Pub Date : 2023-01-01DOI: 10.31544/jtera.v7.i2.2022.223-232
E. Andika, Lani Nurlani, Nila Natalia, Syayidul Muwahhid
{"title":"Implementasi Application Programmable Interface pada Sistem Manajemen Jaringan Berbasis Website","authors":"E. Andika, Lani Nurlani, Nila Natalia, Syayidul Muwahhid","doi":"10.31544/jtera.v7.i2.2022.223-232","DOIUrl":"https://doi.org/10.31544/jtera.v7.i2.2022.223-232","url":null,"abstract":"","PeriodicalId":17680,"journal":{"name":"JTERA (Jurnal Teknologi Rekayasa)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89188909","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 : 2022-12-31DOI: 10.31544/jtera.v7.i2.2022.295-300
Martin Martin, D. Ramdani, Yana Sudarsa, Feni Isdaryani
{"title":"Visual Servoing pada Ball-on-Plate dengan Kendali PID dan Filter Kalman","authors":"Martin Martin, D. Ramdani, Yana Sudarsa, Feni Isdaryani","doi":"10.31544/jtera.v7.i2.2022.295-300","DOIUrl":"https://doi.org/10.31544/jtera.v7.i2.2022.295-300","url":null,"abstract":"","PeriodicalId":17680,"journal":{"name":"JTERA (Jurnal Teknologi Rekayasa)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84349207","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 : 2022-12-31DOI: 10.31544/jtera.v7.i2.2022.275-280
L. Shitophyta, Zahra Lintang Cahyaningtyas, Nurul Aulia Syifa, F. Chusna
Biogas production from lignocellulosic biomass has gained attention in the development of renewable fuels. Corn stover belongs to lignocellulosic biomass. Pretreatment is needed to help the digestion of biomass due to its lignocellulosic recalcitrance. This study aims to compare the different types of acids for enhancing biogas production. The experiment was carried out in a 1 L batch digester at room temperature with different acids of HCl, H2SO4, and C2H2O4 at concentrations of 0%, 5%, 10%, and 15%. The acids pretreatment was performed for 24 hr. Results show that pretreatment of C2H2O4 has a positive impact on increasing biogas yield. The highest cumulative yield of 580.8 mL/gVS is obtained at 15% C2H2O4. The increase in acid concentrations decreases the initial pH value. The pH value below 6 reduces biogas yield.
{"title":"Various Types of Acids on Pretreatment of Corn Stover for Enhancing Biogas Yield","authors":"L. Shitophyta, Zahra Lintang Cahyaningtyas, Nurul Aulia Syifa, F. Chusna","doi":"10.31544/jtera.v7.i2.2022.275-280","DOIUrl":"https://doi.org/10.31544/jtera.v7.i2.2022.275-280","url":null,"abstract":"Biogas production from lignocellulosic biomass has gained attention in the development of renewable fuels. Corn stover belongs to lignocellulosic biomass. Pretreatment is needed to help the digestion of biomass due to its lignocellulosic recalcitrance. This study aims to compare the different types of acids for enhancing biogas production. The experiment was carried out in a 1 L batch digester at room temperature with different acids of HCl, H2SO4, and C2H2O4 at concentrations of 0%, 5%, 10%, and 15%. The acids pretreatment was performed for 24 hr. Results show that pretreatment of C2H2O4 has a positive impact on increasing biogas yield. The highest cumulative yield of 580.8 mL/gVS is obtained at 15% C2H2O4. The increase in acid concentrations decreases the initial pH value. The pH value below 6 reduces biogas yield.","PeriodicalId":17680,"journal":{"name":"JTERA (Jurnal Teknologi Rekayasa)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81483922","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 : 2022-12-31DOI: 10.31544/jtera.v7.i2.2022.315-322
D. Andini, YB Gunawan Sugiarta, Trisno Yuwono Putro, Ryan Dwi Setiawan
Artikel ini mengusulkan sistem presensi kelas berbasis pengenalan wajah. Metode yang diterapkan adalah Convolutional Neural Network (CNN) dengan keterbaruan adalah penyajian dalam file Ms. Excel secara langsung. Metode ini dapat digunakan untuk melakukan proses ekstraksi fitur dari citra dan mengklasifikasikan citra. Aplikasi dirancang menggunakan Graphical User Interface (GUI) untuk pengisian presensi mahasiswa. Pada tampilan dapat digunakan untuk melakukan registrasi secara langsung untuk pembuatan dataset dan model. Perangkat keras sistem terdiri dari kamera, minicomputer, dan LCD. Cara kerja sistem keseluruhan meliputi registrasi, preprocessing, pengenalan citra wajah, dan hasil output identitas mahasiswa. Hasil pengujian menunjukkan bahwa sistem ini memiliki nilai akurasi 85% dan jumlah epoch 40. Waktu dalam proses pengenalan yaitu 3 hingga 9 detik dengan jarak wajah dari kamera 30-50 cm. Sistem bekerja maksmimal jika digunakan pada ruangan dengan pencahayaan berlampu terang. Sudut maksimal wajah menghadap kamera sebesar 10 derajat.
{"title":"Sistem Presensi Kelas Berbasis Pengenalan Wajah Menggunakan Metode CNN","authors":"D. Andini, YB Gunawan Sugiarta, Trisno Yuwono Putro, Ryan Dwi Setiawan","doi":"10.31544/jtera.v7.i2.2022.315-322","DOIUrl":"https://doi.org/10.31544/jtera.v7.i2.2022.315-322","url":null,"abstract":"Artikel ini mengusulkan sistem presensi kelas berbasis pengenalan wajah. Metode yang diterapkan adalah Convolutional Neural Network (CNN) dengan keterbaruan adalah penyajian dalam file Ms. Excel secara langsung. Metode ini dapat digunakan untuk melakukan proses ekstraksi fitur dari citra dan mengklasifikasikan citra. Aplikasi dirancang menggunakan Graphical User Interface (GUI) untuk pengisian presensi mahasiswa. Pada tampilan dapat digunakan untuk melakukan registrasi secara langsung untuk pembuatan dataset dan model. Perangkat keras sistem terdiri dari kamera, minicomputer, dan LCD. Cara kerja sistem keseluruhan meliputi registrasi, preprocessing, pengenalan citra wajah, dan hasil output identitas mahasiswa. Hasil pengujian menunjukkan bahwa sistem ini memiliki nilai akurasi 85% dan jumlah epoch 40. Waktu dalam proses pengenalan yaitu 3 hingga 9 detik dengan jarak wajah dari kamera 30-50 cm. Sistem bekerja maksmimal jika digunakan pada ruangan dengan pencahayaan berlampu terang. Sudut maksimal wajah menghadap kamera sebesar 10 derajat.","PeriodicalId":17680,"journal":{"name":"JTERA (Jurnal Teknologi Rekayasa)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88397281","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 : 2022-12-31DOI: 10.31544/jtera.v7.i2.2022.307-314
Nanda Iryani, Dhanar Yusuf Febriansyah, E. Cahyadi
In the process of exchanging data
在交换数据的过程中
{"title":"Analisis Perbandingan Mod Security dan Deep Packet Inspection pada Web Server Terhadap Serangan DDoS Slow Headers","authors":"Nanda Iryani, Dhanar Yusuf Febriansyah, E. Cahyadi","doi":"10.31544/jtera.v7.i2.2022.307-314","DOIUrl":"https://doi.org/10.31544/jtera.v7.i2.2022.307-314","url":null,"abstract":"In the process of exchanging data","PeriodicalId":17680,"journal":{"name":"JTERA (Jurnal Teknologi Rekayasa)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87317818","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 : 2022-12-31DOI: 10.31544/jtera.v7.i2.2022.267-274
S. M. Ilman, Febi Ariefka Septian Putra, I. Irianto, Eka Prasetyono
{"title":"Kendali Suhu Air Dengan Pengendali PI Menggunakan Metode Ziegler-Nichols I, Chien Servo I, dan Chien Regulator I","authors":"S. M. Ilman, Febi Ariefka Septian Putra, I. Irianto, Eka Prasetyono","doi":"10.31544/jtera.v7.i2.2022.267-274","DOIUrl":"https://doi.org/10.31544/jtera.v7.i2.2022.267-274","url":null,"abstract":"","PeriodicalId":17680,"journal":{"name":"JTERA (Jurnal Teknologi Rekayasa)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75184853","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 : 2022-12-31DOI: 10.31544/jtera.v7.i2.2022.209-216
Rian Rahmanda Putra, Indra Griha Tofik Isa, Ahmad Bahri Joni Malyan, Ema Laila, A. Wardhana
Hyperparameter tuning menjadi bagian yang tidak terpisahkan dalam sebuah pemodelan untuk mencapai performa akurasi yang terbaik. Salah satu parameter yang digunakan dalam hyperparameter tuning adalah epoch, dimana merupakan bagian dari pelatihan dalam sebuah model untuk melihat akurasi terbaiknya. Dalam penelitian ini dilakukan hyperparameter tuning terhadap epoch untuk melihat titik optimum yang dihasilkan dengan 11 skenario epoch yakni epoch 50, epoch 55, epoch 60, epoch 65, epoch 70, epoch 75, epoch 80, epoch 85, epoch 90, epoch 95 dan epoch 100. Adapun batasan dalam penelitian ini adalah: (1) data yang dicermati merupakan citra kebakaran sebanyak 4650 citra yang terdiri dari kondisi kebakaran dan tidak kebakaran dan (2) pemodelan dengan deep learning CNN. Tahapan yang dilakukan meliputi preparation data, image augmentation, pre-processing data, pemodelan menggunakan CNN, epoch hyperparameter tuning, dan data interpretation. Di dalam penelitian ini, acuan hasil yang optimal berpatokan pada nilai rerata accuracy dan val accuracy. Hasil penelitian menunjukkan bahwa secara keseluruhan skenario epoch memiliki rerata accuracy dan val accuracy di atas 89%, dengan terdapat 6 skenario epoch dengan persentasi rerata accuracy dan val accuracy di atas 90%, yakni epoch 85, epoch 90, epoch 80, epoch 95, epoch 75, dan epoch 100. Sedangkan untuk skenario epoch dengan nilai rerata accuracy dan val accuracy tertinggi terdapat pada epoch 85 dimana disimpulkan bahwa epoch 85 merupakan titik optimal dalam penelitian ini.
{"title":"Level Optimum Hyperparameter Tuning Epoch dalam Klasifikasi Citra Bencana Kebakaran","authors":"Rian Rahmanda Putra, Indra Griha Tofik Isa, Ahmad Bahri Joni Malyan, Ema Laila, A. Wardhana","doi":"10.31544/jtera.v7.i2.2022.209-216","DOIUrl":"https://doi.org/10.31544/jtera.v7.i2.2022.209-216","url":null,"abstract":"Hyperparameter tuning menjadi bagian yang tidak terpisahkan dalam sebuah pemodelan untuk mencapai performa akurasi yang terbaik. Salah satu parameter yang digunakan dalam hyperparameter tuning adalah epoch, dimana merupakan bagian dari pelatihan dalam sebuah model untuk melihat akurasi terbaiknya. Dalam penelitian ini dilakukan hyperparameter tuning terhadap epoch untuk melihat titik optimum yang dihasilkan dengan 11 skenario epoch yakni epoch 50, epoch 55, epoch 60, epoch 65, epoch 70, epoch 75, epoch 80, epoch 85, epoch 90, epoch 95 dan epoch 100. Adapun batasan dalam penelitian ini adalah: (1) data yang dicermati merupakan citra kebakaran sebanyak 4650 citra yang terdiri dari kondisi kebakaran dan tidak kebakaran dan (2) pemodelan dengan deep learning CNN. Tahapan yang dilakukan meliputi preparation data, image augmentation, pre-processing data, pemodelan menggunakan CNN, epoch hyperparameter tuning, dan data interpretation. Di dalam penelitian ini, acuan hasil yang optimal berpatokan pada nilai rerata accuracy dan val accuracy. Hasil penelitian menunjukkan bahwa secara keseluruhan skenario epoch memiliki rerata accuracy dan val accuracy di atas 89%, dengan terdapat 6 skenario epoch dengan persentasi rerata accuracy dan val accuracy di atas 90%, yakni epoch 85, epoch 90, epoch 80, epoch 95, epoch 75, dan epoch 100. Sedangkan untuk skenario epoch dengan nilai rerata accuracy dan val accuracy tertinggi terdapat pada epoch 85 dimana disimpulkan bahwa epoch 85 merupakan titik optimal dalam penelitian ini.","PeriodicalId":17680,"journal":{"name":"JTERA (Jurnal Teknologi Rekayasa)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80141497","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 : 2022-12-31DOI: 10.31544/jtera.v7.i2.2022.189-200
Tata Supriyadi, Kemal Taufik Fikri
{"title":"Sistem Keamanan Gerbang Parkir Menggunakan Algoritma YOLO (You Only Look Once) dan Face Recognition","authors":"Tata Supriyadi, Kemal Taufik Fikri","doi":"10.31544/jtera.v7.i2.2022.189-200","DOIUrl":"https://doi.org/10.31544/jtera.v7.i2.2022.189-200","url":null,"abstract":"","PeriodicalId":17680,"journal":{"name":"JTERA (Jurnal Teknologi Rekayasa)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72924454","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：