Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)最新文献_第7页

Evaluating Player Experience for Fear Modeling of 2D East Java Horror Game Alas Tilas 评估2D东爪哇恐怖游戏《Alas Tilas》恐惧建模的玩家体验

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Pub Date : 2023-08-12 DOI: 10.29207/resti.v7i4.5043

Herman Thuan, To Saurik, Harits Ar Rosyid, A. Wibawa, Esther Irawati Setiawan

{"title":"Evaluating Player Experience for Fear Modeling of 2D East Java Horror Game Alas Tilas","authors":"Herman Thuan, To Saurik, Harits Ar Rosyid, A. Wibawa, Esther Irawati Setiawan","doi":"10.29207/resti.v7i4.5043","DOIUrl":"https://doi.org/10.29207/resti.v7i4.5043","url":null,"abstract":"Developing a 2D horror game and evaluating the reliability of the player experience are two things that are interrelated and equally important. Developers must ensure that the game can provide a satisfying and reliable gaming experience for its players. This study aims to evaluate the reliability of the player's experience in the game entitled Alas Tilas, East Java. This study used User Experience Questionnaire (UEQ) in Indonesian as survey approach method which was given to 30 teenager respondents who at least played horror games once. UEQ may provide feedback to developers on the Attractiveness, Clarity, Efficiency, Accuracy, Stimulation, and Novelty aspects of the game. From the results of the UEQ, a reliability test will be carried out using the Cronbach Alpha Technique. The results of the descriptive analysis show that these variables are Attractiveness (mean, 0.933), Clarity (mean, 1.808), Efficiency (mean, 1.508), Accuracy (mean, 0.217), Stimulation (mean, 0.667) and Novelty (mean, 0.242). Attractiveness, Clarity and Efficiency averaged positive results. While the average aspects of accuracy, stimulation and novelty of the game get neutral results. The results of the reliability test conducted on UEQ data obtained a Cronbach alpha value > 0.6 which indicates that the research data used in testing player experience is considered reliable so that it can be used to provide input for future Alas Tilas game development. To increase the average score, the researcher provides recommendations for improvement, namely adjusting the Accuracy and Novelty aspects of the horror scenario game entitled Alas Tilas East Java. So that it is expected to improve the quality of the game. ","PeriodicalId":435683,"journal":{"name":"Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117203281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Folk Games Image Captioning using Object Attention 使用对象注意的民间游戏图像字幕

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Pub Date : 2023-08-12 DOI: 10.29207/resti.v7i4.4708

Saiful Akbar, B. Sitohang, Jasman Pardede, Irfan I. Amal, Kurnianda Yunastrian, M. Ahmada, Anindya Prameswari

引用次数: 0

Solution to Scalability and Sparsity Problems in Collaborative Filtering using K-Means Clustering and Weight Point Rank (WP-Rank 基于k均值聚类和权重点秩的协同过滤可扩展性和稀疏性问题的解决

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Pub Date : 2023-08-12 DOI: 10.29207/resti.v7i4.4543

Mohamad Fahmi Hafidz, Sri Lestari

{"title":"Solution to Scalability and Sparsity Problems in Collaborative Filtering using K-Means Clustering and Weight Point Rank (WP-Rank","authors":"Mohamad Fahmi Hafidz, Sri Lestari","doi":"10.29207/resti.v7i4.4543","DOIUrl":"https://doi.org/10.29207/resti.v7i4.4543","url":null,"abstract":"Collaborative Filtering is a method to be used in recommendation systems. Collaborative Filtering works by analyzing rating data patterns. It is also used to make predictions of user interest. This process begins with collecting data and analyzing large amounts of information about the behavior, activities, and tendencies of users. The results of the analysis are used to predict what users like based on similarities with other users. In addition, Collaborative Filtering is able to produce recommendations with better quality than recommendation systems based on content and demographics. However, Collaborative Filtering still faces scalability and sparsity problems. It is because the data is always evolving so that it becomes big data, besides that there are many data with incomplete conditions or many vacancies are found. Therefore, the purpose of this study proposed a clustering and ranking based approach. The cluster algorithm used K-Means. Meanwhile, the WP-Rank method was used for ranking based. The experimental results showed that the running time was faster with an average execution time of 0.15 second by clustering. In addition, it was able to improve the quality of recommendations as indicated by an increase in the value of NDCG at k=22, the average value of NDCG was 0.82, so that the recommendations produced had more quality and more appropriate with user interests. \u0000 ","PeriodicalId":435683,"journal":{"name":"Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)","volume":"205 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114145269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Classification of Hearing Loss Degrees with Naive Bayes Algorithm 基于朴素贝叶斯算法的听力损失程度分类

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Pub Date : 2023-08-12 DOI: 10.29207/resti.v7i4.4683

Okky Putra Barus, Romindo, Jefri Junifer Pangaribuan

{"title":"Classification of Hearing Loss Degrees with Naive Bayes Algorithm","authors":"Okky Putra Barus, Romindo, Jefri Junifer Pangaribuan","doi":"10.29207/resti.v7i4.4683","DOIUrl":"https://doi.org/10.29207/resti.v7i4.4683","url":null,"abstract":"According to the World Health Organization (WHO), hearing loss is one of the fourth highest causes of disability. The number of people with hearing loss continues to increase yearly. This increase occurred due to delays in recognizing the hearing loss, leading to delays in providing treatment. To solve this problem one solution to deal with this is early identification to detect the degree of hearing loss. This research will use machine learning to classify the degree of hearing loss. The algorithm implemented in this study is naive Bayes. This study uses a dataset from the open-access repository Zenodo with 3105 raw data and 19 features. This study evaluates the performance of overall accuracy, precision, recall, and f1-score and classified four classes: mild, moderate, moderately severe, and severe. The methodology classification stages in this study include data pre-processing, data training, data testing to evaluation. From evaluating the performance of the Naive Bayes algorithm, the classification results obtained the highest impacts in the form of 94% overall accuracy, 100% precision, 100% recall, and 97% f1-score in classifying the degree of hearing loss. \u0000 ","PeriodicalId":435683,"journal":{"name":"Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131235115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Systematic Mapping Study: Research Opportunities on Capacity Planning 系统映射研究:容量规划的研究机会

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Pub Date : 2023-08-12 DOI: 10.29207/resti.v7i4.5037

Yuggo Afrianto, Rendy Munadi, Setyorini, Arief Goeritno

{"title":"Systematic Mapping Study: Research Opportunities on Capacity Planning","authors":"Yuggo Afrianto, Rendy Munadi, Setyorini, Arief Goeritno","doi":"10.29207/resti.v7i4.5037","DOIUrl":"https://doi.org/10.29207/resti.v7i4.5037","url":null,"abstract":"The central idea of the research is to enhance the efficiency and sustainability of data centers by implementing accurate capacity planning, which will also improve their performance and availability. Various literature reviews have been conducted to understand the current status of capacity planning implementation across different domains and perspectives. However, a more organized and systematic approach is required to map research and implementation outcomes in the relevant areas of capacity planning that have the potential for further development. The present study is aimed at filling this gap by conducting a systematic mapping study, which combines both quantitative and qualitative methodologies. The quantitative approach involved the collection of literature and topic classification using the Latent Dirichlet Allocation (LDA) method. In contrast, the qualitative approach utilized content analysis to identify future research directions based on keyword trends and topics. The PRISMA framework was followed to guide the search for relevant studies in electronic research literature databases. The mapping results revealed 15 topics, with topics 8, 10, 11, and 15 showing significant potential for further research and exhibiting increasing trends. The identified topics encompass capacity planning, energy and resource management, computing and technology, data analysis and statistics, engineering, and industry, all crucial for businesses and industries to operate efficiently and sustainably. This study provides a comprehensive overview of the state of capacity planning implementation and highlights areas that require further investigation. \u0000 ","PeriodicalId":435683,"journal":{"name":"Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128103879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Transforming LMS into KMS in Indonesia Educational Institution Case Study in Telkom University Open Library 印尼教育机构LMS向KMS的转变——以电信大学开放图书馆为例

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Pub Date : 2023-08-12 DOI: 10.29207/resti.v7i4.4910

N. Karna, Gede Agung, Ary Wisudiawan, Ni Putu, Nurwita Pratami Wijaya, Kadek Andrean, Pramana Putra, Dewa Ayu, Putu Rahyuni

{"title":"Transforming LMS into KMS in Indonesia Educational Institution Case Study in Telkom University Open Library","authors":"N. Karna, Gede Agung, Ary Wisudiawan, Ni Putu, Nurwita Pratami Wijaya, Kadek Andrean, Pramana Putra, Dewa Ayu, Putu Rahyuni","doi":"10.29207/resti.v7i4.4910","DOIUrl":"https://doi.org/10.29207/resti.v7i4.4910","url":null,"abstract":"Library is an institution that collects printed and recorded knowledge, manages it in a particular way to meet the intellectual needs of its users through various ways of knowledge interaction. In 2021, Indonesia provided 4605 active educational institutions, ranging from universities to community academies. All these institutions are obliged to provide library to create a better learning environment, by providing source of references for each course delivered by the institution. This obligation is encouraged by the government by considering library support within the accreditation system. In this accreditation system, a library should allocate books as a source of reference to each course. This establishes a paradigm that library is where we store books and where a member of the institution may borrow, learn, and return those aforementioned books. Today, educational institution deals with not only books but also thesis, dissertation, technical report, training/workshop report, research paper, etc. Authors believe it will be prudent to leave all these documents of knowledge to librarians, by changing the library's paradigm from managing books to managing knowledge. This study proposes a model of Knowledge Management System as a transformation from Library Management System. This study also explains about expected opportunities and benefits after the transformation. \u0000 ","PeriodicalId":435683,"journal":{"name":"Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130940136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Antlion Optimizer Algorithm Modification for Initial Centroid Determination in K-means Algorithm K-means算法中初始质心确定的Antlion优化算法修正

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Pub Date : 2023-08-12 DOI: 10.29207/resti.v7i4.4997

Nanang Lestio Wibowo, M. Soeleman, A. Z. Fanani

{"title":"Antlion Optimizer Algorithm Modification for Initial Centroid Determination in K-means Algorithm","authors":"Nanang Lestio Wibowo, M. Soeleman, A. Z. Fanani","doi":"10.29207/resti.v7i4.4997","DOIUrl":"https://doi.org/10.29207/resti.v7i4.4997","url":null,"abstract":"Clustering is a grouping of data used in data mining processing. K-means is one of the popular clustering algorithms, easy to use and fast in clustering data. The K-means method groups data based on k distances and determines the initial centroid randomly as a reference for processing. Careless selection of centroids can result in poor clustering processes and local optima. One of the improvements in determining the initial centroid on the k-means method is to use the optimization method for determining the initial centroid. The modified Antlion Optimizer (ALO) method is used to improve poor clustering in the initial centroid determination and as an alternative to determining the initial centroid in the k-means method for better clustering results. The results of the research on the use of the proposed method for determining the initial centroid provide an increase in clustering compared to the usual k-means and k-means++ methods. This is evidenced by the evaluation of the Sum of Intra-Cluster distance (SICD) with UCI datasets, namely iris, wine, glass, ecoli and cancer in each method, the best SICD value was obtained in the proposed method. Then measuring the best SICD value for each method and datasets is measured by providing a ranking proving that the proposed method on the iris, wine, cancer datasets gets the first rank and on the ecoli and glass datasets the proposed method and the k-means++ method both get the first rank. From the average ranking value, the proposed method is ranked first which provides evidence that the proposed method can improve clustering results and can be an alternative method for determining the initial center of a cluster using the k-means method. \u0000 ","PeriodicalId":435683,"journal":{"name":"Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127415865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Density Based Spatial Clustering of Applications and Spatial Pattern Analysis In Mapping the Distribution of ISPA Disease in Bireuen Regency 基于密度的空间聚类应用及空间格局分析在碧伦县ISPA疾病分布制图中的应用

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Pub Date : 2023-06-05 DOI: 10.29207/resti.v7i3.4936

Mutammimul Ula, Tsania Asha Fadilah Daulay, Richki Hardi, Sujacka Retno, Angga Pratama, Ilham Sahputra

{"title":"Density Based Spatial Clustering of Applications and Spatial Pattern Analysis In Mapping the Distribution of ISPA Disease in Bireuen Regency","authors":"Mutammimul Ula, Tsania Asha Fadilah Daulay, Richki Hardi, Sujacka Retno, Angga Pratama, Ilham Sahputra","doi":"10.29207/resti.v7i3.4936","DOIUrl":"https://doi.org/10.29207/resti.v7i3.4936","url":null,"abstract":"ISPA is an infectious disease transmitted through the air. ISPA disease can be detected through the regional distribution maps of disease. ISPA disease can be prevented in the early detection before the disease gets worse. This can prevent the spread of the disease further. Data on ISPA cases in 2019 to 2021 in Bireuen Regency from the medical records of dr. Fauziah Bireuen from an average of 13.18 to 59.24 per year. This research aims to determine the clusters of ISPA spread areas across the district of Bireuen and to analyze the distribution of disease patterns with Spatial Pattern Analysis and to map the respiratory diseases in each area with Flexibly Shaped Spatial Scan Statistics. The methodology in this research are collecting the data at the hospital for each area of ISPA patients and the data processed by using DBSCAN to obtain the cluster points on the map. The result of the DBSCAN model are clusters. The cluster results are next processed with the distribution of hotspot point on the map using the Spatial Pattern Analysis model, and the map of the prone of the spread of ISPA in Bireuen Regency is processed by using the Flexibly Shaped Spatial Scan Statistic method. Information from the map obtained that the first 4 clusters of the area are formed, namely 3 clusters and 1 outlier. Cluster 1 and outliers consist of 6 sub-districts, cluster 2 consist of 4 sub-districts and cluster 3 only has 1 sub-district. There are 6 hotspot areas in 2019, 5 hotspot areas in 2020, and 6 hotspot areas in 2021. The results of this research are each ISPA disease clustering map shows the results of the ISPA distribution on the map and areas that are prone to ISPA disease in Bireuen Regency are Jeunieb and Peusangan","PeriodicalId":435683,"journal":{"name":"Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129064003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Pedestrian Detection System using YOLOv5 for Advanced Driver Assistance System (ADAS) 基于YOLOv5的高级驾驶辅助系统(ADAS)行人检测系统

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Pub Date : 2023-06-05 DOI: 10.29207/resti.v7i3.4884

S. M. Nasution, F. M. Dirgantara

{"title":"Pedestrian Detection System using YOLOv5 for Advanced Driver Assistance System (ADAS)","authors":"S. M. Nasution, F. M. Dirgantara","doi":"10.29207/resti.v7i3.4884","DOIUrl":"https://doi.org/10.29207/resti.v7i3.4884","url":null,"abstract":"The technology in transportation is continuously developing due to reaching the self-driving vehicle. The need of detecting the situation around vehicles is a must to prevent accidents. It is not only limited to the conventional vehicle in which accident commonly happens, but also to the autonomous vehicle. In this paper, we proposed a detection system for recognizing pedestrians using a camera and minicomputer. The approach of pedestrian detection is applied using object detection method (YOLOv5) which is based on the Convolutional Neural Network. The model that we proposed in this paper is trained using numerous epochs to find the optimum training configuration for detecting pedestrians. The lowest value of object and bounding box loss is found when it is trained using 2000 epochs, but it needs at least 3 hours to build the model. Meanwhile, the optimum model’s configuration is trained using 1000 epochs which has the biggest object (1.49 points) and moderate bounding box (1.5 points) loss reduction compared to the other number of epochs. This proposed system is implemented using Raspberry Pi4 and a monocular camera and it is only able to detect objects for 0.9 frames for each second. As further development, an advanced computing device is needed due to reach real-time pedestrian detection. \u0000 ","PeriodicalId":435683,"journal":{"name":"Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115735654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sentiment Analysis of Public Acceptance of Covid-19 Vaccines Types in Indonesia using Naïve Bayes, Support Vector Machine, and Long Short-Term Memory (LSTM) 基于Naïve贝叶斯、支持向量机和LSTM的印尼公众对新型冠状病毒疫苗接受度情绪分析

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Pub Date : 2023-06-05 DOI: 10.29207/resti.v7i3.4737

Dinar Ajeng Kristiyanti, Sri Hardani

{"title":"Sentiment Analysis of Public Acceptance of Covid-19 Vaccines Types in Indonesia using Naïve Bayes, Support Vector Machine, and Long Short-Term Memory (LSTM)","authors":"Dinar Ajeng Kristiyanti, Sri Hardani","doi":"10.29207/resti.v7i3.4737","DOIUrl":"https://doi.org/10.29207/resti.v7i3.4737","url":null,"abstract":"The Covid-19 vaccination is a government program during the pandemic to create herd immunity so that people become more productive in their activities. In Indonesia, the Covid-19 vaccination campaign employs a range of vaccines and has sparked a range of responses from the public on social media, particularly Twitter. Users can tweet and communicate with one another on the social networking site Twitter. This study uses a Sentiment Analysis technique using the Nave Bayes (NB), Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) algorithms to conduct a sentiment analysis of public acceptance of the type of Covid-19 vaccine used in Indonesia using Twitter data. Various types of vaccines in Indonesia include Sinovac, Vaksin Covid-19 Bio Farma, AstraZeneca, Pfizer, Moderna, Sinopharm, Novavax, Sputnik-V, Janssen, Convidencia, Zifivax, often confuse the public in determining the objectivity of this opinion. In addition, theoretically, this study also seeks to contrast the NB, SVM, and LSTM algorithms with experimental techniques to obtain the best algorithm model. The stages of the research involved gathering information based on Twitter user opinions about the type of Covid-19 vaccine on Twitter from January 2021 to January 2022. The researcher used Indonesian language tweet data with the keywords #vaksincorona, #vaksincovid19, #vaksinasi, #ayovaksin, #lawancovid19, and #vaksinindonesia. Before modelling, the pre-processing stage consists of case folding, tokenizing, filtering, stemming, and word weighting using TF-IDF. After that, model testing was carried out using Cross Validation with the Python programming language, and evaluation and validation of the test results using the Confusion Matrix. The results showed that the accuracy score of the SVM method for the best model was 84.89%, while for the Naïve Bayes and LSTM algorithms, they were 84.65% and 82.97%, respectively.","PeriodicalId":435683,"journal":{"name":"Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130265382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0