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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Ngudi Waluyo Empowerment : Jurnal Pengabdian Kepada Masyarakat
Sri Mujiono
Universitas Ngudi Waluyo
Arts Computer Science & IT Education Environmental Science Other

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Sosialisasi Model Pendidikan Nilai dalam Pembentukan Karakter Peserta Didik pada Pendidikan Inklusif Zulmi Roestika Rini; Ela Suryani; Sri Mujiono
Ngudi Waluyo Empowerment: Jurnal Pengabdian Kepada Masyarakat Vol. 1 No. 1 (2022): Ngudi Waluyo Empowerment: Jurnal Pengabdian Kepada Masyarakat
Publisher : Fakultas Komputer dan Pendidikan Universitas Ngudi Waluyo

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Abstract

The purpose of Community Service activities is to find the values ​​needed by teachers for the formation of the character of students in elementary schools for children with special needs (inclusion), as well as to socialize the use of modules for learning models and character education. The method of activity is by means of lectures and dialogues about the socialized model; workshops from teachers to discuss cases that emerged in schools and models designed to design modules; Next, participants compose a module and present it to be selected the best. The result of implementing Community Service activities is that teachers are able to identify the values ​​needed to be applied in schools for children with special needs and design models that will be implemented through the preparation of models in modules. The resulting module there are 28 types of models. Furthermore, from 28 selected 3 models that are considered the most innovative, creative, and inspiring. The school used for this socialization is SD N Jubelan 01 with 15 participants. AbstrakTujuan dari kegiatan Pengabdian pada Masyarakat adalah menemukan nilai yang dibutuhkan oleh guru untuk pembentukan karakter peserta didik di sekolah dasar pada anak berkebutuhan kusus (inklusi), serta mensosialisasikan penggunaan modul untuk model pembelajaran serta Pendidikan kararter. Metode kegiatan dengan cara ceramah dan dialog tentang model yang disosialisasikan; workshop dari guru untuk mendiskusikan kasus-kasus yang muncul di sekolah dan model yang dirancang untuk merancang modul; selanjutnya, peserta menyusun modul dan dipresentasikan untuk dipilih yang terbaik. Hasil pelaksanaan kegiatan Pengabdian pada Masyarakat adalah guru-guru mampu mengidentifikasi nilai-nilai yang dibutuhkan untuk diterapkan di sekolah pada anak berkebutuhan kusus dan merancang model yang akan diimplementasikan melalui penyusunan model di modul. Modul yang dihasilkan ada 28 jenis model. Selanjutnya, dari 28 dipilih 3 model yang dipandang terinovatif, terkreatif, dan terinspiratif. Sekolah yang di guanakan untuk sosialisasi ini adalah SD N Jubelan 01 dengan jumlah peserta yang mengikuti ada 15.
Analysis of User Sentiment Toward Mountain Climbing Content Based on YouTube Comments Using the K-Nearest Neighbors Algorithm Ghiffari Arrozaq; Sri Mujiono
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13518

Abstract

The growth of social media has made YouTube one of the primary sources of information regarding mountain climbing activities. YouTube’s comment feature contains a variety of user opinions that can be used to gauge public perception of climbing content. However, the sheer volume of comments makes manual analysis less effective. This study aims to analyze user sentiment toward mountain climbing content based on YouTube comments using the K-Nearest Neighbor (KNN) algorithm. The research method used is a quantitative approach involving data collection through YouTube comment crawling, manual sentiment abelling, and data preprocessing which includes cleaning, case folding, normalization, tokenization, stopword removal, and stemming followed by feature weighting using Term Frequency–Inverse Document Frequency (TF-IDF) and classification using the K -Nearest Neighbor (KNN) algorithm. The model was evaluated using a confusion matrix with the metrics of accuracy, precision, recall, and F1-score. The results of the study show that the K-Nearest Neighbor (KNN) algorithm is capable of classifying the sentiment of YouTube comments with a maximum accuracy of 79.35% at K = 5. These results indicate that the K-Nearest Neighbor (KNN) algorithm is quite effective in analyzing the sentiment of comments related to mountain climbing content, although it still has limitations in understanding semantic context, such as the use of informal language, irony, and sarcasm. Therefore, future research is recommended to use more complex feature representation methods, such as Word2Vec, FastText, or BERT-based models, to improve classification capabilities and sentiment analysis accuracy.
Co-Authors Ela Suryani Ghiffari Arrozaq Zulmi Roestika Rini