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All Journal JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Pengabdian Masyarakat dan Riset Pendidikan
Weny Nur Afdilla Simangunsong
Universitas Asahan
Humanities Computer Science & IT Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Implementasi Sistem Lampu Otomatis Berbasis Internet of Things (IOT): Penelitian Weny Nur Afdilla Simangunsong; Dicky Apdillah; Dini Farhatun; Zulfa Ar Rahman
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 3 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 3 (Januari 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i3.5107

Abstract

The use of lighting systems is still commonly controlled manually, which often leads to inefficient electricity consumption, especially when lights remain on while not in use. This study aims to design and implement an automatic lighting control and monitoring system based on the Internet of Things (IoT). The research method applied is an experimental method consisting of system design, implementation, and testing stages. The system is developed using NodeMCU ESP8266 as the main controller, a relay as the actuator, and an LDR sensor to detect ambient light intensity. The test results indicate that the system is capable of controlling lights automatically and manually through a web-based monitoring interface with reliable performance. The implementation of this system reduces dependence on manual operation and improves electrical energy efficiency. Therefore, the proposed IoT-based lighting control and monitoring system can serve as an alternative solution to optimize energy usage in residential and public facilities.
KLASIFIKASI TIPE KACA MENGGUNAKAN METODE K-NEAREST NEIGHBOR Muhammad Azwar Al Ayyub; Weny Nur Afdilla Simangunsong; Dini Farhatun; Emi Dea; Selfina Agustin; Zulfa Ar Rahman; Muhammad Ridho
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 1 (2026): February 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i1.5745

Abstract

Abstract: Glass is a material that is widely used in various fields, such as construction, the automotive industry, and household appliances. Each type of glass has different characteristics based on its chemical composition and production process. Problems arise when the process of identifying glass types is still done manually, which is time-consuming, costly, and prone to error. This study aims to apply the K-Nearest Neighbor (K-NN) method in classifying glass types based on their chemical content attributes. The data in this study was sourced from Kaggle, namely the Glass Identification Dataset. The data used consisted of several chemical features, such as Na, Mg, Al, Si, K, Ca, Ba, and Fe, with seven categories of glass classes. The results showed that the K-NN method was able to classify glass types well and could be an effective solution to assist in the automatic glass identification process. Keyword: Classification, K-Nearest Neighbor, Data Mining, Types of Glass. Abstrak: Kaca merupakan material yang banyak digunakan dalam berbagai bidang, seperti konstruksi, industri otomotif, dan peralatan rumah tangga. Setiap jenis kaca memiliki karakteristik yang berbeda berdasarkan komposisi kimia dan proses produksinya. Permasalahan muncul ketika proses identifikasi jenis kaca masih dilakukan secara manual, sehingga membutuhkan waktu, biaya, dan berpotensi menimbulkan kesalahan. Penelitian ini bertujuan untuk menerapkan metode K-Nearest Neighbor (K-NN) dalam mengklasifikasikan jenis kaca berdasarkan atribut kandungan kimianya. Data dalam penelitian ini bersumber dari Kaggle, yaitu Glass Identification Dataset. Data yang digunakan terdiri dari beberapa fitur kimia, seperti Na, Mg, Al, Si, K, Ca, Ba, dan Fe, dengan tujuh kategori kelas kaca. Hasil penelitian menunjukkan bahwa metode KNN mampu mengklasifikasikan jenis kaca dengan baik dan dapat menjadi solusi yang efektif untuk membantu proses identifikasi kaca secara otomatis. Kata kunci: Klasifikasi, K-Nearest Neighbor, Data Mining, Jenis Kaca.
Co-Authors Dicky Apdillah Dini Farhatun Emi Dea Muhammad Azwar Al Ayyub Muhammad Ridho Selfina Agustin Zulfa Ar Rahman