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All Journal Jurnal SOLMA Indonesian Journal of Electrical Engineering and Computer Science SinarFe7 Malcom: Indonesian Journal of Machine Learning and Computer Science
Aziza, Miladina Rizka
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Other

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Optimizing stress resistance in MEMS inertial sensors through material and thickness variations Aziza, Miladina Rizka; Setyawati, Onny; Jumiadi, Jumiadi
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp110-117

Abstract

Stress on the micro-electromechnical system (MEMS) sensors significantly decreases sensor accuracy. Thermomechanical stresses induced by the packaging assembly process and external loads during operation induce a shift in the output signal (offset) of MEMS sensors. To achieve high precision in accelerometers, gyroscopes, and other MEMS devices, it is crucial to employ advanced modeling and simulation techniques to mitigate stress-induced offset drift. Therefore, this paper aims to explore and simulate stress on inertial sensors by designing a gyroscope tuning fork with a perforated proof mass to reduce the damping effect. Our findings provide insights for decreasing stress by varying the material and thickness of the inertial sensor. The least stress was obtained from an inertial silicon sensor with 5 and 20 mm thicknesses.
Strategi Pengolahan Sampah Organik Berkelanjutan melalui Pembuatan Eco-Enzyme di Kalangan Siswa SMA Bahrul Maghfiroh Malang Dwi Hanggara, Fuad; Aziza, Miladina Rizka; Hasanah, Novrindah Alvi; Zafirah, Yasmin; Junikhah, Allin; Zul Fahmi, Fariz Rifqi
Jurnal SOLMA Vol. 13 No. 2 (2024)
Publisher : Universitas Muhammadiyah Prof. DR. Hamka (UHAMKA Press)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22236/solma.v13i2.14981

Abstract

Background: Setiap tahunnya, Indonesia telah mengirimkan 13,8 m3 sampah ke tempat pembuangan sampah, tetapi hanya sekitar 2,6% dari total sampah yang didaur ulang di sumbernya. Adanya eco-enzyme telah memberikan pendekatan baru yang bertujuan untuk mengurangi sampah TPA. Metode: Penelitian ini bertujuan untuk melatih siswa SMA Islam Bahrul Maghfiroh Malang tentang pengelolaan sampah, mulai bulan Mei 2023 hingga Juli 2023. Kegiatan ini merupakan upaya bersama antara tim pengabdian masyarakat Universitas Islam Negeri Maulana Malik Ibrahim Malang dan siswa SMA Bahrul Maghfiroh. Kegiatan yang dilakukan mengikuti model ABCD (Asset Based Community Development), meliputi sosialisasi, demonstrasi, praktik, dan monitoring. Hasil: Setelah dilakukan intervensi melalui kegiatan sosialisasi program pengabdian masyarakat, terjadi peningkatan pada pengetahuan, perilaku, emosi, dan kesadaran ekologi. Sehingga, warga sekolah mulai memilah sampah organik dan mengolahnya menjadi eco-enzyme. Kesimpulan: Kegiatan sosialisasi dan pelatihan pengolahan sampah organik menjadi eco-enzyme dapat disimpulkan bahwa terdapat perubahan pengetahuan, perilaku, emosi dan kesadaran ekologis setelah dilakukan intervensi melalui kegiatan sosialisasi.
Pengendalian Gerak Robot Beroda Menggunakan Sarung Tangan Pintar dengan Neural Network Backpropagation Arif, Yunifa Miftachul; Mustofa, Ahmad Habibil; Holle, Khadijah Fahmi Hayati; Wibowo, Muhammad Ismail Arjun; Aziza, Miladina Rizka; Junikhah, Allin; Hasanah, Novrindah Alvi
SinarFe7 Vol. 7 No. 1 (2025): SinarFe7-7 2025
Publisher : FORTEI Regional VII Jawa Timur

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Abstract

Pengendalian robot berbasis remote control konvensional kerap memerlukan adaptasi dan pembelajaran baru bagi pengguna, khususnya bagi mereka yang belum terbiasa dengan tata letak tombol yang kompleks. Penelitian ini menawarkan solusi yang lebih intuitif melalui pendekatan Hand Gesture Recognition berbasis sarung tangan pintar (smart glove) yang dilengkapi sensor MEMS berupa akselerometer dan giroskop. Data pergerakan tangan yang diperoleh diolah menggunakan metode Neural Network Backpropagation untuk mengenali lima jenis gerakan, yaitu diam, maju, mundur, belok kiri, dan belok kanan. Sistem dikembangkan pada mikrokontroler STM32F10C dengan modul nirkabel NRF24L01 sebagai media transmisi data ke robot beroda. Pengujian dilakukan oleh satu orang pengguna dengan sepuluh kali percobaan untuk setiap gerakan. Hasil klasifikasi menunjukkan tingkat akurasi rata-rata sebesar 82,8%, dengan respon yang cepat dan stabil terhadap perintah yang diberikan. Temuan ini membuktikan bahwa pengendalian robot dapat dilakukan secara lebih natural, efisien, dan responsif hanya dengan gerakan tangan, sehingga berpotensi dikembangkan untuk aplikasi yang lebih luas di masa depan.
Design and Implementation of NodeMCU ESP8266-Based Smart Home System as Residential Energy Efficiency Solution Aziza, Miladina Rizka; Widyanto, Hafidin Bangun; Dwiputra, Febrian Daniel; Cahayanti, Putri Nur; Prawimuliasta, Enggar Kabisafira; Nugroho, Rizky Wahyu
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 3 (2026): MALCOM July 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i3.2902

Abstract

The household sector accounts for approximately 42.41% of Indonesia's total electricity consumption (122,339.69 GWh in 2023), making it a strategic priority for energy efficiency efforts aligned with the Sustainable Development Goals (SDGs), particularly SDG 7, SDG 9, SDG 11, and SDG 13. This study presents the design of a NodeMCU ESP8266-based smart home system integrated with the ThingSpeak platform and an Android application built with MIT App Inventor. This system is capable of integrating three main functions, including controlling and monitoring room lights using an LDR sensor, controlling the AC along with monitoring temperature and humidity using a DHT11 sensor, and monitoring water levels using an HC-SR04 ultrasonic sensor within a single low-cost NodeMCU platform. System performance was evaluated through functional testing of each subsystem using calibrated reference instruments, with accuracy calculated by comparing measured values against theoretical reference values. The DHT11 sensor achieved 95.62% accuracy for temperature and 98.15% for humidity, while the HC-SR04 sensor achieved 97.06% accuracy. With a total component cost below IDR 1.5 million, this system demonstrates that a multi-subsystem smart home solution can be implemented affordably using locally available components, supporting residential energy efficiency and the SDGs 2030 agenda.
Co-Authors Cahayanti, Putri Nur Dwi Hanggara, Fuad Dwiputra, Febrian Daniel Jumiadi, Jumiadi Junikhah, Allin Khadijah Fahmi Hayati Holle Mustofa, Ahmad Habibil Novrindah Alvi Hasanah Nugroho, Rizky Wahyu Onny Setyawati Prawimuliasta, Enggar Kabisafira Wibowo, Muhammad Ismail Arjun Widyanto, Hafidin Bangun Yasmin Zafirah, Yasmin Yunifa Miftachul Arif Zul Fahmi, Fariz Rifqi