Aggregated Sum Product ASsesment or commonly called WASPAS, and Weighted Product or commonly called WP. The purpose of this study is to apply calculation analysis using the SAW, WASPAS, and WP methods to select achievement students in schools. These three methods are applied to determine the ranking of achievement students based on a number of predetermined criteria. There are 7 alternative students who will be determined as one of the achievement students. There are also criteria used to select achievement students, namely based on average report card scores, class rankings, attitude and discipline scores, participation in organizations, and competition winners. Of the 5 criteria used as the basis for selecting one of the achievement students at the school. The results obtained based on the analysis of calculations using the SAW, WASPAS, and WP methods, that a student named Muhammad Rizky Amanullah (A3) was named an achievement student among other students. Students obtained the highest score in the SAW Method with a Vi value of 0.950, then the WASPAS Method with a Qi value of 0.947, and the WP Method with a Vi value of 0.186. From the results obtained, this study can provide input and recommendations in terms of selecting achievement students in schools.
Analisis Sentimen X Terhadap Pemilihan Presiden Indonesia 2024 dengan Metode K-Nearest Neighbor Siddiq, Tri Allan; Ikhsan, 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.v5i4.5802

Abstract

The presidential election which will take place in 2024, sentiment analysis is carried out to determine the tendency of a person's opinion towards an event or problem, whether it tends to be positive or negative. The purpose of this study is to apply the K-Nearest Neighbor Algorithm to the classification of public opinion on the 2024 presidential election and produce a classification of the application of the K-Nearest Neighbor algorithm method to public opinion on the 2024 presidential election on social media X. Based on the results of the research that has been done, it can be concluded that sentiment analysis using the K-Nearest Neighbor (KNN) method has proven effective in identifying and understanding public sentiment related to the 2024 Indonesian presidential election. The community's response to the phenomenon of trends and public opinion towards prospective presidential candidates in 2024 is considered to show a positive attitude, as illustrated in sentiment analysis using a lexicon dictionary. Of the approximately 1,000 tweets that have been analyzed, 211 of them show a positive sentiment, 175 express a negative sentiment, while the other 614 express a Neutral sentiment. This data was collected from November 28, 2023 to April 28, 2024. In addition, this study also identified words that frequently appear in Indonesian tweets. In K-Nearest Neighbor (KNN), the results obtained by the accuracy of the use training set get an accuracy of 100%, precision of 100%, recall of 100% and f-measure of 100%, 10-fold cross validation obtained get an accuracy of 92.5%, precision of 100% recall of 91% and f-measure of 94%, and the last 80% percentage split get an accuracy of 88.55%, precision of 100%, recall of 87% and f-measure of 93.04%. The K-Nearest Neighbor (K-NN) algorithm classification method using 80% percentage split testing is very good in classification testing has greater accuracy, precision, recall and f-measure compared to 10-fold cross validation testing.
Perbandingan Metode Double Exponential Smoothing dan Double Moving Average dalam Penjualan Produk Herbal HNI Tanjung, Sunilfa Maharani; Ikhwan, Ali
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.v5i4.5810

Abstract

Predicting sales of herbal products is the main challenge faced in this research. Currently, forecasting is done solely based on previous sales records, which often prove to be inaccurate. As a result, the company frequently experiences unstable sales and faces difficulties in optimizing inventory and planning product orders efficiently to meet high customer demand. This situation often leads to significant cost losses, forcing the company to reduce capital costs for certain products to cover these losses. The problem arises because the company has not yet implemented an appropriate forecasting method, resulting in estimates that are not supported by a reliable system. This research aims to design and implement a Sales Management Information System for Herbal Products, focusing on the use of the Double Exponential Smoothing (DES) and Double Moving Average (DMA) methods. Additionally, this study aims to compare the two methods in predicting sales by analyzing and calculating the Mean Absolute Percentage Error (MAPE) value for each method. The research uses 200 sales data points from 8 best-selling products, including one of the best-selling products, HNI HEALTH, with data collected from April 2022 to April 2024. The MAPE results from the sales data were then calculated by summing all the MAPE values and dividing them according to the number of MAPEs with an alpha of 0.3. It was found that the DES method is more accurate, with an average Mean Absolute Percentage Error (MAPE) value of 0.285, compared to the DMA method, which has an average MAPE value of 0.292. The DES method is considered more accurate because its MAPE value is smaller than that of the DMA method.
Penerapan Data Mining dalam Menentukan Produk Penjualan Terlaris Menggunakan Algoritma Apriori Randi, Erza Muhammad; Tamara Aldisa, Rima
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.v5i4.5820

Abstract

Information regarding best-selling product data is something that is important to know when analyzing a store's business. Associations between best-selling products can provide useful information for increasing sales profits. However, in reality, the Mabestie Bouquet Store experiences limited problems in analyzing data on best-selling products and their associations using manual calculations only. Therefore, this research aims to create a website for calculating data analysis of best-selling products and their associations using a priori data mining algorithms. The information obtained is data on best-selling products and associations between products in order to develop business strategy progress in the store so that you can know for sure which products need to increase stock and the associations between products that are often purchased by customers. Stock availability of goods that is not managed well has an impact on the shop, for example if goods run out when consumer demand is high then there will be no purchases and this will reduce shop profits. Data mining is the process of utilizing and processing data to find patterns or related relationships in large data sets, and this technique has been widely applied in various fields, including the sale of bouquet products. By using data mining, stores can identify customer preferences through in-depth analysis of complex bouquet sales data. This research focuses on using the Apriori algorithm to analyze sales transaction data at the Mabestie Bouquet Shop. The Apriori algorithm, as a method of association rules, is used to determine combination patterns of itemsets and association rules systematically and accurately. The analysis results show that the items with the highest support value are the Birthday Bouquet Topper and Original Silverqueen Bouquet with a value of 13.3%, and the highest confidence value with a value of 52.6%. Based on this data, it is known that the pattern of purchasing the best-selling product is that if a customer buys a Birthday Bouquet Topper, they will also buy an Original Silverqueen Bouquet product. Based on this data, these two products are products that must be provided with more optimal stock in order to increase sales profits. These findings provide important insights into customer preferences and bouquet purchasing patterns, which can be developed to design more effective marketing strategies and increase the effectiveness of promotions and in-store sales.
Peramalan Stok Penjualan Sahabi Frozen Food dengan Weighted Moving Average Karim, Muhammad Abdul; Yudatama, Uky; Primadewi, Ardhin
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.v5i4.5824

Abstract

Jajan Sahabi Frozen Food (Jajan Sahabi) is a small and medium-sized enterprise (SME) located in Secang, Magelang, offering various types of frozen foods such as burgers, meatballs, and fries from multiple brands. Currently, inventory tracking at Jajan Sahabi is still conducted manually using a logbook, while stock replenishment is carried out weekly based on the remaining inventory. This approach leads to stock imbalances, either resulting in overstock, which may cause losses, or understock, which could lead to customer dissatisfaction. Forecasting is employed as an alternative decision-making tool to predict product demand based on historical data over a certain period. This study analyzes meatball sales reports from January 2022 to December 2022 using the Weighted Moving Average (WMA) method. The accuracy of the forecasts was evaluated using Mean Absolute Deviation (MAD) and Mean Absolute Percentage Error (MAPE). The forecast results for January 2023 indicate that Meatball Ori is expected to sell 216.3 pieces with a MAD of 76.81 and a MAPE of 53.90%. Meatball Kanzler showed the best performance with a MAPE of 28.80% and a MAD of 28.53. In contrast, Meatball Ikan, Meatball Ikan TL, Meatball Fish dan Shrimp, Meatball Cheese Kanzler, and Meatball Wahid exhibited lower accuracy, with MAPE values ranging from 29.03% to 53.90%. These results suggest that the WMA method produces varying outcomes depending on the product, indicating that further adjustments may be necessary to enhance accuracy.

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