Pub Date : 2021-03-30DOI: 10.30998/faktorexacta.v14i1.7833
Hari Hadi S, E. Wati, Tomas Kristiono
Measurement of Peak Particle Velocity (PPV) mm / sec in the Sabo dam construction project was carried out using seismic accelerometers. This study is to determine the value of PPV produced by construction equipment and then compared with the BS 6472-2: 2008 standard. The measurement method is carried out based on the applicable rules. PPV measurement results produced by each machine are different. In heavy equipment dump trucks, excavators, and front end loaders show PPV values at distances of 50 m, 100 m, 150 m and 200 m under safe conditions referring to the standard which is still in the range of 0.2 - 0.4 mm / sec. while for the pile driving device, demolition, vibrator pile driver at a distance of 50 meters are in unsafe conditions, because more than the range of 0.2 - 0.4 mm / sec, but at a distance of 100, 150, and 200 m PPV values are at safe condition
{"title":"ANALISIS GROUND VIBRATION DENGAN METODE PEAK PARTICLE VELOCITY (PPV)","authors":"Hari Hadi S, E. Wati, Tomas Kristiono","doi":"10.30998/faktorexacta.v14i1.7833","DOIUrl":"https://doi.org/10.30998/faktorexacta.v14i1.7833","url":null,"abstract":"<p><em>Measurement of Peak Particle Velocity (PPV) mm / sec in the Sabo dam construction project was carried out using seismic accelerometers. This study is to determine the value of PPV produced by construction equipment and then compared with the BS 6472-2: 2008 standard. The measurement method is carried out based on the applicable rules. PPV measurement results produced by each machine are different. In heavy equipment dump trucks, excavators, and front end loaders show PPV values at distances of 50 m, 100 m, 150 m and 200 m under safe conditions referring to the standard which is still in the range of 0.2 - 0.4 mm / sec. while for the pile driving device, demolition, vibrator pile driver at a distance of 50 meters are in unsafe conditions, because more than the range of 0.2 - 0.4 mm / sec, but at a distance of 100, 150, and 200 m PPV values are at safe condition</em></p><p><em><br /></em></p><p>Key words<strong>: </strong><em>PPV, Ground Vibration, Dam sabo</em></p><p><strong> </strong></p><p><em><br /></em></p>","PeriodicalId":53004,"journal":{"name":"Faktor Exacta","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42362458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-30DOI: 10.30998/faktorexacta.v14i1.8989
N. Dewi, Fiqih Ismawan
Received: Feb 15, 2021 Revised: Feb 22, 2021 Accepted: Mach 12, 2021 Face recognition system is generally divided into two stages, face detection system, which is a pre-processing step followed by a facial recognition system. This step will quickly be done by humans but it takes a long time for the computer. This ability of humans is what researchers want to duplicate in the last few years as biometric technology in computer vision to create a model of face recognition in computer. Deep learning becomes a spotlight in developing machine learning, the reason because deep learning has reached an extraordinary result in computer vision. Based on that, the author came up with an idea to create a face recognition system by implementing deep learning using the CNN method and applying library openFace. The CNN methods are still superior and widely used because they have good accuracy. The initial process was taking a picture of the face to be used as a dataset. From this dataset, face preprocessing will be carried out, that is, to extract the facial vector features into 128-d and to classify the facial vector. The contribution of this research is the addition of features to improve the accuracy of the facial recognition system using the CNN method. The results of this research get a precision value of 98.4%, a recall of 98% and an accuracy of 99.84%.
{"title":"IMPLEMENTASI DEEP LEARNING MENGGUNAKAN CNN UNTUK SISTEM PENGENALAN WAJAH","authors":"N. Dewi, Fiqih Ismawan","doi":"10.30998/faktorexacta.v14i1.8989","DOIUrl":"https://doi.org/10.30998/faktorexacta.v14i1.8989","url":null,"abstract":"Received: Feb 15, 2021 Revised: Feb 22, 2021 Accepted: Mach 12, 2021 Face recognition system is generally divided into two stages, face detection system, which is a pre-processing step followed by a facial recognition system. This step will quickly be done by humans but it takes a long time for the computer. This ability of humans is what researchers want to duplicate in the last few years as biometric technology in computer vision to create a model of face recognition in computer. Deep learning becomes a spotlight in developing machine learning, the reason because deep learning has reached an extraordinary result in computer vision. Based on that, the author came up with an idea to create a face recognition system by implementing deep learning using the CNN method and applying library openFace. The CNN methods are still superior and widely used because they have good accuracy. The initial process was taking a picture of the face to be used as a dataset. From this dataset, face preprocessing will be carried out, that is, to extract the facial vector features into 128-d and to classify the facial vector. The contribution of this research is the addition of features to improve the accuracy of the facial recognition system using the CNN method. The results of this research get a precision value of 98.4%, a recall of 98% and an accuracy of 99.84%.","PeriodicalId":53004,"journal":{"name":"Faktor Exacta","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48684107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-30DOI: 10.30998/faktorexacta.v14i1.7652
Nur Alam, Dian Figana
{"title":"PERANCANGAN MACHINE VISION UNTUK PEMILAH KUALITAS PRODUK AIR MINUM DALAM BOTOL 600ML DI WTP PUTOI PNJ","authors":"Nur Alam, Dian Figana","doi":"10.30998/faktorexacta.v14i1.7652","DOIUrl":"https://doi.org/10.30998/faktorexacta.v14i1.7652","url":null,"abstract":"","PeriodicalId":53004,"journal":{"name":"Faktor Exacta","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45747144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-30DOI: 10.30998/faktorexacta.v14i1.8630
Arham Bakri, Anggraeni Ridwan
{"title":"EVALUASI KUALITAS APLIKASI SISTEM INFORMASI MANAJEMEN KEIMIGRASIAN (SIMKIM) VERSI 2.0 BERBASIS WEB MENGGUNAKAN METODE HUMAN ORGANIZATION TECHNOLOGY FIT (Studi Kasus pada Kantor Imigrasi)","authors":"Arham Bakri, Anggraeni Ridwan","doi":"10.30998/faktorexacta.v14i1.8630","DOIUrl":"https://doi.org/10.30998/faktorexacta.v14i1.8630","url":null,"abstract":"","PeriodicalId":53004,"journal":{"name":"Faktor Exacta","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44263760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-30DOI: 10.30998/faktorexacta.v14i1.9057
Dwi Dani Apriyani
{"title":"Sistem Pendukung Keputusan Pemilihan Siswa Berprestasi Menggunakan Metode Profile Matching","authors":"Dwi Dani Apriyani","doi":"10.30998/faktorexacta.v14i1.9057","DOIUrl":"https://doi.org/10.30998/faktorexacta.v14i1.9057","url":null,"abstract":"","PeriodicalId":53004,"journal":{"name":"Faktor Exacta","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42310694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Analisis Dan Perancangan Simulasi Algoritma Paillier Cryptosystem Pada Pesan Text Dengan Presentation Format Binary, Octal, Hexadecimal dan Base64","authors":"Muhamad Femy Mulya, Nofita Rismawati, Dedy Trisanto","doi":"10.30998/faktorexacta.v13i4.7429","DOIUrl":"https://doi.org/10.30998/faktorexacta.v13i4.7429","url":null,"abstract":"","PeriodicalId":53004,"journal":{"name":"Faktor Exacta","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49355701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-02-16DOI: 10.30998/faktorexacta.v13i4.6598
Habib Nurfaizal, Makhsun Makhsun, Yan Mitha Djaksana
Received July 07, 2020 Revised Feb 06, 2021 Accepted Feb 08, 2021 In this sophisticated era, a lot of human work has begun to be replaced by robots. Physical limitations and human concentration in doing repetitive or dangerous work are important factors in the development of robots. One of the robots that was created to make work easier is a robot that has the ability like a human arm called the arm gripper manipulator robot. This manipulator gripper arm consists of interconnected arms, namely link, joint and endeffector. This research designed a control system of the robot arm gripper manipulator with 2 modes, gesture mode and IoT mode. The microcontroller used is Arduino Mega 2560 with flex sensor control and MPU 6050 inertia measurement unit sensor in gesture mode attached to the glove. And Iot control using a smartphone, the result of testing the error of the average travel time in 5 movements is 2.08%. The overall test results of the robot arm gripper manipulator can be controlled with gesture mode and IoT mode. The hope is that this solution will be useful for humans in reducing the risk of injury when doing heavy work.
在这个复杂的时代，很多人类的工作已经开始被机器人所取代。在机器人的发展中，物理限制和人类在重复或危险工作中的集中是重要的因素。其中一种机器人是为了使工作更容易而创造的，它有像人的手臂一样的能力，被称为手臂抓取机械手机器人。该机械手夹持臂由连接臂组成，即连杆、关节和伸臂。本研究设计了一种具有手势模式和物联网模式两种模式的机械臂抓取机械手控制系统。使用的微控制器是Arduino Mega 2560，带有弯曲传感器控制和MPU 6050惯性测量单元传感器，在手势模式下连接在手套上。并且使用智能手机进行物联网控制，测试结果显示5次运动中平均行程时间的误差为2.08%。机器人手臂夹持机械手的整体测试结果可以通过手势模式和物联网模式进行控制。希望这一解决方案将有助于人类在从事繁重工作时减少受伤的风险。
{"title":"PROTOTYPE SISTEM KENDALI ROBOT ARM GRIPPER MANIPULATOR MENGGUNAKAN FLEX SENSOR DAN MPU6050 BERBASIS INTERNET OF THINGS","authors":"Habib Nurfaizal, Makhsun Makhsun, Yan Mitha Djaksana","doi":"10.30998/faktorexacta.v13i4.6598","DOIUrl":"https://doi.org/10.30998/faktorexacta.v13i4.6598","url":null,"abstract":"Received July 07, 2020 Revised Feb 06, 2021 Accepted Feb 08, 2021 In this sophisticated era, a lot of human work has begun to be replaced by robots. Physical limitations and human concentration in doing repetitive or dangerous work are important factors in the development of robots. One of the robots that was created to make work easier is a robot that has the ability like a human arm called the arm gripper manipulator robot. This manipulator gripper arm consists of interconnected arms, namely link, joint and endeffector. This research designed a control system of the robot arm gripper manipulator with 2 modes, gesture mode and IoT mode. The microcontroller used is Arduino Mega 2560 with flex sensor control and MPU 6050 inertia measurement unit sensor in gesture mode attached to the glove. And Iot control using a smartphone, the result of testing the error of the average travel time in 5 movements is 2.08%. The overall test results of the robot arm gripper manipulator can be controlled with gesture mode and IoT mode. The hope is that this solution will be useful for humans in reducing the risk of injury when doing heavy work.","PeriodicalId":53004,"journal":{"name":"Faktor Exacta","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42938588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-02-16DOI: 10.30998/faktorexacta.v13i4.7074
I. Saputra, Rahmad Singgih AJI PAMBUDI, Hanafi Eko Darono, Fachri Amsury, Muhammad Rizki Fahdia, Benni Ramadhan, Anggi Ardiansyah
Received Sep 9, 2019 Revised May 20, 2020 Accepted December 27, 2020 A collection of tweets from Twitter users about Marketplace Bukalapak and Tokopedia can be used as a sentiment analysis. The data obtained is processed using data mining techniques, in which there is a process of mining the text, tokenize, transformation, classification, stem, etc. Then calculated into three different algorithms to be compared, the algorithm used is the Decision Tree, K-NN, and Naïve Bayes Classifier with the aim of finding the best accuracy. Rapidminer application is also used to facilitate writers in processing data. The highest results from this study are Decision Tree algorithm with 82% accuracy, 81.95% precision and 86% recall.
{"title":"Analisis Sentimen Pengguna Marketplace Bukalapak dan Tokopedia di Twitter Menggunakan Machine Learning","authors":"I. Saputra, Rahmad Singgih AJI PAMBUDI, Hanafi Eko Darono, Fachri Amsury, Muhammad Rizki Fahdia, Benni Ramadhan, Anggi Ardiansyah","doi":"10.30998/faktorexacta.v13i4.7074","DOIUrl":"https://doi.org/10.30998/faktorexacta.v13i4.7074","url":null,"abstract":"Received Sep 9, 2019 Revised May 20, 2020 Accepted December 27, 2020 A collection of tweets from Twitter users about Marketplace Bukalapak and Tokopedia can be used as a sentiment analysis. The data obtained is processed using data mining techniques, in which there is a process of mining the text, tokenize, transformation, classification, stem, etc. Then calculated into three different algorithms to be compared, the algorithm used is the Decision Tree, K-NN, and Naïve Bayes Classifier with the aim of finding the best accuracy. Rapidminer application is also used to facilitate writers in processing data. The highest results from this study are Decision Tree algorithm with 82% accuracy, 81.95% precision and 86% recall.","PeriodicalId":53004,"journal":{"name":"Faktor Exacta","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49221624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-30DOI: 10.30998/faktorexacta.v13i3.6513
Erlin Windia Ambarsari, Herlinda Herlinda
Received June 24, 2020 Revised Oct 14, 2020 Accepted Oct 26, 2020 Students observed Pythagoras for using a plane Geometry and 3D Geometry. However, Pythagoras can also be built for decision trees. Our research regarding Instagram Usage Habit with construct Pythagoras for a single decision tree. The study's results obtained are ambiguous attribute values. Therefore, it is continued with research to build Pythagoras for Random Forest. The purpose of the study is to facilitate the tracking of ambiguous data contained in the attributes. The results obtained that the relationship between characteristics of the target class, thus resulting in misclassification. This error caused invalid data; for example, there are three times the separation of data on the same attribute for age's target for a group of 20. However, although there are misclassifications caused by invalid data, based on the Pythagorean construction for Random Forest, the data is more easily traced to errors, which cannot be done by a single decision tree.
{"title":"Membangun Pythagoras Sebagai Visualisasi Random Forest Untuk Pemodelan Pohon Keputusan","authors":"Erlin Windia Ambarsari, Herlinda Herlinda","doi":"10.30998/faktorexacta.v13i3.6513","DOIUrl":"https://doi.org/10.30998/faktorexacta.v13i3.6513","url":null,"abstract":"Received June 24, 2020 Revised Oct 14, 2020 Accepted Oct 26, 2020 Students observed Pythagoras for using a plane Geometry and 3D Geometry. However, Pythagoras can also be built for decision trees. Our research regarding Instagram Usage Habit with construct Pythagoras for a single decision tree. The study's results obtained are ambiguous attribute values. Therefore, it is continued with research to build Pythagoras for Random Forest. The purpose of the study is to facilitate the tracking of ambiguous data contained in the attributes. The results obtained that the relationship between characteristics of the target class, thus resulting in misclassification. This error caused invalid data; for example, there are three times the separation of data on the same attribute for age's target for a group of 20. However, although there are misclassifications caused by invalid data, based on the Pythagorean construction for Random Forest, the data is more easily traced to errors, which cannot be done by a single decision tree.","PeriodicalId":53004,"journal":{"name":"Faktor Exacta","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46472626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号