Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Internet of Things and Artificial Intelligence Journal
Sitepu, Anggi Jelita
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Perancangan Model Sistem Informasi Pengaduan Masyarakat Ramadani, Wily Supi; Sitepu, Anggi Jelita; Armansyah, Armansyah
Bianglala Informatika Vol 13, No 1 (2025): Bianglala Informatika 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/bi.v13i1.25028

Abstract

Pengaduan masyarakat merupakan sarana penting untuk meningkatkan kualitas pelayanan publik. Namun, pengelolaan pengaduan yang masih bersifat tradisional di Kantor Desa Tuntungan I menyebabkan ketidakefisienan dan kurangnya transparansi. Penelitian ini bertujuan untuk merancang model sistem informasi pengaduan masyarakat berbasis mobile menggunakan metode prototipe. Proses perancangan dimulai dengan pengumpulan data melalui observasi dan wawancara untuk memahami kebutuhan pengguna. Prototipe awal dikembangkan, diuji, dan diperbaiki secara bertahap berdasarkan umpan balik yang diterima. Evaluasi kelayakan sistem dilakukan berdasarkan pengujian dan umpan balik pengguna selama proses pengembangan. Model yang dihasilkan diharapkan dapat meningkatkan efisiensi, transparansi, dan akuntabilitas dalam pengelolaan pengaduan masyarakat, serta memberikan gambaran komprehensif tentang pengajuan pengaduan, pemrosesan, dan pemantauan status pengaduan secara real-time. Berdasarkan evaluasi, kelayakan model sistem ini mencapai 88%, menunjukkan bahwa sistem dapat diimplementasikan dengan baik di Kantor Desa Tuntungan I.Kata Kunci: Pengaduan Masyarakat, Model Sistem Informasi,  Prototipe, Pelayanan Publik.
Sentiment analysis of Faculty of Science and Technology students' satisfaction with the 2024 graduation using the Naïve Bayes method Siregar, Kalfida Eka Wati; Ramadani, Wily Supi; Sitepu, Anggi Jelita; Fadil, Ulfi Muzayyanah; Furqan, Mhd.
Internet of Things and Artificial Intelligence Journal Vol. 5 No. 2 (2025): Volume 5 Issue 2, 2025 [May]
Publisher : Association for Scientific Computing, Electronics, and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/iota.v5i2.940

Abstract

Sentiment analysis of UINSU student graduation based on academic data is one of the efforts to understand the factors that affect the success of student studies. This research aims to analyze the sentiment of UINSU student graduation by utilizing academic data such as cumulative grade point average (GPA), number of credits taken, and other relevant attributes, using the Naive Bayes method. Naive Bayes was chosen because of its ability to classify data efficiently and accurately, even though the data used has noise or inconsistency. The research process begins with collecting student data from the university database, and then data cleaning is carried out to ensure the quality of the data used. Next, the data is processed and classified using the Naive Bayes algorithm in Weka software to predict graduation status based on academic parameters. The results show that the Naive Bayes method is able to produce quite high accuracy in predicting student graduation, with accuracy values ranging from 75% to more than 85% depending on parameter selection and data cleaning. GPA is the most influential attribute on the prediction results, while other attributes such as class activity and organizational experience also contribute, although not as much as GPA. These findings provide important insights for the campus in designing more effective academic coaching and planning programs and can be a reference in the development of data mining-based decision support systems to improve the quality of computer science graduates.
Co-Authors Armansyah Armansyah Fadil, Ulfi Muzayyanah Mhd Furqan Ramadani, Wily Supi Siregar, Kalfida Eka Wati