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All Journal Teknik: Jurnal Ilmu Teknik dan Informatika Jurnal Riset Teknik Komputer
Adhitya Purboyo
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Computer Science & IT Industrial & Manufacturing Engineering Other

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Pengembangan Prototipe Detektor Kebakaran Cerdas dengan Sensor Suhu, Kelembapan, dan Api Berbasis IoT (Studi Kasus: Dinas Pendidikan Kabupaten Semarang) Azani Fajri, Laksamana Rajendra Haidar; Mandaya, Yusuf Wisnu; Adhitya Purboyo; Syafi'i, Imam; Yunus, Ryan
Teknik: Jurnal Ilmu Teknik dan Informatika Vol. 5 No. 2 (2025): Oktober : Teknik: Jurnal Ilmu Teknik dan Informatika
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/teknik.v5i2.1224

Abstract

Fire disasters can occur at any time in residential areas or schools, which are often triggered by electrical short circuits, the use of gas stoves, to minor negligence such as cigarette butts. As a preventive effort of Department of Education of Semarang, this research aims to create a prototype of a microcontroller-based early detection and fire suppression system with C programming. This tool uses NodeMCU as a control center that integrates fire sensors and DHT11 sensors to monitor room temperature in real-time. If the system detects any indication of fire or a significant temperature spike, a buzzer will activate as a warning alarm and the fan will work automatically to assist the initial extinguishing process.
OPTIMASI K-MEANS CLUSTERING PSO UNTUK PENENTUAN JUMLAH CLUSTER OPTIMAL PADA DATA KANKER PAYUDARA Adhitya Purboyo; Laksamana Rajendra Haidar Azani Fajri; Imam Syafii
Jurnal Riset Teknik Komputer Vol. 3 No. 1 (2026): Maret : Jurnal Riset Teknik Komputer (JURTIKOM)
Publisher : CV. Denasya Smart Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69714/drakfm71

Abstract

Breast cancer is one of the most dangerous diseases and a leading cause of death among women worldwide. Clustering methods can assist in diagnosing breast cancer to determine the best course of treatment. K-Means is a widely used clustering algorithm known for its ability to handle large datasets efficiently with fast computational time. However, K-Means has a significant weakness: the number of clusters is determined randomly, resulting in suboptimal clustering outcomes. To overcome this limitation, Particle Swarm Optimization (PSO) is applied for automatic determination of the optimal number of clusters. PSO was selected due to its advantages, including requiring few parameters, ease of implementation, fast convergence, and low computational cost. This study uses the breast cancer dataset from the UCI Machine Learning Repository, consisting of 699 records and 10 attributes. The proposed PSO–K-Means method was evaluated using the Silhouette Coefficient and Davies-Bouldin Index. The results show that the optimal number of clusters is k = 2, achieving a Silhouette Coefficient of 0.92 and a Davies-Bouldin Index of 1.374. These results demonstrate that the PSO–K-Means method significantly outperforms standard K-Means by directly producing optimal clustering results without the need for conducting repeated experiments.
Co-Authors Azani Fajri, Laksamana Rajendra Haidar Imam Syafii Laksamana Rajendra Haidar Azani Fajri Yunus, Ryan Yusuf Wisnu Mandaya