JISKA Jurnal Informatika Sunan Kalijaga最新文献_第5页

Pengenalan Pola Huruf Hijaiyah dengan Metode Local Binary Pattern Menggunakan Algoritma Fuzzy K-Nearest Neighbor

JISKA Jurnal Informatika Sunan Kalijaga Pub Date : 2022-01-25 DOI: 10.14421/jiska.2022.7.1.68-74

Asdar, R. Saputra, Ika Purwanti Ningrum

引用次数: 0

Analisis Sentimen Tweet Tentang UU Cipta Kerja Menggunakan Algoritma SVM Berbasis PSO 基于粒子群算法的支持向量机在求职UU情绪推文分析中的应用

JISKA Jurnal Informatika Sunan Kalijaga Pub Date : 2022-01-25 DOI: 10.14421/jiska.2022.7.1.10-19

Trifebi Shina Sabrila, Yufis Azhar, C. Aditya

{"title":"Analisis Sentimen Tweet Tentang UU Cipta Kerja Menggunakan Algoritma SVM Berbasis PSO","authors":"Trifebi Shina Sabrila, Yufis Azhar, C. Aditya","doi":"10.14421/jiska.2022.7.1.10-19","DOIUrl":"https://doi.org/10.14421/jiska.2022.7.1.10-19","url":null,"abstract":"Support Vector Machine (SVM) is one of the most widely used classification algorithms for sentiment analysis and has been shown to provide satisfactory performance. However, despite its advantages, the SVM algorithm still has weaknesses in selecting the right SVM parameters to optimize the performance. In this study, sentiment analysis was done with the use of data called tweets about Undang-Undang Cipta Kerja which reap many pros and cons by the people in Indonesia, especially the laborers. The classification method used in this study is the Support Vector Machine algorithm which is optimized using the Particle Swarm Optimization method for the SVM parameters selection in the hope of optimizing the performance generated by the SVM algorithm in sentiment analysis. The results of the study using 10 k-fold cross-validations using the SVM algorithm resulted in an accuracy of 92,99%, a precision of 93,24%, and a recall of 93%. Meanwhile, the SVM and PSO algorithms produce an accuracy of 95%, precision of 95,08%, and recall of 94,97%. The results show that the Particle Swarm Optimization method can overcome the weaknesses of the Support Vector Machine algorithm in the problem of parameter selection and has succeeded in improving the resulting performance where the SVM-PSO is more superior to SVM without optimization in sentiment analysis.","PeriodicalId":34216,"journal":{"name":"JISKA Jurnal Informatika Sunan Kalijaga","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46437623","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

Analisis Perbandingan Kinerja TCP Vegas dan TCP New Reno Menggunakan Antrian Drop Tail 分析天津、天津、拉斯维加斯、新里诺、孟古纳坎、安特里安、垂尾

JISKA Jurnal Informatika Sunan Kalijaga Pub Date : 2022-01-25 DOI: 10.14421/jiska.2022.7.1.20-32

Dony Fahrudy, Bambang Sugiantoro

引用次数: 0

Visualisasi Distribusi Densitas Plasma di dalam Tokamak Melalui Persamaan Magnetohidrodinamika Menggunakan Kode BOUT++ 可视化等离子体密度的分布，通过使用代码和+的磁性流体动力学方程进行

JISKA Jurnal Informatika Sunan Kalijaga Pub Date : 2021-11-16 DOI: 10.14421/physics.v3i2.3173

Cecilia Yanuarief, Koddam Rukadi Lubis

引用次数: 1

Peramalan Pelayanan Service Mobil (After-Sale) Menggunakan Backpropagation Neural Network (BPNN) 使用反向传播神经网络（BPNN）的售后服务行为

JISKA Jurnal Informatika Sunan Kalijaga Pub Date : 2021-09-22 DOI: 10.14421/jiska.2021.6.3.149-160

Novianti Puspitasari, Haviluddin, Arinda Mulawardani Kustiawan, H. Setyadi, Gubtha Mahendra Putra

{"title":"Peramalan Pelayanan Service Mobil (After-Sale) Menggunakan Backpropagation Neural Network (BPNN)","authors":"Novianti Puspitasari, Haviluddin, Arinda Mulawardani Kustiawan, H. Setyadi, Gubtha Mahendra Putra","doi":"10.14421/jiska.2021.6.3.149-160","DOIUrl":"https://doi.org/10.14421/jiska.2021.6.3.149-160","url":null,"abstract":"The automotive industry in Indonesia, primarily cars, is getting more and more varied. Along with increasing the number of vehicles, Brand Holder Sole Agents (ATPM) compete to provide after-sale services (mobile service). However, the company has difficulty knowing the rate of growth in the number of mobile services handled, thus causing losses that impact sources of income. Therefore, we need a standard method in determining the forecasting of the number of car services in the following year. This study implements the Backpropagation Neural Network (BPNN) method in forecasting car service services (after-sale) and Mean Square Error (MSE) for the process of testing the accuracy of the forecasting results formed. The data used in this study is car service data (after-sale) for the last five years. The results show that the best architecture for forecasting after-sales services using BPNN is the 5-10-5-1 architectural model with a learning rate of 0.2 and the learning function of trainlm and MSE of 0.00045581. This proves that the BPNN method can predict mobile service (after-sale) services with good forecasting accuracy values.","PeriodicalId":34216,"journal":{"name":"JISKA Jurnal Informatika Sunan Kalijaga","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47941559","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

Optimasi Keamanan Web Server terhadap Serangan Broken Authentication Menggunakan Teknologi Blockchain 基于区块链技术的Web服务器安全优化

JISKA Jurnal Informatika Sunan Kalijaga Pub Date : 2021-09-22 DOI: 10.14421/jiska.2021.6.3.139-148

Imam Riadi, Herman, Aulyah Zakilah Ifani

{"title":"Optimasi Keamanan Web Server terhadap Serangan Broken Authentication Menggunakan Teknologi Blockchain","authors":"Imam Riadi, Herman, Aulyah Zakilah Ifani","doi":"10.14421/jiska.2021.6.3.139-148","DOIUrl":"https://doi.org/10.14421/jiska.2021.6.3.139-148","url":null,"abstract":"The aspect of the internet that needs to be considered a security is the login system. The login system usually uses a username and password as an authentication method because it is easy to implement. However, data in the form of usernames and passwords are very vulnerable to theft, so it is necessary to increase the security of the login system. The purpose of this research is to investigate the security of the system. Whether the system is good at protecting user data or not, minimizing execution errors from the system and minimizing risk errors on the system so that the login system can be used safely. This research is conducted to test the system security with Burp Suite on the login system that has been built. Testing the security of this system by experimenting with POST data which is secured using blockchain technology makes the data sent in the form of hash blocks safer and more confidential so that the system is safer than before. Blockchain technology has successfully secured usernames and passwords from broken authentication attacks. By using the Burp Suite testing system, login is more specific in conducting security testing.","PeriodicalId":34216,"journal":{"name":"JISKA Jurnal Informatika Sunan Kalijaga","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49042751","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

Analisis Perbandingan Algoritma Decision Tree, kNN, dan Naive Bayes untuk Prediksi Kesuksesan Start-up

JISKA Jurnal Informatika Sunan Kalijaga Pub Date : 2021-09-22 DOI: 10.14421/jiska.2021.6.3.178-188

Adhitya Prayoga Permana, Kurniyatul Ainiyah, Khadijah Fahmi Hayati Holle

{"title":"Analisis Perbandingan Algoritma Decision Tree, kNN, dan Naive Bayes untuk Prediksi Kesuksesan Start-up","authors":"Adhitya Prayoga Permana, Kurniyatul Ainiyah, Khadijah Fahmi Hayati Holle","doi":"10.14421/jiska.2021.6.3.178-188","DOIUrl":"https://doi.org/10.14421/jiska.2021.6.3.178-188","url":null,"abstract":"Start-ups have a very important role in economic growth, the existence of a start-up can open up many new jobs. However, not all start-ups that are developing can become successful start-ups. This is because start-ups have a high failure rate, data shows that 75% of start-ups fail in their development. Therefore, it is important to classify the successful and failed start-ups, so that later it can be used to see the factors that most influence start-up success, and can also predict the success of a start-up. Among the many classifications in data mining, the Decision Tree, kNN, and Naïve Bayes algorithms are the algorithms that the authors chose to classify the 923 start-up data records that were previously obtained. The test results using cross-validation and T-test show that the Decision Tree Algorithm is the most appropriate algorithm for classifying in this case study. This is evidenced by the accuracy value obtained from the Decision Tree algorithm, which is greater than other algorithms, which is 79.29%, while the kNN algorithm has an accuracy value of 66.69%, and Naive Bayes is 64.21%.","PeriodicalId":34216,"journal":{"name":"JISKA Jurnal Informatika Sunan Kalijaga","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41690162","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: 8

Implementasi Deep Learning untuk Entity Matching pada Dataset Obat (Studi Kasus K24 dan Farmaku) 药物数据库实体匹配的深度学习实现

JISKA Jurnal Informatika Sunan Kalijaga Pub Date : 2021-09-22 DOI: 10.14421/jiska.2021.6.3.130-138

Rivanda Putra Pratama, Rahmat Hidayat, Nisrina Fadhilah Fano, Adam Akbar, Nur Aini Rakhmawati

{"title":"Implementasi Deep Learning untuk Entity Matching pada Dataset Obat (Studi Kasus K24 dan Farmaku)","authors":"Rivanda Putra Pratama, Rahmat Hidayat, Nisrina Fadhilah Fano, Adam Akbar, Nur Aini Rakhmawati","doi":"10.14421/jiska.2021.6.3.130-138","DOIUrl":"https://doi.org/10.14421/jiska.2021.6.3.130-138","url":null,"abstract":"Data processing speed in companies is important to speed up their analysis. Entity matching is a computational process that companies can perform in data processing. In conducting data processing, entity matching plays a role in determining two different data but referring to the same entity. Entity matching problems arise when the dataset used in the comparison is large. The deep learning concept is one of the solutions in dealing with entity matching problems. DeepMatcher is a python package based on a deep learning model architecture that can solve entity matching problems. The purpose of this study was to determine the matching between the two datasets with the application of DeepMatcher in entity matching using drug data from farmaku.com and k24klik.com. The comparison model used is the Hybrid model. Based on the test results, the Hybrid model produces accurate numbers, so that the entity matching used in this study runs well. The best accuracy value of the 10th training with an F1 value of 30.30, a precision value of 17.86, and a recall value of 100.","PeriodicalId":34216,"journal":{"name":"JISKA Jurnal Informatika Sunan Kalijaga","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48156669","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

Perbandingan Faktor-Faktor Yang Mempengaruhi Penggunaan Electronic-Know Your Customer (e-Kyc) 影响电子了解你的客户（e-Kyc）的因素比较

JISKA Jurnal Informatika Sunan Kalijaga Pub Date : 2021-09-22 DOI: 10.14421/jiska.2021.6.3.189-200

Mr. Fitree Tahe, Maria Ulfah Siregar

引用次数: 0

Implementasi Algoritma RC4 pada Sistem Pengamanan Dokumen Digital Soal Ujian 在数字文件安全系统上执行RC4算法

JISKA Jurnal Informatika Sunan Kalijaga Pub Date : 2021-09-22 DOI: 10.14421/jiska.2021.6.3.171-177

Fauziyah Suwarsita Febriyani, Arief Arfriandi

引用次数: 2