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All Journal Jurnal Ilmiah Wahana Pendidikan
Muhammad Rizqi Warsita
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Perbandingan Support Vector Machine dan Random Forest dalam Analisis Sentimen Komentar YouTube Terkait Isu Hak Veto Amerika Serikat Raival Maulidan Muhamad Akbar; Pandapotan Kristian Sitorus; Fergiano Deren Ryandi; Muhammad Rizqi Warsita; Chaerur Rozikin
Jurnal Ilmiah Wahana Pendidikan Vol 12 No 6.B (2026): Jurnal Ilmiah Wahana Pendidikan
Publisher : Peneliti.net

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This study aims to compare the performance of two classification algorithms Random Forest and Support Vector Machine (SVM) with a sigmoid kernel in conducting sentiment analysis on YouTube comments related to the issue of the United States’ veto power. The dataset consists of 3,363 comments that have undergone comprehensive preprocessing steps (cleaning, normalization, tokenization, etc.) and were manually labeled into two sentiment classes: positive and negative. The findings indicate that SVM provides a more balanced classification across both sentiment categories, although its overall accuracy is slightly lower at 88.00%. In contrast, Random Forest achieves the highest accuracy at 89.33%, making it superior in terms of overall predictive performance. Therefore, SVM is more suitable when balanced class performance is the priority, whereas Random Forest is preferable when maximizing classification accuracy is the primary objective.
Rancang Bangun Pemilah Sampah Organik dan Anorganik Berbasis Arduino Dimas Smeichel Maliseono; Muhammad Rizqi Warsita; Rivan Herdyansyah; Naufal Rifqi Azfar; Susilawati, M.Si
Jurnal Ilmiah Wahana Pendidikan Vol 12 No 6.B (2026): Jurnal Ilmiah Wahana Pendidikan
Publisher : Peneliti.net

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The problem of waste accumulation in Indonesia is often caused by a lack of awareness and inadequate facilities for waste separation at the source. Manual sorting is considered inefficient and poses health risks. The aim of this study is to develop and build a prototype for an automated waste sorting system capable of separating organic and inorganic waste. The research follows an experimental approach using an Arduino Uno R3 as the main controller. The system integrates an HC-SR04 ultrasonic sensor for object detection, an inductive proximity sensor for metal detection, and a TCS3200 color sensor to identify organic waste based on its dominant color (green). Additionally, a DHT22 sensor is used to monitor the ambient temperature inside the waste container. Test results show that the device detects metal waste with 100% accuracy using the inductive sensor and separates organic waste (leaves) from non-metallic inorganic waste with an average success rate of 85% using the color sensor under controlled lighting conditions. This system is intended to provide an effective and hygienic solution for waste separation on a household scale.
Co-Authors Chaerur Rozikin Dimas Smeichel Maliseono Fergiano Deren Ryandi Naufal Rifqi Azfar Pandapotan Kristian Sitorus Raival Maulidan Muhamad Akbar Rivan Herdyansyah Susilawati, M.Si