Web-based STMIK AKBA Student Attendance Information System by Making QR Codes an Auxiliary Medium

Iskandar, Akbar; Rahim, Robbi; Matturungan, Harfawan; Mansyur,

doi:10.56134/jst.v1i2.22

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…Each phase in the Waterfall Method is well-defined, allowing for clear documentation and understanding of the project's progress. This approach is particularly suitable for projects with wellestablished and stable requirements, providing a comprehensive framework for planning, execution, and evaluation throughout the development lifecycle of the Web-Based Student Payment Administration Information System (Iskandar et al, 2022), (Wijanarko & Pangestuti, 2021)namely as follows:…”

Section: Methodsmentioning

confidence: 99%

Web-Based Student Payment Administration Information System Using The Waterfall Method

Mahmud Mustapa,

Erwin Gatot Amiruddin,

Ezra Maharani Chaniago

et al. 2023

ceddi j. edu.

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This research aims to develop an Information System that can assist the school admin in providing the validation process for tuition payments, speed of access, and completeness in providing information. This research aims to overcome administrative challenges that often occur in schools, such as errors in recording payments, late payments, and the difficulty of tracking payment history to save time and effort previously spent processing payments manually. The research method used is the Waterfall method. This application will make it easier for the finance department to record student tuition payments method was chosen because of its deep capabilities and describes a flow or stages that run like a waterfall from one stage to the next. This approach relies heavily on good initial planning because changes made in the early stages may be difficult or expensive to implement in later stages. Based on the results of system design and black box testing, it can run well, and user responses also provide very good responses, with 94% of respondents giving very good responses and 0% saying good and quite good. Based on these findings is hoped that it can be used more efficiently and also ensure data security and can store the results of the data that has been processed.

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Section: Methodsmentioning

confidence: 99%

Web-Based Student Payment Administration Information System Using The Waterfall Method

Mahmud Mustapa,

Erwin Gatot Amiruddin,

Ezra Maharani Chaniago

et al. 2023

ceddi j. edu.

View full text Add to dashboard Cite

show abstract

“…Based on the explanation above, in this study, a Twitter sentiment analysis was carried out to classify opinion tweets and public comments (Valle-Cruz et al, 2020). The data will be processed using the SVM algorithm and generate tweets that have been classified as positive or negative (Iskandar et al, 2022;Suryandari et al, 2022). Of the 2 algorithms tested, namely Naïve Bayes and SVM as comparison SVM is the algorithm with the highest accuracy.…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis Regarding Kanjuruan Stadium Polemics Based on Public Opinion Through Twitter Social Media with SVM Classifier Method

Ghama Wellyandi,

Bayhaqy,

Chandra

et al. 2023

ceddi j. inf. syst. technol.

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Football fans are individuals who promote, motivate, and inspire football. Players of football clubs have both positive and negative fanaticism in both the real world and social media, especially on Twitter. Twitter is one of the communication media. Attracting people worldwide, Twitter saw a record increase in global users, with 313 million monthly active users in 2016 alone; the majority accessed Twitter through mobile devices, accounting for 82 percent of users. Due to the multitude of users tweeting, the latest news and comments become significant worldwide. What happens becomes the main topic, and comments received from many users trigger trending topics on Twitter. This research aims to develop a classification model to predict whether tweets from stadium events are positive or negative from fan perspectives. The classification model is based on a Twitter dataset, and sentiment analysis of tweets was conducted using the Support Vector Machine (SVM) algorithm. The next step involved preprocessing, including case-folding, cleansing, translation to English, and sentiment labeling using VADER. Subsequently, in the preprocessing step 2, tokenization, stopwords, and stemming were applied. For modeling, classic algorithms such as Naïve Bayes and Support Vector Machine were used. The highest accuracy, 87.77%, was achieved using the Support Vector Machine (SVM) algorithm.

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Web-based STMIK AKBA Student Attendance Information System by Making QR Codes an Auxiliary Medium

Cited by 2 publications

References 6 publications

Web-Based Student Payment Administration Information System Using The Waterfall Method

Web-Based Student Payment Administration Information System Using The Waterfall Method

Sentiment Analysis Regarding Kanjuruan Stadium Polemics Based on Public Opinion Through Twitter Social Media with SVM Classifier Method

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