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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Ilmiah Mikrotek Jurnal Simantec REKAYASA Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) Buletin Ilmiah Sarjana Teknik Elektro Journal of Robotics and Control (JRC) International Journal of Science, Engineering and Information Technology
Dafid, Ach
Program Studi Teknik Mekatronika, Fakultas Teknik, UniversitasTrunojoyo Madura Jl. Raya Telang, PO BOX 2, Kamal, Bangkalan - 69162
Aerospace Engineering Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering Physics Other

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Journal : REKAYASA

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Kinerja Pendekatan Convolutional Neural Network dan Dense Network dalam Klasifikasi Citra Malaria Dafid, Achmad; Siwindarto, Ponco; Siswojo, Bambang
Rekayasa Vol 14, No 2: Agustus 2021
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/rekayasa.v14i2.10735

Abstract

Indonesia is an archipelago, which three of its five main island consists mainly, or dense tropical rainforest. This rainforest is main breeding ground for malaria disease that mostly affect regions near said forest. In an effort to treat malaria disease, a diagnostic process is performed to correctly identify the disease. Several image pattern recognition technique been developed and have potential to be utilized as malaria diagnostic tool. In this research, a method is described on designing neural network to detect a blood cell parasitized by malaria. The method consists of utilizing a dense network, and a convolutional neural network, to be trained using publicly available training dataset. Both models’ performance is then compared and analyzed. Before the data is used, a process of padding is performed to resize the input image into 200 x 200 pixels. The resized input data is then used to train both models. From the training and testing, it is found that the dense network achiever 64.78% accuracy. On the other hand, model based on convolutional neural network achiever 94.32%. From analysis, it is found that the size of the model being used is not big enough to achieve better performance. Hence, it is suggested for future research to increase the model size in terms of network width and depth. 
Penerapan Sistem Kontrol Adaptif Proportional Integral Derivative (PID) pada Mesin Penimbang Mie dengan Konveyor Dafid, Ach; Umam, Faikul; Budiarto, Hairil
Rekayasa Vol 18, No 2: Agustus, 2025
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/rekayasa.v18i2.31610

Abstract

Indonesia is the second-largest instant noodle consumer in the world after China, with consumption reaching more than 12 billion packs per year. This high demand drives the need for innovation in the production process, especially in the weighing and cutting aspects, which are still carried out manually in small and medium industries. Manual processes not only require more time and energy, but also result in variations in packaging weight that are not uniform and reduce production efficiency. This study aims to design and implement a Proportional Integral Derivative (PID) adaptive control system on a noodle weighing machine with a conveyor. The system was developed using a load cell sensor to measure the noodle dough weight, a servo motor as a cutting actuator, and a DC motor as a conveyor drive, all of which are controlled by an Arduino ATmega 2560 microcontroller. The research methodology includes mechanical design, electronic design, control system programming, sensor calibration, and performance testing. The test results show that the system is able to produce noodle portions with a target weight of 50 grams consistently. The prototype has conveyor dimensions of 100×20×8 cm with a speed of 26 cm/ms, controlled using tuned PID parameters (Kp=1.5; Ki=1; Kd=1.7). From 20 trials, the system produced an average error of 0.75% and a success rate of 99.25%. Thus, the application of the PID adaptive control system has been proven to improve weighing precision, conveyor speed stability, and production efficiency. This innovation is expected to be a simple and affordable solution to support the automation of small and medium industries in Indonesia in facing increasingly fierce food market competition.
PEMETAAN HARGA RUMAH DENGAN MENGGUNAKAN MODEL STATISTIK : GEOGRAPHICALLY WEIGHTED REGRESSION Winarso, Kukuh; Dafid, Achmad
Rekayasa Vol 15, No 3: Desember 2022
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/rekayasa.v15i3.21818

Abstract

Penentuan harga rumah di sebagian kota-kota besar di Indonesia dipengaruhi oleh banyak faktor, salah satunya adalah lokasi rumah. Lokasi rumah menunjukkan hubungan yang positip dengan harga rumah. Lokasi rumah dekat dengan pusat bisnis adalah salah satu hal yang menyebabkan harga rumah menjadi mahal. Disamping itu pusat pemukiman berdasar kepadatan penduduk di satu sisi menyebabkan harga rumah menjadi naik pada posisi yang lain menyebabkan harga rumah menjadi turun. Penelitian ini berbasis pada pemetaan harga rumah yang dipengaruhi oleh pusat bisnis dan pusat pemukiman penduduk dikota Surabaya. Pemetaan Harga rumah ini menggunakan metode Geographically Weighted Regression (GWR). adalah suatu teknik yang membawa kerangka dari model regresi sederhana menjadi model regresi terboboti.
Co-Authors Achmad Fiqhi Ibadillah Adi Kurniawan Adi Kurniawan Saputro Andharini Dwi Cahyani Andriansyah, Adi Doni Abdul Fatah Faikul Umam Fifin Ayu Mufarroha Firmansyah Adiputra Firmansyah Adiputra Hairil Budiarto Ika Oktavia Suzanti Izzuddin Khusnul Fatimah Kukuh Winarso Kurniawan, Denni Maolana, Firman Morshed, Md Monzur Morshed, Md. Monzur Ponco Siwindarto Rullie Annisa Sholeh, Mahrus Siswojo, Bambang Sitepu Debora , Ribka Sudianto, Achmad Imam Sukri, Hanifudin Sulaiman, Yoga Aulia Sumarto SUTIKNO Thinakaran, Rajermani Triwidyaningrum, Vivi Weny Findiastuti WENY FINDIASTUTI, WENY Yuli Panca Asmara Yusuf, Ahcmad Ziyyad Yassar