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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika JTRM (Jurnal Teknologi dan Rekayasa Manufaktur) SEMNASTERA (Seminar Nasional Teknologi dan Riset Terapan)
Andri Wiyono
Politeknik Manufaktur Bandung
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences

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Robot Lengan Untuk Proses Perakitan Miniatur Body Mobil Sebagai Media Pembelajaran Robotika Andri Wiyono; Ismail Rokhim; Adhitya Sumardi
SEMNASTERA (Seminar Nasional Teknologi dan Riset Terapan) Vol 1 (2019)
Publisher : SEMNASTERA (Seminar Nasional Teknologi dan Riset Terapan)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1149.622 KB)

Abstract

Perkembangan penggunaan teknologi otomasi industri di Indonesia cukup pesat. Diantaranya di bidang industri otomotif terutama mobil telah banyak menggunakan robot sebagai bagian dari proses produksinya. Institusi pendidikan sebagai gerbang pengetahuan dan penguasaan teknologi diharapkan mampu memberikan dukungan terhadap industri. Untuk menciptakan lulusan yang  kompeten dalam bidang tersebut, tentunya perlu sebuah media pembelajaran yang menggambarkan kondisi di industri. Media pembelajaran mengenai proses perakitan tersebut diharapkan mampu menjawab tantangan tersebut, karena dalam media ini peserta didik dapat mengetahui mengenai alur proses produksi, belajar melakukan teaching yaitu membuat program pergerakan robot dengan pendant, membuat program PLC kemudian mengintegrasikannya dengan kontroller robot dan beberapa perangkat lain pendukung sistem pada plant ini. Metode yang digunakan adalah metode perancangan VDI 2206 yaitu perancangan berbasis mekatronika yang dikembangkan dari tiga domain desain spesifik, yaitu domain mekanik, domain elektrik, dan domain informatik. Ketiga domain tersebut akan diintegrasikan sehingga menjadi kesatuan sistem utuh yang menghasilkan suatu produk berupa plant perakitan miniatur body mobil dengan menggunakan robot lengan. Berdasarkan hasil pengujian, PLC sebagai unit kendali utama dapat melakukan pengendalian dan komunikasi dengan baik terhadap robot maupun perangkat pendukung lainnya. Robot dapat melakukan perakitan tercepat dengan waktu 31.79 detik, untuk akurasi perakitan didapatkan hasil yaitu, robot melakukan proses perakitan dengan baik sebanyak tiga kali, dengan tingkat akurasi 78%. Unjuk kerja media pembelajaran robotika berfungsi dengan baik secara keseluruhan. Hasil penilaian ahli mendapat presentase 82,7% dan penilaian mahasiswa mendapat presentase 94% dengan kategori sangat layak dan dapat digunakan sebagai media pembelajaran Praktik Robotika di Jurusan Teknik Otomasi Manufaktur dan Mekatronika Polman Bandung.
Rancang Bangun Teaching Aid Motor Servo AC R88M Berbasis PLC NX1P2 Sebagai Alat Bantu Pengajaran Sarosa Castrena Abadi; Ismail Rokhim; Andri Wiyono; Mustika Rahmah
JTRM (Jurnal Teknologi dan Rekayasa Manufaktur) Vol 5 No 2 (2023): Volume: 5 | Nomor: 2 | Oktober 2023
Publisher : Pusat Penelitian dan Pengabdian kepada Masyarakat (P3M) Politeknik Manufaktur Bandung (Polman Bandung)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.48182/jtrm.v5i2.141

Abstract

Teaching aids are used in the field of education to streamline the implementation of the learning process. In this research, AC servo motor learning media with type R88M-1M10030-S2. The learning media will focus on controlling the position and speed of the servo motor for the x, y and z axes. This research uses the experimental work method. Testing of this research is carried out in Motion Variety parameter testing, NC function testing, HMI function testing, and user evaluation testing. It was found that the percentage of deviation value was 4.9%. In position testing, the deviation value of the X and Z axes has a small average final error with an X-axis error value of 0.21% and a Z-axis of 0.24%. It can be seen that the average Y-axis error is the largest with an average error value of 5.84%. Overall the average value of the HMI evaluation is 89% for the quality of the AC Servo Motor Teaching Aid HMI that has been made. This figure is classified as very good to continue using the tool as an auxiliary medium for the teaching process.
Predictive Analytics for Energy Consumption of Autonomous Mobile Robot Using Hybrid ARIMA-XGBoost Pipit Anggraeni; Wahyu Adhie Candra; Surya Dharma Jatnika; Noval Lilansa; Adhitya Sumardi Sunarya; Nur Jamiludin Ramadhan; Andri Wiyono
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16484

Abstract

The deployment of autonomous mobile robots in smart manufacturing and intralogistics has grown rapidly, yet current battery management systems can only monitor real-time charge levels without predicting future energy consumption. This reactive limitation risks mid-mission battery depletion and production disruption. The study presents a predictive analytics system for the Polebot autonomous mobile robot integrated with a Robot Operating System 2 data historian pipeline and an InfluxDB time-series database. The objective is to evaluate whether an autoregressive time-series model or a gradient-boosted machine learning model better suits different operational conditions, specifically constant-velocity static operation versus acceleration-heavy dynamic operation. Data were collected from three sensor sources across six operational protocols covering baseline, high-load, stop-and-go, creep, burst acceleration, and mixed conditions, yielding 10,800 synchronized data points at 1 Hz after resampling. Results show that the Autoregressive Integrated Moving Average model with parameters (2,1,3) achieves a Mean Absolute Error of 1.047% and a symmetric Mean Absolute Percentage Error of 1.74% for battery State of Charge prediction under static conditions. Extreme Gradient Boosting achieves a Mean Absolute Error of 0.022 watts for motor power prediction, 136 times more accurate than the time-series model for the same variable. The proposed Condition-Based Temporal Switching framework was validated on 1,803 data points and autonomously produced 265 model transitions during a 30-minute mixed operational test, with static conditions comprising 83.9% of the validation window. Adaptive model selection outperforms single-model strategies for energy prediction in autonomous mobile robot platforms.
Co-Authors Adhitya Sumardi Sunarya Ismail Rokhim Mustika Rahmah Nur Jamiludin Ramadhan Pipit Novita Anggraeni Sarosa Castrena Abadi Surya Dharma Jatnika Wahyu Adhie Candra