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Pandapotan Kristian Sitorus
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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.
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

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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.
Analisis Sentimen Program Makan Bergizi Gratis (MBG) untuk Anak Sekolah Menggunakan Algoritma Support Vector Machine Muhammad Rizqi Warsita; Chaerur Rozikin; Pandapotan Kristian Sitorus
Jurnal Ilmiah Wahana Pendidikan Vol 12 No 10.D (2026): Jurnal Ilmiah Wahana Pendidikan
Publisher : Peneliti.net

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The Support Vector Machine with a linear kernel has proven to be highly effective and balanced in classifying public perceptions of the Free Nutritious Meals Programme (MBG). This conclusion was drawn from an analysis of public opinion on the social media platform X, which aimed to map public sentiment towards the programme. The analysis was conducted using a linear kernel SVM algorithm with the SEMMA methodology, utilising 1,000 initial data points from X, which were filtered down to 578 binary data points (401 positive and 177 negative) through a TF-IDF-based cleaning and labelling process. The data was split in an 80:20 ratio and tested across five variations of the hyperparameter C. The best configuration was achieved at C=1, with a global accuracy of 96.55 per cent, a negative F1-score of 94.44 per cent, and a positive F1-score of 97.50 per cent. In terms of content, positive sentiment centred on improvements in children’s nutrition (keywords ‘nutrition’ and ‘food’), whilst negative sentiment related to the risk of food poisoning and budget efficiency (keywords ‘poison’ and ‘budget’). Given this high and balanced performance across both positive and negative classes, the linear kernel SVM is considered reliable for the task of classifying public policy perceptions.
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