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INDONESIA
Jurnal Sistem Cerdas
Published by Asosiasi Prakarsa Indonesia Cerdas
ISSN : -     EISSN : 26228254     DOI : -
Core Subject : Science, Education, Engineering,
Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering
Jurnal Sistem Cerdas dengan eISSN : 2622-8254 adalah media publikasi hasil penelitian yang mendukung penelitian dan pengembangan kota, desa, sektor dan kesistemam lainnya. Jurnal ini diterbitkan oleh Asosiasi Prakarsa Indonesia Cerdas (APIC) dan terbit setiap empat bulan sekali.
Arjuna Subject : Umum - Umum
Articles 176 Documents
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Real-Time Multiface Mask Automatic Detection System in Classroom Learning using YOLOv4 Deep Learning Arif Fadllullah; Rahmatuz Zulfia; Tegar Palyus Fiqar; Awang Pradana
Jurnal Sistem Cerdas Vol. 7 No. 2 (2024)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v7i2.391

Abstract

During the Covid-19 pandemic, students were required to wear masks in classroom learning. However, students often do not use masks, so they are prone to transmission of Covid-19. For this reason, this study proposes the development of a real-time multi-face mask automatic detection system in classroom learning using YOLOv4 deep learning. Experimental results on 22 samples of students who collected real-time/live video data every 3 minutes for 20 scenarios proved that the proposed system was successful in detecting objects wearing masks (PM) and not wearing masks (TPM) with the average percentage of precision was 95.63% for PM and 97.33% for TPM, the average percentage of recall was 61.61% for PM and 60.23% for TPM, and the average percentage of F-measure was 74.55% for PM and 74.00% for TPM. This results indicate an effective, valid and accurate proposed system for monitoring the use of masks in classroom learning easily and automatically.
Classification of Beef and Pork with Deep Learning Approach Akhiril Anwar Harahap; Novita, Rice; Ahsyar, Tengku Khairil; Zarnelly, Zarnelly
Jurnal Sistem Cerdas Vol. 7 No. 1 (2024)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v7i1.393

Abstract

Beef is one of the most consumed meats in Indonesia. However, the high price of beef has led to rogue traders mixing pork with beef. This condition occurs due to the lack of public knowledge about the difference between the two meats. To maintain food safety in Indonesia and especially in Riau province, the Livestock Service Office of Riau province conducts market surveys. There are several methods that are usually used to check the content of beef or pork, including Rapid Test Kit and Elisa. Both methods are time consuming and costly. One other solution that can be used is the artificial intelligence method, namely deep learning. In this research, a classification approach using deep learning is used to distinguish between beef and pork in the form of a web application. This research compares Convolutional Neural Network algorithm with Inception-V3 and Inception-Resnet-V2 architecture with hyperparameter optimization. From several experiments that have been carried out, the best model is the Inception-Resnet-V2 architecture with an experimental scenario using a learning rate of 0.001, and an optimizer Adam with an accuracy of 96.50%, Precision 96.48%, Recall 96.55% and F1-Score 96.50%. By using this model, web-based applications can be developed using the flask framework well and can perform classification accurately.
Comparison of Service and Ease of e-Commerce User Applications Using BERT Yuda, Afi Ghufran; Novita, Rice; Mustakim; Afdal, M.
Jurnal Sistem Cerdas Vol. 7 No. 2 (2024)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v7i2.403

Abstract

The development of e-commerce has transformed shopping patterns by harnessing the internet, enabling consumers to shop online. In Indonesia, e-commerce has experienced rapid growth, with numerous options such as Tokopedia, Shopee, and Lazada, leading to intense competition. Sentiment analysis using machine learning techniques has become crucial for understanding consumer views on these e-commerce services. This study analyzes user comments on Tokopedia, Shopee, and Lazada e-commerce platforms from Instagram social media, totaling 3900 data points, using the Bidirectional Encoder Representations from Transformers (BERT) model with 5 epochs and a batch size of 32. Sentiment analysis utilizes 3 types of labels: positive, neutral, and negative. The final results of the study include the performance analysis of the BERT model, as well as comparisons for each predefined category, namely Promotions & Offers, and Services. The final results of the model indicate good performance, with accuracy rates of 95%, 97%, and 99%, respectively.
Sentiment Analysis on the Impact of Artificial Intelligence (AI) Development to Determine Technology Needs Abror, Naufal; Novita, Rice; Mustakim; Afdal, M.Afdal
Jurnal Sistem Cerdas Vol. 7 No. 2 (2024)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v7i2.404

Abstract

Artificial Intelligence (AI) has become a hot topic in recent years in Indonesia. To determine the influence of AI developments in determining technology needs, a sentiment analysis needs to be carried out. Sentiment analysis is a process used to help identify the contents of a dataset in the form of opinions or views (sentiments) in text form regarding an issue or event that is positive, negative or neutral. The algorithm applied in this research is the Multinominal Naive Bayes Classifier method. The Multinominal Naive Bayes Classifier method was chosen because it has quite high processing speed and accuracy when used on large, varied and large amounts of data. In this research, the sentiment results were "Negative" for the topic of data security and privacy with a testing accuracy of 75%, "Positive" for Economic Topics with a testing accuracy of 50%, "Negative" for Industrial Topics with a testing accuracy of 58%, "Positive" for Field Topics jobs with a testing accuracy of 75%, “Negative” Transportation Topics with a testing accuracy of 50%, and “Negative” for Education Topics with a testing accuracy of 67%.
Eliminating Production Process Waste with Lean Six Sigma in the Gresik Ceramic Industry Pristyanto, Yafie; Rochmoeljati
Jurnal Sistem Cerdas Vol. 7 No. 2 (2024)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v7i2.408

Abstract

The Gresik Ceramic Industry is a company operating in the ceramic industry. In the ceramic production area at PT XYZ, waste is still found, namely waste defects, safety health environment (SHE), excess processing. and motion. This research aims to identify and reduce waste in the ceramic production process using the Lean six sigma (LSS) method and Failure Mode Effect and Analysis (FMEA) as proposed recommendations for improvement. Lean six sigma uses 5 stages, namely define, measure, analyze, improve, control. However, research is carried out to the improvement stage to provide recommendations for improvement. The results of reducing the lead time value in the ceramic production process, which was originally 1880 minutes, changed to 1816 minutes or decreased by 64 minutes, thereby reducing the cycle time in the ceramic production process. Then the DPMO value obtained was13311 with a sigma level of 3.71. The research results show that crack defects have the highest RPN value of 576 with the cause being that the punches on the press machine have reached their useful life, causing the printing process to be less than perfect. The proposed recommendation for improvement is replacing the punches components on the press machine
Streamlined A* for Faster Robotic Inspections in Ports Hartanto Kusuma Wardana; Rumaksari, Atyanta; Prischa Wilhelmina Picanussa; Budihardja Murtianta; Adri Sooai; King Harold Recto
Jurnal Sistem Cerdas Vol. 7 No. 2 (2024)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v7i2.416

Abstract

Research on automatic port inspections using robots has been carried out in the state-owned company Indonesia Port Corporation, Semarang, Indonesia. However, increasing the efficiency of robotic inspections is critical because robots need to perform these tasks at much higher speeds than humans, while maintaining a high level of accuracy. The robot is equipped with sensors and computer vision technology to detect defects or problems that humans might miss. The aim is to increase overall inspection accuracy at a lower cost. In this research, we introduce an optimized A* path planning algorithm that incorporates the flood algorithm, node reductions process, and linear path planning optimization for an autonomous navigated port inspection robot. Our primary objective is to significantly increase the efficiency of the conventional A* algorithm in guiding robotic systems through complex paths. The proposed algorithm demonstrates exceptional efficiency in generating feasible paths, with success attributed to optimization steps that specifically target reducing node processing and enhancing route finding. The experimentation phase involves a comprehensive assessment of the algorithm using six key parameters: running time, number of nodes, number of turns, maximum turning angle, expansion nodes, and the total distances output. Through rigorous testing, the algorithm's performance is evaluated and compared against seven other current algorithms, namely A*, BestFirst, Dijkstra, BFS, DFS, Bidirectional A*, and Geometric A*. Results from the experiments reveal the algorithm's outstanding running time efficiency, surpassing all other algorithms tested. Notably, it exhibits a remarkable 6.5% improvement over the widely recognized Geometric A* algorithm.
The IoT Integrated Electric Vehicle Fire Detection System: Case Study of IMEI TEAM UMSIDA Muhamad Husaini; Sulistiyowati, Indah; Anshory, Izza; Syahrorini, Syamsudduha; Aliffudin, Muchammad
Jurnal Sistem Cerdas Vol. 7 No. 2 (2024)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v7i2.420

Abstract

Abstract—In the modern era, electric vehicle technology is rapidly developing and emerging as a more environmentally friendly option than fossil fuel vehicles. Despite its many advantages, electric vehicles face safety concerns, especially fire hazards caused by problems with the battery, electrical system, or human error. Fires in electric vehicles can occur due to problems with the lithium-ion battery, accidents, or improper maintenance. Although rare, this risk needs to be considered by following maintenance and safety guidelines. Fire detection systems in electric vehicles are designed to detect and respond to potential fires, provide early warning, and take preventive or emergency action. The system uses NodeMCU esp8266 as the microcontroller, which is connected with a smartphone via WiFi. Data from the MQ2 sensor and the fire sensor are sent and recorded on the IoT platform on the smartphone. This tool is able to detect fires in real-time, but unstable internet or WiFi quality can affect data transmission. Thus, this system is expected to increase safety and reduce the risk of fire in electric vehicles, so that people feel safer and more confident in using this environmentally friendly technology. Keywords— Electric Vehicles; Fie Detector; Monitoring
A Reliability Analysis of Steam Condenser Instrumentation Using Failure Mode and Effect Analysis (FMEA) M.Padol Padilla; Jufrizel; Putut Son Maria; Ahmad Faizal
Jurnal Sistem Cerdas Vol. 7 No. 2 (2024)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v7i2.426

Abstract

Abstract—PT PLN Nusantara Power UP Tenayan is a steam power plant (PLTU) with a capacity of 2 x 110 MW which is a buffer for the electricity system in the Central Sumatra section. In the production process of PT PLN Nusantara Power UP Tenayan it does not always go well,. So that it will have an impact on the production stages of the power plant. One of the production machines that often fails and disrupts the production stage is the steam condenser. This research uses the Failure Mode And Effect Analysis Method which aims to identify the type of failure, cause of failure, effect of failure, and determine the RPN value. The research phase begins with data collection from literature studies, interviews, field observations, data analysis, FMEA analysis, results and discussion, conclusions. Based on the results of the RPN calculation, each component of the steam condenser instrumentation does not exceed the RPN standard limit of less than 200, although the temperature indicator has the highest RPN value but is still categorized as reliable and not recommended for immediate maintenance action. The results of identifying the type of failure are that there are inaccurate indications, there are switches with sticky conditions, there are displays that are unclear or blurry, and there is a mismatch between the data displayed in the DCS and the data displayed in the local area. For recommended actions on the steam condenser instrumentation components, among others, carry out more incentive maintenance every 3 months, make improvements to the specifications of the switch according to the amount of pressure to be measured, move the sensor to a safer area and protected from water exposure, carry out a zero calibration process on the steam condenser instrumentation components every six months.
Design and Implementation of a Fire Detection and Extinguishing System Using Dual Axis Mechanics Hidayat, Sharul; Maria, Putut Son; Hilman Zarory; Ahmad Faizal
Jurnal Sistem Cerdas Vol. 7 No. 2 (2024)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v7i2.429

Abstract

Fire detection and extinguishing systems play an essential role in anticipating the impact of more severe damage to residential installations, warehouses, and buildings that store valuable goods or important documents. This research develops an automatic fire detection and extinguishing system using the dual-axis rotational principle. The flame channel sensor is used by modifying its default position to be collateral (facing the same plane at the same angle). The dual-axis principle allows panning and tilting movements to yield a broader scope for detecting and extinguishing fires. The Dual Axis built in the research has a panning angle range of 113° and a tilting angle of 45°. The results show good performance with a fire detection accuracy rate of 73-100% and a successful extinguishing rate of 76-96%. The implementation of dual-axis rotational construction in the automatic fire extinguishing system not only improves the detection coverage but also increases the chance ratio of more targeted extinguishing.
Face Recognition-Based Surveillance System in Mining Industry Hidayat, Fadhil; Elviani, Ulva; Agil Alunjati, Figo; Furqan Alfuady, Muhammad
Jurnal Sistem Cerdas Vol. 7 No. 2 (2024)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v7i2.434

Abstract

Access control in mining construction areas is crucial for the operations of mining companies. This access control functions to secure and restrict unauthorized parties from mining activities. Violations of access rights in the mining industry result in significant losses for companies. This access control can also be utilized to record employee attendance, serving as input for the contract work system commonly applied in mining areas. Closed-circuit television (CCTV) is commonly used to monitor activities; however, the current use of CCTV still requires direct human observation, which may result in important events being overlooked. The functionality of these CCTVs can be enhanced to manage access rights and monitor employee attendance to support company operations through face recognition methods. In this study, a system design was carried out through a research approach to determine the technology to be used in the system. The development of a face recognition-based access control system was conducted based on system engineering methodology. This development includes system requirements analysis, the design of a face recognition-based access control system, implementation, and system performance evaluation. The resulting system was tested through simulation processes based on actual field conditions, and the test results showed that the system could recognize faces registered in the dataset and identify subjects not registered in the dataset with an accuracy of 60%, precision of 96%, recall of 58%, and an F-score of 72%. Additionally, the system was able to connect to a database to store face recognition results and then display them on an employee attendance monitoring dashboard. The delay between the face recognition system and actual time ranged from 2-4 seconds and was still tolerable.

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