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All Journal Sistemasi: Jurnal Sistem Informasi JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING INOVTEK Polbeng - Seri Informatika Jurnal Teknologi Sistem Informasi dan Aplikasi J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Sistem Cerdas JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JOISIE (Journal Of Information Systems And Informatics Engineering) Building of Informatics, Technology and Science Jurnal Sistem informasi dan informatika (SIMIKA) Jurnal Pendidikan dan Teknologi Indonesia Bulletin of Computer Science Research J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Algoritma CONSEN: Indonesian Journal of Community Services and Engagement INOVTEK Polbeng - Seri Informatika
Mona Fronita, Mona
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Religion Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Physics Social Sciences Other

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Journal : Building of Informatics, Technology and Science

 1 
Analysis of Academic and Administration Information Systems Using Servqual and Kano Methods Sari, Cahya Metta; Hamzah, Muhammad Luthfi; Angraini, Angraini; Saputra, Eki; Fronita, Mona
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2713

Abstract

Academic and Administrative Information System (SIAKAdm) is an online-based information system service for students of Hangtuah University Pekanbaru. With the development of information systems in the academic field, we must also test information systems, there are several problems that users feel that the quality of service of the Academic Information System (SIAKAdm) has not run effectively and efficiently, such as, there are often delays when filling in KRS, color contrast in the system is too disturbing to the user's eyes, there is no edit menu on the student profile, and finally there is no complaint lyanan menu or C3 servicedesk menu. This research was conducted using the ServQual method and the Kano method. The ServQual Method can be said to be a method used to measure the quality of service attributes of a dimension, while the Kano Method can be interpreted as a model built to understand how well their product or service meets the needs of users. This data collection process is by conducting interviews and distributing questionnaires of 98 respondents using the Simple Random Sampling technique. The data was obtained using IBM SPSS 26 and calculated the GAP value using Microsoft Excel. The results of this study The highest gap value was in the Assurance variable, with a GAP value of -4.54. While the lowest gap value is in the Responsivennes variable of -2.51.
Analisis Sentimen Tanggapan Publik di Twitter Terkait Program Kerja Makan Siang Gratis Prabowo–Gibran Menggunakan Algoritma Naïve Bayes Classifier dan Support Vector Machine Ramadhani, Annisa; Permana, Inggih; Afdal, M; Fronita, Mona
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6188

Abstract

Indonesia faces a serious challenge related to stunting, with rates reaching 21% in 2024, although this represents a decrease from 24% in 2021. In response, the government has launched various programs to address this issue, including nutrition education, health check-ups for pregnant women, and supplementary food provisions. Amid these efforts, the proposed free lunch program aims to improve nutritional quality for children and pregnant women. However, this program has sparked controversy over the required budget, estimated at IDR 450 trillion, which could impact the national budget balance and lead to inflation.This study analyzes public sentiment toward the free lunch program using the Naïve Bayes Classifier (NBC) and Support Vector Machine (SVM) algorithms. An analysis of 1,028 tweets revealed that negative sentiment predominates at 44.84%, followed by positive sentiment (32.39%) and neutral sentiment (22.76%). SVM outperformed NBC with an accuracy of 75.39%, compared to NBC's 68.97%. The findings provide important insights into public perceptions of the program and highlight the need for further research to improve sentiment analysis methodologies.
Co-Authors 'alimah, Muta Afifah, Afifah Pendri Ahsyar, Tengku Khairil Angraini Angraini Angraini Annisa Ramadhani Anofrizen Anofrizen Arif Marsal Arsyad, Ilham Butar, Fajar Rido Butar Daulay, Suandi Eki Saputra Fahlepi, Ridho Febi Nur Salisah, Febi Nur Hamzah, Muhammad Luthfi Hardianto, Arvin Indriani, Monalisa Inggih Permana Intan, Sri Irfan Faruqi, Nouval Jazman, Muhammad Luthfi Hamzah, Muhammad M Afdal M Yogi Megawati Megawati - Muhammad Lutfi Hamzah Mukti, Raihan Nazhifatunnisa, Nazhifatunnisa Ningrum, Meriana Prihati Putra, Defri Indra Raditya, Muhammad Zacky Rahmawita M, Medyantiwi Rahmawita, Medyantiwi Renaldi, Rengga Sari, Cahya Metta Sherly Rahayu Siregar, Syafril Subakja, Triyawan Bagus Susilawati, Susilawati Syaifullah Syaifullah Syakirin, Fakhrusy Winata, Rahmad Waviq Zarnelly Zarnelly