SINTECH (Science and Information Technology) Journal最新文献_第2页

Implementasi Metode Convolutional Neural Network Pada Pengenalan Aksara Bali Berbasis Game Edukasi 基于游戏教育的巴厘岛字母转换网络的应用

SINTECH (Science and Information Technology) Journal Pub Date : 2023-04-30 DOI: 10.31598/sintechjournal.v6i1.1298

Gusti Ngurah, Anom Cahyadi Putra, I. Kadek, Agus Andika Putra, I. Bagus, Gede Dwidasmara, I. M. Widiartha, Ngurah Agus, E. Sanjaya, Putu Gede, Hendra Suputra

{"title":"Implementasi Metode Convolutional Neural Network Pada Pengenalan Aksara Bali Berbasis Game Edukasi","authors":"Gusti Ngurah, Anom Cahyadi Putra, I. Kadek, Agus Andika Putra, I. Bagus, Gede Dwidasmara, I. M. Widiartha, Ngurah Agus, E. Sanjaya, Putu Gede, Hendra Suputra","doi":"10.31598/sintechjournal.v6i1.1298","DOIUrl":"https://doi.org/10.31598/sintechjournal.v6i1.1298","url":null,"abstract":"Balinese script or also known as hanacaraka, is the writing used by Balinese people to write their language. In general, this script is used to write everyday language and literary language. Balinese script in the past was not only used for writing literature or sacred texts but also for writing everyday language. Balinese script plays an important role in literary writing. The sacred text of the Vedas uses Balinese script in the Sanskrit language. In preserving the Balinese script, itself, Balinese script lessons are mandatory for students from elementary school to high school. In addition to studying at school, interesting learning is certainly needed to attract students' interest. One way is by way of game applications or educational games. This Balinese script recognition application receives input in the form of Balinese script writing characters from the user, then it will be processed by preprocessing and continued with the classification training process using the Convolutional Neural Network (CNN) and Backpropagation methods. The result is a web-based application that can recognize Balinese script writing with the CNN classification method with an accuracy rate of 81.3% and gets a positive response from respondents who have tested the application.","PeriodicalId":302091,"journal":{"name":"SINTECH (Science and Information Technology) Journal","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123767243","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}

引用次数: 0

Perbandingan Metode Pembobotan TF-RF Dan TF-ABS Pada Kategorisasi Berita Di BDI Denpasar BDI登巴萨新闻类别新闻的tf -射频偷袭和TF-ABS比较

SINTECH (Science and Information Technology) Journal Pub Date : 2023-04-30 DOI: 10.31598/sintechjournal.v6i1.1252

I. Kadek, Wahyu Dananjaya, Gusti Ayu, Agung Diatri Indradewi

{"title":"Perbandingan Metode Pembobotan TF-RF Dan TF-ABS Pada Kategorisasi Berita Di BDI Denpasar","authors":"I. Kadek, Wahyu Dananjaya, Gusti Ayu, Agung Diatri Indradewi","doi":"10.31598/sintechjournal.v6i1.1252","DOIUrl":"https://doi.org/10.31598/sintechjournal.v6i1.1252","url":null,"abstract":"BDI Denpasar is a government agency tasked with carrying out training and education for human resources of animation, crafts and art. BDI Denpasar in managing news classes in the Kabar Insan Oke service still uses conventional methods. Therefore an automatic news classification module is needed. This study was made to compare the performance level of news classification at BDI Denpasar using K-NN classification with the TF-RF and TF-ABS term weighting methods. Methods that have a high level of performance will be implemented in the news classification module. This research was carried out by collecting news documents, text preprocessing, term weighting, classification, model validation and testing. The K-NN classification uses the n_neighbhor (k), namely k=3, k=5, k=7 and k=9 using a dataset of 324 documents containing 7 classes taken from BDI Denpasar website. Based on the results of the tests performed, TF-RF method obtained a higher performance at k=5 with an accuracy of 71% with a precision of 73% and a recall of 71%. TF-ABS method with the highest performance value is found at k=9 which obtains 70% accuracy, 63% precision and 70% recall. So the method that will be implemented in the news classification module is TF-RF at k=5 with an accuracy of 71% with a precision of 73% and a recall of 71%.","PeriodicalId":302091,"journal":{"name":"SINTECH (Science and Information Technology) Journal","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130734582","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}

引用次数: 0

Penerapan Metode E-Service Quality Terhadap Pengukuran Tingkat Kepuasan Penggunaan Marketplace e - services方法的应用，测量市场使用满意度的水平

SINTECH (Science and Information Technology) Journal Pub Date : 2022-10-28 DOI: 10.31598/sintechjournal.v5i2.1236

W. G. S. Parwita, I. G. A. A. D. Indradewi, M. Ariantini, N. L. W. S. R. Ginantra, I. K. A. Putra

{"title":"Penerapan Metode E-Service Quality Terhadap Pengukuran Tingkat Kepuasan Penggunaan Marketplace","authors":"W. G. S. Parwita, I. G. A. A. D. Indradewi, M. Ariantini, N. L. W. S. R. Ginantra, I. K. A. Putra","doi":"10.31598/sintechjournal.v5i2.1236","DOIUrl":"https://doi.org/10.31598/sintechjournal.v5i2.1236","url":null,"abstract":"Intense competition makes various existing markets must be able to provide the best and satisfaction for users to win the existing competition. In the review of the JD.ID application during versions 6.3 and 6.4 there were several user complaints that led to system and service quality problems. Service quality is one of the factors supporting the success or failure of an information system to provide satisfaction to its users. The purpose of this study was to determine how the influence of electronic service quality (e-service quality) on user satisfaction in the JD.ID application. The type of analysis used in this study is simple regression analysis with descriptive analysis to describe a generalization or explain the research subject based on the dimensions of e-service quality, so that an overview of the effect of e-service quality on user satisfaction can be obtained. The processed data was obtained from distributing questionnaires by using the google form. The results showed that the majority of JD.ID marketplace users in Badung Regency were women. The test results show that e-service quality which consists of dimensions of efficiency, system availability, fulfillment, privacy, responsiveness, compensation, and contact has a significant influence on user satisfaction and has a strong correlation, meaning that the higher the service quality JD has. ID, the higher the level of satisfaction of JD.ID users. It can be said that the service quality of JD.ID is quite good in providing user satisfaction.","PeriodicalId":302091,"journal":{"name":"SINTECH (Science and Information Technology) Journal","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115500951","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}

引用次数: 0

Self-Isolation Monitoring of COVID-19 Patients Using Fuzzy Inference System-Tsukamoto 基于模糊推理系统的COVID-19患者自我隔离监测

SINTECH (Science and Information Technology) Journal Pub Date : 2022-10-28 DOI: 10.31598/sintechjournal.v5i2.1114

Trisna Ari Roshinta, Masbahah Masbahah

{"title":"Self-Isolation Monitoring of COVID-19 Patients Using Fuzzy Inference System-Tsukamoto","authors":"Trisna Ari Roshinta, Masbahah Masbahah","doi":"10.31598/sintechjournal.v5i2.1114","DOIUrl":"https://doi.org/10.31598/sintechjournal.v5i2.1114","url":null,"abstract":"In self-isolation of Covid-19 patients, it is very important to carry out regular condition checks. Currently, the examination of severity of patien’s condition can be carried out by the patient himself online with the tools as measurement provided by public health center, and the data can be monitored by medic team. Several applications for monitoring the daily condition of Covid-19 patients have been developed but the parameters used in the monitoring application are not standardized and the accuracy of the application is unknown. This study aims to develop a Covid-19 patient monitoring application using more complete and accurate parameters. The input parameters used are body temperature, O2 saturation, pulse rate, and respiratory rate. The output is the level of the Covid-19 patient's condition which is divided into mild, moderate, and severe, as well as information on the actions that must be taken. This research uses the Fuzzy Inference System-Tsukamoto method. The test results between the system output and expert testing related to the condition of Covid-19 patients show that this self-checking application for monitoring has an accuracy of 95%.","PeriodicalId":302091,"journal":{"name":"SINTECH (Science and Information Technology) Journal","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123518641","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}

引用次数: 0

Prediksi Jumlah Pasien Covid-19 Dengan Menggunakan Klasifikasi Algoritma Machine Learning 通过使用分类算法学习机器来预测病人的数量

SINTECH (Science and Information Technology) Journal Pub Date : 2022-10-28 DOI: 10.31598/sintechjournal.v5i2.1163

Aidia Khoiriyah Firdausy Aidia, Putri Juli Amelia, Vina Rahmayanti Setyaning Nastiti

{"title":"Prediksi Jumlah Pasien Covid-19 Dengan Menggunakan Klasifikasi Algoritma Machine Learning","authors":"Aidia Khoiriyah Firdausy Aidia, Putri Juli Amelia, Vina Rahmayanti Setyaning Nastiti","doi":"10.31598/sintechjournal.v5i2.1163","DOIUrl":"https://doi.org/10.31598/sintechjournal.v5i2.1163","url":null,"abstract":"Corona virus or servere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a disease that results in the occurrence of mild to moderate respiratory tract infections. Positive cases of Covid-19 in Indonesia were first detected on March 2, 2020 and continue until 2022. The additional number of deaths caused by COVID-19 has also increased. Therefore, the author is interested in making a predictive model of the cumulative number of COVID-19 patients who died in Indonesia. Therefore, in this study is how to predict the number of patients who die from COVID-19 in Indonesia by creating an appropriate accuracy model to help estimate the number of deaths associated with COVID-19 in Indonesia and assist the government in dealing with cases of new variants of COVID-19. In this study, the authors used the Decision Tree model using entropy criteria as well as Information Gain and Random Forest which resulted in accuracy rates of 91.83% (Decission Tree) and 73.80% (Random Forest). The results, explain that the model used is good. The more the R-squared error value is close to 1, the better the model used will be","PeriodicalId":302091,"journal":{"name":"SINTECH (Science and Information Technology) Journal","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125116433","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}

引用次数: 0

Klasifikasi Penyakit Infeksi Pada Ayam Berdasarkan Gambar Feses Menggunakan Convolutional Neural Network 根据粪便图片对鸡肉感染的分类使用了神经对联性网络

SINTECH (Science and Information Technology) Journal Pub Date : 2022-10-28 DOI: 10.31598/sintechjournal.v5i2.1179

Moch. Kholil, Heri Priya Waspada, Rafika Akhsani

{"title":"Klasifikasi Penyakit Infeksi Pada Ayam Berdasarkan Gambar Feses Menggunakan Convolutional Neural Network","authors":"Moch. Kholil, Heri Priya Waspada, Rafika Akhsani","doi":"10.31598/sintechjournal.v5i2.1179","DOIUrl":"https://doi.org/10.31598/sintechjournal.v5i2.1179","url":null,"abstract":"Convolutional Neural Network (CNN) is one of the Deep Learning methods that is able to carry out an independent learning process that is popular and appropriate in classifying. The development of technology in the field of Deep Learning, this study aims to assist farmers in identifying the types of infectious diseases that attack chickens based on faecal images using Convolutional Neural Network (CNN) so as to increase production yields. Several infectious diseases that attack chickens can be identified through their feces, including newcastle disease caused by a virus, pullorum caused by bacteria, and coccidiosis caused by parasites. To identify, it is necessary to classify the types of diseases that attack by using images of chicken feces. With deep learning using Keras/TensorFlow, 95.40% of chicken feces images are predicted to be infected with coccidiosis, 94.97% chicken feces images are predicted to be healthy, 90.21% chicken feces images are predicted to be infected with tetelo disease, and 96.50% chicken feces images are predicted to be infected with pullorum disease","PeriodicalId":302091,"journal":{"name":"SINTECH (Science and Information Technology) Journal","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115083642","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}

引用次数: 2

Deteksi Tingkat Kematangan Tandan Buah Segar Kelapa Sawit dengan Algoritme K-Means

SINTECH (Science and Information Technology) Journal Pub Date : 2022-10-28 DOI: 10.31598/sintechjournal.v5i2.1146

W. Sari, M. Muslimin, Annafi’ Franz, Putu Sugiartawan

引用次数: 0

Densely Connected dan Residual Convolutional Neural Network Untuk Estimasi Jumlah Keluarga Tingkat Desa Dengan Citra Satelit

SINTECH (Science and Information Technology) Journal Pub Date : 2022-10-28 DOI: 10.31598/sintechjournal.v5i2.1191

Jodi jhouranda Siregar, A. Kurnia, Kusman Sadik

{"title":"Densely Connected dan Residual Convolutional Neural Network Untuk Estimasi Jumlah Keluarga Tingkat Desa Dengan Citra Satelit","authors":"Jodi jhouranda Siregar, A. Kurnia, Kusman Sadik","doi":"10.31598/sintechjournal.v5i2.1191","DOIUrl":"https://doi.org/10.31598/sintechjournal.v5i2.1191","url":null,"abstract":"Indonesia conducts a population census every ten years to collect population data. Variables such as family count are collected to provide general population information for policy making and sampling frames. Indonesia as an archipelagic country with an area of 8.3 million km2 will require a lot of resources to collect such data. In the age of big data, satellite imagery has become more available and inexpensive. In this study, we used West Java as a case study for applying deep learning to estimate family counts at the village level. Sentinel-2 and SPOT-67 data are used to model family counts. Using xgboost, we regress the family count with the softmax probability, resulting from family density classification using deep learning (densenet121 and resnet50 ) as the input. With an R2 of 0.93 and a MAPE of 19%, the regression model provides a good prediction of the number of families in the census. Regarding the input data, Sentinel-2 is sufficient to accomplish this task as there is no significant difference from the modeling results with higher resolution images (SPOT 6-7). The input level in the form of a segment of the estimation area and using structured auxiliary variables also deliver better predictions","PeriodicalId":302091,"journal":{"name":"SINTECH (Science and Information Technology) Journal","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131628349","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}

引用次数: 0

Perbandingan Algoritma Klasifikasi Data Mining Untuk Prediksi Penyakit Stroke

SINTECH (Science and Information Technology) Journal Pub Date : 2022-10-28 DOI: 10.31598/sintechjournal.v5i2.1222

Yufis Azhar, Aidia Khoiriyah Firdausy, Putri Juli Amelia

{"title":"Perbandingan Algoritma Klasifikasi Data Mining Untuk Prediksi Penyakit Stroke","authors":"Yufis Azhar, Aidia Khoiriyah Firdausy, Putri Juli Amelia","doi":"10.31598/sintechjournal.v5i2.1222","DOIUrl":"https://doi.org/10.31598/sintechjournal.v5i2.1222","url":null,"abstract":"Data mining is often called knowledge Discovery in Database (KDD). Data mining is usually used to improve future decision making based on information obtained from the past. For example for prediction, estimation, association, clustering, and description. Stroke is the second most deadly disease in the world according to WHO. The sufferer has an injury to the nervous system. Because of this, health experts, especially in the field of nursing, need special attention. Currently, the development of the Industrial Revolution Era 4.0 is collaborating in the fieldsof technology and health science so that it becomes something useful by using Machine Learning. There are so many benefits that are used in predicting several diseases that can be anticipated. In this study the dataset is dividedinto 2 parts, namely training data and testing data using split validation. Based on the results of the test that have been carried out in this study, the algorithm that has the highest accuracyvalue on balanced data is Logistic Regression with an accuracy rate of 75.65%, while for unbalanced data, the algorithm that has the highest accuracy results is Logistic Regression, Random Forest, SVM, and KNN with an accuracy rate of 98.63%. This testing process is carried out to identify stroke with data mining algorithms","PeriodicalId":302091,"journal":{"name":"SINTECH (Science and Information Technology) Journal","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116082544","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}

引用次数: 2

SPK Penerima Bantuan Sosial Menggunakan Metode BWM-SAW dengan Metodologi Team Data Science Process (TDSP) SPK社会援助收纳采用bwsaw方法与科学数据团队处理方法(TDSP)

SINTECH (Science and Information Technology) Journal Pub Date : 2022-10-28 DOI: 10.31598/sintechjournal.v5i2.983

Gede Surya Mahendra

引用次数: 2