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All Journal Jurnal Intake : Jurnal Penelitian Ilmu Teknik dan Terapan Jurnal JEETech
Lailia Rahmawati
Universitas Darul ulum
Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Mechanical Engineering

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Deteksi Abnormality Dengan Menggunakan Algortima KLT Pada Citra Video CCTV Lailia Rahmawati
Jurnal Intake : Jurnal Penelitian Ilmu Teknik dan Terapan Vol. 8 No. 2 (2017): Oktober 2017
Publisher : FT- UNDAR

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.48056/jintake.v8i2.27

Abstract

Abnormality Detection on CCTV video images that are shown on a particular individual or a particular object where the movement is captured by a minority. The use of KLT Algorithm (Kanade-Lucas-Tomasi) as Algortima Tracing results of a video that has been done Residual Spectral process that aims to produce a global movement that allocates an interesting area. From the comparison results it is known that the number of pixels in the detection move smaller than the detection is not moving. Then the result is that, the abnormal minority is that there is an object moving on its own compared to the other objects
Implementasi Sensor Gas MQ-136 Dan MQ-137 Untuk Mendeteksi Kesegaran Daging Sapi Menggunakan Metode Neural Network Lailia Rahmawati; Achmad Maulana Hakimuddin; Izzatul Umami
Jurnal Intake : Jurnal Penelitian Ilmu Teknik dan Terapan Vol. 12 No. 1 (2021): Jurnal Intake : Jurnal Penelitian Ilmu Teknik dan Terapan
Publisher : FT- UNDAR

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (741.013 KB) | DOI: 10.48056/jintake.v12i1.138

Abstract

Daging sapi merupakan pangan penting dalam budaya dan tradisi makanan di Indonesia, walaupun konsumsi daging sapi relative lebih rendah dibandingkan dengan konsumsi ikan ataupun ayam broiler[5]. Bahan olahan makanan dari daging sapi haruslah dipilih yang berkualitas baik. Kualitas daging sapi bergantung dengan tingkat kesegaran daging sapi tersebut. Tingkat kesegaran daging sapi ditentukan dari warna, tekstur, rasa dan aroma. Penelitian ini merancang suatu sistem untuk menentukan tingkat kesegaran daging sapi dengan menggunakan neural network. Sistem ini memanfaatkan electronic nose dengan menggunakan sensor gas dengan jenis MQ-136 dan MQ-137. Data sensor diproses ke mikrokontroler dan mikrokontoler mengirimkan data sensor ke PC yang telah terprogram neural network. Hasil percobaan menunjukkan tingkat keberhasilan 70% dari 3 kali pengujian daging sapi segar dan tingkat keberhasilan terbaik 100% dari 3 kali pengujian daging busuk. Pada sistem ini diharapkan dapat menggantikan indra penciuman manusia dan membantu manusia untuk mendapatkan daging sapi yang segar dan layak konsumsi.
PENERAPAN CHATBOT AUTO REPLY PADA WHATSAPP SEBAGAI PELAYANAN INFORMASI FIFGROUP MENGGUNAKAN ARTIFICIAL INTELEGENCE Lailia Rahmawati; Fiana Kusuma Agis Kuncara; Izzatul Umami
Jurnal Intake : Jurnal Penelitian Ilmu Teknik dan Terapan Vol. 12 No. 2 (2021): Jurnal Intake : Jurnal Penelitian Ilmu Teknik dan Terapan
Publisher : FT- UNDAR

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.48056/jintake.v12i2.172

Abstract

Abstrak − Chatbot adalah pesan robot yang otomatis akan berjalan seperti robot yang otomatis bergerak. Chatbot akan bisa membalas pertanyaan pengguna dengan materi yang telah ditentukan. Pada masa sekarang sudah banyak program chatbot yang telah berjalan dalam membantu melakukan kegiatan yang bersifat online untuk memudahkan berinteraksi dan memperoleh informasi dari suatu materi yang di ingikan respon dari chatbot bisa berupa jawaban langsung atau pun juga pilihan kata dan pilihan nomor terlebih dahulu sampai nanti akan sampai pada respon yang di inginkan konsumen. Dalam chatbot auto replay sebagai layanan informasi FIFGROUP ini menggunakan aplikasi platform landbot app dan bahasa dilaogflow dalam menganalisis pertanyaan yang akan diajukan oleh konsumen. Dan juga memanfaatkan aplikasi Whatsapp yang sudah banyak konsumen gunakan untuk komunikasi menggunakan android pada masa sekarang. Dan aplikasi penghubung ke whatsappnya adalah whatsauto. Karena chatbot ini nanti di program menyerupai customer virtual yang dapat setiap saat akan membantu berbagai kebutuhan tentang layanan informasi FIFGROUP .
Pemanfaatan Teknologi Computer Vision Untuk Implementasi Deteksi Masker Menggunakan Metode Supervised Learning Lailia Rahmawati; Muhammad Rifki Arrosid; Mohammad Gugus Azhari
Jurnal JEETech Vol. 4 No. 2 (2023): Nomor 2 November
Publisher : Universitas Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32492/jeetech.v4i2.4201

Abstract

The use of masks is still very strict in public places, especially in hospitals, this is solely done to prevent the spread of the corona virus again. The purpose of this research is to assist examination workers or health protocol workers in supervising the use of masks in public places. Mask detection is a solution to this problem, by utilizing computer vision technology and applying supervised learning algorithms. For this mask detection classification method, this system uses the Naive Bayes method. The output of this mask detection system is planned to distinguish people wearing masks and not wearing masks, by giving red labels to people who are not wearing masks and green labeling to people wearing masks. The distance aspect is used in testing this mask detection system, the system is able to work well by getting an error rate presentation below 2% and getting the highest accuracy of 100% with an average percentage value of 98%. On the other hand, there are still weaknesses in this system, the use of brown masks that are in harmony with skin color can worsen the results of the classification system
Prototype Sistem Monitoring Kebakaran Berbasis IoT Menggunakan Node MCU Dengan Penyemprot Air Otomatis Lailia Rahmawati; Yoga Yusuf Pratama; Muhammad Gugus Azhari
Jurnal JEETech Vol. 3 No. 1 (2022): Nomor 1 May
Publisher : Universitas Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.48056/jeetech.v3i1.189

Abstract

Fires are very detrimental, ranging from loss of property to even loss of life. Fires can be detected when the fire has grown or smoke has billowed out of buildings. Urban areas have densely populated housing so that it has the potential to cause problems in the event of a fire. These fires can be caused by by factors of human negligence and natural factors. Such as the occurrence of short-circuited electricity, negligence in cooking, negligence in burning garbage, and others. Therefore, we need a tool that can detect the presence of an early source of fire so that it does not spread. The problem that often occurs is when a fire occurs, the fire department often arrives late, so there are many losses due to the fire. And the losses can be material and economic. Therefore we need a system that works to control the fire automatically and to extinguish the fire in every house. This study aims to design and build an efficient and affordable prototype. Which later can be applied to housing or warehouses. This prototype is designed using NodeMCU ESP8266 as the main controller, Flame sensor can respond to infrared light beams on the modulation spectrum from 5 to 30 cycles per second, Web Thinger.Io as a server, data storage on the internet of things , and notification of fire in the Blynk application. This system uses a flame sensor based on the Node MCU ESP8266 microcontroller and is equipped with a Buzzer for Alarm and a Mini Water Pump to automatically spray water if a fire is detected. This system utilizes the Blynk Application as an interface to provide notification information about a fire to find out notifications if a fire is detected. And if a fire is detected, it will automatically get a notification from the Blynk application on the "There is Fire" Smartphone and the buzzer will automatically sound and the mini water pump will automatically spray water. In this study, the tool can automatically send Safe and Fire notifications using the blynk application on a smartphone with a delay of < 3 seconds.
Co-Authors Achmad Maulana Hakimuddin Fiana Kusuma Agis Kuncara Izzatul Umami Mohammad Gugus Azhari Muhammad Gugus Azhari Muhammad Rifki Arrosid Yoga Yusuf Pratama