Article Per Year (5 Year)
p-Index From 2021 - 2026
2.663
P-Index
This Author published in this journals
All Journal E-Bisnis : Jurnal Ilmiah Ekonomi dan Bisnis Jurnal Ekonomi, Manajemen, dan Akuntansi Universitas Ngudi Waluyo ProBisnis : Jurnal Manajemen Jurnal Algoritma The International Conference on Education, Social Sciences and Technology (ICESST) Indonesian Journal of Midwifery (IJM) "JAMASTIKA" Jurnal Mahasiswa Teknik Informatika AMMA : Jurnal Pengabdian Masyarakat International Journal of Economics and Management Sciences Jurnal Informatika dan Kesehatan INOVTEK Polbeng - Seri Informatika Management Dynamics: International Journal of Management and Digital Sciences
Kustiyono
Universitas Ngudi Waluyo
Humanities Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Medicine & Pharmacology Nursing Public Health Social Sciences

Published : 17 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : INOVTEK Polbeng - Seri Informatika

 1 
a Sentiment Analysis of Free Meal Plans on Social Media using Naïve Bayes Algorithms Yoga Zaen Vebrian; Kustiyono
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 1 (2025): March
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/3m2fcz69

Abstract

This study analyses public sentiment towards the "Free Meal Plan" initiative introduced by the political pair Prabowo-Gibran. This policy aims to assist underprivileged communities in Indonesia and is a significant issue in the social and political context. Data was collected from the social media platform X (formerly Twitter), gathering 501 relevant comments based on their connection to the topic and high levels of engagement (such as retweets and likes). The comments were then processed using Text Preprocessing and TF-IDF techniques and applied to a Naïve Bayes model. The model achieved an accuracy of 69.3%, a precision of 72%, a recall of 57.05%, and an F1 score of 54.5%. These results indicate that the model is capable of classifying public sentiment, though it has challenges in accurately detecting negative sentiment. These findings provide valuable insights for policymakers to design more effective communication and policy strategies, particularly in addressing criticism or public dissatisfaction. The study highlights the importance of using text processing and machine learning techniques to analyze social media data in a structured way.
Customer Data Management For Citynet Using Geolocation-Based Internet Broadband Registration Form Application Alfiano Aldo Pamungkas; Kustiyono
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 1 (2025): March
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/60hw1d67

Abstract

This study aims to address the inefficiencies in CityNet's customer data management by developing a geolocation-based registration form application. Currently, CityNet relies on Google Forms for data collection and WhatsApp for location sharing, leading to inaccuracies, inefficiencies, and delays in the installation process. The proposed system integrates geolocation to automatically capture customer locations, reducing input errors and streamlining data processing. This research follows the waterfall development model, encompassing needs analysis, system design, implementation, and usability testing involving CityNet administrators and customers. The results indicate an 80% reduction in input errors and a 30% improvement in operational efficiency. Additionally, the system seamlessly integrates with Google Spreadsheet, Telegram, and email, ensuring real-time data synchronisation and faster response times. While the application significantly enhances CityNet's operational workflow, challenges such as user adoption and dependency on internet connectivity remain. This study provides a scalable solution for broadband providers seeking efficient customer data management with location-based automation.
Co-Authors Abdul Aziz Abeda Muhammad Iqbal Ade Pratama Agung Wibowo Alfiano Aldo Pamungkas Asrini Mahdia Aulia, Izza Dedi Muliadi Ghufronul A, M Mufti gulo, titusman Hendri Saputra Hendrike Priventa Hevi Alvina Muhammad Fathoni, Muhammad Najib Sadam Rinto Sigit Ambar Widyawati Sigit Kharisma Silvi Meilani Sofiyanti, Ida Umi Handayani Wahyu Riani , Rosalina Wira Wati , Adilla Yoga Zaen Vebrian