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INDONESIA
Sistemasi: Jurnal Sistem Informasi
Published by Universitas Islam Indragiri
ISSN : 23028149     EISSN : 25409719     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Sistemasi adalah nama terbitan jurnal ilmiah dalam bidang ilmu sains komputer program studi Sistem Informasi Universitas Islam Indragiri, Tembilahan Riau. Jurnal Sistemasi Terbit 3x setahun yaitu bulan Januari, Mei dan September,Focus dan Scope Umum dari Sistemasi yaitu Bidang Sistem Informasi, Teknologi Informasi,Computer Science,Rekayasa Perangkat Lunak,Teknik Informatika
Arjuna Subject : -
Articles 1,011 Documents
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Continuous Integration/ Continuous Delivery Optimization on Network Automation using Gray Wolf Optimizer Ronald Adrian; Anni Karimatul Fauziyyah; Sahirul Alam
Sistemasi: Jurnal Sistem Informasi Vol 11, No 3 (2022): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v11i3.2322

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.
Recommendations of Thesis Supervisor using the Cosine Similarity Method Hairani Hairani; Mujahid Mujahid
Sistemasi: Jurnal Sistem Informasi Vol 11, No 3 (2022): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v11i3.2003

Abstract

During the thesis writing process, the role of the supervisor is needed so that the completion of the thesis is timely so that the quality of the thesis is maintained. One of the problems in determining the thesis supervisor for undergraduate computer science at Bumigora University is the subjectivity of the head of the study program in determining the thesis supervisor. Not only that, the selection process for supervisors is done manually so that it can take a long time and can slow down student thesis work.  Students' thesis work will be late and not on time if the thesis topic is not in accordance with the competence of the lecturer. This study aims to apply the cosine similarity method to the recommendation of a thesis supervisor for undergraduate computer science at Bumigora University. The stages of this research consist of collecting thesis documents, pre-processing text (Case Folding, Tokenization, Filtering, Stemming), word weighting with TF-IDF, implementation of the cosine similarity method, and accuracy testing. The data used are 113 thesis documents which are divided as training data as many as 90 documents and testing data 23 documents. Based on the testing data on the test, the cosine similarity method can correctly recommend 21 of 23 thesis documents with an accuracy of 91.3%. Thus, the cosine similarity method can be applied to the case of selecting a thesis supervisor for undergraduate computer science at Bumigora University because it has very good accuracy.
Analysis of SOR Framework Concerning Online Shopping Value and Web Satisfaction on E-Commerce Tika Sartika; Muhammad Fikry Aransyah
Sistemasi: Jurnal Sistem Informasi Vol 11, No 3 (2022): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v11i3.1941

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.
Naive Bayes Optimization with PSO for Predicting ICU Needs for Covid-19 Patients Lusiana Dwi Lestari; Iqbal Harifal; Taslim Taslim; Yogi Yunefri; Susi Handayani; Eka Sabna; Kursiah Warti Ningsih
Sistemasi: Jurnal Sistem Informasi Vol 11, No 3 (2022): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v11i3.2094

Abstract

Covid-19 is a global pandemic that requires a coordinated worldwide response across all national health and healthcare systems. Identifying patients who are at high risk of contracting the Covid-19 virus is important to increase awareness before patients are further infected by the Covid-19 virus which can cause severe respiratory illness that requires special treatment in intensive care units (ICU). This study aims to predict ICU needs in patients infected with the Covid-19 virus. The value results from the prediction of ICU needs are used as a reference for hospitals to meet ICU needs for patients infected with Covid-19 so that they can increase ICU supplies. The prediction will be carried out using the Naïve Bayes algorithm method with optimization using the PSO algorithm. Based on the results of the study, the population size 20 with an accuracy value of the NBC algorithm was 87.03%, population size 40 with an accuracy value of 87.28, population size 60 obtained an accuracy of 87.13%, population size 80 with an accuracy value of 87.16 % and population size 100, the results obtained are 87.26% so that each population has an increase in the accuracy value.
X-Ray Classification of Pneumonia by Neural Networks Convolution using Vgg Architecture Toni Arifin; Naufal Hidayah Surya
Sistemasi: Jurnal Sistem Informasi Vol 11, No 3 (2022): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v11i3.1597

Abstract

Pneumonia is one of the deadliest diseases, killing 2-3 million people in developed countries like the United States. Based on WHO's view, pneumonia is one of the leading causes of death in children worldwide, WHO says many children under the age of 5 have died from the disease. And in 2017, the World Health Organization (commonly known as WHO) reported that  pneumonia had claimed the lives of 800,000 children under the age of 5. This is  why  researchers developed this program,  to help the  public  diagnose pneumonia. In this study, we generated a Deep Learning model using the CNN (Convolutional neural network) approach using the VGG16 architecture for thoracic pneumonia classification and normalization. The results of this study show that the Convolution neural network method can classify chest X-ray results  with pneumonia with the highest accuracy of 0.9772
Systematic Literature Review Comparative Analysis Factors Influencing the Success of Enterprise Systems Ryo Pambudi; Dinda Ayu Aprilia; Eko Sediyono; Aris Puji Widodo
Sistemasi: Jurnal Sistem Informasi Vol 11, No 3 (2022): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v11i3.2018

Abstract

Enterprise System (ES) is a software system that helps companies operate their businesses and manage their data. The importance of achieving ES adaptation to the organization's business behavior requires a systematic effort to measure the success factors in enterprise systems so that the objectives of using ES can be achieved. Some journals examine several factors that influence the success of enterprise system.  Analysis needs to be carried out as a basis and consideration to produce measurement methods that continue to develop so as to encourage the emergence of various new successful system measurement models. Analysis and evaluation of the success factors that influence the success factors of ES can be done using Systematic Literature Review (SLR) method. This study uses the SLR method of comparison of success factors in ES, which includes journals published from 2000 to 2022. From the results of this SLR research, it can be seen that several success factors influence the success of the enterprise system.
Best Alpha for Forecasting Stock using Brown's Weighted Exponential Moving Average Muhammad Amfahtori Wijarnoko; Mochammad Kautsar Sophan; Kurniawan Eka Permana
Sistemasi: Jurnal Sistem Informasi Vol 11, No 3 (2022): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v11i3.1588

Abstract

Stocks are a very profitable type of investment, one of the most profitable stock sectors is mining. PT. Aneka Tambang Tbk (ANTM) is the largest mining company in Indonesia engaged in metals and minerals. The problem that occurs in stock investment is the fluctuation of stock prices. Stock price fluctuations occur because of the process of buying and selling shares, rumors about the company, or certain policies made by the government that can affect the company. These rapid stock price fluctuations are very dangerous for investors because these fluctuations are not always directly proportional to the company's fundamental conditions. Therefore we need a system that can perform forecasting to monitor stock price fluctuations that occur. The author uses the Brown's Weighted Exponential Moving Average (B-WEMA) method for forecasting. The data used are 241 daily PT Aneka Tambang Tbk (ANTM) in the Covid-19 era, starting January 1, 2020 - December 31, 2020. The experiment was carried out into 3 scenarios, namely using 3 months, 6 months, and 12 months data. Alpha tested is 0.1 to 0.9 (1 digit behind the comma) while the number of moving averages is 3, 5, 10, 20, 50, 100, and 200 adjusted to the amount of data. From the experiment it was found that the best forecasting was obtained in scenario 2 with the best manual alpha value having a MAPE error value of 2.84% using an alpha of 0.9 and a moving average of 100.
Comparison of Logistic Regression and Random Forest using Correlation-based Feature Selection for Phishing Website Detection Farida Farida; Ali Mustopa
Sistemasi: Jurnal Sistem Informasi Vol 12, No 1 (2023): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v12i1.1832

Abstract

The world is currently experiencing mass developments in information technology, especially during the current pandemic, which requires all of us to learn and even work online. They are triggered much crime in the internet world. One of them is stealing internet user data through a fake website built like the original or called a phishing website. In this research , a classification model is needed to detect phishing websites using the best performance from one of the logistic regression and random forest classification algorithms to overcome the rise of phishing websites in cyberspace. Classification performance can be improved using the correlation-based feature selection (CFS) method to select the most influential attribute in detecting web phishing. Based on the test results, applying the logistic regression and random forest classification algorithm in the classification of web phishing resulted in an accuracy of 93.035% and 96.834%. After feature selection with CFS, the accuracy was 92.718% and 97.015%, respectively. On the Testing, There was an increase in accuracy in RandomForest by 0.181% and an insignificant decrease in logistic regression. The test results prove that feature selection with CFS can eliminate redundant attributes and the resulting classification algorithm accuracy is not much different when the details are complete and Random Forest has accuracy better than after using CSF.Keywords: website phishing, classification, logistic regression, random forest, correlation-based 
Comparison of Ensemble Learning Method: Random Forest, Support Vector Machine, AdaBoost for Classification Human Development Index (HDI) Ressa Isnaini Arumnisaa; Arie Wahyu Wijayanto
Sistemasi: Jurnal Sistem Informasi Vol 12, No 1 (2023): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v12i1.2501

Abstract

Classification in supervised learning is a way to find patterns in database that the classes are already known. In the classification of machine learning, there is a term called ensemble classifier. The workings of the ensemble classifier aimed to improve model accuracy and optimize classification performance. This study aims to analyze the comparison of algorithms that work with ensemble learning, including Random Forest, Support Vector Machine (SVM), and AdaBoost. The data used is the Human Development Index (HDI) of districts/cities in Indonesia. Other variables that are strongly related to human development are GRDP per capita, gross enrollment rate, net enrollment rate, labor force participation rate, unemployment rate, poverty rate, poverty depth, poverty severity, and average consumption per capita. The reason for using HDI is that apart from being an important macroeconomic variable in describing the condition of human resources in Indonesia, HDI already has an obvious classification according to the Badan Pusat Statistik (BPS) so that supervised learning can be applied. Comparison of model evaluation using accuracy, specificity, sensitivity, and kappa statistics. The analysis flow starts with data preprocessing, resampling and cross-validation, then modeling using the Random Forest, Support Vector Machine (SVM), and AdaBoost algorithm. The final stage is the model evaluation by comparing the best models in the classifications of districts/ cities according to HDI. The results showed that the Random Forest model had the best performance compared to the Support Vector Machine (SVM) and AdaBoost models with an accuracy value of 85,23%, specificity of 71,63%, sensitivity of 95,05%, and kappa coefficient of 0,7698. From this research, the an ensemble classifier can be developed to help classify scores on the Human Development Index in Indonesia.Keywords: AdaBoost, Random Forest, Support Vector Machine, Ensemble Learning, Human Development Index
Determinants of the Acceptance of Ambon City Local Government Information Systems using the Technology Acceptance Model Sherly Toding; Evi Maria
Sistemasi: Jurnal Sistem Informasi Vol 12, No 1 (2023): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v12i1.2312

Abstract

The research aims to find empirical evidence on the determinants of the acceptability of Ambon City regional apparatus on implementing the Regional Government Information System (SIPD) using the Technology Acceptance Model (TAM). The research sample is SIPD users in Ambon City, with a total sample of 90 respondents. This research data was collected from a questionnaire that was measured using a Likert scale. Data were analyzed using simple linear regression analysis. The research results found empirical evidence that the perceived convenience of SIPD users affected perceptions of SIPD use in Ambon City and attitudes towards using SIPD in Ambon City, and attitudes towards SIPD use had an effect on actual SIPD use in Ambon City. This research did not find evidence of an influence between perceptions of the usefulness of SIPD on attitudes towards using SIPD in Ambon City. It means that the Ambon City regional apparatus will use SIPD in supporting development planning and regional financial management work if the apparatus perceives that SIPD is easy to use. If the regional apparatus accepts SIPD, then the regional apparatus will use the SIPD to manage development activities in Ambon City. Therefore, the government of Ambon City can be more active in socializing SIPD and holding training programs for SIPD users so that local officials can use SIPD optimally to support planning and financial management work.Keywords:. local government information system, Technology Acceptance Model, acceptability of regional apparatus.

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