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<![CDATA[Penerapan Kontroler Neural Fuzzy Untuk Pengendalian Kecepatan Motor Induksi 3 Fasa Pada Mesin Sentrifugal ]]>
<![CDATA[Putri, Ratna Ika]]>;
<![CDATA[Fauziyah, Mila]]>;
<![CDATA[Setiawan, Agus]]>
<![CDATA[ INKOM Journal Vol 3, No 1-2 (2009) ]]>
Publisher : <![CDATA[Pusat Penelitian Informatika - LIPI ]]>
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DOI: 10.14203/j.inkom.41
<![CDATA[Motor induksi 3 fasa adalah alat penggerak yang paling banyak digunakan dalam dunia industri. Salah satu aplikasi motor induksi pada industri gula adalah pada mesin sentrifugal yang digunakan pada proses sentrifugasi. Mesin sentrifugal menggunakan motor induksi sebagai penggerak untuk memutar chamber mesin sentrifugal yang berisi massacuite yang akan diolah. Tujuan penelitian ini adalah merancang kontroler neural fuzzy yang digunakan untuk mengendalikan kecepatan motor induksi sehingga dapat mempertahankan kecepatan sesuai setpoint walaupun terjadi perubahan beban. Motor induksi dimodelkan dengan menggunakan transformasi dq dan algoritma kontroler disimulasikan dengan MATLAB. Kontroler neural fuzzy menggunakan 2 layer dengan jumlah neuron yang diubah yaitu 20 dan 50 neuron. Dari hasil simulasi didapatkan, kontroler neural fuzzy 50 neuron memiliki settling time lebih cepat tetapi error steady state lebih besar dan tidak menghasilkan overshoot pada respon kecepatan. Sedangkan pada kondisi terbebani, kontroler dapat mempertahankan kecepatan sesuai dengan setpoint. Waktu pemulihan kontroler 50 neuron lebih cepat dibandingkan kontroler neural fuzzy dengan 20 neuron.]]>
<![CDATA[Conditioning of Temperature and Soil Moisture in Chrysanthemum Cut Flowers Greenhouse Prototype based on Internet of Things (IoT) ]]>
<![CDATA[Fauziyah, Mila]]>;
<![CDATA[Safitri, Hari Kurnia]]>;
<![CDATA[Dewatama, Denda]]>;
<![CDATA[Aulianta, Erdin]]>
<![CDATA[ ELKHA : Jurnal Teknik Elektro Vol. 13 No. 1 April 2021 ]]>
Publisher : <![CDATA[Faculty of Engineering, Universitas Tanjungpura ]]>
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DOI: 10.26418/elkha.v13i1.43078
<![CDATA[Currently, cut chrysanthemum cultivation in a greenhouse is still using a conventional system. Temperature and soil moisture are the most important factors in the growth process. If the temperature and humidity of the soil are not conditioned, the roots will quickly rot and slow the growth process of chrysanthemums. Internet of Things is an integrated system with a data-based server that stores data in the cloud from sensors so that the system can be monitored remotely in real-time. Based on this, an integrated system was designed to make it easier for farmers to condition the temperature and humidity of the chrysanthemum flower soil. In temperature conditioning, if the temperature of cut chrysanthemum is detected <24 ℃ then the heater will be "on" by adjusting the fan rotation and vice versa. Meanwhile, soil moisture conditioning is carried out by distributing water if the detected soil moisture is <50%, then the water pump is in the "on" state. The data on the degree of temperature and the percent of soil moisture will be recorded into the cloud which will then be displayed in the form of graphs and history data on the webserver and Android. By using this system, it is found that the growth process of cut chrysanthemums can grow 7 days faster than the standard harvest time of 30 days.]]>
<![CDATA[Implementasi JST Backpropagation pada Face Recognition untuk Percepatan Proses Sistem Absensi ]]>
<![CDATA[Hijriah, Ayu]]>;
<![CDATA[Fauziyah, Mila]]>;
<![CDATA[Dewatama, Denda]]>
<![CDATA[ Jurnal Elektronika Otomasi Industri Vol 1, No 1 (2014) ]]>
Publisher : <![CDATA[Politeknik Negeri Malang ]]>
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DOI: 10.33795/elkolind.v1i1.29
<![CDATA[Sistem absensi merupakan salah satu bagian terpenting pada kegiatan yang berorientasi pada kehadiran. Karakteristik penting dari sistem absensi adalah sistemnya ringkas untuk pengguna, komputasinya cepat, tidak memerlukan peralatan tambahan bagi pengguna, dan tidak mudah untuk dimanipulasi. Skripsi ini mengakomodasi karakteristik penting tersebut dengan menerapkan face recognition. Hasil face recognition ini diharapkan dapat tercapai dengan menggunakan backpropagation JST. Proses identifikasi diawali dengan pengambilan data citra digital menggunakan kamera Logitech E1000. Selanjutnya data citra digital tersebut dibentuk dalam citra berukuran 30×30 pixel. Komputasi pengolahan data citra dilakukan dengan metode JST melalui proses pelatihan backpropagation. Sistem absensi ini didukung dengan hardware berbentuk miniatur rumah dengan fokus pada pergerakan pintu. Pintu tersebut digerakkan oleh servo yang dikontrol oleh mikrokontroller Atmega32 dan dilengkapi dengan LCD matriks agar memudahkan proses debugging. Kesuluruhan sistem mekanik dan elektrik tersebut merupakan hasil dari perintah dalam bentuk program pada sistem software. Sistem software ini terdiri bahasa C pada Codeblock & Codevision AVR dan M-File pada MATLAB. Hasil dari pengujian penelitian ini menunjukkan bahwa hasil citra]]>
<![CDATA[IMPLEMENTASI KONTROLER NEURAL FUZZY PADA PENGATURAN KECEPATAN MOTOR INDUKSI 3 FASA ]]>
<![CDATA[Ratna Ika Putri]]>;
<![CDATA[Mila Fauziyah]]>
<![CDATA[ Seminar Nasional Aplikasi Teknologi Informasi (SNATI) 2010 ]]>
Publisher : <![CDATA[Jurusan Teknik Informatika, Fakultas Teknologi Industri, Universitas Islam Indonesia ]]>
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<![CDATA[Motor induksi 3 fasa adalah alat penggerak yang paling banyak digunakan dalam dunia industri. Salah satuaplikasi motor induksi pada industri gula adalah pada mesin sentrifugal yang digunakan pada prosessentrifugasi. Mesin sentrifugal menggunakan motor induksi sebagai penggerak untuk memutar chamber mesinsentrifugal yang berisi massacuite yang akan diolah. Tujuan penelitian ini adalah merancang kontroler neuralfuzzy yang digunakan untuk mengendalikan kecepatan motor induksi sehingga dapat mempertahankankecepatan sesuai setpoint walaupun terjadi perubahan beban. Motor induksi dimodelkan dengan menggunakantransformasi dq dan algoritma kontroler disimulasikan dengan MATLAB. Kontroler neural fuzzy menggunakan2 layer dengan jumlah neuron yang diubah yaitu 20 dan 50 neuron. Dari hasil simulasi didapatkan, kontrolerneural fuzzy 50 neuron memiliki settling time lebih cepat dibanding tetapi error steady state lebih besar dantidak menghasilkan overshoot pada respon kecepatan. Sedangkan pada kondisi terbebani, kontroler dapatmempertahankan kecepatan sesuai dengan setpoint. Waktu pemulihan kontroler 50 neuron lebih cepatdibandingkan kontroler neural fuzzy dengan 20 neuron..Kata Kunci: Motor Induksi, Kontroler, Neural Fuzzy]]>
<![CDATA[PI Controller Untuk Mengatur Kecepatan Motor Induksi 1 Fasa ]]>
<![CDATA[Siti Nur Alima]]>;
<![CDATA[Mila Fauziyah]]>;
<![CDATA[Denda Dewatama]]>
<![CDATA[ Aviation Electronics, Information Technology, Telecommunications, Electricals, Controls (AVITEC) Vol 2, No 2 (2020): August ]]>
Publisher : <![CDATA[Institut Teknologi Dirgantara Adisutjipto ]]>
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DOI: 10.28989/avitec.v2i2.647
<![CDATA[Induction motors are widely used in the industrial world, home-based businesses as well as in households. Currently in the process of making tofu an induction motor is used as a motor to drive soy blending blades. At this time the use of induction motors is still manually by requiring the operator to regulate the speed of the motor. To reduce operator work, it is necessary to apply PI control as a motor speed controller so that a constant motor rotation is obtained. 1 phase induction motor can be adjusted with variable speed drive (VSD) 0.75KW 1 phase. Blending blade drive uses 0.5HP 1 phase induction motor. In the application of PI control requires some hardware namely Arduino Uno as a minimum system that gives PWM circuit input commands. And the speed sensor as a motor blending speed reader. PI tuning values obtained from the application of the Ziegerl-Nichols I method with the best Kp and Ki tuning values are 1.35 and 0.02673. This research was conducted with 3 speed variables namely 1400 rpm, 1300 and 1200 rpm. From the application of Kp and Ki tuning values, the smallest error value is 4.08% at 1400 rpm with the system response time peak (tp) 5s, rise time (tr) 3s faster, delay time (td) 3s, and settling time (ts) 9s , and a maximum overshoot of 9.8%.]]>
<![CDATA[Penerapan Kontroler Neural Fuzzy Untuk Pengendalian Kecepatan Motor Induksi 3 Fasa Pada Mesin Sentrifugal ]]>
<![CDATA[Ratna Ika Putri]]>;
<![CDATA[Mila Fauziyah]]>;
<![CDATA[Agus Setiawan]]>
<![CDATA[ INKOM Journal Vol 3, No 1-2 (2009) ]]>
Publisher : <![CDATA[Pusat Penelitian Informatika - LIPI ]]>
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Download Original
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DOI: 10.14203/j.inkom.41
<![CDATA[Motor induksi 3 fasa adalah alat penggerak yang paling banyak digunakan dalam dunia industri. Salah satu aplikasi motor induksi pada industri gula adalah pada mesin sentrifugal yang digunakan pada proses sentrifugasi. Mesin sentrifugal menggunakan motor induksi sebagai penggerak untuk memutar chamber mesin sentrifugal yang berisi massacuite yang akan diolah. Tujuan penelitian ini adalah merancang kontroler neural fuzzy yang digunakan untuk mengendalikan kecepatan motor induksi sehingga dapat mempertahankan kecepatan sesuai setpoint walaupun terjadi perubahan beban. Motor induksi dimodelkan dengan menggunakan transformasi dq dan algoritma kontroler disimulasikan dengan MATLAB. Kontroler neural fuzzy menggunakan 2 layer dengan jumlah neuron yang diubah yaitu 20 dan 50 neuron. Dari hasil simulasi didapatkan, kontroler neural fuzzy 50 neuron memiliki settling time lebih cepat tetapi error steady state lebih besar dan tidak menghasilkan overshoot pada respon kecepatan. Sedangkan pada kondisi terbebani, kontroler dapat mempertahankan kecepatan sesuai dengan setpoint. Waktu pemulihan kontroler 50 neuron lebih cepat dibandingkan kontroler neural fuzzy dengan 20 neuron.]]>
<![CDATA[Teknologi Tepat Guna Pemanfaatan Listrik Daya Kecil untuk Pengelolaan Kandang pada Budidaya Ayam Pedaging di Desa Pucangsongo Kabupaten Malang ]]>
<![CDATA[Andriani Parastiwi]]>;
<![CDATA[Mila Fauziyah]]>;
<![CDATA[Dwi Puspitasari]]>
<![CDATA[ Jurnal Pengabdian kepada Masyarakat (Indonesian Journal of Community Engagement) Vol 4, No 1 (2018): September ]]>
Publisher : <![CDATA[Direktorat Pengabdian kepada Masyarakat Universitas Gadjah Mada ]]>
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DOI: 10.22146/jpkm.32973
<![CDATA[One of the problems faced by small-scale broiler cultivators in Pucangsongo-Malang was the use of PLN electricity that exceeds the installed capacity. Partners feel it was too expensive to raise the power capacity. To overcome the problem, timing settings to power the equipment is needed. The cage-smell’ problem needs to be handled because the cage-locations are in settlement. The purpose of this activity was helping partners to increase productivity by helping solve problems from production aspects, as well as the safety. The implementation method was providing appropriate science and technology to partners in the form of electricity utilization management equipped with a cage monitoring system. In addition, it also developed a backup power source safety system that works when the electricity goes off to keep the cage remain conducive. Prior this activity, the cage-capacity was 3300 with an average death of 200 chickens. Currently, the cage-capacity increased to 3500 chickens with deaths down to 110 chickens. With the developed and installed electric power management equipped with monitoring system of cage condition, broiler cultivators in Pucangsongo-Malang can monitor the cage’s state at any time and power shortage problem can be resolved.]]>
<![CDATA[PEMANFAATAN PANEL SURYA UNTUK SISTEM PENERANGAN KANDANG AYAM DI KEPANJEN, KABUPATEN MALANG ]]>
<![CDATA[Ratna Ika Putri]]>;
<![CDATA[Mila Fauziyah]]>;
<![CDATA[Muhammad Rifa’i]]>;
<![CDATA[Supriatna Adhisuwignjo]]>;
<![CDATA[Yulianto Yulianto]]>
<![CDATA[ Aptekmas Jurnal Pengabdian pada Masyarakat Vol 4 No 3 (2021): APTEKMAS Volume 4 Nomor 3 2021 ]]>
Publisher : <![CDATA[Politeknik Negeri Sriwijaya ]]>
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<![CDATA[The partner in this community service program is the Dua Putra farm located in the village of Sengguruh, Kepanjen, Malang Regency. Apart from the cage for laying hens, this Dua Putra farm also has a hatchery cage with a capacity of 1000 chicks. Lighting in the chicken coop is important, especially at night because in addition to functioning as lighting, it also functions as a heater. Currently, the lighting in the chicken coop still uses the PLN electricity network, so that every month the owner has to pay electricity costs, which of course will increase production costs. The purpose of this activity is to provide understanding, knowledge and skills to farmers regarding the use of renewable energy, especially solar heat as a source of electrical energy used for lighting in partner chicken coops. The methods used in this program are appropriate technology innovation, training and coaching as well as monitoring and evaluation of activities. Technological innovation is carried out through the manufacture of a chicken coop lighting system using a microcontroller-based solar panel. The lighting of the chicken coop is supplied by solar heat which is converted into electrical energy by solar panels. The lighting will turn on automatically in the afternoon. Training and coaching are carried out through the socialization of the use of solar panels for lighting and the practice of using and maintaining solar systems using solar panels.]]>
<![CDATA[PENDAMPINGAN BUDIDAYA SAYUR ORGANIK DENGAN SISTEM HIDROPONIK DI DESA BUMIAJI KOTA BATU ]]>
<![CDATA[Hari Kurnia Safitri]]>;
<![CDATA[Bambang Priyadi]]>;
<![CDATA[Indrazno Siradjuddin]]>;
<![CDATA[Mila Fauziyah]]>
<![CDATA[ Jurnal Pengabdian Kepada Masyarakat (J-ABDIMAS) Vol 8 No 2 (2021): JURNAL PENGABDIAN KEPADA MASYARAKAT 2021 ]]>
Publisher : <![CDATA[Publisher UPT P2M Politeknik Negeri Malang ]]>
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DOI: 10.33795/jabdimas.v8i2.136
<![CDATA[Bumiaji village is located on the slopes of the mountains with an altitude of 150mdpl. The majority of bumiaji villagers work as farmers. PKM in activities aim to help farmers in terms of increasing income and diverse agricultural produce on partner land. Currently the partner has about 800 m2 of land that is used for citrus cultivation. Because the old citrus harvest process is about 4-5 months, the partner needs additional income in addition to citrus cultivation. The training conducted by the PKM team is: (1) the introduction of hydroponic system knowledge; (2) training to grow vegetables with hydroponic system. By knowing and knowing the hydroponic system farmers / partners can develop hydroponic systems for various organic vegetables in the rest of the land, so that the results obtained are diverse and increase the income of partners.]]>
<![CDATA[Pembacaan Jarak dan Kecepatan dengan ArUco Marker pada Sistem Koper Follow Me Beroda ]]>
<![CDATA[Agus Triwahyudin]]>;
<![CDATA[Hari Kurnia Safitri]]>;
<![CDATA[Mila Fauziyah]]>
<![CDATA[ Jurnal Teknologi Elektro Vol 21 No 1 (2022): (Januari - Juni) Majalah Ilmiah Teknologi Elektro ]]>
Publisher : <![CDATA[Universitas Udayana ]]>
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DOI: 10.24843/MITE.2022.v21i01.P14
<![CDATA[The Wheeled Follow Me Suitcase is an automatic robot that can follow human journeys to carry goods to meet their needs. This suitcase has 2 modes; manual and automatic modes. The manual mode of the suitcase will be used like a suitcase in general. While the automatic mode, the suitcase will always follow the owner who already has a special mark (markers). The main components of this system are raspberry pi camera, raspberry pi 4, push button, buzzer, motor driver and dc motor. By doing image processing on special marks (markers) so as to produce a variable distance. The results of processing the reading of the distance variable will be processed into a speed variable, so that the error data range will be obtained. The use of markers of the ArUcoMarker type, limits the distance for reading special marks to a maximum distance of 2 meters which is as stable as possible at a distance of 1 meter. To anticipate failure in reading, a buzzer is used as a sign that the follow me suitcase has been far from the user. In the test results using markers of the ArUco Markers type, a minimum distance of 74 mm is produced, a maximum of 2170 mm. With the smallest reading error rate at a distance of 400 mm and 1100 mm while the largest reading error at a distance of 2000 mm and for the average distance reading error of 0.55%.]]>
