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All Journal Jurnal INKOM Journal of Robotics and Control (JRC) Jurnal Instrumentasi
Maftukhah, Tatik
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Chemistry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Mechanical Engineering

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Comparative Study of Fuzzy Query Vector Modification and Fuzzy Radial Basis Function method for Images Retrieval Maftukhah, Tatik
INKOM Journal Vol 5, No 1 (2011)
Publisher : Pusat Penelitian Informatika - LIPI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (319.462 KB) | DOI: 10.14203/j.inkom.88

Abstract

Pada makalah ini diuraikan tentang penggunaan metode Modifikasi Vektor Kueri (MVK) Fuzzy dan Fungsi Basis Radial (FBR) Fuzzy untuk perolehan data citra. Kedua metode tersebut digunakan dalam proses umpanbalik relevansi untuk mendapatkan citra yang sesuai dengan keinginan pengguna. Penelitian yang diusulkan adalah metode MVK Fuzzy dengan enam tingkat relevansi yang terdiri dari: sangat relevan, relevan, sedikit relevan, samar-samar, tidak relevan, dan sangat tidak relevan. Metode FBR Fuzzy menggunakan tiga tingkatan yang terdiri dari relevan, fuzzy, dan tidak relevan. Pengujian dilakukan untuk membandingan kinerja metode MVK Fuzzy dengan FBR Fuzzy melalui perhitungan nilai precision recall. Dari penelitian ini dapat disimpulkan bahwa metode MVK Fuzzy mempunyai kinerja yang lebih baik dibandingkan dengan metode FBR Fuzzy.
COMPARISONS BETWEEN MEASUREMENT AND CALCULATION METHODS IN OBTAINING VIRTUAL WATER FOR HOME MADE YOGURT Wijonarko, Sensus; Sirenden, Bernadus; Maftukhah, Tatik; Rustandi, Dadang
Instrumentasi Vol 43, No 2 (2019)
Publisher : LIPI Press, Anggota IKAPI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31153/instrumentasi.v43i2.173

Abstract

The aim of this study was to get shadow water result comparisons for home industry yogurt using practical and theoretical methods. The embodied water obtained from the empirical technique was 20.2 liters, namely 3.8 liters lower than the mathematical technique. The differences of the embedded water between two methods were caused especially on one side the producer did not optimize the water potential to absorb the heat, but on the other side the producer did not pay any attention to the regulation in disposing the waste water that should not be higher than its threshold level. The consumption of exogenous water could be decreased if the cooling water was recycled, water heat was dissipated using natural air, and the unused milk was consumed by cattle.
A Performance Evaluation of Repetitive and Iterative Learning Algorithms for Periodic Tracking Control of Functional Electrical Stimulation System Kurniawan, Edi; Pratiwi, Enggar B.; Adinanta, Hendra; Suryadi, Suryadi; Prakosa, Jalu A.; Purwowibowo, Purwowibowo; Wijonarko, Sensus; Maftukhah, Tatik; Rustandi, Dadang; Mahmudi, Mahmudi
Journal of Robotics and Control (JRC) Vol 5, No 1 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i1.20705

Abstract

Functional electrical stimulation (FES) is a medical device that delivers electrical pulses to the muscle, allowing patients with spinal cord injuries to perform activities such as walking, cycling, and grasping. It is critical for the FES to generate stimuli with the appropriate controller so that the desired movements can be precisely tracked. By considering the repetitive nature of the movements, the learning-based control algorithms are utilized for regulating the FES. Iterative learning control (ILC) and repetitive control (RC) are two learning algorithms that can be used to accomplish accurate repetitive motions. This study investigates a variety of ILC and RC designs with distinct learning functions; this constitutes our contribution to the field. The FES model of ankle angle, which is in a class of discrete-time linear systems is considered in this study. Two learning functions, i.e., proportional, and zero-phase learning functions, are simulated for the second-order FES model running at a sampling time of 0.1 s. The results indicate the superior performance of the ILC and RC in terms of convergence rate using the zero-phase learning function. ILC and RC with a zero-phase learning function can reach a zero root-mean-square error in two iterations if the model of the plant is correct. This is faster than proportional-based ILC and RC, which takes about 40 iterations. This indicates that the zero-phase learning function requires two iterations to ensure that the patient's ankle angle precisely tracks the intended trajectory. However, the tracking performance is degraded for both control methods, especially when the model is subject to uncertainties. This specific problem can lead to future research directions.
Co-Authors Adinanta, Hendra Edi Kurniawan Mahmudi Mahmudi Prakosa, Jalu A. Pratiwi, Enggar B. Purwowibowo Purwowibowo, Purwowibowo Rustandi, Dadang Sirenden, Bernadus Suryadi Suryadi Wijonarko, Sensus Wijonarko, Sensus