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All Journal Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer Journal Of Informatics And Busisnes JTH: Journal of Technology and Health Jurnal Penelitian Teknologi Informasi dan Sains Polygon: Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi
Alexander Adis
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Computer Science & IT Economics, Econometrics & Finance Health Professions Industrial & Manufacturing Engineering Medicine & Pharmacology Nursing

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Journal : Jurnal Penelitian Teknologi Informasi dan Sains

 1 
Application of the Naive Bayes Method in Predicting the Level of Community Satisfaction with the Performance of the Magho Linyo Village Head Seselia Soli Lede; Gergorius Kopong Pati; Alexander Adis
Jurnal Penelitian Teknologi Informasi dan Sains Vol. 2 No. 3 (2024): SEPTEMBER : JURNAL PENELITIAN TEKNOLOGI INFORMASI DAN SAINS
Publisher : Institut Teknologi dan Bisnis (ITB) Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jptis.v2i3.2457

Abstract

Village monies are sourced from the State Budget (APBN) and are allocated in accordance with Law Number 6 of 2016 for the purposes of development, coaching, social activities, and community empowerment. The existence of village finances is necessary to support all sources of revenue for the village, and as the government's revenue grows, so too must the village's public service infrastructure. As a result, the sentiment analysis of village officials will be done in this study. The analysis will include the classification of community sentiment using the Naive Bayes technique. To assess each method's accuracy, two will be contrasted. The village administration, as the highest social institution in the community, is crucial in establishing norms, distributing funds, and creating a help-related socialization process. Furthermore, some Pada Eweta village inhabitants may not receive help, which may cause social rivalry amongst locals. Sentiment categorization will be used to classify responses as positive or negative. Based on feedback from visitors, this study seeks to evaluate the validity of the two approaches put to the test and provide insight into the level of service provided by village officials. The accuracy of both methods will be verified by evaluating the outcomes with the RapidMiner tool.
Penerapan Metode Naïve Bayes Classification dalam Analisis Tingkat Pemahaman Siswa Terhadap Guru pada Mata Pelajaran Matematika Sovia Umbu; Andreas Ariyanto Rangga; Alexander Adis
Jurnal Penelitian Teknologi Informasi dan Sains Vol. 2 No. 4 (2024): Desember : JURNAL PENELITIAN TEKNOLOGI INFORMASI DAN SAINS
Publisher : Institut Teknologi dan Bisnis (ITB) Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jptis.v2i4.2482

Abstract

For students, Dasar Elementary School offers a formal education pathway. Mengubah pemahaman siswa terhadap materi pembelajaran is quite important in the learning process. Perlu dicapainya, siswa atau didik adalah fungsi dari adanya seorang pendidik.This is due to the fact that in the process of teaching mathematics, understanding concepts is a foundation that is extremely important for making decisions on the solution of mathematical problems as well as daily problems. Due to a few factors that make it difficult for students to understand math lessons, they find it difficult to understand or even understand them at all. To achieve the maximum learning outcomes, education must be provided by qualified teachers. One of the most important educational resources for achieving the highest learning outcomes is a teacher. A teacher is one of the most important educational resources; learning will be more enjoyable if the teacher can implement a flexible learning model. In addition, teachers are expected to be constantly creative in their approach to teaching. Due to this, a system is required to determine the student's level of proficiency in comparison to the advanced learning environment in order to assess student learning. The collected data from the survey will be divided into two categories: training data and testing data. The model's training data results will be used to assess the accuracy of the data testing. The classification results indicate that the Naïve Bayes algorithm is a good choice for reducing student anxiety levels in foreign language learning, with a remarkably high accuracy rate of 95.24%.
Co-Authors Adelbertus Umbu Janga Andreas Ariyanto Rangga dama nuna, ersita Elfira Umar Elfrida Susanti Tanggu Friska Priskila Wanda Gergorius Kopong Pati Margaretha Inya Tuku Marlince Saingo Paulina Indriani Mori Uma Seselia Soli Lede Siska Kahi Ninda Sofia Dada Gole Sovia Umbu Yulius Nahak Tetik Yunita Ngongo