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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Ilmiah Teknologi dan Komputer (JITTER)
Dwi Suarjaya, I Made Agus
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Computer Science & IT

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Browser-Based Detection of Harmful Content with Deep Learning Model Sikiandani, Ni Made Deni; Dwi Suarjaya, I Made Agus; Perdana Putra, Yohanes
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9804

Abstract

This study presents a browser extension that detects harmful content on both web pages and TikTok using a deep learning-based approach. The core model employs a Bidirectional Long Short-Term Memory (BiLSTM) network for multi-label classification, targeting six categories: Toxic, Severe Toxic, Obscene, Threat, Insult, and Identity Hate. The dataset combines 13,057 labeled samples from a public Kaggle dataset (2021) and 2,884 manually labeled tweets scraped from Twitter (X) between October–November 2024. Three feature extraction methods were tested: learned embeddings, FastText, and Word2Vec. The BiLSTM model architecture includes one embedding layer, a 32-unit bidirectional LSTM, three dense layers (128,256,128) using ReLU activation, and a six-unit sigmoid output layer. The model was trained using the Adam optimizer and binary cross-entropy loss, with early stopping applied after five stagnant validation checks across a maximum of 200 epochs. While the FastText-based model showed the best performance, the final deployed model used learned embeddings in Scenario 1 due to its smaller size (1.6M parameters) and near-optimal performance (Recall: 0.9786; Hamming Loss: 0.0052). The extension also integrates Whisper ASR for detecting harmful speech in video-based platforms like TikTok and supports five customizable censorship filters. User evaluation via Customer Satisfaction Score (CSAT) indicated strong acceptance, with 95.45% rating the user experience as Excellent, 84.09% confirming detection relevance, and 79.55% rating the system performance as Good. This highlights the extension’s effectiveness in promoting safer digital interaction across text and audiovisual content.
News Recommendation System Using Content-Based Filtering through RSS Customization Service Nandita, Ida Ayu Widya; Dwi Suarjaya, I Made Agus; Bayupati, I Putu Agung
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9807

Abstract

News refers to stories or information about current events or incidents. Several news websites offer a service called RSS (Really Simple Syndication), which enables users to easily receive updates on the latest news. News RSS feeds are typically generated based on the order of publication time or general categories. The content of these news RSS feeds can be customized to align with user interests or preferences. A recommendation system can be utilized as an approach to customize RSS feeds. This study was conducted to design a system capable of generating RSS feeds based on news recommendations using the content-based TF-IDF method and cosine similarity. Data scraping and preprocessing of news articles from various RSS feeds of Indonesian news websites were automated using cron jobs. Content-based filtering modeling was carried out using TF-IDF and cosine similarity. The design and customization of RSS feeds were implemented in a Flask application and packaged within several endpoints. The recommendations generated based on user click interactions were reasonably relevant, as they successfully presented news titles similar to the clicked articles, with cosine similarity scores ranging from 0.2 to 1.0. The majority of respondents agreed that the recommended news articles were relevant to the articles they had clicked and aligned with their interests. The RSS feed evaluation yielded highly satisfactory results, with all aspects assessed in the user acceptance survey achieving an average score exceeding 80%, and the overall results of the customer satisfaction survey indicated scores starting from 90%.
Implementation of a Waste Filtration Control System and Monitoring of Waste Quality in an Internet of Things-Based Laundry Business Pio, Priska; Dwi Suarjaya, I Made Agus; Buana, Putu Wira
JITTER: Jurnal Ilmiah Teknologi dan Komputer Vol. 6 No. 3 (2025): JITTER Vol.6, No.3, December 2025
Publisher : Program Studi Teknologi Informasi, Fakultas Teknik, Universitas Udayana

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

Abstract

An Internet of Things (IoT)-based wastewater filtration control system is developed to monitor water quality in real-time and automatically manage the filtration process. pH, turbidity, and Total Dissolved Solids (TDS) sensors are employed to detect the quality of wastewater generated from laundry a This section presents the results of the design and implementation stages of the waste filtration control system and Internet of Things-based water quality monitoring. ctivities. Sensor data is transmitted via Arduino and ESP8266 microcontrollers to Firebase, then displayed through an Android application. Based on the sensor readings, the system activates the filtration pump or channels the water to either a drainage outlet or a storage tank depending on the water condition. Test results indicate a 70% success rate in producing cleaner water from ten tested samples. The system also demonstrates stable and fast data transmission to Firebase and the Android application. This solution is suitable for small-scale applications, especially for household and laundry wastewater management.
Co-Authors I Made Sunia Raharja, I Made Sunia I Putu Agung Bayupati Jaye, M. Tirje Sukme Kadek Suar Wibawa Nandita, Ida Ayu Widya Perdana Putra, Yohanes Pio, Priska Prayogo, Bagus Dwi Putu Wira Buana Rusjayanthi, Ni Kadek Dwi Saskara, Ketut Dimas Aresta Sikiandani, Ni Made Deni Suarnata Wahyu Putra, Putu Bayu