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All Journal Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) INTERNATIONAL JOURNAL OF SOCIETY REVIEWS (INJOSER) International Journal of Teaching and Learning (INJOTEL)
Mohammad Aldinugroho Abdullah
Universitas Nasional
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Mathematics Social Sciences Other

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Analisis Sentimen Mengenai Vaksin Sinovac di Media Sosial Twitter Menggunakan Metode Naïve bayes Classification Rima Tamara Aldisa; Azizah Azizah; Mohammad Aldinugroho Abdullah
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 6 No 3 (2022): JULY-SEPTEMBER 2022
Publisher : KITA Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v6i3.479

Abstract

The Sinovac vaccine is an example of a type of inactivated vaccine. The government bought Sinovac, Novavax, AstraZeneca, and Pfizer vaccines. This vaccine is used to treat the Covid-19 pandemic. This vaccine is used to treat the Covid-19 pandemic. The role of the Indonesian people in expressing and stating the pros and cons often involves public services that are easily accessible by many people, namely social media, one of which is Twitter. This can be used as material to analyze who produces data in support of decisions. The technique that can be used is sentiment analysis. The method used in this study is the Naïve bayes Classification. The purpose of this study was to determine the value of sentiment analysis on the Sinovac vaccine using the Naive Bayes Classification method on Twitter social media using Indonesian. The result of this research is the final probability value based on the condition 0.000002765 for positive and 0.000000359 for negative. A response with a positive comment has a greater probability of a response with a negative comment.
Sistem Pendukung Keputusan untuk Menentukan Hasil Bisnis Pujasera Terbaik dimasa Pandemi Covid 19 dengan Metode Fuzzy Tahani dan Simple Additive Weighting (SAW) berbasis Website (Studi Kasus: Pujasera Hangout Salihara) Mohammad Aldinugroho Abdullah; Iskandar Fitri; Novi Dian Nathasia
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 5 No 1 (2021): JANUARI-MARET 2021
Publisher : KITA Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v5i1.202

Abstract

Pujasera Hangout Salihara is a company engaged in the business of serving food and beverages for the public. During the Covid 19 pandemic, there were many impacts, one of which was in the restaurant business sector. With the Fuzzy database method Tahani model and Simple Additive Weighting are used in determining the destination of the favorite food menu at the food court according to customer desires accurately, quickly, and easily understandable, helping the food court owner and tenant of the food court at the salihara hangout food court in providing food menu recommendations, the most popular drink menu and the highest and lowest rating of each food court in a week or a month. The method of Fuzzy database model Tahani and Simple Additive Weighting are applied in making a decision support system with stages determined by the researcher. The result of the Decision Support System is a system that can assist in making decisions that are carried out accurately and according to the desired goals. In applications that have been built, the results are based on the value of the degree of membership and the truth value of the calculation process in the application. Testing is done by means of the BlackBox testing.
GROUPING PRODUCTS IN SUPERMARKETS USING THE K-MEANSALGORITHM Mohammad Aldinugroho Abdullah; Rima Tamara Aldisa
INTERNATIONAL JOURNAL OF SOCIETY REVIEWS Vol. 1 No. 8 (2024): INTERNATIONAL JOURNAL OF SOCIETY REVIEWS (INJOSER)
Publisher : Adisam Publisher

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Abstract

Supermarket, a shop that provides various products for use, especially for daily life, including food products, drinks, kitchen utensils, clothing, electronic equipment and others. It is not surprising that many mothers now choose to shop for daily necessities at supermarkets rather than the nearest stall. With self-service, it can make it easier for us consumers to buy different products in one place. So there is no need to change shops to buy other items. Of course, products have different levels of popularity, not only because of taste but also because of price. The number of products provided by supermarkets is relatively large and if you look at the level of popularity, it is difficult to determine, so data mining is needed. The data mining used is clustering. After implementing and using the K-Means algorithm in clustering (grouping) supermarket products, there are two centroids used (C1 for Not Selling Products and C2 for Best Selling Products). The initial centroid value is determined randomly, while the subsequent centroids are adjusted according to the results of calculating the closest distance (maximum distance). The final result obtained is that the best-selling group consists of 12 products, namely products with serial numbers 1, 4, 5, 6, 7, 8, 9, 11, 14, 15, 16 and 17. Meanwhile, the product group does not There are 6 best-selling products, namely products with serial numbers 2, 3, 10, 12, 13 and 18.
THE EFFECTIVENESS OF GOOGLE CLASSROOM IN INCREASING STUDENT UNDERSTANDING AND INTERACTIVITY IN ONLINE LEARNING Rima Tamara Aldisa; Frenda Farahdinna; Mohammad Aldinugroho Abdullah
International Journal of Teaching and Learning Vol. 1 No. 9 (2024): International Journal of Teaching and Learning (INJOTEL)
Publisher : Adisam Publisher

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Abstract

In the current digital era, the use of online learning platforms such as Google Classroom is becoming increasingly relevant in supporting an effective learning process. This research aims to investigate the effectiveness of Google Classroom in increasing learning interactions and student involvement in learning. The research method used was an experiment with a pretest and posttest design as a group. Data was collected through the results of student engagement questionnaires, observations of learning interactions, and in-depth interviews with students. The results of the research show that the use of Google Classroom significantly increases learning interactions, student understanding in using technology and student engagement compared to conventional learning methods. Students in the experimental group showed increased motivation to learn, participation in class discussions, and collaboration between students. These findings confirm the potential of Google Classroom as an effective supporting tool in improving the quality of learning interactions and student involvement in the learning process. This research provides insight for educators and helps students to better utilize online learning technology in designing and delivering interactive and interesting learning materials.
Co-Authors Azizah Azizah Frenda Farahdinna Iskandar Fitri Novi Dian Nathasia Rima Tamara Aldisa Rima Tamara Aldisa