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All Journal Jurnal Teknik Sipil Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control INFOKUM Jurnal Pengabdian UNDIKMA
Fadhillah Azmi
Electrical Engineering Department, Faculty Of Engineering, Universitas Medan Area
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Nursing Public Health Social Sciences Other

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Smart Door System using Face Recognition Based on Raspberry Pi Fadhillah Azmi; Insidini Fawwaz; Rina Anugrahwaty
INFOKUM Vol. 10 No. 1 (2021): Desember, Data Mining, Image Processing, and artificial intelligence
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (865.796 KB)

Abstract

A smart door system is a door with a smart digital lock system where someone can open the door or give permission to enter the house by authenticating the user. Basically, the technology used for smart door implementation uses a microcontroller as its controller and is combined with identification in the form of a password. The technology can be combined with other techniques, such as using facial recognition. This is done because data security using alphanumeric combination passwords is no longer used, so it is necessary to add security that is difficult to manipulate by certain people. The type of security offered is facial recognition biometric technology which has different characteristics. This study will design a smart door system that is built using Raspberry Pi-based facial recognition as a controller. The facial recognition algorithm will interact with the webcam and solenoid lock using the Raspberry Pi.Based on the results of the study, it was found that the smart door system with facial recognition can be done well and obtains an accuracy of 94%. The application of the smart door system proposed in this study is considered capable of increasing home security which can be controlled automatically using facial recognition. A smart door system is a door with a smart digital lock system where someone can open the door or give permission to enter the house by authenticating the user. Basically, the technology used for smart door implementation uses a microcontroller as its controller and is combined with identification in the form of a password. The technology can be combined with other techniques, such as using facial recognition. This is done because data security using alphanumeric combination passwords is no longer used, so it is necessary to add security that is difficult to manipulate by certain people. The type of security offered is facial recognition biometric technology which has different characteristics. This study will design a smart door system that is built using Raspberry Pi-based facial recognition as a controller. The facial recognition algorithm will interact with the webcam and solenoid lock using the Raspberry Pi.Based on the results of the study, it was found that the smart door system with facial recognition can be done well and obtains an accuracy of 94%. The application of the smart door system proposed in this study is considered capable of increasing home security which can be controlled automatically using facial recognition.
STUDI PERBANDINGAN ANALISIS STRUKTUR BALOK MENGGUNAKAN APLIKASI BERBASIS ANDROID dan SAP2000 Samsul A Rahman Sidik Hasibuan; Fadhillah Azmi; Yuan Anisa
Jurnal Teknik Sipil Vol 6 No 1 (2022): Jurnal Gradasi Teknik Sipil - Juni 2022
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/gradasi.v6i1.1337

Abstract

Struktur balok merupakan tipe elemen struktural yang penting untuk para profesional konstruksi serta mayoritas insinyur wajib akrab dengan balok. Tipe struktur balok yang dibahas dalam tulisan ini merupakan struktur balok sederhana (simple beam). Dalam menghitung gaya-gaya serta deformasi struktur secara numerik memerlukan waktu yang lebih lama serta memerlukan ketelitian yang baik. Pada masa yang semakin canggih ini, telah tersedia bermacam software serta aplikasi yang dapat digunakan untuk mendapatkan gaya-gaya dalam serta deformasi struktur dengan cepat dan akurat. Salah satu software yakni SAP2000 dan salah satu aplikasi yakni Easy Beam dapat digunakan untuk mendapatkan gaya-gaya dalam serta deformasi struktur dengan cepat dan akurat. Dalam tulisan ini struktur balok sederhana dimodelkan serta dianalisis menggunakan software SAP2000 dan aplikasi Easy Beam. Tulisan ini bertujuan untuk membandingkan hasil analisis dengan software SAP2000 dan aplikasi Easy Beam khususnya balok sederhana. Selanjutnya, hasil analisis dengan software SAP2000 dan aplikasi Easy Beam telah diperoleh serta telah bahas. Hasil analisis menunjukan bahwa nilai-nilai yang diperoleh tidak memiliki perbedaan yang signifikan. Kata kunci— simple beam, Easy Beam analysis, SAP2000
The Implementation of a Learning Management System for Improving Teacher Knowledge and Skills in MTs. Teladan Medan Fadhillah Azmi; Amir Saleh; N P Dharshinni; Despaleri Perangin-Angin
Jurnal Pengabdian UNDIKMA Vol. 4 No. 2 (2023): May
Publisher : LPPM Universitas Pendidikan Mandalika (UNDIKMA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/jpu.v4i2.6611

Abstract

The implementation of community service (PKM) aims to improve teachers' ability to teach by applying the website-based learning management system (LMS) application at MTs. Teladan Medan is the location of the dedication that has been done. The application that had been developed was a contribution to PKM partners to helped the school conduct learned management, especially in implementing online learned.  PKM activities were carried out through several activities, namely: socialization, training, and assistance in the used of the website-based learned management system (LMS) application that had been developed for several 16 teachers. The instruments used in the form of questionnaires, pretests, and posttests were distributed before and after the training. Based on the results of the activities carried out, there was an increase in teacher knowledge about integrated learning technology using the learning management system application, with a score of 75.65%, and an increase in teacher skills in using or applying the learning management system application, with a score of 80.47%.
A Gradient Boosting–Based Platform with Fuzzy Linguistic Representation for Cardiovascular Disease Risk Prediction Amir Saleh; Fadhillah Azmi
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 3, August 2026 (Article in Progress)
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i3.2699

Abstract

Cardiovascular disease (CVD) is one of the most common causes of death around the world. In order to effectively prevent and manage CVD, early detection and prediction of risk are essential. This research introduces a healthcare platform based on CVD risk prediction using advanced machine learning (ML) methods. This platform is designed to provide accurate risk assessment by integrating the gradient boosting (GB) classifier method. Additionally, other ML models are used as comparison algorithms. Initially, this research used preprocessing techniques such as data normalization and data cleaning to tackle outliers in the dataset. Recursive feature elimination (RFE) feature selection approaches are utilized to find features that affect prediction performance, hence lowering the amount of data dimensions and enhancing model performance. Then, using metrics such as accuracy, precision, recall, and F1-score, each model’s performance is evaluated. The modeling results of the suggested approach are then used to create a digital health platform that predicts new input from users. Additionally, fuzzy logic is applied to transform data into linguistic variables to help users find simpler information. Using the proposed GB model and preprocessing method, the platform can make more accurate CVD risk predictions during data validation than other ML methods. When compared to other approaches with lower accuracy, the evaluation results demonstrate that the GB method can achieve the highest prediction accuracy of 94.30%.
Co-Authors Achmad Ridwan Amir Saleh Despaleri Perangin-Angin Insidini Fawwaz M Khalil Gibran N P Dharshinni Nanda Novita Rina Anugrahwaty Samsul A Rahman Sidik Hasibuan Yuan Anisa