Article Per Year (5 Year)
p-Index From 2021 - 2026
0.983
P-Index
This Author published in this journals
All Journal Jurnal Otomasi Kontrol dan Instrumentasi Jurnal ULTIMA Computing Journal of Robotics and Control (JRC)
Mukhlish, Faqihza
Kelompok Keahlian Fisika Teknik, Fakultas Teknologi Industri, Institut Teknologi Bandung
Aerospace Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

Published : 8 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 8 Documents
Search

 1 
Kontrol Model Prediksi Robas Pada Waste Heat Boiler : Parametric Ellipsoidal Uncertainty Faqihza Mukhlish; Endra Joelianto
Jurnal Otomasi Kontrol dan Instrumentasi Vol 2 No 1 (2010): Jurnal Otomasi Kontrol dan Instrumentasi
Publisher : Pusat Teknologi Instrumentasi dan Otomasi (PTIO) - Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/joki.2010.2.1.4

Abstract

Kontrol Model Prediksi adalah suatu strategi kontrol yang dirancang dengan menggunakan model dari proses suatu sistem. Model proses ini digunakan untuk menghitung himpunan prediksi ke depan dari proses tersebut berdasarkan himpunan sinyal kontrol yang diberikan pada model. Himpunan sinyal kontrol tersebut diperoleh dari suatu algoritma optimisasi yang meminimalkan fungsi kinerja MPC dan solusinya memenuhi kendala sistem. Oleh karena itu, pemilihan model yang tepat akan mempengaruhi kinerja dari pengontrol.Optimisasi merupakan bagian terpenting pada perhitungan sinyal control ke depan pada MPC. Pada tugas akhir ini digunakan optimisasi robas. Optimisasi robas atau Robust Optimization (RO) adalah metoda optimisasi yang dapat menangani ketidakpastian data. Ketidakpastian dalam hal ini adalah data dari masalah optimisasi tidak diketahui secara pasti saat menentukan solusi optimal.PT.Petrokimia Gresik Pabrik Pupuk Nitrogen memiliki boiler bertipe Waste Heat Boiler (WHB), pembangkit uap yang memanfaatkan gas panas sisa dari generator turbin gas. Sistem ini digunakan untuk melihat kinerja MPC dengan optimisasi robas dalam sistem di industri dan melakukan penghematan bahan bakar pada sistem WHB. Mekanisme pengontrolan dilakukan dengan keadaan awal 0 untuk setiap masukan dan keluaran, lalu diamati kestabilan sistem dan variabel prosesnya terhadap kendala sistem.RO-MPC dengan ketidakpastian elips parametrik dapat mengkompensasi gangguan terukur dari generator turbin gas. Nilai RMSE dan IAE dari keluaran yang mempengaruhi kualitas steam (uap), Tekanan uap RMSE 1 = 0.1327 dan IAE 1 = 32.1710, Flow uap RMSE 4 = 1.7132 dan IAE 4 = 2.3603, Temperatur uap RMSE 5 = 8.9758 dan IAE5  = 2242.4. Kata Kunci: Kontrol Model Prediksi, Optimisasi Robas, Optimisasi Konik, Robust Counterpart, receding horizon, waste heat boiler.
Pembuatan Prototipe Viskometer Bola Jatuh Menggunakan Sensor Magnet dan Bola Magnet Bias M. L. Putri; Sissilia O. Putri; Farida I. Muchtadi; Faqihza Mukhlish
Jurnal Otomasi Kontrol dan Instrumentasi Vol 5 No 2 (2013): Jurnal Otomasi Kontrol dan Instrumentasi
Publisher : Pusat Teknologi Instrumentasi dan Otomasi (PTIO) - Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/joki.2013.5.2.6

Abstract

Viskometer bola jatuh merupakan alat ukur viskositas dengan cara mengukur waktu yang dibutuhkan sebuah bola untuk melewati cairan dengan jarak tertentu berdasarkan prinsip Hukum Stokes dan Hukum Newton. Perhitungan secara manual pada waktu tempuh bola dan  nilai viskositas cairan menyebabkan kesalahan paralaks. Oleh sebab itu, pada penelitian ini dibuat prototipe viskometer bola jatuh yang dapat mengukur waktu tempuh bola dan mengolahnya untuk mendapatkan nilai koefisien viskositas secara otomatis.Prototipe pada penelitian ini menggunakan dua buah closed-circuit magnetic sensor untuk mendeteksi waktu tempuh bola magnet saat dijatuhkan pada cairan uji. Waktu tempuh diolah menjadi kecepatan bola magnet dan nilai koefisien viskositas cairan (dPa.s) lalu ditampilkan di komputer menggunakan perangkat arduino dan LabView 8.5.Prototipe dapat mengukur waktu tempuh bola secara otomatis dan menampilkan nilai koefisien viskositas pada komputer. Hasil pengukuran nilai viskositas menggunakan prototipe sebagai berikut minyak goreng 5,46 dPa.s ; SAE 40 24,67 dPa.s dan silicone oil 22,97 dPa.s. Nilai tersebut jauh dari nilai viskositas referensi disebabkan faktor konstanta yang digunakan pada persamaan viskometer bola jatuh tidak dapat disesuaikan seperti teori yang ada karena syarat ukuran gelas ukur sebagai wadah cairan pada prototipe dibandingkan dengan ukuran bola tidak terpenuhi. Sehingga ada faktor lain yang perlu dipertimbangkan agar diperoleh hasil pengukuran nilai viskositas cairan yang mendekati nilai sebenarnya.Kata Kunci : viskositas cairan, sensor magnet, bola jatuh
Pembuatan Prototipe Thermal Mass Flowmeter Tipe Heat Transfer untuk Pengukuran Laju Aliran Massa Udara Ghina A. Nudiani; Syafri Firmansyah; Farida I. Muchtadi; Faqihza Mukhlish
Jurnal Otomasi Kontrol dan Instrumentasi Vol 6 No 1 (2014): Jurnal Otomasi Kontrol dan Instrumentasi
Publisher : Pusat Teknologi Instrumentasi dan Otomasi (PTIO) - Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/joki.2014.6.1.2

Abstract

Thermal mass flowmeter sebagai alat ukur laju aliran massa mulai banyak digunakan dalam sektor industri migas karena memiliki akurasi yang baik untuk fluida gas dan dapat mengukur secara langsung nilai laju aliran massa tanpa ada hilang energi yang besar. Dalam penelitian ini dibuat prototipe thermal mass flowmeter tipe heat transfer sebagai media pembelajaran.Dalam perancangan, material prototipe dibuat dari bahan pipa akrilik transparan sehingga prinsip kerjanya dapat diamati secara langsung. Udara dipilih sebagai fluida yang diukur karena memiliki nilai massa jenis yang telah diketahui. Laju aliran massa dihitung berdasarkan perbedaan temperatur udara sebelum dan sesudah dipanaskandengan memperhitungkan temperatur pemanas. Kalibrasi nilai laju aliran massa dilakukan dengan cara membandingkan hasil pengukuran prototipe dengan nilai yang didapatkan secara teoretis.Hasil pengujian menunjukkan bahwa prototipe memiliki sensitivitas pengukuran naik dan turun masing-masing sebesar 1,080 dan 1,084 serta ketidakpastian pengukuran naik dan turun masing-masing sebesar ± 0,642 kg/jam dan ± 0,929 kg/jam dengan daerah kerja pengukuran 10,157 kg/jam sampai dengan 14,845 kg/jam. Dengan input yang tetap, prototipe memiliki rata-rata akurasi dan presisi masing-masing sebesar 97,158% dan 95,092%.Kata Kunci: thermal mass flowmeter, temperatur, laju aliran massa, udara
Predictive Maintenance Automatic Weather Station Sensor Error Detection using Long Short-Term Memory Santoso, Bayu; Ryan, Muhammad; Wicaksana, Haryas Subyantara; Ananda, Naufal; Budiawan, Irvan; Mukhlish, Faqihza; Kurniadi, Deddy
Ultima Computing : Jurnal Sistem Komputer Vol 15 No 2 (2023): Ultima Computing : Jurnal Sistem Komputer
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/sk.v15i2.3403

Abstract

Weather information plays a crucial role in various sectors due to Indonesia's wide range of weather and extreme climate phenomena. Automatic Weather Stations (AWS) are automated equipment designed to measure and collect meteorological parameters such as atmospheric pressure, rainfall, relative humidity, atmospheric temperature, wind speed, and wind direction. Occasionally, AWS sensors may produce erroneous values without the technicians' awareness. This study aims to develop sensors error detection system for predictive maintenance on AWS using the Long Short-Term Memory (LSTM) model. The AWS dataset from Jatiwangi, West Java, covering the period from 2017 to 2021, will be utilized in the study. The study revolves around developing and testing four distinct LSTM models dedicated to each sensor: RR, TT, RH, and PP. The research methodology involves a phased approach, encompassing model training on 70% of the available dataset, subsequent validation using 25% of the data, and finally, testing on 5% of the dataset alongside the calibration dataset. Research outcomes demonstrate notably high accuracy, exceeding 90% for the RR, TT, and PP models, while the RH model achieves above 85%. Moreover, the research highlights that Probability of Detection (POD) values generally trend high, surpassing 0.8, with a low False Alarm Rate (FAR), typically below 0.1, except for the RH model. Sensor condition requirements will adhere to the rules set by World Meteorological Organization (WMO) and adhere to the permitted tolerance limits for each weather parameter.
Adaptive Strategies for Dynamic Obstacle Avoidance and Formation Control in Multi-Agent Drone Systems: A Review Argiliana, Shania; Ekawati, Estiyanti; Mukhlish, Faqihza
Journal of Robotics and Control (JRC) Vol. 6 No. 4 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i4.26243

Abstract

Obstacle avoidance in multi-agent systems is a critical area of research driven by advancements in autonomous technology and artificial intelligence. This review examines various approaches to path planning, formation control, and communication architectures, focusing on their effectiveness in static and dynamic environments. The research contribution is a comprehensive analysis of current techniques based on a structured selection process evaluating peer-reviewed studies through computational efficiency, real-time adaptivity, and scalability. The findings highlight the strengths and limitations of classical methods, such as the Improved Artificial Potential Field (IAPF), and modern techniques like Reinforcement Learning (RL) and Model Predictive Control (MPC). Comparative analysis reveals that while these approaches improve adaptivity, they also introduce challenges such as high computational loads, difficulties in large-scale multi-agent coordination, and sensitivity of parameter tuning. Additionally, existing formation control strategies depend highly on stable inter-agent communication, making them vulnerable to delays and failures in decentralized networks. This review identifies key research gaps and suggests future directions, including hybrid RL-MPC formation control, adaptive path planning algorithms, and scalable communication protocols to enhance multi-agent system performance in real-world applications.
Predictive Maintenance Automatic Weather Station Sensor Error Detection using Long Short-Term Memory Santoso, Bayu; Ryan, Muhammad; Wicaksana, Haryas Subyantara; Ananda, Naufal; Budiawan, Irvan; Mukhlish, Faqihza; Kurniadi, Deddy
ULTIMA Computing Vol 15 No 2 (2023): Ultima Computing : Jurnal Sistem Komputer
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/sk.v15i2.3403

Abstract

Weather information plays a crucial role in various sectors due to Indonesia's wide range of weather and extreme climate phenomena. Automatic Weather Stations (AWS) are automated equipment designed to measure and collect meteorological parameters such as atmospheric pressure, rainfall, relative humidity, atmospheric temperature, wind speed, and wind direction. Occasionally, AWS sensors may produce erroneous values without the technicians' awareness. This study aims to develop sensors error detection system for predictive maintenance on AWS using the Long Short-Term Memory (LSTM) model. The AWS dataset from Jatiwangi, West Java, covering the period from 2017 to 2021, will be utilized in the study. The study revolves around developing and testing four distinct LSTM models dedicated to each sensor: RR, TT, RH, and PP. The research methodology involves a phased approach, encompassing model training on 70% of the available dataset, subsequent validation using 25% of the data, and finally, testing on 5% of the dataset alongside the calibration dataset. Research outcomes demonstrate notably high accuracy, exceeding 90% for the RR, TT, and PP models, while the RH model achieves above 85%. Moreover, the research highlights that Probability of Detection (POD) values generally trend high, surpassing 0.8, with a low False Alarm Rate (FAR), typically below 0.1, except for the RH model. Sensor condition requirements will adhere to the rules set by World Meteorological Organization (WMO) and adhere to the permitted tolerance limits for each weather parameter.
Evaluasi Spasial Estimasi Curah Hujan pada Radar Cuaca Menggunakan Metode Z-R Marshall-Palmer di Wilayah Jawa Barat Ananda, Naufal; Mukhlish, Faqihza; Wicaksana, Haryas Subyantara; Budiawan, Irvan
Jurnal Otomasi Kontrol dan Instrumentasi Vol 16 No 1 (2024): Jurnal Otomasi Kontrol dan Instrumentasi
Publisher : Pusat Teknologi Instrumentasi dan Otomasi (PTIO) - Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/joki.2024.16.1.4

Abstract

Rainfall is one of the weather parameters that affect various sectors. High rainfall intensity can trigger hydrometeorological disasters, so rainfall observation data is vital to monitor rainfall conditions in an area. An automatic rain gauge is an instrument that measures rainfall at an observation point, but the instrument has reasonably low coverage and has yet to reach the entire region. Weather radar is a remote sensing instrument capable of spatially estimating rainfall. Weather radar data can be used to estimate rainfall using the Marshall-Palmer Z-R method. The application of the method can be an alternative for areas that do not have rainfall observation equipment. However, the estimation needs to be evaluated to improve the accuracy of the estimation value. Based on the evaluation, the highest coefficient of determination was 0.92, and the lowest was 0.67. The lowest RMSE value was 2.40, the highest was 6.76, the highest ME value was 16.59, and the lowest was 5.93; the highest bias was 12.90, and the lowest was 5.30. The study results show that the weather radar can operate according to the specifications of the maximum observation distance of up to 220 KM, but the farther the observation distance to a point, the higher the performance of rainfall estimation accuracy.
Estimasi Kecepatan Angin Permukaan pada Jaringan Anemometer Menggunakan Temporal Convolutional Network Wicaksana, Haryas; Mukhlish, Faqihza; Ananda, Naufal; Budiawan, Irvan; Khamdi, Arif Nur; Habib , Abdul Hamid Al
Jurnal Otomasi Kontrol dan Instrumentasi Vol 16 No 1 (2024): Jurnal Otomasi Kontrol dan Instrumentasi
Publisher : Pusat Teknologi Instrumentasi dan Otomasi (PTIO) - Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/joki.2024.16.1.5

Abstract

Surface winds in various locations are measured simultaneously using a multisite anemometer network. This network is susceptible to system failures due to sensor damage, causing a data gap during sensor removal and reinstallation. This research develops a wind speed estimation model on a multisite anemometer using the Temporal Convolutional Network (TCN) algorithm. TCN processes time domain signals in parallel, thus significantly cutting the computation time. Minutely wind speed data set was obtained from four anemometers at Juanda International Airport in Surabaya from January 1, 2022 – December 24, 2023. The model design comprises data pre-processing, dominant wind direction analysis, hyperparameter determination, training, and testing on actual data. TCN estimation models are divided into easterly, westerly, transitional, and all-directional models. These wind speed estimation models strongly correlate with actual data, with correlation coefficients of 0.70, 0.77, and 0.87. Overall, the accuracy of the TCN-based estimation model conforms to World Meteorological Organization (WMO) requirements for wind speed measurements. It achieves RMSE<5 m/s and MAE<3 m/s. As for computation duration, TCN processes the training for 87 seconds per epoch and completes the estimation in 37 seconds, much faster than CNN-BiDLSTM’'s training duration of 2206 seconds per epoch and estimation completion of 548 seconds.
Co-Authors Ananda, Naufal Argiliana, Shania Bias M. L. Putri Budiawan, Irvan Deddy Kurniadi, Deddy Ekawati, Estiyanti Endra Joelianto Farida I. Muchtadi Ghina A. Nudiani Habib , Abdul Hamid Al Khamdi, Arif Nur Ryan, Muhammad Santoso, Bayu Sissilia O. Putri Syafri Firmansyah Wicaksana, Haryas Wicaksana, Haryas Subyantara