Pub Date : 2021-10-22DOI: 10.30998/faktorexacta.v14i3.9365
Nopri Santi, Suryarini Widodo
{"title":"ALGORITMA NEURAL NETWORK BACKPROPAGATION UNTUK PREDIKSI HARGA SAHAM PADA TIGA GOLONGAN PERUSAHAAN BERDASARKAN KAPITALISASINYA","authors":"Nopri Santi, Suryarini Widodo","doi":"10.30998/faktorexacta.v14i3.9365","DOIUrl":"https://doi.org/10.30998/faktorexacta.v14i3.9365","url":null,"abstract":"","PeriodicalId":53004,"journal":{"name":"Faktor Exacta","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46624523","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 : 2021-10-22DOI: 10.30998/faktorexacta.v14i3.9567
Frederick Alexander, I. Imelda
Received Apr 14, 2021 Revised Agus 1, 2021 Accepted Sep 06, 2021 Flood disaster remains a natural phenomenon that often occurs in Indonesia, especially in the Wisma Tajur Housing Complex area, Tangerang City which causes property losses including the safety of the souls of the affected community. The difficulty experienced so far is how to measure the water level to obtain alert status information as an indicator of flood warning. As a solution in overcoming these problems, this research proposes a method based on digital image processing with canny edge detection algorithms and image contouring to measure river water levels. Canny edge detection and image contouring were chosen due to their accuracy in detecting the edges of the image and the ease of the computation process. The steps taken in this research are to conduct a simulation experiment of measuring the water level using a water container that can describe the situation in the river, then doing field testing. Canny edge detection produces an outline that can then be detected by the contour, then water level measurements can be made on the bounding rectangle that is formed and changes dynamically with fluctuations in water level. The contribution of this research is the use of black measuring lines that are processed using thresholding techniques to facilitate the process of measuring water level using a combination of canny edge detection and image contouring techniques as well as adding attributes/features using threshold, MinVal, and MaxVal values on the canny edge. Sampling testing produces an accuracy of 99.96%, prototype testing produces 100% accuracy, and direct testing produces an accuracy of 99.85%.
{"title":"Analisis Model Pengukuran Tinggi Permukaan Air Dengan Metode Canny Edge Detection dan Image Contouring Sebagai Indikator Peringatan Dini Bencana Banjir","authors":"Frederick Alexander, I. Imelda","doi":"10.30998/faktorexacta.v14i3.9567","DOIUrl":"https://doi.org/10.30998/faktorexacta.v14i3.9567","url":null,"abstract":"Received Apr 14, 2021 Revised Agus 1, 2021 Accepted Sep 06, 2021 Flood disaster remains a natural phenomenon that often occurs in Indonesia, especially in the Wisma Tajur Housing Complex area, Tangerang City which causes property losses including the safety of the souls of the affected community. The difficulty experienced so far is how to measure the water level to obtain alert status information as an indicator of flood warning. As a solution in overcoming these problems, this research proposes a method based on digital image processing with canny edge detection algorithms and image contouring to measure river water levels. Canny edge detection and image contouring were chosen due to their accuracy in detecting the edges of the image and the ease of the computation process. The steps taken in this research are to conduct a simulation experiment of measuring the water level using a water container that can describe the situation in the river, then doing field testing. Canny edge detection produces an outline that can then be detected by the contour, then water level measurements can be made on the bounding rectangle that is formed and changes dynamically with fluctuations in water level. The contribution of this research is the use of black measuring lines that are processed using thresholding techniques to facilitate the process of measuring water level using a combination of canny edge detection and image contouring techniques as well as adding attributes/features using threshold, MinVal, and MaxVal values on the canny edge. Sampling testing produces an accuracy of 99.96%, prototype testing produces 100% accuracy, and direct testing produces an accuracy of 99.85%.","PeriodicalId":53004,"journal":{"name":"Faktor Exacta","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48969347","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 : 2021-10-22DOI: 10.30998/faktorexacta.v14i3.10010
Za’imatun Niswati, Rahayuning Hardatin, Meia Noer Muslimah, S. Hasanah
{"title":"Perbandingan Arsitektur ResNet50 dan ResNet101 dalam Klasifikasi Kanker Serviks pada Citra Pap Smear","authors":"Za’imatun Niswati, Rahayuning Hardatin, Meia Noer Muslimah, S. Hasanah","doi":"10.30998/faktorexacta.v14i3.10010","DOIUrl":"https://doi.org/10.30998/faktorexacta.v14i3.10010","url":null,"abstract":"","PeriodicalId":53004,"journal":{"name":"Faktor Exacta","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47278305","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 : 2021-10-22DOI: 10.30998/faktorexacta.v14i3.10325
Ariansyah Ariansyah, Mira Kusmira
{"title":"ANALISIS SENTIMEN PENGARUH PEMBELAJARAN DARING TERHADAP MOTIVASI BELAJAR DI MASA PANDEMI MENGGUNAKAN NAIVE BAYES DAN SVM","authors":"Ariansyah Ariansyah, Mira Kusmira","doi":"10.30998/faktorexacta.v14i3.10325","DOIUrl":"https://doi.org/10.30998/faktorexacta.v14i3.10325","url":null,"abstract":"","PeriodicalId":53004,"journal":{"name":"Faktor Exacta","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48800889","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 : 2021-10-22DOI: 10.30998/faktorexacta.v14i3.9841
T. Harjanti, Himawan Himawan
{"title":"Teknologi Pengolahan Citra Digital Untuk Ekstraksi Ciri pada Citra Daun untuk Identifikasi Tumbuhan Obat","authors":"T. Harjanti, Himawan Himawan","doi":"10.30998/faktorexacta.v14i3.9841","DOIUrl":"https://doi.org/10.30998/faktorexacta.v14i3.9841","url":null,"abstract":"","PeriodicalId":53004,"journal":{"name":"Faktor Exacta","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47483265","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 : 2021-10-22DOI: 10.30998/faktorexacta.v14i3.9807
Syah Alam, Gunawan Tjahjadi, Nur Rahma Yenita, S. Supriyadi
{"title":"RANCANG BANGUN PROTOTYPE PENGENDALIAN LENGAN ROBOT (ROBOTIC ARM) SEBAGAI PEMINDAH BARANG BERBASIS INTERNET OF THINGS","authors":"Syah Alam, Gunawan Tjahjadi, Nur Rahma Yenita, S. Supriyadi","doi":"10.30998/faktorexacta.v14i3.9807","DOIUrl":"https://doi.org/10.30998/faktorexacta.v14i3.9807","url":null,"abstract":"","PeriodicalId":53004,"journal":{"name":"Faktor Exacta","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42434198","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 : 2021-08-10DOI: 10.30998/faktorexacta.v14i2.8482
E. K. Wati
The instrument correction method is a way to eliminate interference with the signal from the recording instrument response. Signal processing by the instrument correction method using the inverse filter method created using the MATLAB program. In this research using Honshu earthquake data, Japan with Mw 7.4 (dated September 5, 2004) recorded by the MERAMEX seismometer type L4C-3D type short seismometer and Japan Tohoku-Oki earthquake with a strength of Mw 9.0 (March 11, 2011) the data from four seismic stations in Padang, West Sumatra with a DS-4A type short-period seismometer. From the research known, the signal can clearly show the phase of the P and S waves. This can help to determine the parameters of the hypocenter, receiver function, moment tensors, studies of . The surface wave phase can be reconstructed well. This is very useful for studies using surface wave data, moment tensor solutions, seismic wave dispersion studies. Based on the amplitude of the instrument correction results compared with theoretical data, the gain or amplification .
{"title":"Processing The Ground Motion Signal Recording Using Correction Instrument Method","authors":"E. K. Wati","doi":"10.30998/faktorexacta.v14i2.8482","DOIUrl":"https://doi.org/10.30998/faktorexacta.v14i2.8482","url":null,"abstract":"The instrument correction method is a way to eliminate interference with the signal from the recording instrument response. Signal processing by the instrument correction method using the inverse filter method created using the MATLAB program. In this research using Honshu earthquake data, Japan with Mw 7.4 (dated September 5, 2004) recorded by the MERAMEX seismometer type L4C-3D type short seismometer and Japan Tohoku-Oki earthquake with a strength of Mw 9.0 (March 11, 2011) the data from four seismic stations in Padang, West Sumatra with a DS-4A type short-period seismometer. From the research known, the signal can clearly show the phase of the P and S waves. This can help to determine the parameters of the hypocenter, receiver function, moment tensors, studies of . The surface wave phase can be reconstructed well. This is very useful for studies using surface wave data, moment tensor solutions, seismic wave dispersion studies. Based on the amplitude of the instrument correction results compared with theoretical data, the gain or amplification .","PeriodicalId":53004,"journal":{"name":"Faktor Exacta","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48938900","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 : 2021-08-10DOI: 10.30998/faktorexacta.v14i2.9429
Octaviani Hutapea, Aini Suri Talita
Received April 6, 2021 Revised July 17, 2021 Accepted July 22, 2021 Based on data from the National Cyber And Crypto Agency (BSSN) of the Republic of Indonesia from 2018 to 2021, the threat of cyber attacks continues to experience a significant increase. In 2021, a significant change that is likely to be faced is with the emergence of new smart devices, which are more than just end-users and remotely connected networked devices. Surely, gives it the attention of all parties. There are many types of cyberattacks including Malware, Phishing, Ransomeware, etc. IDS (Intrusion Detection System) is a method that can detect suspicious activity in a system or network. Implementation of the Fuzzy K-Medoids method by using the Matlab programming language that retrieves data from KDDCUP’99 which has been normalized. The data used are normal data and anomaly attack data which are categorized as DoS, Probe, R2L, and U2R. From the research conducted the accuracy percentage is around 6089% with three types of data preprocessing.
{"title":"Implementasi Metode K-Medoids Untuk Masalah Intrusion Detection System Menggunakan Bahasa Pemrograman Matlab","authors":"Octaviani Hutapea, Aini Suri Talita","doi":"10.30998/faktorexacta.v14i2.9429","DOIUrl":"https://doi.org/10.30998/faktorexacta.v14i2.9429","url":null,"abstract":"Received April 6, 2021 Revised July 17, 2021 Accepted July 22, 2021 Based on data from the National Cyber And Crypto Agency (BSSN) of the Republic of Indonesia from 2018 to 2021, the threat of cyber attacks continues to experience a significant increase. In 2021, a significant change that is likely to be faced is with the emergence of new smart devices, which are more than just end-users and remotely connected networked devices. Surely, gives it the attention of all parties. There are many types of cyberattacks including Malware, Phishing, Ransomeware, etc. IDS (Intrusion Detection System) is a method that can detect suspicious activity in a system or network. Implementation of the Fuzzy K-Medoids method by using the Matlab programming language that retrieves data from KDDCUP’99 which has been normalized. The data used are normal data and anomaly attack data which are categorized as DoS, Probe, R2L, and U2R. From the research conducted the accuracy percentage is around 6089% with three types of data preprocessing.","PeriodicalId":53004,"journal":{"name":"Faktor Exacta","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43559229","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}
Received May 1, 2021 Revised May 25, 2021 Accepted May 28, 2021 During the Covid-19 pandemic, many people access information and even consult health problems online with the best doctors via smartphones. The Halodoc application is considered the most popular with 18 million users in 2020. So that many people have reviewed the application on the Google Play Store application provider. It may take a while to read the full review. However, if only a few comments are read, they are biased. For that, a platform is needed which can automatically identify positive or negative opinions. Sentiment analysis is a solution for the technique of classifying texts or sentiments into positive or negative opinion categories. The method used in this research is an experiment using the Naive Bayes algorithm, Support Vector Machine, and K-Nearest Neighbors. Evaluation is carried out using 10 Fold Cross-Validation. The results showed that K-Nearest Neighbors (KNN) had the best and most accurate performance in the sentiment classification because it produced the highest accuracy value of 95.00% and the largest AUC value of 0.985 compared to the Naive Bayes and Support Vector Machine algorithm.
{"title":"Implementasi Algoritma Naive Bayes, Support Vector Machine, dan K-Nearest Neighbors untuk Analisa Sentimen Aplikasi Halodoc","authors":"Elly Indrayuni, Acmad Nurhadi, Dinar Ajeng Kristiyanti","doi":"10.30998/faktorexacta.v14i2.9697","DOIUrl":"https://doi.org/10.30998/faktorexacta.v14i2.9697","url":null,"abstract":"Received May 1, 2021 Revised May 25, 2021 Accepted May 28, 2021 During the Covid-19 pandemic, many people access information and even consult health problems online with the best doctors via smartphones. The Halodoc application is considered the most popular with 18 million users in 2020. So that many people have reviewed the application on the Google Play Store application provider. It may take a while to read the full review. However, if only a few comments are read, they are biased. For that, a platform is needed which can automatically identify positive or negative opinions. Sentiment analysis is a solution for the technique of classifying texts or sentiments into positive or negative opinion categories. The method used in this research is an experiment using the Naive Bayes algorithm, Support Vector Machine, and K-Nearest Neighbors. Evaluation is carried out using 10 Fold Cross-Validation. The results showed that K-Nearest Neighbors (KNN) had the best and most accurate performance in the sentiment classification because it produced the highest accuracy value of 95.00% and the largest AUC value of 0.985 compared to the Naive Bayes and Support Vector Machine algorithm.","PeriodicalId":53004,"journal":{"name":"Faktor Exacta","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49187690","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 : 2021-08-10DOI: 10.30998/faktorexacta.v14i2.9297
Devi Fitrianah, Saruni Dwiasnati, Hanny Hikmayanti H, Kiki Ahmad Baihaqi
Received March 20, 2021 Revised June 4, 2021 Accepted June 13, 2021 Customers are people who trust the management of their money in a bank or other financial service party to be used in banking business operations, thereby expecting a return in the form of money for their savings. To reach information to increase company profits, a method is needed to be able to provide knowledge in supporting the data that the company has. The model can be obtained by using predictive data processing of customer data that is categorized as potential or not potential. Data processing can be done using Machine Learning, namely classification techniques. This technique will produce a churn prediction model for determining the category of customers who fall into the Potential or Not Potential category and find out what accuracy value will be generated by applying the classification technique using the Naïve Bayes Algorithm. The parameters used in this study are Gender, Age, Marital Status, Dependent, Occupation, Region, Information. The data used are 150 data from customers who have participated in the savings program to find out whether the customer is in the Potential or NonPotential category. The accuracy results generated using this data are 86.17% of the tools used by Rapidminner.
{"title":"Penerapan Metode Machine Learning untuk Prediksi Nasabah Potensial menggunakan Algoritma Klasifikasi Naïve Bayes","authors":"Devi Fitrianah, Saruni Dwiasnati, Hanny Hikmayanti H, Kiki Ahmad Baihaqi","doi":"10.30998/faktorexacta.v14i2.9297","DOIUrl":"https://doi.org/10.30998/faktorexacta.v14i2.9297","url":null,"abstract":"Received March 20, 2021 Revised June 4, 2021 Accepted June 13, 2021 Customers are people who trust the management of their money in a bank or other financial service party to be used in banking business operations, thereby expecting a return in the form of money for their savings. To reach information to increase company profits, a method is needed to be able to provide knowledge in supporting the data that the company has. The model can be obtained by using predictive data processing of customer data that is categorized as potential or not potential. Data processing can be done using Machine Learning, namely classification techniques. This technique will produce a churn prediction model for determining the category of customers who fall into the Potential or Not Potential category and find out what accuracy value will be generated by applying the classification technique using the Naïve Bayes Algorithm. The parameters used in this study are Gender, Age, Marital Status, Dependent, Occupation, Region, Information. The data used are 150 data from customers who have participated in the savings program to find out whether the customer is in the Potential or NonPotential category. The accuracy results generated using this data are 86.17% of the tools used by Rapidminner.","PeriodicalId":53004,"journal":{"name":"Faktor Exacta","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45925587","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}
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