Alvien Muhammad Kannabi, Norhikmah M.Kom (SCOPUS ID: 57216417658)
{"title":"Implementation of the Fisher-Yates Shuffle Game Algorithm in Learning Hijaiyah Letters","authors":"Alvien Muhammad Kannabi, Norhikmah M.Kom (SCOPUS ID: 57216417658)","doi":"10.32520/stmsi.v11i3.2053","DOIUrl":"https://doi.org/10.32520/stmsi.v11i3.2053","url":null,"abstract":"In 2020 the Indonesian government and the world health agency (WHO) declared the Covid-19 virus a pandemic. As a result, some activities cannot be carried out offline, especially in learning activities. The government requires that learning activities be carried out online or online. The number of children who really need help to understand the learning material provided, for example, is a game which is one of the media in learning for children to make it more interesting in its delivery. With the aim of providing basic Koran education for early childhood with interactive and fun games and the application of the Fisher-Yates Shuffle algorithm in the quiz feature that is used to randomize hijaiyah letter questions. Stages of research 1). Collecting data using library studies, namely Iqra books and related research papers. 2). Game design using story board. 3). Application of the Fisher-Yates Shuffle algorithm. 4). Testing the Fisher-Yates algorithm, black box and question. The results obtained from 10 question and 96 respondents resulted in 89.5% concluding that the hijaiyah letter game uses the Android-based Construct 2 game engine. implementation of the Fisher-Yates Shuffle algorithm on the quiz feature is feasible to use.","PeriodicalId":32367,"journal":{"name":"Sistemasi Jurnal Sistem Informasi","volume":"219 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75886257","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}
{"title":"Survey of IT Governance Influence on Digital Transformation and Bank Organization Performance","authors":"Nurul Afifah, Rahmat Mulyana, L. Abdurrahman","doi":"10.32520/stmsi.v11i3.2179","DOIUrl":"https://doi.org/10.32520/stmsi.v11i3.2179","url":null,"abstract":"The disruption of emerging technologies, startups, changes in stakeholders' behavior, and the COVID-19 pandemic acceleration have forced many companies to perform a digital transformation (DT). However, the facts show many related investment failures because of poor governance practices. Previous research has proven that IT governance (ITG) has a significant role in improving organizational performance. Unfortunately, few studies still show the ITG mechanisms, structures, processes, and relational influence on successful DT. Recent research has found the hybrid ITG influence on DT, but not their relationship to organizational performance. Therefore, we hypothesized the traditional and agile/adaptive ITG mechanisms influence DT The survey method was used by distributing a questionnaire to the organization's three lines of defense, from the top-level and IT managers (planners, architects, developers, operations, services, security, and quality assurance) to risk managers and internal auditors. The model was analyzed using Structural Equation Modeling assisted by SmartPLS. Bank B was chosen because the banking industry is greatly affected in the digital era along with the rise of fintech. The results showed a significant correlation between agile/adaptive and traditional ITG mechanisms on DT and a positive impact on organizational performance. This manuscript contributes to researchers exploring the ITG mechanism details for successful DT and practitioners to implement them in related industries.","PeriodicalId":32367,"journal":{"name":"Sistemasi Jurnal Sistem Informasi","volume":"100 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74724037","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}
Ronald Adrian, Anni Karimatul Fauziyyah, Sahirul Alam
{"title":"Continuous Integration/ Continuous Delivery Optimization on Network Automation using Gray Wolf Optimizer","authors":"Ronald Adrian, Anni Karimatul Fauziyyah, Sahirul Alam","doi":"10.32520/stmsi.v11i3.2322","DOIUrl":"https://doi.org/10.32520/stmsi.v11i3.2322","url":null,"abstract":"Continuous Integration/ Continuous Delivery is the latest method used in network automation. In-network programming has helped network admins a lot in managing all their devices. One of the real-time networks needs to force network admins to be able to provide data quickly. Deployment speed can be increased to provide up-to-date data or network configuration. To tackle these problems, we propose implementing the GWO algorithm in the Continuous Integration/Continuous Delivery process. This algorithm is proven to be superior in the speed of finding the value of the objective function compared to other similar algorithms. The results obtained indicate that the convergence time is faster by 74%. This value has an impact on increasing program deployment speed by 41.2%. These results indicate that the GWO algorithm can be an alternative to increasing the speed of Continuous Integration/ Continuous Delivery.","PeriodicalId":32367,"journal":{"name":"Sistemasi Jurnal Sistem Informasi","volume":"86 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83264131","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}
Sonya Meitarice, M. Sari, Meidiana Meidiana, Rosa Adhawiyah, Vira Febriyanti
{"title":"Information System Strategic Planning DPMPTSP Riau Province using Ward and Peppard Method","authors":"Sonya Meitarice, M. Sari, Meidiana Meidiana, Rosa Adhawiyah, Vira Febriyanti","doi":"10.32520/stmsi.v11i3.2101","DOIUrl":"https://doi.org/10.32520/stmsi.v11i3.2101","url":null,"abstract":"metode and Peppard metode tersebut mremiliki kerangka berfokus teknologi, berfokus kebutuhan organisasi. rekomendasi portofolio digunakan Abstract Appropriate application of information systems and information technology can increase efficiency and effectiveness and create a competitive advantage. Dinas Penanaman Modal dan Pelayanan Terpadu Satu Pintu (DPMPTSP) Riau Province is one of the agencies for providing integrated licensing services spread throughout the Provinces and Regencies/Cities in Indonesia. DPMPTSP is required to carry out the task of licensing services that are fast, accurate, in accordance with existing regulations and transparent costs to the entire community. Thus, it is necessary to design a strategic information system so that the implementation of IS/IT is in line with the vision and mission as well as the company's goals. This research uses the Ward and Peppard method because the method has a fairly clear and complete framework besides that it does not only focus on technology, but also focuses on the business needs of the organization. The result of this research are recommendations for future portfolio applications that can be used within the next four years.","PeriodicalId":32367,"journal":{"name":"Sistemasi Jurnal Sistem Informasi","volume":"B1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85206891","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}
{"title":"Exploratory Data Analysis (EDA): A Study of Olympic Medallist","authors":"Noviyanti T M Sagala, Fonggi Yudi Aryatama","doi":"10.32520/stmsi.v11i3.1857","DOIUrl":"https://doi.org/10.32520/stmsi.v11i3.1857","url":null,"abstract":"Olympic games are one of the most popular international sports events in the world where thousands of athletes participate in different types of sports categories. The winner has rewarded a medal (Bronze, Silver, Gold) according to the rank. An analysis can be carried out on the Olympic data to understand the changes in medalists over time. Furthermore, it helps to determine the progress of participating countries and strategies that can be used in the future. Exploratory Data Analysis (EDA) is a method for analyzing and summarizing the properties of data, either in graphical or non-graphical, to get insights from the dataset being studied. The approaches can be classified as univariate, bivariate, or multivariate. EDA is widely used in various domains including sport. The main purpose of this study is to analyze the changes of Olympic Medalist data throughout the provided years in the form of univariate, bivariate, and multivariate analysis. This analysis provides detailed, statistical, and interesting information about the changes in medal winners from time to time.","PeriodicalId":32367,"journal":{"name":"Sistemasi Jurnal Sistem Informasi","volume":"48 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79554770","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}
{"title":"Analysis of SOR Framework Concerning Online Shopping Value and Web Satisfaction on E-Commerce","authors":"Tika Sartika, M. F. Aransyah","doi":"10.32520/stmsi.v11i3.1941","DOIUrl":"https://doi.org/10.32520/stmsi.v11i3.1941","url":null,"abstract":"Online shopping transactions on e-commerce in Indonesia are increasing. E-commerce players continue to attempt and develop strategies to attract users in shopping online. This research analyze through the SOR (Stimulus, Organism, Response) framework on the driving factors for e-commerce users, especially Shopee in making online purchases regarding online shopping value and web satisfaction provided on Shopee services, problems related to the high number of e-commerce shopping. and the amount of impulsive spending money in the large number from big sale programs that are displayed. The approach of this research was qualitative through descriptive analysis of the results of questionnaires obtained by 385 respondents in Indonesia using the Guttman Scale and interviewing 10 informants as supporting empirical data. The results of this study was reproducibility coefficient is 0.94 and a scalability coefficient is 0.63, with reliable test using the Richard Kuderson formula (KR20) is 0.2 which was declared valid and reliable. In addition, the most important driving factor for purchase intention was obtained from this study, namely the free shipping feature with a percentage of 48.1%. Recommendations for future research is to focus on research on the peak moments of online shopping, such as the Ramadan big sale, and the celebration of the national shopping day and others. In addition, expanding respondents and informants, in order to expand the study and re-design related to the framework used.","PeriodicalId":32367,"journal":{"name":"Sistemasi Jurnal Sistem Informasi","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74878449","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}
Ullya Mega Wahyuni, J. Rahmadoni, Afriyanti Dwi Kartika, Hafizah Hanim
{"title":"The Combination of R&D and MDLC Models in WEB-Based Interactive Learning Media","authors":"Ullya Mega Wahyuni, J. Rahmadoni, Afriyanti Dwi Kartika, Hafizah Hanim","doi":"10.32520/stmsi.v11i3.2085","DOIUrl":"https://doi.org/10.32520/stmsi.v11i3.2085","url":null,"abstract":"The Technopreneur is a core course offered in semester IV (four) in the Information Systems department, Faculty of Information Technology, Unand. Based on observations, the learning process using the lecture method via video conference makes students more inclined to be subordinated. At the same time, the lecturer position tends to appear as a one-man show. The problems make students more-passive and weak in the creative power. To attract students' interest in learning, it is necessary to develop interactive learning media. This research aims to produce a WEB-based interactive learning media with a combination of R&D and MDLC model. Learning media is designed based result course learning process on the previous semester and user needs. Based on the results of system functionality testing, it shows 100% of the features are functioning properly. While testing user satisfaction on multimedia elements from 20 respondents with the Mean Opinion Score (MOS) scheme was obtained at 4.11, which means the learning media is quite good. For interactive elements, it gets a score of 4.31, which means it is included in the category ready to be applied in the Technopreneur class.","PeriodicalId":32367,"journal":{"name":"Sistemasi Jurnal Sistem Informasi","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77594374","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}
{"title":"Comparison of Bagging and Adaboost Methods on C4.5 Algorithm for Stroke Prediction","authors":"Nur Diana Saputri, Khalid Khalid, Dwi Rolliawati","doi":"10.32520/stmsi.v11i3.1684","DOIUrl":"https://doi.org/10.32520/stmsi.v11i3.1684","url":null,"abstract":"cerebrovascular disease secara cepat dapat menyebabkan kematian. Tujuan dari penelitian ini adalah untuk mengatasi masalah tersebut adalah membuat model prediksi berbasis machine learning untuk membantu ahli medis menangani penyakit stroke untuk mengurangi risiko kematian. Metode yang diterapkan untuk penelitian ini adalah menerapkan metode klasifikasi algoritma C4.5 serta metode bagging dan Adaboost dari Ensemble Learning . Data stroke diolah menggunakan 2 Abstract Stroke is a non-communicable disease and is very dangerous because of functional disorders of the brain caused by blockage of blood circulation. This disease is classified as a cerebrovascular disease because it requires treatment for 24 hours, if not treated quickly it can cause death. The purpose of this research is to overcome this problem is to create a machine learning-based prediction model for medical experts in dealing with diseases to help reduce the risk of death. The method applied for this research is to apply the C4.5 algorithm classification method as well as the bagging and Adaboost methods from Ensemble Learning. Stroke data is processed using 2 stages of data processing, namely the data cleaning","PeriodicalId":32367,"journal":{"name":"Sistemasi Jurnal Sistem Informasi","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90277007","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}
Suardinata Suardinata, Rusdisal Rusmi, Muhammad Amrin Lubis
{"title":"Determining Travel Time and Fastest Route Using Dijkstra Algorithm and Google Map","authors":"Suardinata Suardinata, Rusdisal Rusmi, Muhammad Amrin Lubis","doi":"10.32520/stmsi.v11i2.1836","DOIUrl":"https://doi.org/10.32520/stmsi.v11i2.1836","url":null,"abstract":"Dijkstra's algorithm is commonly used to determine the shortest route connecting a point as a starting node to another which acts as the end node. In this study, the UNP student dormitory acted as the starting node, while the library which is frequently visited by students was sampled from the campus as the end node. Due to the fact that students generally live around campus and move on foot, an alternative route is needed to determine the fastest travel time. Therefore, this study aims to determine the route with the fastest travel time from the start to the end of nodes using the Dijkstra algorithm, in comparison with the route displayed by Google Map. Data were obtained from Google Map, which showed the availability of many routes with the possibility of students taking the fastest travel time. The result showed that the fastest route using the Dijkstra algorithm and Google Map were 14 and 3 alternatives at 15 and 21 minutes intervals. Based on these data, it is concluded that the travel time through the fastest route obtained using the Dijkstra algorithm was 6 minutes faster than data found in the Google Map.","PeriodicalId":32367,"journal":{"name":"Sistemasi Jurnal Sistem Informasi","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85248983","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}
{"title":"Hyperparameter Tuning on Classification Algorithm with Grid Search","authors":"Wahyu Nugraha, A. Sasongko","doi":"10.32520/stmsi.v11i2.1750","DOIUrl":"https://doi.org/10.32520/stmsi.v11i2.1750","url":null,"abstract":"Saat ini algoritma machine learning terus dikembangkan untuk bisa dilakukan optimasi dengan berbagai metode agar menghasilkan model dengan performa terbaik. Salah satu cara optimasi dengan menerapkan tuning hyperparameter . Pada Supervised learning atau klasifikasi sebagian besar algoritmanya memiliki hyperparameter . Tuning hyperparameter merupakan arsitektur dari deep learning untuk meningkatkan performa dari model prediksi. Metodologi hyperparameter yang populer diantaranya adalah Grid Search . Grid Search menggunakan Cross Validation memberikan kemudahan dalam menguji coba setiap parameter model tanpa harus melakukan validasi manual satu persatu. Pada penelitian ini akan menggunakan metode dalam optimasi hyperparameter yaitu Grid Search . Tujuan dari penelitian ini ingin mengetahui optimasi terbaik dari hyperparameter terhadap 7 algoritma klasifikasi machine learning. Validasi terhadap hasil eksperimen menggunakan metrik pengukuran Mean Cross Validation . Dari hasil eksperimen menunjukkan bahwa model XGBoost memperoleh nilai terbaik sedangkan Decision tree memiliki nilai terendah. Abstract Currently, machine learning algorithms continue to be developed to perform optimization with various methods to produce the best-performing model. In Supervised learning or classification, most of the algorithms have hyperparameters. Tuning hyperparameter is an architecture of deep learning to improve the performance of predictive models. One of the popular hyperparameter methodologies is Grid Search. Grid Search using Cross Validation provides convenience in testing each model parameter without having to do manual validation one by one. In this study, we will use a method in hyperparameter optimization, namely Grid Search. The purpose of this study is to find out the best optimization of hyperparameters against 7 machine learning classification algorithms. Validation of experimental results using the Mean Cross Validation. The experimental results show that the XGBoost model gets the best value while the Decision tree has the lowest value.","PeriodicalId":32367,"journal":{"name":"Sistemasi Jurnal Sistem Informasi","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76157219","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}