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Implementasi Algoritma PID untuk Pengontrolan Suhu Pada Mesin Pengering Cabai Toar, Handri; Wasdoni Alfi; Illa Aryeni; Nanta Fakih Prebianto; Hana Mutialif Maulidiah; Muammar Khadapi Arif Nasution; Micko Tomas
Journal of Applied Electrical Engineering Vol. 9 No. 1 (2025): JAEE, June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaee.v9i1.9314

Abstract

Penelitian ini mengimplementasikan kontrol PID pada mesin pengering cabai untuk mempertahankan stabilitas suhu selama proses pengeringan. Pengaturan suhu optimal dicapai melalui mekanisme tuning PID dengan metode Ziegler–Nichols untuk mengatur parameter Kp, Ki, dan Kd yang dapat menghasilkan respon sistem yang stabil pada suhu setpoint. Mesin pengering terdiri dari sensor DHT22, pemanas, dan mikrokontroler ESP32, serta terintegrasi dengan aplikasi Home Assistant untuk pemantauan dan pengontrolan jarak jauh. Hasil pengujian menunjukkan bahwa pada suhu 60°C menghasilkan keseimbangan optimal antara kecepatan pengeringan dan kestabilan kelembaban. Implementasi kontrol PID berhasil menjaga suhu mendekati setpoint dengan steady-state error sebesar 0,98%, overshoot yang minimal dan settling time yang optimal. Proses pengeringan menghasilkan penurunan kadar air yang signifikan, dengan berat awal 1 kg menjadi 261 gr setelah pengeringan. Tuning PID menunjukkan efektivitas dalam meningkatkan kestabilan suhu dan kualitas akhir produk cabai kering, serta menjadi solusi dalam mengatasi kendala pengeringan cabai secara konvensional.
REDUCTION OF LIFTED STITCH DEFECTS IN WIRE BONDING PROCESS THROUGH ROOT CAUSE ANALYSIS Puspita, Widya Rika; Manurung, Anjunius; budiana, Budiana; Nur Sakinah Asaad; Jefiza, Adlian; Nakul, Fitriyanti; Putra, Irwanto Zarma; Diputra, Muhammad Naufal Airlangga; Illa Aryeni; Handri Toar; Anggraini, Ria
Jurnal Teknologi Dan Riset Terapan (JATRA) Vol. 7 No. 2 (2025): Jurnal Teknologi dan Riset Terapan (JATRA) - December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jatra.v7i2.9604

Abstract

Reliable connection inspection is crucial for the quality of semiconductor products. A frequent issue is stitch defects during wire bonding. To address this, an investigation was conducted using flow charts. Additionally, an analysis was performed by identifying root causes using the Fishbone Diagram method and the 5 Whys technique. After improvements, including targeted operator training, a significant reduction of 96.75% in stitch defects was achieved. This study demonstrates that the combination of root cause analysis methods and operator training effectively enhances the reliability of the wire bonding process and the quality of semiconductor products. This study did not account for environmental factors that might influence the wire bonding process, such as temperature and humidity variations. Therefore, the findings may be limited in settings with different environmental conditions.
Stator Flux Estimator Using Feed-Forward Neural Network for Evaluating Hysteresis Loss Curve in Three Phase Induction Motor Praharsena, Bayu; Purwanto, Era; Jaya, Arma; Rusli, Muhammad Rizani; Toar, Handri; wk, Ridwan
EMITTER International Journal of Engineering Technology Vol 6 No 1 (2018)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (704.235 KB) | DOI: 10.24003/emitter.v6i1.263

Abstract

The operation of induction motors with high performance contributes significantly to the global energy savings but hysteresis loss is one of the factors causing decreased performance. Stator flux density (B) and magnetic field intensity (H) must be plotted to know hysteresis loss quantity. Unfortunately, since the rotor rotates in time series, the stator flux density is unmeasurable quantities, it’s hard to direct sensored this properties because of limited airgap space and costly to install additional instrument. The purpose of this paper is to evaluate the hysteresis loss quantity in induction motor using a novel method of multilayer perceptron feed forward neural network as stator flux estimator and magnetizing current model as magnetic field intensity properties. This method is effective, because it’s non-destructive method, without an additional instrument, low cost, and suitable for real-time motor drive systems. The FFNN estimator response is satisfying because accurately estimate stator flux density for evaluating hysteresis loss quantity including its magnitude and phase angle. By using the proposed model, the stator flux density and magnetizing current can be plotted become hysteresis loss curve. The performance of flux response, speed response, torque response and error deviation of stator flux estimator has been presented, investigated, compared and verified in Simulink Matlab.