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Perancangan dan Implementasi Lampu Otomatis Berbasis IoT Menggunakan Aplikasi Blynk: Penelitian Daniswara; Triana Puspa Handayani; Muhammad Fakhri Fauzar; Dicky Apdilah
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.v4i3.5086

Abstract

The development of Internet of Things (IoT) technology allows remote control of electrical devices via the internet network. One application is in lighting systems to improve energy efficiency and user comfort. This study aims to design and implement an IoT-based automatic lighting system using the Blynk application as a control and monitoring medium. The method used is an experimental method with an engineering approach, including the system design stage, hardware assembly, programming, integration with the Blynk application, and system testing. The main devices used are NodeMCU/ESP32, relay modules, and AC lamps. The test results show that the system is able to control the lights in real-time via a smartphone with a good response as long as the internet connection is stable. This system has proven effective in providing convenience, flexibility, and has the potential to save electricity usage, making it suitable for application as a simple smart home solution and IoT learning medium.
IMPLEMENTASI ALGORITMA FUZZY C-MEANS DALAM PENGELOMPOKAN SKALA PRODUKSI PABRIK KELAPA SAWIT Daniswara; Helmi Fauzi Siregar
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 2 (2026): April 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i2.6261

Abstract

Abstract: Recording production output is one of the key activities in the oil palm production management system at PT Socfindo Kebun Aek Loba. However, the production data generated is still presented solely as production figures without any categorization by production scale, necessitating further data processing to identify production scales more systematically. Therefore, this study aims to design and develop a clustering application using the Fuzzy C-Means (FCM) algorithm based on PHP and MySQL to group the levels of oil palm production at PT Socfindo Kebun Aek Loba. The data used in this study were obtained from the archives and production reports of the oil palm mill at PT Socfindo Kebun Aek Loba. A total of 211 palm oil production data points were obtained from PT Socfindo Kebun Aek Loba. Based on the results of the system’s computational testing, the Fuzzy C-Means algorithm successfully achieved convergence and objectively clustered the data into three clusters. Of the total 211 processed data points, 42 production data points were classified into the Low Production cluster (C1), 75 into the Medium Production cluster (C2), and 94 into the High Production cluster (C3). The benefit of this study is that it helps provide clear, precise, and accurate information regarding the classification of oil palm production scales at PT Socfindo Kebun Aek Loba. Keywords: Clustering, Data Mining, Production Scale, Palm Oil Mill, Fuzzy C-Means.   Abstrak: Pencatatan hasil produksi merupakan salah satu kegiatan penting dalam sistem pengelolaan produksi kelapa sawit di PT Socfindo Kebun Aek Loba. Namun, data produksi yang dihasilkan masih disajikan dalam bentuk angka produksi tanpa adanya pengelompokkan skala produksi kelapa sawit, sehingga diperlukan pengolahan data lanjutan agar skala produksi dapat diidentifikasi secara lebih sistematis. Oleh karena itu, penelitian ini bertujuan untuk merancang dan membangun aplikasi clustering menggunakan algoritma Fuzzy C-Means (FCM) berbasis PHP dan MySQL untuk mengelompokkan tingkat produksi kelapa sawit di PT Socfindo Kebun Aek Loba. Data yang digunakan dalam penelitian ini merupakan data yang diperoleh dari arsip dan laporan produksi pabrik kelapa sawit di PT Socfindo Kebun Aek Loba. Data yang didapat sebanyak 211 data produksi kelapa sawit di PT Socfindo Kebun Aek Loba.  Berdasarkan hasil pengujian komputasi sistem, algoritma Fuzzy C-Means berhasil mencapai konvergensi dan mengelompokkan data kedalam tiga klaster secara objektif. Dari total 211 data yang diolah, didapatkan hasil sebanyak 42 data produksi masuk ke dalam klaster Produksi Rendah (C1), 75 data produksi masuk ke dalam klaster Produksi Sedang (C2), 94 data produksi masuk ke dalam klaster Produksi Tinggi (C3). Manfaat dari penelitian ini adalah membantu dalam memperoleh informasi yang jelas, tepat dan akurat mengenai pengelompokkan skala produksi kelapa sawit di PT Socfindo Kebun Aek Loba. Kata Kunci: Clustering, Data Mining, Skala Produksi, Pabrik Kelapa Sawit, Fuzzy C-Means.
Co-Authors Dicky Apdilah Helmi Fauzi Siregar Muhammad Fakhri Fauzar Triana Puspa Handayani