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KIT PRAKTIKUM GERAK LURUS BERBASIS LABVIEW : RANCANG BANGUN KIT PRAKTIKUM GERAK LURUS MENGGUNAKAN SENSOR OBSTACLE AVOINDANCE
(POLITEKNIK NEGERI BATAM, 2025-01-09) Simamora, Aprian Yosua Hery Boy; Mahdaliza, Rahmi
KIT Praktikum Gerak Lurus Berbasis Labview memperkenalkan alat peraga yang bertujuan untuk membantu pembelajaran bagi mahasiswa terkhusus dalam bidang ilmu fisika pada gerak lurus berupa papan lintasan dan gerak jatuh bebas berupa tabung. Sistem ini menggunakan Arduino Uno sebagai mikrokontroler dan sensor obstacle, yang dimana sensor obstacle ini bermanfaat sebagai penangkap nilai waktu dari alat uji coba yang melintas dan akan diproses dalam Labview serta ditampilkan pada antarmuka yang dibuat pada Labview , hasil data yang diperoleh berupa hasil data secara real – time.Dengan adanya KIT Praktikum ini dilingkungan pembelajaran, diharapkan agar mahasiswa dapat meningkatkan pemahaman mereka mengenai gerak lurus dan gerak jatuh bebas, serta mampu menghubungkan teori fisika dengan aplikasi praktis dalam kehidupan sehari -hari. KIT Praktikum ini menjadi sebuah inovasi yang memiliki potensi untuk meningkatkan hasil belajar dan minat siswa terhadap ilmu fisika. Berdasarkan data yang diperoleh dari hasil percobaan, penerapan ilmu fisika pada KIT Praktikum ini telah sesuai.
Implementasi jaringan syaraf tiruan sebagai pendeteksi kecacatan pada Printed Circuit Board (PCB)
(Politeknik Negeri Batam, 2025-07-30) Gunawan, Rayhan; Risandriya , Sumantri Kurniawan
Abstract—Papan sirkuit cetak (Printed Circuit Board/PCB) merupakan komponen vital pada perangkat elektronik modern, namun proses manufakturnya rentan menimbulkan cacat seperti open circuit, short, spur, dan mouse bite. Keterbatasan metode inspeksi visual manual dalam hal konsistensi dan efisiensi mendorong kebutuhan akan sistem inspeksi otomatis yang andal. Penelitian ini menerapkan sistem deteksi cacat otomatis pada PCB menggunakan model berbasis arsitektur Convolutional Neural Network (CNN), yaitu You Only Look Once versi 8 (YOLOv8). Dengan menggunakan dataset citra PCB yang telah dianotasi, model dilatih dengan teknik augmentasi data dan dievaluasi kinerjanya menggunakan metrik Precision, Recall, F1-Score, serta analisis visual. Hasil pengujian menunjukkan bahwa model yang dikembangkan mencapai kinerja yang baik dengan akurasi keseluruhan 99,5%, Precision 100%, Recall 99,2%, dan F1-Score 99,5%. Analisis lebih lanjut juga mengindikasikan kemampuan model untuk mendeteksi cacat berukuran kecil yang dominan di area tengah PCB. Hasil ini menunjukkan bahwa pendekatan yang diterapkan mampu memberikan deteksi yang cepat, akurat, dan konsisten, sehingga berpotensi besar untuk diimplementasikan pada sistem kontrol kualitas di industri manufaktur elektronik.
ANALYSIS OF 3 KG LPG INVENTORY CONTROL USING THE ECONOMIC ORDER QUANTITY (EOQ) METHOD AT PT PUTRA JAYA NUSA PERKASA
(Politeknik Negeri Batam, 2026-08-03) Dewi, Meylinda; Syafrina, Mia
Inventory is one of the important aspects of the operations of a distribution company because it affects distribution continuity and cost efficiency. PT Putra Jaya Nusa Perkasa, as a 3 kg LPG distribution agent in Batam City, faces inventory control problems in the form of demand fluctuations, the risk of excess inventory, and high inventory costs due to the absence of an optimal inventory calculation method. The main objective of this study is to evaluate the extent to which the application of the Economic Order Quantity (EOQ) method by PT Putra Jaya Nusa Perkasa in controlling 3 kg LPG inventory has succeeded in reducing inventory costs. This study uses a descriptive quantitative approach with a case study method. Data were collected through record keeping, interviews, and field observations. The results show that the company’s current inventory management system does not use optimal inventory estimates, but is instead based on distribution provisions from Pertamina. The company’s actual total inventory costs in 2023 amounted to IDR 2,136,794,625, in 2024 to IDR 2,313,364,233, and in 2025 to IDR 1,173,579,450. After applying the EOQ method, the optimal order quantities were 520,528 cylinders in 2023, 551,987 cylinders in 2024, and 383,988 cylinders in 2025. The application of the EOQ method reduced total inventory costs to IDR 974,323,384 in 2023, IDR 1,036,180,553 in 2024, and IDR 695,141,515 in 2025.
Raw Material Inventory Control at PT Pegaunihan Technology Indonesia Using the Economic Order Quantity (EOQ) Method
(Politeknik Negeri Batam, 2026-08-18) Cristina, Fikky; Sari, Desi Ratna
This purpose of study this to evaluate the raw material inventory control system at PT Pegaunihan Technology Indonesia by comparing the company's conventional inventory management policy with the Economic Order Quantity (EOQ) approach. The study employs a quantitative descriptive method, with data collected through interviews and documentation. Interviews were conducted with three employees of PT Pegaunihan Technology Indonesia who were directly involved in inventory management activities. The results indicate that inventory control using the EOQ method is more efficient than the inventory management system currently implemented by the company. In 2024, the company's inventory cost amounted to IDR 87,656,612, with raw material procurement conducted 42 times per year and an order quantity of 1,228 sheets per order. By applying the EOQ approach, inventory costs could be reduced to IDR 74,798,750, while the optimal order quantity was reduced to 679 sheets per order with an ordering frequency of 39 times per year. This resulted in potential cost savings of IDR 12,857,862.In 2025, the company's inventory cost reached IDR 99,294,293 with procurement activities conducted 43 times per year and an order quantity of 1,228 sheets per order. By implementing the EOQ method, inventory costs could be reduced to IDR 82,380,041, with an optimal ordering frequency of 43 times per year. The results demonstrate potential inventory cost savings of IDR 16,941,252.
Comparison of the Application of YOLOv5, YOLOv8, and YOLOv11 for Training Chinese Chess Objects
(Polihteknik Negeri Batam, 2026-07-23) Panisti, Ahmad Mufid; Wijaya, Ryan Satria
The use of YOLO (You Only Look Once)-based object detection algorithms has become one of the main approaches in visual object recognition and training. This study aims to compare the performance of three versions of YOLO, namely YOLOv5, YOLOv8, and YOLOv11, in training models to detect objects in Chinese chess images. The dataset used consists of images of Chinese chess boards and pieces in various positions and lighting variations. The training process was carried out using uniform parameters to ensure fair evaluation, including batch size, number of epochs, and image resolution. The performance of each model was evaluated based on detection accuracy, inference speed, and computational efficiency metrics. The results of the study show that each version of YOLO has specific advantages in certain aspects, such as training speed or detection precision. From the 7224 images used as the dataset, several results were obtained that were necessary in helping to compile this journal. These included Precision (YOLOv5: 0.94, YOLOv8: 0.96, YOLOv11: 0.98), Recall (YOLOv5: 0.93, YOLOv8: 0.98, YOLOv11: 0.96), and mAP (YOLOv5: 0.96, YOLOv8: 0.98, YOLOv11: 0.99). This study provides important insights into the advantages and disadvantages of each version of YOLO in the specific application of Chinese chess object recognition, as well as providing guidance for developers in choosing the model that suits their project needs. This study provides insights into the strengths and limitations of each YOLO version, offering guidance for selecting appropriate models in real-time Chinese chess object detection applications.