Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Jurnal Ilmiah Wahana Pendidikan
Pandapotan Kristian Sitorus
Unknown Affiliation
Religion Education Social Sciences Other

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
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

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

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.
Perancangan Prototipe Alat Pendeteksi Banjir berbasis esp32 dan IoT di Perumahan Tridaya Indah Bekasi Fahmi Al Ashri; Pandapotan Kristian Sitorus; Naufal Ilham Ramadhan; Danendra Ganijan Sunarso; Susilawati, M.Si
Jurnal Ilmiah Wahana Pendidikan Vol 12 No 6.B (2026): Jurnal Ilmiah Wahana Pendidikan
Publisher : Peneliti.net

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Flooding is a serious and recurring problem in the Tridaya Indah Housing complex, Bekasi. Delays in information and emergency response often exacerbate the disaster's impact. This research aims to design an Internet of Things (IoT) based prototype for a flood early warning system (EWS) and mitigation. The research method used is Prototyping, which includes the stages of requirements gathering, design, prototype building, evaluation, and iteration. This system integrates an ESP32 microcontroller as the main processing unit, a JSN-SR04T ultrasonic sensor for water level detection, a flame sensor for secondary hazard (fire) detection, a buzzer as a local alarm, and a relay-controlled mini water pump for automatic mitigation. Remote monitoring and real-time notifications are implemented using the Blynk platform via a smartphone application. The black-box testing results show that the prototype functions 100% as designed. The system successfully differentiated three condition levels (Safe, Alert, Danger) accurately, and successfully activated the buzzer and water pump when the "Danger" level was reached, while simultaneously sending notifications to the user. This 4-in-1 integrated system (detection, local alarm, remote alarm, and mitigation) is proven feasible for implementation as a proactive early warning and flood response solution for the case study at Tridaya Indah Housing.
Co-Authors Chaerur Rozikin Danendra Ganijan Sunarso Fahmi Al Ashri Fergiano Deren Ryandi Muhammad Rizqi Warsita Naufal Ilham Ramadhan Raival Maulidan Muhamad Akbar Susilawati, M.Si