Pub Date : 2021-01-01DOI: 10.14710/jtsiskom.2021.13931
Irsandy Maulana Satya Viddin, Antonius Cahya Prihandoko, D. M. Firmansyah
This study aims to develop an authentication alternative by applying the Histogram shifting steganography method. The media used for authentication is image media. Histogram shifting utilizes the histogram of an image to insert a secret message. The developed authentication has implemented the Histogram shifting to insert user credentials into the carrier image. Users can use the steganographic image to log into their accounts. The method extracts the credentials from the image during the login. PSNR test of the steganographic images produces an average value of 52.52 dB. The extraction capability test shows that the method can extract all test images correctly. In addition, this authentication method is also more resistant to attacks common to password authentication.
{"title":"An authentication alternative using histogram shifting steganography method","authors":"Irsandy Maulana Satya Viddin, Antonius Cahya Prihandoko, D. M. Firmansyah","doi":"10.14710/jtsiskom.2021.13931","DOIUrl":"https://doi.org/10.14710/jtsiskom.2021.13931","url":null,"abstract":"This study aims to develop an authentication alternative by applying the Histogram shifting steganography method. The media used for authentication is image media. Histogram shifting utilizes the histogram of an image to insert a secret message. The developed authentication has implemented the Histogram shifting to insert user credentials into the carrier image. Users can use the steganographic image to log into their accounts. The method extracts the credentials from the image during the login. PSNR test of the steganographic images produces an average value of 52.52 dB. The extraction capability test shows that the method can extract all test images correctly. In addition, this authentication method is also more resistant to attacks common to password authentication.","PeriodicalId":56231,"journal":{"name":"Jurnal Teknologi dan Sistem Komputer","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67019419","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 : 2020-12-09DOI: 10.14710/jtsiskom.2021.14007
Agus Subhan Akbar, R. H. Kusumodestoni
This correct the article "Optimasi nilai k dan parameter lag algoritme k-nearest neighbor pada prediksi tingkat hunian hotel (Optimization of k value and lag parameter of k-nearest neighbor algorithm on the prediction of hotel occupancy rates)" in vol. 8, no. 3, pp. 246-254, Jul. 2020; https://doi.org/10.14710/jtsiskom.2020.13648In the original published article, the placement of Figure 8 and Figure 9 less appropriate, which causes the manuscript hard to read. In addition, Table 2 through Table 6 need to be repositioned. These placing errors have been corrected online.The publisher apologizes for these errors.
本文对《酒店入住率预测的k值和k近邻算法的滞后参数优化》(第八卷第8期)一文中的“Optimasi nilai k- dan参数滞后算法k-nearest neighbor pada prediksi tingkat huunian hotel”进行了修正。3, pp. 246-254, july 2020;https://doi.org/10.14710/jtsiskom.2020.13648In原发表的文章，图8和图9的位置不太合适，这导致稿件难以阅读。此外，表2到表6需要重新定位。这些放置错误已在网上更正。出版商对这些错误表示歉意。
{"title":"Erratum: Optimization of k value and lag parameter of k-nearest neighbor algorithm on the prediction of hotel occupancy rates","authors":"Agus Subhan Akbar, R. H. Kusumodestoni","doi":"10.14710/jtsiskom.2021.14007","DOIUrl":"https://doi.org/10.14710/jtsiskom.2021.14007","url":null,"abstract":"This correct the article \"Optimasi nilai k dan parameter lag algoritme k-nearest neighbor pada prediksi tingkat hunian hotel (Optimization of k value and lag parameter of k-nearest neighbor algorithm on the prediction of hotel occupancy rates)\" in vol. 8, no. 3, pp. 246-254, Jul. 2020; https://doi.org/10.14710/jtsiskom.2020.13648In the original published article, the placement of Figure 8 and Figure 9 less appropriate, which causes the manuscript hard to read. In addition, Table 2 through Table 6 need to be repositioned. These placing errors have been corrected online.The publisher apologizes for these errors. ","PeriodicalId":56231,"journal":{"name":"Jurnal Teknologi dan Sistem Komputer","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47927731","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 : 2020-12-08DOI: 10.14710/JTSISKOM.2020.13747
Agil Aditya, B. Sari, T. N. Padilah
K-medoids is a clustering method that uses the distance method to find and classify data that have similarities and inequalities between data. This shows that the determination of the distance measurement method is important because it affects the performance of the k-medoids cluster results. From several studies, it is stated that the Euclidean and Gower methods can be used as measurement methods in clustering with numerical data. This study aims to compare the performance of the k-medoids clustering results on a numerical dataset using the Euclidean and Gower methods. The method used is the Knowledge Discovery in Database (KDD) method. In this study, seven numerical datasets were used and the evaluation of clustering results used silhouette, Dunn, and connectivity values. The Euclidean distance method is superior to the two values of silhouette evaluation and connectivity, it shows that Euclidean has a good data grouping structure, while the Gower is superior to the Dunn value, which shows that the Gower has good cluster separation compared to Euclidean. This study shows that the Euclidean method is superior to the Gower method in the application of the k-medoids algorithm with a numeric dataset.
{"title":"Comparison analysis of Euclidean and Gower distance measures on k-medoids cluster","authors":"Agil Aditya, B. Sari, T. N. Padilah","doi":"10.14710/JTSISKOM.2020.13747","DOIUrl":"https://doi.org/10.14710/JTSISKOM.2020.13747","url":null,"abstract":"K-medoids is a clustering method that uses the distance method to find and classify data that have similarities and inequalities between data. This shows that the determination of the distance measurement method is important because it affects the performance of the k-medoids cluster results. From several studies, it is stated that the Euclidean and Gower methods can be used as measurement methods in clustering with numerical data. This study aims to compare the performance of the k-medoids clustering results on a numerical dataset using the Euclidean and Gower methods. The method used is the Knowledge Discovery in Database (KDD) method. In this study, seven numerical datasets were used and the evaluation of clustering results used silhouette, Dunn, and connectivity values. The Euclidean distance method is superior to the two values of silhouette evaluation and connectivity, it shows that Euclidean has a good data grouping structure, while the Gower is superior to the Dunn value, which shows that the Gower has good cluster separation compared to Euclidean. This study shows that the Euclidean method is superior to the Gower method in the application of the k-medoids algorithm with a numeric dataset.","PeriodicalId":56231,"journal":{"name":"Jurnal Teknologi dan Sistem Komputer","volume":"9 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2020-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48411754","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 : 2020-10-31DOI: 10.14710/jtsiskom.2020.13591
F. Budiman, E. Susanto, D. Perdana, Husneni Mukhtar, Yulius Anggoro Pamungkas, Yakobus Yulyanto Kevin
This study examines the application of a landslide disaster monitoring system based on soil activity information that utilizes humidity, temperature, and accelerometer sensors. An artificial highland was built as the research object, and the landslide process was triggered by supplying the system with continuous artificial rainfall. The soil activities were observed through its slope movement, temperature, and moisture content, utilizing an accelerometer, temperature, and humidity sensors both in dry and wet conditions. The system could well observe the soil activities, and the obtained data could be accessed in real-time and online mode on a website. The time delay in sending the data to the server was 2 seconds. Moreover, the characteristics of soil porosity and its relevance to soil saturation level due to water pressure were studied as well. Kinetic study showed that the water adsorption to soil followed the intraparticle diffusion model with a coefficient of determination R 2 0.99043. The system prototype should be used to build the information center of disaster mitigation, particularly in Indonesia.
{"title":"Landslide monitoring system based on water adsorption rate utilizing humidity, accelerometer, and temperature sensors","authors":"F. Budiman, E. Susanto, D. Perdana, Husneni Mukhtar, Yulius Anggoro Pamungkas, Yakobus Yulyanto Kevin","doi":"10.14710/jtsiskom.2020.13591","DOIUrl":"https://doi.org/10.14710/jtsiskom.2020.13591","url":null,"abstract":"This study examines the application of a landslide disaster monitoring system based on soil activity information that utilizes humidity, temperature, and accelerometer sensors. An artificial highland was built as the research object, and the landslide process was triggered by supplying the system with continuous artificial rainfall. The soil activities were observed through its slope movement, temperature, and moisture content, utilizing an accelerometer, temperature, and humidity sensors both in dry and wet conditions. The system could well observe the soil activities, and the obtained data could be accessed in real-time and online mode on a website. The time delay in sending the data to the server was 2 seconds. Moreover, the characteristics of soil porosity and its relevance to soil saturation level due to water pressure were studied as well. Kinetic study showed that the water adsorption to soil followed the intraparticle diffusion model with a coefficient of determination R 2 0.99043. The system prototype should be used to build the information center of disaster mitigation, particularly in Indonesia.","PeriodicalId":56231,"journal":{"name":"Jurnal Teknologi dan Sistem Komputer","volume":"8 1","pages":"255-262"},"PeriodicalIF":0.0,"publicationDate":"2020-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42472514","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 : 2020-10-31DOI: 10.14710/JTSISKOM.2020.13625
Ahmad Taufiq Akbar, Rochmat Husaini, Bagus Muhammad Akbar, S. Saifullah
Blood type still leads to an assumption about its relation to some personality aspects. This study observes preprocessing methods for improving the classification accuracy of MBTI data to determine blood type. The training and testing data use 250 data from the MBTI questionnaire answers given by 250 respondents. The classification uses the k-Nearest Neighbor (k-NN) algorithm. Without preprocessing, k-NN results in about 32 % accuracy, so it needs some preprocessing to handle data imbalance before the classification. The proposed preprocessing consists of two-stage, the first stage is the unsupervised resample, and the second is the supervised resample. For the validation, it uses ten cross-validations. The result of k-Nearest Neighbor classification after using these proposed preprocessing stages has finally increased the accuracy, F-score, and recall significantly.
{"title":"A proposed method for handling an imbalance data in classification of blood type based on Myers-Briggs type indicator","authors":"Ahmad Taufiq Akbar, Rochmat Husaini, Bagus Muhammad Akbar, S. Saifullah","doi":"10.14710/JTSISKOM.2020.13625","DOIUrl":"https://doi.org/10.14710/JTSISKOM.2020.13625","url":null,"abstract":"Blood type still leads to an assumption about its relation to some personality aspects. This study observes preprocessing methods for improving the classification accuracy of MBTI data to determine blood type. The training and testing data use 250 data from the MBTI questionnaire answers given by 250 respondents. The classification uses the k-Nearest Neighbor (k-NN) algorithm. Without preprocessing, k-NN results in about 32 % accuracy, so it needs some preprocessing to handle data imbalance before the classification. The proposed preprocessing consists of two-stage, the first stage is the unsupervised resample, and the second is the supervised resample. For the validation, it uses ten cross-validations. The result of k-Nearest Neighbor classification after using these proposed preprocessing stages has finally increased the accuracy, F-score, and recall significantly.","PeriodicalId":56231,"journal":{"name":"Jurnal Teknologi dan Sistem Komputer","volume":"8 1","pages":"276-283"},"PeriodicalIF":0.0,"publicationDate":"2020-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48422531","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 : 2020-10-31DOI: 10.14710/jtsiskom.2020.13668
Deden Istiawan
Currently, the identification of critical land, that has been physically, chemically, and biologically damaged, uses a geographic information system. However, it requires a high cost to get the high resolution of satellite images. In this study, a comparison framework is proposed to determine the performance of the classification algorithms, namely C.45, ID3, Random Forest, k-Nearest Neighbor, and Naive Bayes. This research aims to find out the best algorithm for the classification of critical land in agricultural cultivation areas. The results show that the highest accuracy Random Forest algorithm was 93.10 % in predicting critical land, and the naive Bayes has the lowest performance, with 89.32 % of accuracy in predicting critical land.
{"title":"Comparative analysis of classification algorithms for critical land prediction in agricultural cultivation areas","authors":"Deden Istiawan","doi":"10.14710/jtsiskom.2020.13668","DOIUrl":"https://doi.org/10.14710/jtsiskom.2020.13668","url":null,"abstract":"Currently, the identification of critical land, that has been physically, chemically, and biologically damaged, uses a geographic information system. However, it requires a high cost to get the high resolution of satellite images. In this study, a comparison framework is proposed to determine the performance of the classification algorithms, namely C.45, ID3, Random Forest, k-Nearest Neighbor, and Naive Bayes. This research aims to find out the best algorithm for the classification of critical land in agricultural cultivation areas. The results show that the highest accuracy Random Forest algorithm was 93.10 % in predicting critical land, and the naive Bayes has the lowest performance, with 89.32 % of accuracy in predicting critical land.","PeriodicalId":56231,"journal":{"name":"Jurnal Teknologi dan Sistem Komputer","volume":"8 1","pages":"270-275"},"PeriodicalIF":0.0,"publicationDate":"2020-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47103051","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 : 2020-10-31DOI: 10.14710/jtsiskom.2020.13744
Edwin R. Arboleda, Kimberly M. Parazo, C. Pareja
This study aimed to design and develop a watermelon ripeness detector using Near-Infrared Spectroscopy (NIRS). The research problem being solved in this study is developing a prototype wherein the watermelon ripeness can be detected without the need to open it. This detector will save customers from buying unripe watermelon and the farmers from harvesting an unripe watermelon. The researchers attempted to use the NIRS technique in determining the ripeness level of watermelon as it is widely used in the agricultural sector with high-speed analysis. The project was composed of Raspberry Pi Zero W as the microprocessor unit connected to input and output devices, such as the NIR spectral sensor and the OLED display. It was programmed by Python 3 IDLE. The detector scanned a total of 200 watermelon samples. These samples were grouped as 60 % for the training dataset, 20 % for testing, and another 20 % for evaluation. The data sets were collected and are subjected to the Support Vector Machine (SVM) algorithm. Overall, experimental results showed that the detector could correctly classify both unripe and ripe watermelons with 92.5 % accuracy.
{"title":"Watermelon ripeness detector using near infrared spectroscopy","authors":"Edwin R. Arboleda, Kimberly M. Parazo, C. Pareja","doi":"10.14710/jtsiskom.2020.13744","DOIUrl":"https://doi.org/10.14710/jtsiskom.2020.13744","url":null,"abstract":"This study aimed to design and develop a watermelon ripeness detector using Near-Infrared Spectroscopy (NIRS). The research problem being solved in this study is developing a prototype wherein the watermelon ripeness can be detected without the need to open it. This detector will save customers from buying unripe watermelon and the farmers from harvesting an unripe watermelon. The researchers attempted to use the NIRS technique in determining the ripeness level of watermelon as it is widely used in the agricultural sector with high-speed analysis. The project was composed of Raspberry Pi Zero W as the microprocessor unit connected to input and output devices, such as the NIR spectral sensor and the OLED display. It was programmed by Python 3 IDLE. The detector scanned a total of 200 watermelon samples. These samples were grouped as 60 % for the training dataset, 20 % for testing, and another 20 % for evaluation. The data sets were collected and are subjected to the Support Vector Machine (SVM) algorithm. Overall, experimental results showed that the detector could correctly classify both unripe and ripe watermelons with 92.5 % accuracy.","PeriodicalId":56231,"journal":{"name":"Jurnal Teknologi dan Sistem Komputer","volume":"8 1","pages":"317-322"},"PeriodicalIF":0.0,"publicationDate":"2020-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48832363","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 : 2020-10-31DOI: 10.14710/jtsiskom.2020.13669
Tamunopriye Ene Dagogo-George, H. A. Mojeed, A. Balogun, M. Mabayoje, S. A. Salihu
Diabetic Retinopathy (DR) is a condition that emerges from prolonged diabetes, causing severe damages to the eyes. Early diagnosis of this disease is highly imperative as late diagnosis may be fatal. Existing studies employed machine learning approaches with Support Vector Machines (SVM) having the highest performance on most analyses and Decision Trees (DT) having the lowest. However, SVM has been known to suffer from parameter and kernel selection problems, which undermine its predictive capability. Hence, this study presents homogenous ensemble classification methods with DT as the base classifier to optimize predictive performance. Boosting and Bagging ensemble methods with feature selection were employed, and experiments were carried out using Python Scikit Learn libraries on DR datasets extracted from UCI Machine Learning repository. Experimental results showed that Bagged and Boosted DT were better than SVM. Specifically, Bagged DT performed best with accuracy 65.38 %, f-score 0.664, and AUC 0.731, followed by Boosted DT with accuracy 65.42 %, f-score 0.655, and AUC 0.724 when compared to SVM (accuracy 65.16 %, f-score 0.652, and AUC 0.721). These results indicate that DT's predictive performance can be optimized by employing the homogeneous ensemble methods to outperform SVM in predicting DR.
{"title":"Tree-based homogeneous ensemble model with feature selection for diabetic retinopathy prediction","authors":"Tamunopriye Ene Dagogo-George, H. A. Mojeed, A. Balogun, M. Mabayoje, S. A. Salihu","doi":"10.14710/jtsiskom.2020.13669","DOIUrl":"https://doi.org/10.14710/jtsiskom.2020.13669","url":null,"abstract":"Diabetic Retinopathy (DR) is a condition that emerges from prolonged diabetes, causing severe damages to the eyes. Early diagnosis of this disease is highly imperative as late diagnosis may be fatal. Existing studies employed machine learning approaches with Support Vector Machines (SVM) having the highest performance on most analyses and Decision Trees (DT) having the lowest. However, SVM has been known to suffer from parameter and kernel selection problems, which undermine its predictive capability. Hence, this study presents homogenous ensemble classification methods with DT as the base classifier to optimize predictive performance. Boosting and Bagging ensemble methods with feature selection were employed, and experiments were carried out using Python Scikit Learn libraries on DR datasets extracted from UCI Machine Learning repository. Experimental results showed that Bagged and Boosted DT were better than SVM. Specifically, Bagged DT performed best with accuracy 65.38 %, f-score 0.664, and AUC 0.731, followed by Boosted DT with accuracy 65.42 %, f-score 0.655, and AUC 0.724 when compared to SVM (accuracy 65.16 %, f-score 0.652, and AUC 0.721). These results indicate that DT's predictive performance can be optimized by employing the homogeneous ensemble methods to outperform SVM in predicting DR.","PeriodicalId":56231,"journal":{"name":"Jurnal Teknologi dan Sistem Komputer","volume":"8 1","pages":"297-303"},"PeriodicalIF":0.0,"publicationDate":"2020-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46421531","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 : 2020-10-21DOI: 10.14710/jtsiskom.2020.13874
Sugriyono Sugriyono, M. U. Siregar
The existence of outliers in the dataset can cause low accuracy in a classification process. Outliers in the dataset can be removed from a preprocessing stage of classification algorithms. Clustering can be used as an outlier detection method. This study applies K-means and a distance matrix to detect outliers and remove them from datasets with class labels. This research used a dataset of students’ academic performance totaling 6847 instances, having 18 attributes and 3 class labels. Preprocessing applies the K-means method to get centroid in each class. The distance matrix is used to evaluate the distance of instance to the centroid. Outliers, which are a different class, will be removed from the dataset. This preprocessing improves the classification accuracy of the kNN algorithm. Data without preprocessing has 72.28 % accuracy, preprocessed data using K-means with Euclidean has 98.42 % accuracy (an increase of 26.14 %), while the K-means with Manhattan has 97.76 % accuracy (an increase of 25.48 %).
{"title":"Preprocessing kNN algorithm classification using K-means and distance matrix with students’ academic performance dataset","authors":"Sugriyono Sugriyono, M. U. Siregar","doi":"10.14710/jtsiskom.2020.13874","DOIUrl":"https://doi.org/10.14710/jtsiskom.2020.13874","url":null,"abstract":"The existence of outliers in the dataset can cause low accuracy in a classification process. Outliers in the dataset can be removed from a preprocessing stage of classification algorithms. Clustering can be used as an outlier detection method. This study applies K-means and a distance matrix to detect outliers and remove them from datasets with class labels. This research used a dataset of students’ academic performance totaling 6847 instances, having 18 attributes and 3 class labels. Preprocessing applies the K-means method to get centroid in each class. The distance matrix is used to evaluate the distance of instance to the centroid. Outliers, which are a different class, will be removed from the dataset. This preprocessing improves the classification accuracy of the kNN algorithm. Data without preprocessing has 72.28 % accuracy, preprocessed data using K-means with Euclidean has 98.42 % accuracy (an increase of 26.14 %), while the K-means with Manhattan has 97.76 % accuracy (an increase of 25.48 %).","PeriodicalId":56231,"journal":{"name":"Jurnal Teknologi dan Sistem Komputer","volume":"465 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67018442","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 : 2020-10-13DOI: 10.14710/jtsiskom.2020.13725
Hendi Santoso, T. Hestirianoto, I. Jaya
This study aims to develop a turtle nests real-time monitoring system using the Arduino Uno to measure the temperature and moisture of sand used conveniently for certain applications. Sand temperature measurement uses a DS18B20 waterproof sensor, sand moisture uses SKU:SEN0193, and air temperature and humidity using DHT22. The micro SD card module is used to store data from sensor calculations in real-time and continuously. The measuring instrument was designed to be portable and easy to use. The material used is polypropylene that has dimensions of 11x6x18 cm3. Using the regression linear analysis, there was no significant difference in temperature measurements using the DS18B20 sensor and analog thermometer and sand humidity using an SKU:SEN0193 sensor and analog humidity measuring instrument.
{"title":"Sand temperature and moisture monitoring system for turtle nests using Arduino Uno","authors":"Hendi Santoso, T. Hestirianoto, I. Jaya","doi":"10.14710/jtsiskom.2020.13725","DOIUrl":"https://doi.org/10.14710/jtsiskom.2020.13725","url":null,"abstract":"This study aims to develop a turtle nests real-time monitoring system using the Arduino Uno to measure the temperature and moisture of sand used conveniently for certain applications. Sand temperature measurement uses a DS18B20 waterproof sensor, sand moisture uses SKU:SEN0193, and air temperature and humidity using DHT22. The micro SD card module is used to store data from sensor calculations in real-time and continuously. The measuring instrument was designed to be portable and easy to use. The material used is polypropylene that has dimensions of 11x6x18 cm3. Using the regression linear analysis, there was no significant difference in temperature measurements using the DS18B20 sensor and analog thermometer and sand humidity using an SKU:SEN0193 sensor and analog humidity measuring instrument.","PeriodicalId":56231,"journal":{"name":"Jurnal Teknologi dan Sistem Komputer","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45558041","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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