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All Journal JTIK (Jurnal Teknik Informatika Kaputama) Kohesi: Jurnal Sains dan Teknologi
Meirynda Lastika Rahimsyah
Unknown Affiliation
Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Education Energy Engineering Other

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ANALISIS TERHADAP APLIKASI WHATSAPP DAN LINE MENGGUNAKAN METODE USABILITY DALAM TEKNOLOGI KOMUNIKASI Meirynda Lastika Rahimsyah; Azmi Nurfauziah Hayati; Rahmi Nurul Arapah
JTIK (Jurnal Teknik Informatika Kaputama) Vol 5, No 2 (2021): Volume 5, Nomor 2 Juli 2021
Publisher : STMIK KAPUTAMA

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Abstract

The mobile messenger application is a messaging application that is currently very often used by all humans to make communication easier and even a necessity for humans, because from this mobile messenger application we get information, namely the WhatsApp and LINE applications. In using the mobile messenger application, often some applications cannot use it efficiently and effectively, such as how to use it is too complicated, it becomes a consideration for users in using the mobile messenger application. This study aims to compare the two mobile messenger applications, namely the WhatsApp and LINE applications, assessed from their effectiveness and efficiency, as well as the interests of these mobile messenger users. And from the results of the research that we tried on 40 respondents using the questionnaire method that the WhatsApp mobile messenger has a high value compared to LINE with a total of 92% of respondents and the remaining 8% for the level of effectiveness and efficiency and from the level of most users all respondents use the Whatsapp application, 5 of them use the LINE application.
ANALISIS ALGORITMA FP-GROWTH DAN APRIORI UNTUK MENEMUKAN MODEL ASOSIASI TERBAIK PADA DATASET ONLINE RETAIL Meirynda Lastika Rahimsyah; Yudi Ramdhani
Kohesi: Jurnal Sains dan Teknologi Vol. 3 No. 1 (2024): Kohesi: Jurnal Sains dan Teknologi
Publisher : CV SWA Anugerah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.3785/kohesi.v3i1.2866

Abstract

In the digital era, the online retail industry is growing rapidly and is becoming an important sector. However, challenges arise in the analysis of sales transaction data on the Online Retail dataset. This study aims to overcome problems in the analysis of sales transaction data in the Online Retail dataset. The main focus includes selecting the optimal association algorithm between FP-Growth and Apriori, identifying relevant association models on complex datasets, and the efficiency and performance of algorithms in processing sales transaction data. The method used is association data processing using the FP-Growth and Apriori algorithms. Implementation of the association rule involves adding a lift metric as a measure of association strength. Measurement of processing time is also carried out to determine the efficiency of implementation. The results showed that FP-Growth and Apriori could produce an association model with the same frequent itemset and value matrix, namely a support value of 0.12 and a confidence value of 0.96, but there were differences in the resulting model order. The Apriori algorithm produces a model with the highest support value at index 18, while FP-Growth at index 10. In addition, the FP-Growth algorithm shows an advantage in faster processing time (0.004 seconds) compared to Apriori (0.007 seconds). This research provides a better understanding of the use of association algorithms in the context of the online retail industry.
Co-Authors Azmi Nurfauziah Hayati Rahmi Nurul Arapah Yudi Ramdhani