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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jupiter Coding: Jurnal Komputer dan Aplikasi ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Journal of Electrical Technology Linguista: Jurnal Ilmiah Bahasa, Sastra, dan Pembelajarannya BULETIN AL-RIBAATH JURIKOM (Jurnal Riset Komputer) Abdi: Jurnal Pengabdian dan Pemberdayaan Masyarakat Abdimas Galuh: Jurnal Pengabdian Kepada Masyarakat Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Informatika dan Rekayasa Perangkat Lunak Bulletin of Computer Science Research Jurnal Fokus Elektroda (Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali) Mekongga: Jurnal Pengabdian Masyarakat
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Universitas Tanjungpura
Agriculture, Biological Sciences & Forestry Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Journal : jupiter

 1 
Smart Lamp: Kendali dan Monitor Lampu Berbasis Internet Of Things (IoT) Suhardi; Rahmi Hidayati; Irma Nirmala
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 14 No 2-c (2022): Jupiter Edisi Oktober 2022
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281./5591/5.jupiter.2022.10

Abstract

Generally, people control the house light manually. The problem is that sometimes people forget to turn off the lights when they go. It caused a waste of energy and increased the bills. The light can be automated controlled or controlled from far by the Internet of Things (IoT) technology. This research developed automated several room lights in a miniature house by a website. The system has been successfully implemented. The user can control and monitor the condition of house lights by a web application. The control and monitoring process can be carried out in two modes. First, manual mode by pressing the on/off button on the application. Second, automatic mode by setting the time for each room light according to the conditions required by the user.
Sistem Penentuan Kelayakan Minyak Jelantah Menggunakan Metode Naïve Bayes Irmina Rika; Suhardi Suhardi; Rahmi Hidayati
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 18 No 1 (2026): Jurnal Penelitian Ilmu dan Teknologi Komputer (JUPITER)
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.18168112

Abstract

Used cooking oil is cooking oil that has been used repeatedly, causing its quality to deteriorate and potentially harm human health. Many people continue to use used cooking oil due to cost-saving considerations and the difficulty of directly assessing its quality. Therefore, a system capable of determining the suitability of used cooking oil is needed. This research aims to classify the suitability of used cooking oil based on clarity, viscosity, and color parameters using the Naïve Bayes method. The classification system utilizes an LDR sensor to detect clarity levels, a YF-S401 water flow sensor to measure viscosity, and a TCS3200 color sensor to read RGB values, with a NodeMCU ESP32 microcontroller as the processing unit. The test results show that the LDR sensor successfully detects clarity levels, the YF-S401 sensor achieves an accuracy of 97.73%, and the TCS3200 color sensor reads RGB values with accuracies of 99.85% for red, 97% for green, and 83% for blue. The dataset used in this study consists of 120 training samples and 30 test samples. The classification process using the Naïve Bayes method produced an accuracy of 96.67%, a precision of 94.74%, and a recall of 100%.
Co-Authors Ade Muhamad Nurhadi Andi Hairil Alimuddin Arie Pratama Cani Cani Cucu Suhery Cucu Suhey Dedi Triyanto Dedi Triyanto Dedi Triyanto Desyandri Desyandri Dwi Marisa Midyanti Epin Aji Farhan Fajar Luthfi Fatma Agus Setyanigsih Fatma Agus Setyaningsih Febriyanto, Ferdy Firman Saputra Hafiz Muhardi hamdi hamdi Hasanuddin Hasanuddin Hendra Perdana Hendrikus Hendrikus Hirzen Hasfani Ikhwan Ruslianto Irma Nirmala Irmina Rika Irwan Argi Himawan Kartika Sari Kasliono Kasliono L Lusi Lily Thamrin Midyanti, Hafizhah Insani Muhardi NINA RAHMADANI Nugra Zurus Pratama Rahmi Hidayati Risko, Risko Roynal Kadepi Saharman Saharman Sampe Hotlan Sitorus Syamsul Bahri Tedy Rismawan Tejo Dwi Prasetyo Tjen Veronica Uray Ristian Yakobus Dapi Yogi Ramadhan Putra Yosep Robert Yudhi