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Artificial Neural Network Application for Aroma Monitoring on The Coffe Beans Blending Process Susanti Roza; Zas Ressy Aidha; Milda Yuliza; - Suryadi; Surfa Yondri
JOIV : International Journal on Informatics Visualization Vol 2, No 3 (2018)
Publisher : Politeknik Negeri Padang

This study aims to identify the type of coffee powder aroma from the coffee beans blending using backpropagation artificial neural network (ANN). Backpropagation is a controlled training implementing a weight adjustment pattern to achieve a minimum error value between the the predicted and the actual output. Within this study, the coffee aroma testing utilized electronic tasting sensor system consisted of 4 sensors namely TGS 2611, TGS 2620, TGS 2610 and TGS 2602. The coffee aroma monitoring and data collection in this system applied LabVIEW software as a virtual instrumentation. The testing result of this ANN was able to distinguish the coffee variety of Robusta, Arabica coffee powder and the one without any coffee aroma. The backpropagation architecture was formed by 3 layers consisting of 1 input layer with 4 input nerve cells, 1 hidden layer with 8 neural cells, and 2 output layers by applying the backpropagation training algorithm. The training data was taken from 70 data samples of each circumstance of coffee with 5 testing times. The results of the training and testing showed that the established backpropagation was capable to identify and differenciate the coffee powder in accordance with the given input with different average success rate; 91.96% for Robusta coffee, 100 % for Arabica coffee, and no 84.24% for without coffee aroma.

Monitoring Suhu Proses Sangrai Biji Kopi Roza Susanti -; Zas Ressy Aidha; Fedri Tri Satrio
Jurnal Ilmiah Poli Rekayasa Vol 12, No 2 (2017)
Publisher : Pusat Penelitian dan pengabdian kepada Masyarakat (P3M) Politeknik Negeri Padang

Temperature set of the coffee bean roasting to get the roasting temperature of arabica using thermocouple temperature cencor. Temperature monitoring is controlled by microcontroller. The suitable temperature set of roasting process determine the temperature of three conditions, they are light roast, medium roast, and dark roast. Roasting temperature set of the coffee bean have a goal to get the best quality of coffee bean.

The Use of Artificial Neural Networks (ANN) in the Chayote Chips Dough Mixer Roza Susanti; Zas Ressy Aidha; Surfa Yondri; Sir Anderson; Tri Oktaviandra
Andalas Journal of Electrical and Electronic Engineering Technology Vol. 2 No. 2 (2022): November 2022
Publisher : Electrical Engineering Dept, Engineering Faculty, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/ajeeet.v2i2.27

This study uses a backpropagation neural network to determine the evenness of the chayote chip dough. The Tcs3200 Color Sensor mounted on the stirrer alt is used as a sensor to determine the color of the chayote emping dough. A regression score of 1 indicates that the input and target data match in the test results of the artificial neural network, which has an objective error (MSE) value of 0.0096306 achieved in the 313th epoch. Changes in RGB color readings on the TCS sensor from min values ??<40 and max values>52 in mixing dough are influenced by distance and light intensity which will be converted in the form of frequency.

Planning Penerangan Jalan Umum (PJU) Mushalla Al-Barkah Lambung Bukit Kec. Pauh Kota Padang Yona Mayura; Zulka Hendri; Akbar Abadi; Aldi Rahman; Fibriyanti Fibriyanti; Zas Ressy Aidha
Jurnal Andalas: Rekayasa dan Penerapan Teknologi Vol. 3 No. 1 (2023): Juni 2023
Publisher : Jurusan Teknik Elektro, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jarpet.v3i1.41

Penerangan jalan akan lebih bermanfaat apabila ditempatkan pada area jalan menuju tempat-tempat ibadah seperti, masjid, mushola, gereja, pura, vihara, klenteng, dan tempat-tempat ibadah lainnya. Sebab dengan adanya penerangan jalan menuju tempat ibadah akan mempermudah akses umat beragama untuk melaksanakan ibadah di tempat ibadahnya, misalnya saja umat beragama islam yang selalu melaksanakan ibadah sholat lima waktu sehari semalam secara berjamaah di masjid atau di mushola. Terutama pada malam hari penerangan jalan sangat bermanfaat bagi masyarakat di sekitar masjid atau mushola yang terletak di kawasan yang belum terdapat PJU dari pemerintah setempat, seperti pada kasus mushalla Al-barkah Lambung Bukit kecamatan Pauh kota Padang. Mushalla Al-barkah Lambung Bukit kecamatan Pauh kota Padang merupakan salah satu tempat ibadah umat muslim yang terletak di kawasan yang belum terdapat penerangan jalan dari pemerintah setempat, karena mushola ini dalam kondisi baru dibangun di bagian sisi sebelah jalan. oleh sebab itu, dosen prodi D3 Teknik Listrik PNP berinisiatif untuk melakukan planning PJU pada mushalla Al-barkah yang berpedoman pada SNI 7391:2008. Berdasarkan perhitungan sesuai dengan SNI 7391:2008, maka diperoleh sebanyak 47 titik lampu PJU yang akan dipasang disekitar jalan mushalla Al-Barkah. Namun untuk penerangan jalan sementara mushalla Al-Barkah, maka dipasang lampu sebanyak 6 buah di teras mushalla.

The Use of Artificial Neural Networks (ANN) in the Chayote Chips Dough Mixer Roza Susanti; Zas Ressy Aidha; Surfa Yondri; Sir Anderson; Tri Oktaviandra
Andalas Journal of Electrical and Electronic Engineering Technology Vol. 2 No. 2 (2022): November 2022
Publisher : Electrical Engineering Dept, Engineering Faculty, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/ajeeet.v2i2.27

This study uses a backpropagation neural network to determine the evenness of the chayote chip dough. The Tcs3200 Color Sensor mounted on the stirrer alt is used as a sensor to determine the color of the chayote emping dough. A regression score of 1 indicates that the input and target data match in the test results of the artificial neural network, which has an objective error (MSE) value of 0.0096306 achieved in the 313th epoch. Changes in RGB color readings on the TCS sensor from min values ??<40 and max values>52 in mixing dough are influenced by distance and light intensity which will be converted in the form of frequency.

Identifikasi Warna Adonan Kerupuk Labusiam Menggunakan Sensor Warna Berbasis IoT Susanti, Roza; Alfitri, Nadia; Aidha, Zas Ressy; Hidayat, Anton; Rahmawati, Anisa
Elektron : Jurnal Ilmiah Volume 15 Nomor 2 Tahun 2023
Publisher : Teknik Elektro Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/eji.0.0.416

Chayote (Sechium edule) is a fruit vegetable with nutritional content, which rich in minerals and vitamins. In West Sumatra, precisely in Tanah Datar Regency, Salimpauang District, chayote has become a household product, not only is it processed into vegetables, but chayote is also processed into snacks such as chayote chips. The process of processing chayote is generally still carried out conventionally by the Salimpauang community. To overcome the problem of automatic and remote monitoring of equipment during dough mixing by applying the IoT method. This monitoring system integrated with IoT was created using a NodeMCU 8266 microcontroller device and a TCS3200 color sensor. The TCS3200 color sensor is used to monitor color changes in chayote dough. Apart from that, this sensor is also used to determine differences in the evenness of the dough color. The RGB color of the dough so that the average value for a smooth texture is R>53, G>92 and B>42.

Identification of Bioethanol Quality for Motorcycle Fuel Angraini, Tuti; Aidha, Zas Ressy; Anton; Kurniadi, Dedi; Prabowo, Cipto
International Journal of Advanced Science Computing and Engineering Vol. 5 No. 3 (2023)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/ijasce.5.3.173

The availability of crude oil as a raw material for vehicle fuel is dwindling and limited in nature. One of the renewable energies worth developing is bioethanol, which is one of the alternative fuels that can be used as a biofuel and can be processed from plants containing starch and glucose. In this research, the entire bioethanol identification system in a sugar cane drip distillation apparatus was examined. The distillation process using MQ3 and MQ135 sensors resulted in an alcohol percentage of 42% and 46%. The maximum temperature measured by a thermocouple during distillation was 88°C, while the minimum temperature recorded was 87°C. This study utilized a backpropagation artificial neural network method to identify the detected bioethanol. The architecture of the artificial neural network included 2 input nodes, 4 neuron nodes, and 2 output nodes. The training and testing results showed that the formed backpropagation was able to identify and differentiate the detection of bioethanol according to the given inputs with a success rate of 88.86% for detected bioethanol and 94.86% for undetected bioethanol..

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