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Journal : Infotekmesin

SIMPENDI-PHB: Sistem Informasi Manajemen Pengelolaan Penelitian dan Pengabdian Masyarakat Berbasis Website Dairoh; Ginanjar Wiro Sasmito; M Fikri Hidayatullah; Ratono; Fina Yuniarti; Riszki Wijayatun Pratiwi; Dwi Intan Af’idah; Sharfina Febbi Handayani
Infotekmesin Vol 13 No 1 (2022): Infotekmesin: Januari, 2022
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v13i1.861

Abstract

Document management for submission of proposals, assessments, and reports on research and service activities at the Research and Community Service Center of the Harapan Bersama Polytechnic is still manually carried out and not computerized. To facilitate the process of implementing, managing research and service management as well as supervising research, service, publication, and evaluation activities, an information system was built using the waterfall method. This system has been created under the name SIMPENDI PHB by involving 9 actors. The result is that the system runs according to the functions of the actors involved. This system was tested with black box and usability. The results of system testing (black box) is that 9 actors involved in this system have been running according to the function of each actor. As for the usability results by involving several actor menus, namely as many as 30 users consisting of 6 managers in related units, 4 users from the assessment team, and the proposer users (lecturers) is a UI/UX score of 86% in the "Very good" category.
Sistem Informasi Manajemen Legalisir Online Berbasis Website Dairoh Dairoh; Riszki Wijayatun Pratiwi; Dwi Intan Af’idah; Sharfina Febbi Handayani; Ferian Andhika Toarsi
Infotekmesin Vol 15 No 1 (2024): Infotekmesin: Januari, 2024
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v15i1.1778

Abstract

Management and submission of legalization at the Harapan Bersama Polytechnic have not been computerized. The legalization process is carried out face-to-face by leaving legalized documents and returning them when the legalized documents are ready. This process is problematic because going to campus requires quite a long time, and there is no certainty or travel history from the management of legalized documents. The research objective is to build a legalization system to facilitate the process of implementing legalization in BAA units using the website-based waterfall method. This system validates alumni data in the form of NIM at registration, processes payments through payment gateways, pays for selected shipments, and tracks travel history from the submitted legalized documents. This system is called Simaleja, and there are two actors involved. As a result, the system runs according to the functions of the actors involved and has been tested. As for the results of the black box (actor) test, the system has been running according to the function of each actor, and the UI/UX usability results were obtained for 38 users, 82% of whom fall into the very good category.
Klasifikasi Opini Publik di Twitter Terhadap Bakal Calon Presiden Indonesia Tahun 2024 Menggunakan LSTM Secara Realtime Berbasis Website Muhammad Rizki; Muhammad Fikri Hidayattullah; Dwi Intan Af'idah
Infotekmesin Vol 14 No 2 (2023): Infotekmesin: Juli, 2023
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v14i2.1908

Abstract

The analysis of public opinions from Indonesian netizens regarding the potential presidential candidates for Indonesia in 2024 on Twitter is challenging. Human-based classification of the candidates on Twitter has limitations as it requires expertise and a considerable amount of time to process the data. Therefore, a system that provides realtime visualization of public opinion classification is necessary. Previous research only focused on model evaluation, while this study aims to implement the best model on a website. The objective of this research is to develop a system for monitoring the Twitter-based public opinion classification of the potential presidential candidates for Indonesia in 2024 within specific time frames. The training process utilizes the LSTM method, resulting in a model with an accuracy of 76%. Parameters such as batch size, dropout, and learning rate were tested. The data used in this study was obtained by crawling Twitter using the keywords Ganjar Pranowo, Anies Baswedan, and Prabowo Subianto. The LSTM model was then implemented in a website-based system that generates a dashboard with features such as a color-coded map displaying the highest levels of positive sentiment for each candidate in each province, the overall classification count for each candidate, and filters for sentiment classification based on province and specific time frames.