Pub Date : 2022-06-29DOI: 10.31544/jtera.v7.i1.2022.173-180
Lani Nurlani, E. Andika
Reporting family health data is useful for knowing the level of public health in a certain area, so that policy makers can make decisions quickly and accurately. This family health data consists of family health indicators whose target values have been set by the Health Office. This target value or data is given to each Puskesmas which is then lowered down to the village level. Therefore, an information system is needed to monitor the achievement of the target data for the family health indicators. This study aims to build a system that can facilitate the reporting of Puskesmas to monitor the achievement of family health indicators. The data reported by the Puskesmas is data collected from the Posyandu level which is verified by the village midwife. The system development method used is Rapid Application Development (RAD) which consists of three main phases, namely requirements planning, design workshop, and implementation. The test method used is the black box method, where this method prioritizes system functionality. The results of this study indicate that the functionally built system has been able to meet the reporting needs to determine the achievement of health targets by Posyandu, midwives, and Puskesmas.
{"title":"Sistem Monitoring Pencapaian Indikator Kesehatan Keluarga melalui Pelaporan Puskesmas","authors":"Lani Nurlani, E. Andika","doi":"10.31544/jtera.v7.i1.2022.173-180","DOIUrl":"https://doi.org/10.31544/jtera.v7.i1.2022.173-180","url":null,"abstract":"Reporting family health data is useful for knowing the level of public health in a certain area, so that policy makers can make decisions quickly and accurately. This family health data consists of family health indicators whose target values have been set by the Health Office. This target value or data is given to each Puskesmas which is then lowered down to the village level. Therefore, an information system is needed to monitor the achievement of the target data for the family health indicators. This study aims to build a system that can facilitate the reporting of Puskesmas to monitor the achievement of family health indicators. The data reported by the Puskesmas is data collected from the Posyandu level which is verified by the village midwife. The system development method used is Rapid Application Development (RAD) which consists of three main phases, namely requirements planning, design workshop, and implementation. The test method used is the black box method, where this method prioritizes system functionality. The results of this study indicate that the functionally built system has been able to meet the reporting needs to determine the achievement of health targets by Posyandu, midwives, and Puskesmas.","PeriodicalId":17680,"journal":{"name":"JTERA (Jurnal Teknologi Rekayasa)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73571496","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}
The use of electric motor drives in the process industry has the risk of causing sparks due to short circuit currents and the formation of electromagnetic fields. Chemical contamination with sparks and electromagnetic fields poses a risk of fire. Thus, the use of pneumatic motors can be used as an alternative solution. The focus of this research is to control the rotational stability of the pneumatic motor drive so that it can move stable under normal conditions or is disturbed using fuzzy logic algorithms. The pneumatic motor rotates with the wind energy source coming from the compressor. To maintain the stability of rotation, the flow of wind through the pneumatic motor is regulated through the size of the proportional solenoid valve opening. The size of the valve opening is regulated electronically through the control device by paying attention to feedback from the speed sensor. The speed sensor value is compared with the setpoint value so as to produce error and delta error variables as a reference for controller input. The output of the control system is a decision making of the size of the valve opening based on the results of Sugeno's fuzzy inference. The overall test results show that the pneumatic motor can rotate stably with a steady state error of 0.656% at a source pressure of 3.2 bar.
{"title":"Aplikasi Algoritma Fuzzy Sugeno pada Kendali Kestabilan Putaran Motor Pneumatik","authors":"Budi Setiadi, Sarjono Wahyu Jadmiko, Hari Purnama, Fryma Zhafran Raihan, Varian Andika Wijayakusuma, Tata Supriyadi, Ridwan Solihin, S. Sudrajat, Hilmi Dhiya Ulhaq","doi":"10.31544/jtera.v7.i1.2022.143-148","DOIUrl":"https://doi.org/10.31544/jtera.v7.i1.2022.143-148","url":null,"abstract":"The use of electric motor drives in the process industry has the risk of causing sparks due to short circuit currents and the formation of electromagnetic fields. Chemical contamination with sparks and electromagnetic fields poses a risk of fire. Thus, the use of pneumatic motors can be used as an alternative solution. The focus of this research is to control the rotational stability of the pneumatic motor drive so that it can move stable under normal conditions or is disturbed using fuzzy logic algorithms. The pneumatic motor rotates with the wind energy source coming from the compressor. To maintain the stability of rotation, the flow of wind through the pneumatic motor is regulated through the size of the proportional solenoid valve opening. The size of the valve opening is regulated electronically through the control device by paying attention to feedback from the speed sensor. The speed sensor value is compared with the setpoint value so as to produce error and delta error variables as a reference for controller input. The output of the control system is a decision making of the size of the valve opening based on the results of Sugeno's fuzzy inference. The overall test results show that the pneumatic motor can rotate stably with a steady state error of 0.656% at a source pressure of 3.2 bar.","PeriodicalId":17680,"journal":{"name":"JTERA (Jurnal Teknologi Rekayasa)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88667180","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 : 2022-06-26DOI: 10.31544/jtera.v7.i1.2022.17-24
R. W. Tri Hartono, Regina Nur Shabrina, Nadya Sarah, M. Fadhlan, Rida Hudaya, S. Supriyanto, Adyatma Adyatma
Virus yang menyebabkan Covid-19 disebut SARS-CoV-2 menyebar secara cepat bila ada kontak erat dalam jarak sekitar 2 meter. Penggunaan masker merupakan salah satu cara menghindari penularan penyakit ini. Dalam penelitian ini dikembangkan alat pendeteksi penggunaan masker yang selanjutnya disebut E-Pindai. E-Pindai merupakan inovasi berbasis teknologi pengolahan citra menggunakan metoda Convolution Neural Network (CNN) dan Internet of Things (IoT). Sistem ini dipasang di Abstract The virus that causes Covid-19, called SARS-CoV-2, spreads quickly when there is close contact within about 2 meters. Wearing a mask is one way to prevent the spread of this disease. In this study, a mask detection tool was developed, hereinafter referred to as E-Scan. E-Scan is an innovation based on image processing technology using the Convolution Neural Network (CNN) and Internet of Things (IoT) methods. This system is installed at the entrance gate of a public area where every visitor who enters his face will be scanned. If it is detected that you are not wearing a mask, the door remains closed, the buzzer sounds, and a photo of the face is sent to the Covid-19 Task Force via the Telegram application as a notification. If all visitors wear masks, the door will open automatically. Data processing is carried out using a Raspberry Pi that has been filled with the program using the Python programming language. The processed data will produce a logic number 1 or 0 which becomes the command code to move the servo motor to open or close the gate, and activate or deactivate the buzzer. The results of testing on 17 types of masks using the confusion matrix method resulted in a percentage of 94% accuracy, 100% precision, 94.11% sensitivity, 100% specificity, and 5.56% error rate. Analysis of image capture distance and response time was also carried out to see the response of the device made.
{"title":"E-Pindai: Pengolahan Citra Wajah Pendeteksi Penggunaan Masker dengan Metode Convolution Neural Network","authors":"R. W. Tri Hartono, Regina Nur Shabrina, Nadya Sarah, M. Fadhlan, Rida Hudaya, S. Supriyanto, Adyatma Adyatma","doi":"10.31544/jtera.v7.i1.2022.17-24","DOIUrl":"https://doi.org/10.31544/jtera.v7.i1.2022.17-24","url":null,"abstract":"Virus yang menyebabkan Covid-19 disebut SARS-CoV-2 menyebar secara cepat bila ada kontak erat dalam jarak sekitar 2 meter. Penggunaan masker merupakan salah satu cara menghindari penularan penyakit ini. Dalam penelitian ini dikembangkan alat pendeteksi penggunaan masker yang selanjutnya disebut E-Pindai. E-Pindai merupakan inovasi berbasis teknologi pengolahan citra menggunakan metoda Convolution Neural Network (CNN) dan Internet of Things (IoT). Sistem ini dipasang di Abstract The virus that causes Covid-19, called SARS-CoV-2, spreads quickly when there is close contact within about 2 meters. Wearing a mask is one way to prevent the spread of this disease. In this study, a mask detection tool was developed, hereinafter referred to as E-Scan. E-Scan is an innovation based on image processing technology using the Convolution Neural Network (CNN) and Internet of Things (IoT) methods. This system is installed at the entrance gate of a public area where every visitor who enters his face will be scanned. If it is detected that you are not wearing a mask, the door remains closed, the buzzer sounds, and a photo of the face is sent to the Covid-19 Task Force via the Telegram application as a notification. If all visitors wear masks, the door will open automatically. Data processing is carried out using a Raspberry Pi that has been filled with the program using the Python programming language. The processed data will produce a logic number 1 or 0 which becomes the command code to move the servo motor to open or close the gate, and activate or deactivate the buzzer. The results of testing on 17 types of masks using the confusion matrix method resulted in a percentage of 94% accuracy, 100% precision, 94.11% sensitivity, 100% specificity, and 5.56% error rate. Analysis of image capture distance and response time was also carried out to see the response of the device made.","PeriodicalId":17680,"journal":{"name":"JTERA (Jurnal Teknologi Rekayasa)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73883771","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 : 2022-06-26DOI: 10.31544/jtera.v7.i1.2022.57-64
Enceng Sulaeman, Griffani Megiyanto Rahmatullah, Rifa Hanifatunnisa, A. Ashari
penelitian penelitian instrumen data digital menjadi sinyal digital dalam bentuk line coding jenis unipolar yaitu dan RZ serta yaitu HDB-3 menggunakan prosesor OMAP L-138 yang berupa prototipe. Hasil dari penelitian ini merupakan representasi nilai data biner menjadi sinyal digital sesuai dengan jenis line coding telah sesuai berdasarkan teori yang berlaku dan juga sesuai berdasarkan proses pada diagram blok sistem. Abstract Practicum of Communication Systems and Telecommunication Transmission Engineering which has been carried out at the Bandung State Polytechnic Telecommunication Engineering study program is carried out using hands-on instruments in the laboratory. However, the performance of the practicum is increasingly less than optimal because the performance of the instruments used decreases due to the age factor. One solution that can be done is to use signal processing techniques and apply the communication system concepts learned to the digital signal processor. This study aims to utilize the OMAP-L138 module for line coding generation as a learning module. This research was carried out in stages, starting with the generation of pulse code modulation (PCM) which has been running in previous studies, followed by the generation of the current line coding. The data generated in the previous research was continued as input data in this study. The result is a digital data processing instrument into a digital signal in the form of line coding with unipolar types, namely NRZ and RZ and bipolar types, namely AMI and HDB-3 using an OMAP L-138 processor in the form of a prototype. The result of this research is a representation of the value of binary data into a digital signal according to the type of line coding that is appropriate based on the applicable theory and is also appropriate based on the process on the system block diagram.
{"title":"Pemanfaatan Modul OMAP-L138 untuk Pembangkitan Line Coding sebagai Modul Pembelajaran","authors":"Enceng Sulaeman, Griffani Megiyanto Rahmatullah, Rifa Hanifatunnisa, A. Ashari","doi":"10.31544/jtera.v7.i1.2022.57-64","DOIUrl":"https://doi.org/10.31544/jtera.v7.i1.2022.57-64","url":null,"abstract":"penelitian penelitian instrumen data digital menjadi sinyal digital dalam bentuk line coding jenis unipolar yaitu dan RZ serta yaitu HDB-3 menggunakan prosesor OMAP L-138 yang berupa prototipe. Hasil dari penelitian ini merupakan representasi nilai data biner menjadi sinyal digital sesuai dengan jenis line coding telah sesuai berdasarkan teori yang berlaku dan juga sesuai berdasarkan proses pada diagram blok sistem. Abstract Practicum of Communication Systems and Telecommunication Transmission Engineering which has been carried out at the Bandung State Polytechnic Telecommunication Engineering study program is carried out using hands-on instruments in the laboratory. However, the performance of the practicum is increasingly less than optimal because the performance of the instruments used decreases due to the age factor. One solution that can be done is to use signal processing techniques and apply the communication system concepts learned to the digital signal processor. This study aims to utilize the OMAP-L138 module for line coding generation as a learning module. This research was carried out in stages, starting with the generation of pulse code modulation (PCM) which has been running in previous studies, followed by the generation of the current line coding. The data generated in the previous research was continued as input data in this study. The result is a digital data processing instrument into a digital signal in the form of line coding with unipolar types, namely NRZ and RZ and bipolar types, namely AMI and HDB-3 using an OMAP L-138 processor in the form of a prototype. The result of this research is a representation of the value of binary data into a digital signal according to the type of line coding that is appropriate based on the applicable theory and is also appropriate based on the process on the system block diagram.","PeriodicalId":17680,"journal":{"name":"JTERA (Jurnal Teknologi Rekayasa)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87365276","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}
The development of social media has made it easier for people to disseminate information. One form of information in question is the freedom to express opinions on social media. The development of research related to sentiment analysis on text review data aims to determine the polarity of opinion on social media which has increased. One of the methods applied in the sentiment analysis of the review text is the use of the Long Short-Term Memory (LSTM) method. The purpose of this study was to determine the performance of the LSTM model on various sentiments of reviews of Indonesian-language Twitter texts. The testing process is carried out based on the calculation of the hyperparameter tuning accuracy value. Testing the accuracy of this study using Word2Vec parameters, activation function, number of epochs, and number of neurons. The optimal LSTM performance test results from this study were obtained based on tuning the Word2Vec Continuous Bag of Words (CBOW) architecture with an accuracy of 57.15%, tuning the number of neurons as much as 150 producing an accuracy value of 57.35%, tuning the number of epochs at 30 producing an accuracy value of 57. .40%, and tuning the softmax activation function produces an accuracy value of 57.35%.
社交媒体的发展使人们更容易传播信息。受到质疑的一种信息形式是在社交媒体上表达意见的自由。对文本评论数据进行情感分析相关研究的发展旨在确定社交媒体上日益增加的意见极性。在评论文本情感分析中应用的方法之一是长短期记忆(LSTM)方法。本研究的目的是确定LSTM模型在印尼语Twitter文本评论的各种情绪上的表现。测试过程基于超参数整定精度值的计算。使用Word2Vec参数、激活函数、epoch数、神经元数来检验本研究的准确性。优化Word2Vec Continuous Bag of Words (CBOW)架构，优化准确率为57.15%，优化神经元数最多为150，优化准确率为57.35%，优化epoch数为30，优化softmax激活函数，优化准确率为57.40%，得到了最优的LSTM性能测试结果。
{"title":"Analisis Sentimen pada Data Ulasan Twitter dengan Long-Short Term Memory","authors":"Sharfina Febbi Handayani, Riszki Wijayatun Pratiwi, Dairoh Dairoh, Dwi Intan Af’idah","doi":"10.31544/jtera.v7.i1.2022.39-46","DOIUrl":"https://doi.org/10.31544/jtera.v7.i1.2022.39-46","url":null,"abstract":"The development of social media has made it easier for people to disseminate information. One form of information in question is the freedom to express opinions on social media. The development of research related to sentiment analysis on text review data aims to determine the polarity of opinion on social media which has increased. One of the methods applied in the sentiment analysis of the review text is the use of the Long Short-Term Memory (LSTM) method. The purpose of this study was to determine the performance of the LSTM model on various sentiments of reviews of Indonesian-language Twitter texts. The testing process is carried out based on the calculation of the hyperparameter tuning accuracy value. Testing the accuracy of this study using Word2Vec parameters, activation function, number of epochs, and number of neurons. The optimal LSTM performance test results from this study were obtained based on tuning the Word2Vec Continuous Bag of Words (CBOW) architecture with an accuracy of 57.15%, tuning the number of neurons as much as 150 producing an accuracy value of 57.35%, tuning the number of epochs at 30 producing an accuracy value of 57. .40%, and tuning the softmax activation function produces an accuracy value of 57.35%.","PeriodicalId":17680,"journal":{"name":"JTERA (Jurnal Teknologi Rekayasa)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86859575","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 : 2022-06-26DOI: 10.31544/jtera.v7.i1.2022.73-82
Nur Andita Prasetyo, Bambang Setiawan
Information Security Culture is indispensable in securing personal data and company data. The assessment of the cultural level is generally done by calculating the index value (composite indicator) which is formed from the dimensions or factors that influence the culture. This study aims to obtain information security culture factors that can be used to form an index of the level of information security culture. The method used is Systematic Literature Review (SLR). The SLR is used to identify, review, discuss and discuss all available research on the phenomenon area of interest, with specific relevant research questions. This study examines 39 research papers related to information security culture in organizations and individuals between 2012 and 2021. There are nine types of organizations discussed, including: health, government, Small and Medium industrial, public organizations, finance, general organizations, trading companies, telecommunications, and academics. The results showed that there were 11 factors used in the assessment of information security culture, namely awareness, policy, training, monitoring, compliance, knowledge, education, behavior, strategy, change management and communication. Where there are four factors used by more than 25% of papers, namely awareness, policy, training and monitoring.
{"title":"Kajian Dimensi Budaya Keamanan Informasi dalam Berbagai Organisasi","authors":"Nur Andita Prasetyo, Bambang Setiawan","doi":"10.31544/jtera.v7.i1.2022.73-82","DOIUrl":"https://doi.org/10.31544/jtera.v7.i1.2022.73-82","url":null,"abstract":"Information Security Culture is indispensable in securing personal data and company data. The assessment of the cultural level is generally done by calculating the index value (composite indicator) which is formed from the dimensions or factors that influence the culture. This study aims to obtain information security culture factors that can be used to form an index of the level of information security culture. The method used is Systematic Literature Review (SLR). The SLR is used to identify, review, discuss and discuss all available research on the phenomenon area of interest, with specific relevant research questions. This study examines 39 research papers related to information security culture in organizations and individuals between 2012 and 2021. There are nine types of organizations discussed, including: health, government, Small and Medium industrial, public organizations, finance, general organizations, trading companies, telecommunications, and academics. The results showed that there were 11 factors used in the assessment of information security culture, namely awareness, policy, training, monitoring, compliance, knowledge, education, behavior, strategy, change management and communication. Where there are four factors used by more than 25% of papers, namely awareness, policy, training and monitoring.","PeriodicalId":17680,"journal":{"name":"JTERA (Jurnal Teknologi Rekayasa)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79984033","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 : 2022-06-26DOI: 10.31544/jtera.v7.i1.2022.163-172
Monika Natalia, Jajang Atmaja, Dini Sekar Putri, Putri Helena
Konsep nilai hasil memberikan informasi kinerja proyek dan menghasilkan estimasi biaya dan waktu untuk penyelesaian seluruh pekerjaan proyek. Proyek rumah susun PIK Pulo Gadung Tahap II Jakarta Timur mengalami deviasi sebesar -12,70% pada hari ke-441 (minggu ke-63) pelaksanaan dari total durasi 569 hari akibat pandemi Covid-19. Hal ini karena pembatasan tenaga kerja, pembatasan pemasokan material, dan pembatasan waktu kerja instansi pemerintahan menyebabkan kepengurusan administrasi tidak maksimal. Penelitian ini bertujuan untuk mengukur kinerja proyek rumah susun PIK Pulo Gadung Jakarta Timur. Metode penelitian yang digunakan yaitu dengan analisis konsep nilai hasil menggunakan aplikasi Microsoft Project untuk mendapatkan kinerja proyek. Selanjutnya untuk percepatan waktu penyelesaian proyek dilakukan dengan metode Time Cost Trade-Off (TCTO) menggunakan crashing program. Hasil analisis didapat perkiraan waktu penyelesaian proyek adalah 666 hari atau selisih 97 hari dibandingkan rencana awal. Sisa durasi untuk menyelesaikan pekerjaan yang tersisa sebesar 225 hari dengan proyeksi biaya akhir sekitar 57 milyar rupiah atau terjadi penyimpangan sekitar 6 milyar rupiah dibandingkan biaya awal. Alternatif percepatan durasi proyek dengan metode TCTO yaitu dengan menambah shift kerja dimana proyek selesai lebih cepat tiga hari daripada penambahan jam kerja dan biaya lebih hemat sebesar 1,8 milyar rupiah.
{"title":"Analisis Konsep Nilai Hasil Dengan Metode Time Cost Trade-Off pada Proyek Rumah Susun","authors":"Monika Natalia, Jajang Atmaja, Dini Sekar Putri, Putri Helena","doi":"10.31544/jtera.v7.i1.2022.163-172","DOIUrl":"https://doi.org/10.31544/jtera.v7.i1.2022.163-172","url":null,"abstract":"Konsep nilai hasil memberikan informasi kinerja proyek dan menghasilkan estimasi biaya dan waktu untuk penyelesaian seluruh pekerjaan proyek. Proyek rumah susun PIK Pulo Gadung Tahap II Jakarta Timur mengalami deviasi sebesar -12,70% pada hari ke-441 (minggu ke-63) pelaksanaan dari total durasi 569 hari akibat pandemi Covid-19. Hal ini karena pembatasan tenaga kerja, pembatasan pemasokan material, dan pembatasan waktu kerja instansi pemerintahan menyebabkan kepengurusan administrasi tidak maksimal. Penelitian ini bertujuan untuk mengukur kinerja proyek rumah susun PIK Pulo Gadung Jakarta Timur. Metode penelitian yang digunakan yaitu dengan analisis konsep nilai hasil menggunakan aplikasi Microsoft Project untuk mendapatkan kinerja proyek. Selanjutnya untuk percepatan waktu penyelesaian proyek dilakukan dengan metode Time Cost Trade-Off (TCTO) menggunakan crashing program. Hasil analisis didapat perkiraan waktu penyelesaian proyek adalah 666 hari atau selisih 97 hari dibandingkan rencana awal. Sisa durasi untuk menyelesaikan pekerjaan yang tersisa sebesar 225 hari dengan proyeksi biaya akhir sekitar 57 milyar rupiah atau terjadi penyimpangan sekitar 6 milyar rupiah dibandingkan biaya awal. Alternatif percepatan durasi proyek dengan metode TCTO yaitu dengan menambah shift kerja dimana proyek selesai lebih cepat tiga hari daripada penambahan jam kerja dan biaya lebih hemat sebesar 1,8 milyar rupiah.","PeriodicalId":17680,"journal":{"name":"JTERA (Jurnal Teknologi Rekayasa)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91379688","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 : 2022-06-26DOI: 10.31544/jtera.v7.i1.2022.149-156
Imam Sapuan, Muhammad Hilmi Fauzan, Christina Juliane
tahap selanjutnya, sedangkan metode decision tree digunakan sebagai algoritma prediksinya. Hasil penelitian menunjukkan bahwa kedua metode ini berhasil menyelesaikan masalah dengan tingkat akurasi yang sangat tinggi yaitu sebesar 95,3%, presisi sebesar 95,4%, dan recall sebesar 95,3%. Abstract The grouping of families into rich and poor clusters is very much needed as a reference for various future activities such as government assistance or other related parties. Data mining is one approach that can be used to solve this problem. Data mining methods that are suitable are clustering and prediction. This study aims to implement data mining for clustering and predicting family groups. There are two algorithms used in this study, namely kModes and decision tree. The kModes algorithm functions to generate clusters that will be used in the next stage, while the decision tree method is used as the prediction algorithm. The results showed that these two methods succeeded in solving the problem with a very high level of accuracy, namely 95.3%, precision 95.4%, and recall of 95.3%.
Tahap selanjutnya, sedangkan方法决策树digunakan sebagai算法预测。Hasil penelitian menunjukkan bahwa kedua mede ini berhasil menyelesaikan masalah dengan tingkat akurasi yang sangat tinggi yitu sebesar 95,3%， presisi sebesar 95,4%， dan recall sebesar 95,3%。将家庭划分为富裕和贫穷的集群是非常必要的，作为政府援助或其他相关方今后各种活动的参考。数据挖掘是一种可以用来解决这个问题的方法。适合的数据挖掘方法是聚类和预测。本研究的目的是实现数据挖掘的聚类和预测家庭群体。本研究使用了两种算法，即kModes和decision tree。kModes算法用于生成将在下一阶段使用的聚类，而决策树方法被用作预测算法。结果表明，这两种方法都能很好地解决问题，准确率为95.3%，精密度为95.4%，召回率为95.3%。
{"title":"Implementasi Data Mining untuk Klasterisasi dan Prediksi Kelompok Keluarga","authors":"Imam Sapuan, Muhammad Hilmi Fauzan, Christina Juliane","doi":"10.31544/jtera.v7.i1.2022.149-156","DOIUrl":"https://doi.org/10.31544/jtera.v7.i1.2022.149-156","url":null,"abstract":"tahap selanjutnya, sedangkan metode decision tree digunakan sebagai algoritma prediksinya. Hasil penelitian menunjukkan bahwa kedua metode ini berhasil menyelesaikan masalah dengan tingkat akurasi yang sangat tinggi yaitu sebesar 95,3%, presisi sebesar 95,4%, dan recall sebesar 95,3%. Abstract The grouping of families into rich and poor clusters is very much needed as a reference for various future activities such as government assistance or other related parties. Data mining is one approach that can be used to solve this problem. Data mining methods that are suitable are clustering and prediction. This study aims to implement data mining for clustering and predicting family groups. There are two algorithms used in this study, namely kModes and decision tree. The kModes algorithm functions to generate clusters that will be used in the next stage, while the decision tree method is used as the prediction algorithm. The results showed that these two methods succeeded in solving the problem with a very high level of accuracy, namely 95.3%, precision 95.4%, and recall of 95.3%.","PeriodicalId":17680,"journal":{"name":"JTERA (Jurnal Teknologi Rekayasa)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90914908","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 : 2022-06-26DOI: 10.31544/jtera.v7.i1.2022.25-30
A. Ramdan
Increasing production in agriculture, especially vegetables, needs to be done by utilizing technology in line with the increasing public demand for vegetables. Artificial Intelligence (AI) technology can support business processes in agriculture that can be used to increase agricultural production. One of the uses of this technology is to implement a machine learning-based plant growth monitoring system. Plant monitoring system during the growth period is needed to increase agricultural production. This research aims to design a monitoring system by applying the Support Vector Machine (SVM) algorithm as a classifier with the color feature extraction method using the Hue, Saturation, Intensity (HIS) method on the Raspberry Pi. The results showed that this mustard plant growth monitoring system can detect plants that have good and bad growth with accuracy of 90%.
{"title":"Implementasi Sistem Monitoring Pertumbuhan Tanaman Sawi Hijau Berbasis Pembelajaran Mesin","authors":"A. Ramdan","doi":"10.31544/jtera.v7.i1.2022.25-30","DOIUrl":"https://doi.org/10.31544/jtera.v7.i1.2022.25-30","url":null,"abstract":"Increasing production in agriculture, especially vegetables, needs to be done by utilizing technology in line with the increasing public demand for vegetables. Artificial Intelligence (AI) technology can support business processes in agriculture that can be used to increase agricultural production. One of the uses of this technology is to implement a machine learning-based plant growth monitoring system. Plant monitoring system during the growth period is needed to increase agricultural production. This research aims to design a monitoring system by applying the Support Vector Machine (SVM) algorithm as a classifier with the color feature extraction method using the Hue, Saturation, Intensity (HIS) method on the Raspberry Pi. The results showed that this mustard plant growth monitoring system can detect plants that have good and bad growth with accuracy of 90%.","PeriodicalId":17680,"journal":{"name":"JTERA (Jurnal Teknologi Rekayasa)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87798545","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 : 2022-06-26DOI: 10.31544/jtera.v7.i1.2022.65-72
Agus Sugiyono, Ira Fitriana, I. Rahardjo, Joko Santosa
Battery electric vehicles have several advantages compared to conventional vehicles, i.e. more energy efficient, better performance, more environmentally friendly, and reduce dependence on the use of fossil energy. The dominance of conventional vehicles that use oil fuel needs to be reduced and replaced with battery electric vehicles. This study aims to examine the role of battery-based electric motorized vehicles in reducing fuel demand. Reducing oil fuel use through the development of battery electric vehicles is modeled using LEAP (Low Emissions Analysis Platform) software. The model uses the base year of 2018 and the projection period until 2050. Analysis is carried out in the model using two scenarios, namely the BASE scenario and the KBL scenario. The model results show that the use of battery electric vehicles in 2050 can save oil fuel by 358 million BOE and only increase electricity usage by 119 million BOE. Reducing the use of oil fuel will reduce imports which can have a positive impact on the national trade balance.
{"title":"Peran Kendaraan Bermotor Listrik Berbasis Baterai dalam Mengurangi Permintaan BBM di Indonesia","authors":"Agus Sugiyono, Ira Fitriana, I. Rahardjo, Joko Santosa","doi":"10.31544/jtera.v7.i1.2022.65-72","DOIUrl":"https://doi.org/10.31544/jtera.v7.i1.2022.65-72","url":null,"abstract":"Battery electric vehicles have several advantages compared to conventional vehicles, i.e. more energy efficient, better performance, more environmentally friendly, and reduce dependence on the use of fossil energy. The dominance of conventional vehicles that use oil fuel needs to be reduced and replaced with battery electric vehicles. This study aims to examine the role of battery-based electric motorized vehicles in reducing fuel demand. Reducing oil fuel use through the development of battery electric vehicles is modeled using LEAP (Low Emissions Analysis Platform) software. The model uses the base year of 2018 and the projection period until 2050. Analysis is carried out in the model using two scenarios, namely the BASE scenario and the KBL scenario. The model results show that the use of battery electric vehicles in 2050 can save oil fuel by 358 million BOE and only increase electricity usage by 119 million BOE. Reducing the use of oil fuel will reduce imports which can have a positive impact on the national trade balance.","PeriodicalId":17680,"journal":{"name":"JTERA (Jurnal Teknologi Rekayasa)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86947139","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：