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All Journal Equator Journal of Management and Entrepreneurship Jupiter Coding: Jurnal Komputer dan Aplikasi Techno.Com: Jurnal Teknologi Informasi SEMIRATA 2015 Jurnal Edukasi dan Penelitian Informatika (JEPIN) CESS (Journal of Computer Engineering, System and Science) SMARTICS Journal JURNAL REKAYASA TEKNOLOGI INFORMASI Martabe : Jurnal Pengabdian Kepada Masyarakat CYBERNETICS Digital Zone: Jurnal Teknologi Informasi dan Komunikasi JURIKOM (Jurnal Riset Komputer) Jurnal Informatika dan Rekayasa Elektronik Abdi: Jurnal Pengabdian dan Pemberdayaan Masyarakat Journal of Computer System and Informatics (JoSYC) Bulletin of Computer Science Research Jurnal PkM (Pengabdian kepada Masyarakat) Komatika: Jurnal Pengabdian Kepada Masyarakat Jurnal Media Informasi Teknologi Mekongga: Jurnal Pengabdian Masyarakat
Cucu Suhery
Universitas Tanjungpura
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Library & Information Science Medicine & Pharmacology Public Health Social Sciences Other

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Algoritma Kalman Filter Penerapan Algoritma Kalman Filter Untuk Mengurangi Noise Pada Sistem Pembuka Kunci Pintu Dengan Pengenalan Suara Carlos Aprillio Angger Gindaong; Irma Nirmala; Cucu Suhery
SMARTICS Journal Vol 12 No 1 (2026): Journal SMARTICS (April 2026)
Publisher : Universitas PGRI Kanjuruhan Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/smartics.v12i1.13931

Abstract

Acoustic noise refers to unwanted sound disturbances occurring during data recording and collection. This interference often mixes with desired signals, creating challenges in accurately detecting intended voice commands. This study focuses on the implementation of the Kalman Filter Algorithm to mitigate acoustic noise in a voice recognition-based door lock system. The system utilizes an INMP441 microphone module interfaced with an ESP32 microcontroller, which transmits audio data to an edge server. The Kalman Filter Algorithm is deployed on the ESP32 to denoise audio signals from environmental interference. Keyword detection is performed using six comprehensive analysis algorithms, which examine syllable structure, energy patterns, consonant-vowel differentiation, temporal patterns, spectral analysis, and voice characteristics. Experiments were conducted under various noise intensity levels and pronunciation volume variations, comparing performance both with and without the Kalman Filter. The results indicate that the Kalman Filter improved system accuracy from 69.6% to 83.2%, while effectively mitigating false positives—instances where incorrect inputs are erroneously validated as correct. The system successfully recognized the keyword "buka" (open), achieving an average response time of 6.54 seconds to unlock the door following keyword confirmation by the edge server. However, high-intensity noise, such as that produced by grinding machinery, presents significant detection challenges. Recommendations for future research include enhancing microphone sensitivity and developing adaptive keyword detection methods to manage diverse acoustic environments and noise conditions more effectively.
Implementasi Algoritma Prophet dengan Grid Search Hyperparameter Tuning untuk Prediksi Konsumsi Energi Listrik Berbasis IoT Suhardi Suhardi; Tedy Rismawan; Cucu Suhery; Irma Nirmala
Bulletin of Computer Science Research Vol. 6 No. 5 (2026): August 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i5.1182

Abstract

?Real-time monitoring of household electricity consumption is not yet sufficient to support adaptive energy management. Therefore, an accurate yet easily interpretable prediction capability is required. This study implements the Prophet algorithm, an additive time series model based on trend and seasonal components, as the core method for predicting daily energy consumption in an Internet of Things (IoT)-based system with per-room granularity. Data were obtained from PZEM-004T sensors and NodeMCU ESP32 modules in three rooms over 30 days, processed through a two-stage grid search procedure for model hyperparameter optimization. The evaluation results show a testing MAPE of 1.05%–2.01% across the three rooms, all falling into the highly accurate category (<10%). Furthermore, the average MAPE difference between the training and testing data reached only 0.42 percentage points, indicating good model generalization without overfitting. Component decomposition analysis reveals that the consumption pattern is dominated by a stable linear trend with a low-amplitude weekly seasonal pattern (±0.06 kWh), thereby providing a higher level of interpretability compared to black-box models. The 30-day-ahead projection yields a total estimated consumption of approximately 384 kWh (~IDR 554,817) for the three rooms, which can be utilized as a basis for budget planning and adaptive electrical load management. The main contribution of this study is a transparent and reproducible Prophet tuning procedure for per-room electricity consumption data with limited historical volume, supported by metrological validation of the acquisition sensor as an input quality assurance step, a context that has not been widely explored in prior Prophet literature
Co-Authors Abdul Malik Asrul Gunawan Baiti, Nurul Bestari, Umi Cahyani, Ika Carlos Aprillio Angger Gindaong Destia Arini Hairunnisa Dwi Kusumayani Dwi Marisa Midyanti Elvi Rusmiyanto Pancaning Wardoyo, Suci Lestari, Mukarlina, Evi Noviani Gunawan Gunawan Gusrizal Gusrizal Hafidza Nur &#039;Ainika Rani Hafiz Muhardi Hariyadi, Hadiyan Fahmi Hestivera, Eva Novianti Hirzen Hasfani Ibnur Rusi Ika Nurul Hidayah Ikhwan Ruslianto Ilhamsyah Indriana, Fitrialfi Irma Nirmala Kafriandeni, Okhari Kartika Sari Kasliono Kasliono Marisa Midyanti, Dwi Muhammad Luthfi Mulyana, Dandi Rahmanita Widiyanti Rahmi Hidayati Renny Puspita Sari Risny Sari Gumala Suhardi Suhardi Suhardi Suhardi Surayya Safira Milania Syahru Rahmayudha, Syahru Syamsul Bahri Syamsul Bahri Syarifah Iin Safitri Sylviana Kusuma Tedy Rismawan Trianny Syantikha Uray Ristian Willy Surya Hidayat Yudha Arman