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All Journal Jurnal Media Informatika Intelligent Computing and Advanced Data Science
Dicky Ramadhan Hutasuhut
Universitas Satya Terra Bhinneka
Computer Science & IT

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Deteksi Toxic Comment TikTok dan Auto Filtering pada TikTok Menggunakan Support Vector Machine Dewi Purnama Sari; Dicky Ramadhan Hutasuhut; Nadya Balqis Nasution; Risya Amalia Putri Nasution
Journal of Intelligent Computing and Advanced Data Science Vol. 1 No. 2 (2026): July 2026
Publisher : Universitas Satya Terra Bhinneka

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Abstract

The rapid growth of social media, particularly TikTok, has increased user interaction through its comment feature. However, the large number of comments has also led to the emergence of toxic comments containing hate speech, insults, and cyberbullying. Manual moderation is considered ineffective due to the massive volume of comments and the complexity of informal language commonly used on social media. This study aims to implement a text mining approach using the Support Vector Machine (SVM) algorithm to detect toxic comments and develop an automatic filtering mechanism for TikTok comments. The dataset was collected by scraping comments directly from several TikTok videos. The preprocessing stage included case folding, cleaning, tokenization, stopword removal, and stemming. Feature extraction was performed using TF-IDF, followed by text classification using the SVM algorithm. Model performance was evaluated using accuracy, precision, recall, and F1-score. The experimental results show that the SVM model achieved an accuracy of 95.28%, with a precision of 1.00, recall of 0.67, and F1-score of 0.80 for the toxic class. Furthermore, the developed auto-filtering system successfully filters toxic comments automatically, making the content moderation process faster and more efficient. The proposed approach demonstrates that combining TF-IDF feature extraction with the SVM algorithm can effectively support automated content moderation and help reduce the spread of harmful comments on social media platforms.
DETEKSI PENYAKIT BLAS, TUNGRO & BERCAK COKLAT PADA TANAMAN PADI MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK Mustika Tiara; Charistian Hia; Dicky Ramadhan Hutasuhut; Enjelina Megawati Hutauruk
Jurnal Media Informatika Vol. 6 No. 3 (2025): Jurnal Media Informatika
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jumin.v6i3.6462

Abstract

Produksi padi di Indonesia terus menghadapi tantangan serius akibat serangan penyakit tanaman seperti blas, tungro, dan bercak coklat. Penyakit ini dapat menurunkan hasil panen secara signifikan jika tidak dideteksi secara dini. Penelitian ini dilakukan untuk mengembangkan sistem deteksi otomatis menggunakan metode Convolutional Neural Network (CNN) yang mampu mengklasifikasikan jenis penyakit padi berdasarkan citra daun. Dataset yang digunakan terdiri dari citra daun padi yang diambil langsung dari lapangan (primer) dan juga dataset publik RiceLeafs (sekunder). Data citra diproses melalui tahapan preprocessing dan augmentasi untuk meningkatkan kualitas pelatihan model. Arsitektur CNN dibangun dengan beberapa lapisan konvolusi, pooling, dan fully connected yang dilatih menggunakan optimizer Adam dan fungsi loss categorical crossentropy. Hasil pelatihan menunjukkan akurasi validasi mencapai 100%, dengan loss yang sangat rendah, menandakan kinerja model yang sangat baik. Model juga mampu memprediksi kelas penyakit dari citra baru secara akurat. Penelitian ini menunjukkan bahwa penerapan CNN berpotensi besar sebagai alat bantu diagnosis awal penyakit tanaman padi, sehingga dapat memberikan solusi nyata dalam meningkatkan produktivitas pertanian melalui pendekatan teknologi kecerdasan buatan.
Co-Authors Charistian Hia Dewi Purnama Sari Enjelina Megawati Hutauruk Mustika Tiara Nadya Balqis Nasution Risya Amalia Putri Nasution