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All Journal Jurnal Teliska VOLT : Jurnal Ilmiah Pendidikan Teknik Elektro Indonesian Journal of Artificial Intelligence and Data Mining JTEIN: Jurnal Teknik Elektro Indonesia
Dewi Permata Sari
Politeknik Negeri Sriwijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Analysis of The Measurement of PH Levels and Levels of Water Clarity on The Ship's Robot Bayu Perdana Dinambar; Dewi Permata Sari; Yeni Irdayanti
VOLT : Jurnal Ilmiah Pendidikan Teknik Elektro Vol 2, No 2 (2017): October 2017
Publisher : Department of Electrical Engineering Education, Faculty of Teacher Training and Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1477.316 KB) | DOI: 10.30870/volt.v2i2.1929

Abstract

The ship robot uses a pH meter sensor to measure the water acidity and LDR sensors for measuring water clarity. The measurement results will be sent directly using bluetooth communication. This ship robot is made with the aim to facilitate in doing research on pH levels and water clarity level To get accurate results required sensitivity and proper sensor placement. Testing sensitivity on ship's pH robot sensors by comparing two gauges to see the difference in measurement values obtained. Sensory clarity test by looking at the change in Lux value (Lighting Refraction) based on sensor placement distance. Lux value is the readable value of the smartphone to display the clarity level in the water. Sensory clarity test using 3 samples with the clear category, somewhat clear, cloudy. The distance of sensor placement ranging from 1-5 cm.  For acidity measurements, the SKU pH sensor: SEN0169 has an average accuracy of 0.06-2.33%. While on the measurement of clarity, the higher the value of lux then the higher the level of clarity and the lower the value of lux then the lower the level of clarity.
SISTEM PERHITUNGAN KWH METER LISTRIK PRABAYAR (LPB) UNTUK PELANGGAN DAYA 900 VA PT. PLN (PERSERO) AREA PALEMBANG Dewi Permata Sari
TELISKA Vol. 5 No. 2 (2013): Edisi 14, Volume 5, Nomor 2, Mei 2013
Publisher : Teknik Elektro Polsri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1040.921 KB)

Abstract

Sejak diluncurkan pada Januari 2008, listrik prabayar menjadi salah satu pilihan masyarakat dalam kemudahan pengelolaan pemakaian listrik mereka. Listrik prabayar merupakan cara pembelian listrik dimana pelanggan membayar terlebih dahulu baru kemudian menikmati aliran listrik. Berupa voucher isi ulang yang telah tersedia di ribuan loketloket yang tersebar diseluruh indonesia, voucher Listrik Prabayar STROOM ini diharap mampu menjangkau lebih luas masyarakat melalui kemitraan dengan bank-bank, PT. POS Indonesia, dan mitra pihak ketiga lainnya. Layanan listrik prabayar ini menggunakan alat khusus yang berbeda dengan layanan listrik pasca bayar/biasa. Alat khusus ini dinamakan KWH Meter (meteran listrik) Pra Bayar, atau lebih dikenal sebagai Meter prabayar. Setiap pelanggan prabayar akan dilengkapi dengan meter prabayar ini beserta 1 Kartu Prabayar. Meter tersebut yang akan mencatat penggunaan listrik anda. Sedang kartu prabayar, selain sebagai nomor identitas pelanggan prabayar, juga berfungsi sebagai alat transaksi pembelian energi listrik. Kartu prabayar tersebut dipakai oleh pelanggan selama masih berlangganan listrik PLN. Jadi, saat membeli energi listrik (isi ulang), pelanggan harus menunjukkan dan memberikan kartu prabayar kepada petugas PLN untuk dilakukan pengisian energi listrik. Tanpa kartu prabayar, pengisian ulang tidak dapat dilakukan. Tarif listrik prabayar bila dibandingkan dengan tarif reguler, listrik prabayar boleh dikatakan lebih murah 3-5%. Itu dikarena pelanggan tidak perlu lagi membayar Uang Jaminan Langganan (UJL), biaya pencatatan meter, dll. Sementara harga per kWh-nya tetap (flat). Sistem Prabayar merupakan bentuk paling efisien pembayaran listrik. Karena pelanggan hanya dibebankan membeli sejumlah kredit (isi ulang) untuk kemudian dipergunakan sampai kWh listrik tersebut habis. Pilihan besaran isi ulang bebas, dengan nilai minimum Rp 20.000,- s/d Rp. 1.000.000,-Kata kunci voucher Listrik Prabayar STROOM, KWH Meter,
Kendali Kestabilan Putaran Motor DC Robot Pemindah Barang dengan Metode Ziegler-Nichols pada Industri Dewi Permata Sari; Iskandar Lutfi; Firnan Saputra
Jurnal Teknik Elektro Indonesia Vol 4 No 2 (2023): JTEIN: Jurnal Teknik Elektro Indonesia
Publisher : Jurusan Teknik Elektro Fakultas Teknik Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtein.v4i2.448

Abstract

Robot salah satu keperluan yang dapat dikatakan sangat membantu dalam pekerjaan industri. Dalam suatu robot sistem kendali berperan penting menjaga kestabilan dari kerja yang dihasilkan oleh robot itu sendiri. Robot pemindah barang dengan sistem yang dibuat untuk memindahkan barang ke tempat yang tinggi tentu harus memiliki tanggapan yang stabil. Sehingga Tujuan dari penelitian diterapkannya kendali PID (propotional, integral, derevative) untuk mengontrol kecepatan motor dc sebagai pemutar katrol untuk mengangkat barang dengan sistem seperti lift terhadap pemindahan barang. Supaya pergerakan motor bisa mengatur kecepatan stabil dengan kondisi tertentu. Metode yang digunakan pada penelitian ini yaitu Ziegler-Nichols model sistem close loop dengan penentuan parameter PID berdasarkan osilasi, hasil parameter yang diperoleh dimasukkan kedalam sistem dan melihat respon dalam mengontrol kecepatan motor jika respon tidak sesuai, maka dilakukan tunning manual trial end error untuk mendapat respon yang diinginkan sehingga didapatlah motor yang dapat bergerak stabil dan dapat mengurangi dan memperbaiki kesalahan saat mengangkat barang. Hasil yang didapat tanggapan bisa stabil dan telah mencapai baik dengan error pembacaan rpm dari sensor rotary encoder motor dc dibawah 5%. Pengendali PID dapat diimplementasikan pada robot pemindah barang ini, motor dapat memperbaiki error yang diakibatkan beban dan tanggapan atau respon motor seimbang dalam mengangkat beban berat sekaligus.
MATLAB-Based Performance Evaluation of Lightweight YOLO Models for Waste Object Detection Nadhirah Meidiasty Maharani; Dewi Permata Sari; Ibnu Maja; Destra Andika Pratama; Ozkar F. Homzah
Indonesian Journal of Artificial Intelligence and Data Mining Vol. 9 No. 2 (2026): July 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

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Abstract

Accurate waste object detection is important for enabling efficient automated recycling and environmental management. While lightweight YOLO models are often managed in Python, integration and evaluation of the models in MATLAB remains a technical challenge due to limited support and documentation. This study intends to fill that gap by evaluating the performance of YOLOv5s, YOLOv7-Tiny, and YOLOv8n within the MATLAB environment for waste object detection using the TrashNet dataset. A semi-automatic labeling approach was employed, combining manual annotation with pseudo-labeling using a pretrained YOLOv8n model. The models were trained and exported to the ONNX format for MATLAB-based inference and analysis. Experimental results show that YOLOv8n achieved the highest mAP@0.5 of 0.954, while YOLOv5s demonstrated the most stable inference performance in MATLAB, consistently producing confidence scores above 90% and real-time speeds of up to 15.9 fps. In contrast, YOLOv7-Tiny achieved the fastest inference speed (up to 24.4 fps) but exhibited reduced classification consistency. Notably, YOLOv8n experienced confidence score degradation during MATLAB inference, suggesting post-processing discrepancies between native Python and ONNX-imported workflows. This research highlights MATLAB’s capability to serve as a functional evaluation platform for modern lightweight detectors and emphasizes its potential for expanding accessible AI applications in waste management systems.
Authorized Occupant Detection System in Smart Rooms under Daylight and Nighttime Lighting Conditions Reza Fahlevi; Dewi Permata Sari; Ibnu Maja
Indonesian Journal of Artificial Intelligence and Data Mining Vol. 9 No. 2 (2026): July 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

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Abstract

This study discusses the development of a legitimate occupant detection system in smart rooms using the YOLOv8 algorithm, tested under daytime and nighttime lighting conditions. The system is designed using a Raspberry Pi connected to a webcam for real-time monitoring. The aim of this study is to evaluate the system's performance under different light intensities. Data were obtained by capturing images during the day and night, which were then used as a training dataset for the YOLOv8 model. With a mAP@0.5 of 0.91 and precision, recall, and F1-score values of 0.90, 0.88, and 0.89, respectively, the evaluation findings demonstrate that the system operates effectively under ideal lighting conditions. This shows that the model can recognize things accurately and consistently in real time. However, performance drastically declines in low light, with mAP@0.5 falling to 0.68 and precision, recall, and F1-score falling to 0.70, 0.65, and 0.67, respectively. This indicates a rise in false and missed detections (FP and FN). Reduced image quality, including inadequate illumination, noise, and poor feature visibility, is the primary cause of this degradation. However, it has been demonstrated that using more light sources increases detection accuracy
Comparative Study: Performance Comparison of You Only Look Once and Convolutional Neural Networks Algorithms in Human Object Detection Dewi Permata Sari; M. Akbar Tri Ramadhani; Abdurrahman Abdurrahman
Indonesian Journal of Artificial Intelligence and Data Mining Vol. 8 No. 3 (2025): November 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

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Abstract

The evolution of object identification technologies, particularly for person detection applications, has increasingly accelerated due to the merger of deep learning and artificial intelligence with computer vision. This study intends to test the efficacy of two object detection algorithms, YOLOv8n and CNN MobileNetSSD, in identifying human objects in digital photos. A dataset of 12,334 human-labeled photos from the Roboflow platform was utilized to train the YOLOv8n model, while performance results for the CNN MobileNetSSD model were acquired from a prior article. The precision, recall, and F1-score of each model were examined. Experimental results reveal that YOLOv8n attains 94% precision, 92% recall, and a 92.9% F1-score, representing a considerable enhancement over MobileNetSSD. Conversely, MobileNetSSD got an F1-score of 85.2%, with a precision of 86.5% and a recall of 84.1%. The findings show that CNN MobileNetSSD is more ideal for non-time-sensitive or resource-limited scenarios; however, YOLOv8n is preferable for real-time human identification tasks due to its greater accuracy and faster inference. This comparative analysis is important for differentiating object detection models matched to certain application needs.
Co-Authors Abdurrahman Abdurrahman Bayu Perdana Dinambar Destra Andika Pratama Firnan Saputra Homzah, Ozkar F Ibnu Maja Iskandar Lutfi M. Akbar Tri Ramadhani Nadhirah Meidiasty Maharani Reza Fahlevi Yeni Irdayanti