Nurmaylina, Vivi
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Perancangan Sistem Informasi Posyandu Berbasis Website pada Posyandu Edelweis Babelan Desa Bahagia Kabupaten Bekasi Bimantoro, Dava Sevtiandra; Raihan, Farid; Siahaan, Bangun; Nurmaylina, Vivi; Sarimole, Frencis Matheos
AJAD : Jurnal Pengabdian kepada Masyarakat Vol. 4 No. 1 (2024): APRIL 2024
Publisher : Lembaga Mitra Solusi Teknologi Informasi (L-MSTI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59431/ajad.v4i1.283

Abstract

The use of digital technology, primarily through the use of the Posyandu website, has positively impacted the efficiency and effectiveness of recording and reporting Posyandu activities. This article explores digitalization's role in optimizing community health services at the Posyandu level. The author used the data collection method: direct observation of the Edelweis Posyandu activities. The results show that implementing the Posyandu website has simplified the process of recording and reporting activities, reduced the workload for cadres, and increased data accessibility for health workers. Apart from that, digitalization also helps increase cadres' digital literacy, enabling them to use technology more effectively in their tasks. In conclusion, the digitization of Posyandu has excellent potential to strengthen its role as a provider of integrated and efficient public health services.
Sentiment Analysis of Social Media X Users Towards Legislators Engaged in Online Gambling Using Naïve Bayes Algorithm Nurmaylina, Vivi; Akbar, Yuma
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 3 (2024): DECEMBER 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i3.3079

Abstract

This research analyzes public feelings toward legislative members participating in online gambling applying the Naïve bayes classification technique. The collected data were processed, labeled, cleaned, preprocessed, and classified using RapidMiner Studio software, while conducting the sentiment analysis according to a systematic approach from each of those steps described above, namely, data crawling, cleaning, preprocessing, and classification of the Twitter data. Sentiment distribution yielded 286 negative and 90 positive sentiments with a prediction accuracy of 73.10%. These findings illustrate an overwhelmingly negative public response to this behavior and the expectation society has for legislators as public figures.