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All Journal Go Infotech: Jurnal Ilmiah STMIK AUB Go Infotech: Jurnal Ilmiah STMIK AUB
Nur Rokhman
Sistem Informasi Universitas Sain dan Teknologi Komputer Semarang
Computer Science & IT

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Prototype Sistem Pendeteksi Kebakaran Di Ruang Server Berbasis Arduino Pada Dinas Kesehatan Kendal Nur Rokhman; Sumaryanto Sumaryanto; Puteri Anindya Maulan; Fitro Nur Hakim
Go Infotech: Jurnal Ilmiah STMIK AUB Vol 32, No 1 (2026): June
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer AUB - Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36309/goi.v32i1.449

Abstract

Arduino-Based Fire Detection System Prototype in the Server Room at the Kendal Health Service. Fire disaster is a disaster that can occur anywhere and at any time. Fires can also be caused by human negligence, natural conditions, and so on. Fire is also a problem faced by the community due to officers' delays in handling it, resulting in a lot of losses that can occur, including loss of life or material. Residents who are at the location of a fire must receive early warning information if the fire starts to get bigger. Therefore, the aim of this research is to design a fire detection tool using an Arduino Uno equipped with an extinguisher with an SMS gateway notification. This system will work where if the flame sensor and MQ-2 sensor have received fire or smoke input, the GMS module will automatically provide notification in the form of SMS to the cellphone. This fire detection system can also reduce the impact of fires which will grow and be extinguished by the water pump. installed on the tool. This fire detection tool solution can solve the problems faced by sending SMS notifications to officers and can be handled quickly by this tool.
Prototype Sistem Pendeteksi Kebakaran Di Ruang Server Berbasis Arduino Pada Dinas Kesehatan Kendal Nur Rokhman; Sumaryanto Sumaryanto; Puteri Anindya Maulan; Fitro Nur Hakim
Go Infotech: Jurnal Ilmiah STMIK AUB Vol 32, No 1 (2026): June
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer AUB - Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36309/goi.v32i1.449

Abstract

Arduino-Based Fire Detection System Prototype in the Server Room at the Kendal Health Service. Fire disaster is a disaster that can occur anywhere and at any time. Fires can also be caused by human negligence, natural conditions, and so on. Fire is also a problem faced by the community due to officers' delays in handling it, resulting in a lot of losses that can occur, including loss of life or material. Residents who are at the location of a fire must receive early warning information if the fire starts to get bigger. Therefore, the aim of this research is to design a fire detection tool using an Arduino Uno equipped with an extinguisher with an SMS gateway notification. This system will work where if the flame sensor and MQ-2 sensor have received fire or smoke input, the GMS module will automatically provide notification in the form of SMS to the cellphone. This fire detection system can also reduce the impact of fires which will grow and be extinguished by the water pump. installed on the tool. This fire detection tool solution can solve the problems faced by sending SMS notifications to officers and can be handled quickly by this tool.
Integrasi Machine Learning dalam Homebase Sistem Informasi untuk Analisis Produktivitas Akademik Nur Rokhman; Sumaryanto Sumaryanto; Fitro Nur Hakim; Puteri Anindya Maulan
Go Infotech: Jurnal Ilmiah STMIK AUB Vol 31, No 2 (2025): December
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer AUB - Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36309/goi.v31i2.424

Abstract

Penelitian ini bertujuan untuk mengintegrasikan teknik Machine Learning (ML) dalam sistem informasi berbasis homebase guna menganalisis produktivitas akademik. Sistem informasi akademik konvensional sering mengalami keterbatasan dalam memberikan analisis mendalam terkait data akademik mahasiswa, dosen, dan staf administrasi (Turban et al., 2021). Oleh karena itu, penelitian ini mengembangkan sistem yang memanfaatkan algoritma ML untuk menganalisis, memprediksi, dan memberikan wawasan terkait kinerja akademik (Yusuf et al., 2022). Implementasi ML diharapkan dapat meningkatkan akurasi dan efektivitas pengolahan data akademik, seperti nilai mahasiswa, tingkat kehadiran, dan interaksi dalam proses pembelajaran (Zhou et al., 2023). Sistem ini diuji menggunakan data historis untuk menghasilkan rekomendasi bagi pihak universitas dalam pengambilan keputusan yang lebih baik. Hasil penelitian menunjukkan bahwa integrasi ML dalam sistem informasi homebase meningkatkan akurasi analisis dan efisiensi dalam pemantauan serta evaluasi produktivitas akademik (Mendoza & Bastias, 2020).
Integrasi Machine Learning dalam Homebase Sistem Informasi untuk Analisis Produktivitas Akademik Nur Rokhman; Sumaryanto Sumaryanto; Fitro Nur Hakim; Puteri Anindya Maulan
Go Infotech: Jurnal Ilmiah STMIK AUB Vol 31, No 2 (2025): December
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer AUB - Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36309/goi.v31i2.424

Abstract

Penelitian ini bertujuan untuk mengintegrasikan teknik Machine Learning (ML) dalam sistem informasi berbasis homebase guna menganalisis produktivitas akademik. Sistem informasi akademik konvensional sering mengalami keterbatasan dalam memberikan analisis mendalam terkait data akademik mahasiswa, dosen, dan staf administrasi (Turban et al., 2021). Oleh karena itu, penelitian ini mengembangkan sistem yang memanfaatkan algoritma ML untuk menganalisis, memprediksi, dan memberikan wawasan terkait kinerja akademik (Yusuf et al., 2022). Implementasi ML diharapkan dapat meningkatkan akurasi dan efektivitas pengolahan data akademik, seperti nilai mahasiswa, tingkat kehadiran, dan interaksi dalam proses pembelajaran (Zhou et al., 2023). Sistem ini diuji menggunakan data historis untuk menghasilkan rekomendasi bagi pihak universitas dalam pengambilan keputusan yang lebih baik. Hasil penelitian menunjukkan bahwa integrasi ML dalam sistem informasi homebase meningkatkan akurasi analisis dan efisiensi dalam pemantauan serta evaluasi produktivitas akademik (Mendoza & Bastias, 2020).
Co-Authors Fitro Nur Hakim Puteri Anindya Maulan Puteri Anindya Maulan Sumaryanto Sumaryanto