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Binti Kholifah
Universitas Negeri Surabaya
Computer Science & IT Electrical & Electronics Engineering Engineering

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Designing an Internet of Things-Based Fire and Gas Leak Detection System M Adamu Islam Mashuri; Binti Kholifah; Faris Abdi El Hakim
Journal of Applied Informatics Research Vol. 1 No. 1 (2025): July
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jair.v1i1.44451

Abstract

LPG currently plays a crucial role in daily human activities, both in households and industries. However, gas leaks from LPG cylinders have often led to fire incidents, primarily due to undetected gas emissions. To address this issue, a real-time gas and smoke detection system has been developed using the MQ-2 gas sensor and the ESP32 microcontroller. The system is designed to monitor gas concentration levels and provide early warnings through the Blynk application on smartphones, utilizing the Internet of Things (IoT) concept. The system can detect dangerous gas concentrations above 80 ppm and responds by activating a buzzer and LED indicators. In addition, warning notifications are sent to the user’s smartphone. Test results show the system can accurately detect gas within a 10 cm radius of the sensor, with wireless notification capabilities reaching up to 500 meters. The system proves to be effective in enhancing safety and minimizing fire risks caused by undetected LPG leaks.
Optimization of Personalized Fashion Recommendations for H&M: A Collaborative Filtering Algorithm Approach with Temporal Time Interval Analysis Moch Deny Pratama; Dimas Novian Aditia Syahputra; M Adamu Islam Mashuri; Binti Kholifah; Rifqi Abdillah; Adinda Putri Pratiwi
Journal of Applied Informatics Research Vol. 1 No. 1 (2025): July
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jair.v1i1.44593

Abstract

This study presents a personalized fashion recommendation system for the H&M dataset, utilizing a cosine similarity-based collaborative filtering algorithm. This study investigates the effect of temporal segmentation on recommendation performance by conducting three experiments using datasets divided into two-week, one-month, and two-year time intervals. The experimental results show that the two-year interval achieves the best performance, producing a Mean Average Precision (MAP) of 0.02254 with a computational time of 2741.7 seconds. In contrast, the two-week interval achieves a MAP of 0.00915 in 1609.2 seconds, while the one-month interval produces a MAP of 0.00554 with a computational time of 3118.9 seconds. The main contribution of this study lies in the optimization of data structure transformation through dictionary-based modeling, which significantly improves training efficiency. These findings underscore the crucial role of temporal granularity in improving the accuracy and computational efficiency of collaborative filtering-based personalized fashion recommendation systems.
Enhancing Clickbait Headline Identification Performance Without Preprocessing Through Feature Reduction and Sentiment Analysis Moch Deny Pratama; Anisa Nur Azizah; Misbachul Falach Asy'ari; Dimas Novian Aditia Syahputra; M Adamu Islam Mashuri; Binti Kholifah; Rifqi Abdillah; Adinda Putri Pratiwi; Dina Zatusiva Haq
Journal of Applied Informatics Research Vol. 1 No. 1 (2025): July
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jair.v1i1.44659

Abstract

This study addresses the challenge of identifying clickbait headlines without relying on conventional text preprocessing, which can be resource-intensive and may degrade contextual integrity. To enhance detection performance, we examine three feature extraction methods: TF-IDF, Word2Vec, and Headline2Vec, an embedding technique designed for short texts like headlines. These features are optimized using feature selection algorithms, including Pearson Correlation Coefficient (PCC), Neighborhood Component Analysis (NCA), and Relief, to reduce dimensionality and enhance relevant signal retention. Sentiment polarity is also integrated as a complementary feature. A comparative evaluation is conducted using several machine learning classifiers, namely Support Vector Classifier (SVC), Random Forest, LightGBM, and XGBoost, across all combinations of feature extraction and selection methods. Results show that the optimal configuration Headline2Vec with Relief and SVC achieves the highest accuracy at 94.40%, outperforming other approaches. This demonstrates the effectiveness of combining semantic vectorization and feature selection for clickbait detection in the absence of traditional preprocessing. The findings support the development of streamlined and scalable classification models capable of maintaining high accuracy while reducing preprocessing overhead, making the proposed method particularly suitable for real-time and large-scale content moderation and news verification systems.
Co-Authors Adinda Putri Pratiwi Anisa Nur Azizah Dimas Novian Aditia Syahputra Dina Zatusiva Haq Faris Abdi El hakim M Adamu Islam Mashuri Misbachul Falach Asy'ari Moch Deny Pratama Rifqi Abdillah