Pub Date : 2024-03-31DOI: 10.33020/saintekom.v14i1.489
A. Wardani, Miftahurrahma Rosyda
Tarakan City has road facilities with various degrees of damage, ranging from minor to severe conditions. To address this issue, a WebGIS application is required to provide information about the road conditions. This research aims to develop the "Laporjalanku" application, designed to assist the public in reporting road damage in Tarakan City and mapping information about damaged roads. The application enables the public to easily report complaints via the web or their Android smartphones. Its features include information such as images of damaged roads, the location of damage, and estimated repair times. The development methodology used is the waterfall model, ensuring a systematic and sequential system development process. Black-box testing showed that the system functions well. Meanwhile, the System Usability Scale (SUS) test resulted in a score of 84.5%, categorized as good, and meeting user experience requirements. This application streamlines the road mapping and reporting process, providing accurate information to the Department of Public Works. Additionally, it serves as a vital tool for the government and the public to improve collaboration in maintaining the city's road conditions.
{"title":"Implementasi Aplikasi Laporjalanku untuk Pemetaan dan Pelaporan Jalan Rusak di Wilayah Kota Tarakan","authors":"A. Wardani, Miftahurrahma Rosyda","doi":"10.33020/saintekom.v14i1.489","DOIUrl":"https://doi.org/10.33020/saintekom.v14i1.489","url":null,"abstract":"Tarakan City has road facilities with various degrees of damage, ranging from minor to severe conditions. To address this issue, a WebGIS application is required to provide information about the road conditions. This research aims to develop the \"Laporjalanku\" application, designed to assist the public in reporting road damage in Tarakan City and mapping information about damaged roads. The application enables the public to easily report complaints via the web or their Android smartphones. Its features include information such as images of damaged roads, the location of damage, and estimated repair times. The development methodology used is the waterfall model, ensuring a systematic and sequential system development process. Black-box testing showed that the system functions well. Meanwhile, the System Usability Scale (SUS) test resulted in a score of 84.5%, categorized as good, and meeting user experience requirements. This application streamlines the road mapping and reporting process, providing accurate information to the Department of Public Works. Additionally, it serves as a vital tool for the government and the public to improve collaboration in maintaining the city's road conditions.","PeriodicalId":359182,"journal":{"name":"Jurnal SAINTEKOM","volume":"38 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140358045","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 : 2024-03-31DOI: 10.33020/saintekom.v14i1.528
S. Sakur, Miske Silangen, Desmin Tuwohingide
Capture fisheries production is decreasing due to natural resources or weather conditions, so other resources are needed to support fisheries production. One alternative is to increase the production of aquaculture commodities through seawater, freshwater, or brackish water cultivation. Various potentials for developing aquaculture have been developed in various regions, including the North Sulawesi region. Grouping aquaculture commodity production according to container type is very important to maintain and increase aquaculture production. This research aims to cluster aquaculture production in the North Sulawesi area using the K-Means method using Euclidean, Manhattan, and Minkowsky distances. The results of the research obtained three clusters, namely the first cluster, the region with the highest production of pond container types, namely the Sitaro Islands, and for second cluster consisting of 13 regions that have variations in production ranging from low to high for the types of floating net containers and ponds, while third cluster is the region Bitung with moderate production for pond types. It is hoped that this research can help related agencies to create policies to increase the production potential of aquaculture.
{"title":"Penerapan Algoritma K-Means untuk Klasterisasi Produksi Budidaya Perikanan Provinsi Sulawesi Utara","authors":"S. Sakur, Miske Silangen, Desmin Tuwohingide","doi":"10.33020/saintekom.v14i1.528","DOIUrl":"https://doi.org/10.33020/saintekom.v14i1.528","url":null,"abstract":"Capture fisheries production is decreasing due to natural resources or weather conditions, so other resources are needed to support fisheries production. One alternative is to increase the production of aquaculture commodities through seawater, freshwater, or brackish water cultivation. Various potentials for developing aquaculture have been developed in various regions, including the North Sulawesi region. Grouping aquaculture commodity production according to container type is very important to maintain and increase aquaculture production. This research aims to cluster aquaculture production in the North Sulawesi area using the K-Means method using Euclidean, Manhattan, and Minkowsky distances. The results of the research obtained three clusters, namely the first cluster, the region with the highest production of pond container types, namely the Sitaro Islands, and for second cluster consisting of 13 regions that have variations in production ranging from low to high for the types of floating net containers and ponds, while third cluster is the region Bitung with moderate production for pond types. It is hoped that this research can help related agencies to create policies to increase the production potential of aquaculture.","PeriodicalId":359182,"journal":{"name":"Jurnal SAINTEKOM","volume":"38 44","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140357985","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 : 2024-03-31DOI: 10.33020/saintekom.v14i1.512
Hendri Mahmud Nawawi, Agung Baitul Hikmah, Ali Mustopa, Ganda Wijaya
The complexity of the job market requires individuals and organizations to understand the trends and needs of the world of work. One of the main challenges is the right career placement. That is becoming increasingly popular is the use of Machine Learning algorithms in the decision-making process. ML classification models such as Random Forest, Decision Tree, Naïve Bayes, KNN, and SVM have demonstrated their potential in uncovering hidden patterns from data, including a person's educational history, work experience and interests. In this research, the application of the ML classification model is aimed at predicting career placement. From the data sample used of 215, this research evaluates the effectiveness of various ML models in the context of career placement. As a result, the Random Forest Model is superior to other proposed models with an accuracy value of 87% and an AUC/ROC value of 0.93 which indicates a very good classification value. Meanwhile, the SVM model with Linear Kernel shows the lowest performance with an accuracy value of 67%. Apart from getting information on the best accuracy and AUC/ROC values, the results of this research found that the 'ssc_presentage' attribute (high school exam percentage) is an important factor in career placement decisions.
就业市场的复杂性要求个人和组织了解职场的趋势和需求。其中一个主要挑战就是正确的职业定位。在决策过程中使用机器学习算法正变得越来越流行。随机森林（Random Forest）、决策树（Decision Tree）、奈夫贝叶斯（Naïve Bayes）、KNN 和 SVM 等 ML 分类模型已经证明了它们在从数据中发现隐藏模式（包括个人的教育历史、工作经验和兴趣）方面的潜力。在本研究中，应用 ML 分类模型的目的是预测职业安置。从 215 个数据样本中，本研究评估了各种 ML 模型在职业安置方面的有效性。结果显示，随机森林模型的准确率为 87%，AUC/ROC 值为 0.93，分类效果非常好，优于其他建议的模型。与此同时，采用线性核的 SVM 模型准确率最低，仅为 67%。除了获得最佳准确率和 AUC/ROC 值的信息外，本研究结果还发现，"ssc_presentage "属性（高中考试百分比）是职业安置决策的一个重要因素。
{"title":"Model Klasifikasi Machine Learning untuk Prediksi Ketepatan Penempatan Karir","authors":"Hendri Mahmud Nawawi, Agung Baitul Hikmah, Ali Mustopa, Ganda Wijaya","doi":"10.33020/saintekom.v14i1.512","DOIUrl":"https://doi.org/10.33020/saintekom.v14i1.512","url":null,"abstract":"The complexity of the job market requires individuals and organizations to understand the trends and needs of the world of work. One of the main challenges is the right career placement. That is becoming increasingly popular is the use of Machine Learning algorithms in the decision-making process. ML classification models such as Random Forest, Decision Tree, Naïve Bayes, KNN, and SVM have demonstrated their potential in uncovering hidden patterns from data, including a person's educational history, work experience and interests. In this research, the application of the ML classification model is aimed at predicting career placement. From the data sample used of 215, this research evaluates the effectiveness of various ML models in the context of career placement. As a result, the Random Forest Model is superior to other proposed models with an accuracy value of 87% and an AUC/ROC value of 0.93 which indicates a very good classification value. Meanwhile, the SVM model with Linear Kernel shows the lowest performance with an accuracy value of 67%. Apart from getting information on the best accuracy and AUC/ROC values, the results of this research found that the 'ssc_presentage' attribute (high school exam percentage) is an important factor in career placement decisions.","PeriodicalId":359182,"journal":{"name":"Jurnal SAINTEKOM","volume":"19 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140361087","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 : 2024-03-31DOI: 10.33020/saintekom.v14i1.623
Lucia Devlina Adventia Jelita, Moh. Noor Al Azam, A. Nugroho
In the era of digital transformation, data has become something valuable but vulnerable to leaks. According to Lanskap Keamanan SIber Indonesia 2022, there were 311 data leakage incidents that occurred in Indonesia. Many tools can be used to evaluate information security, one of which is the Information Security Index (ISI). KAMI is a tool to assess the level of information security readiness of an organization based on the SNI ISO / IEC 27001 standard. PT Kano Teknologi Utama is a company that uses information technology in its daily operations. Therefore, researchers conducted an information technology security evaluation to determine the condition of information security and can evaluate it so that security becomes better. The evaluation is carried out by conducting observations and interviews at the company. The data that has been obtained will then be assessed according to OUR Index. Based on the results of the Our Index assessment, the electronic system category score obtained is 19 and is included in the high category. While the final evaluation result is "Good Enough" with a final score of 674 and the level of completeness of implementation based on the ISO 27001 standard is at levels II to IV.
在数字化转型时代，数据已成为有价值的东西，但也容易被泄露。根据《2022 年印度尼西亚信息安全报告》（Lanskap Keamanan SIber Indonesia 2022），印度尼西亚共发生了 311 起数据泄漏事件。许多工具可用于评估信息安全，信息安全指数（ISI）就是其中之一。KAMI 是根据 SNI ISO / IEC 27001 标准评估组织信息安全准备程度的工具。PT Kano Teknologi Utama 是一家在日常运营中使用信息技术的公司。因此，研究人员进行了一次信息技术安全评估，以确定信息安全状况，并对其进行评估，从而使安全状况变得更好。评估是通过对公司进行观察和访谈进行的。获得的数据将根据 OUR 指数进行评估。根据 "我们的指数 "评估结果，电子系统类别得分 19 分，属于高分类别。最终评估结果为 "足够好"，最终得分为 674 分，根据 ISO 27001 标准，实施的完整程度为 II 至 IV 级。
{"title":"Evaluasi Keamanan Teknologi Informasi Menggunakan Indeks Keamanan Informasi 5.0 dan ISO/EIC 27001:2022","authors":"Lucia Devlina Adventia Jelita, Moh. Noor Al Azam, A. Nugroho","doi":"10.33020/saintekom.v14i1.623","DOIUrl":"https://doi.org/10.33020/saintekom.v14i1.623","url":null,"abstract":"In the era of digital transformation, data has become something valuable but vulnerable to leaks. According to Lanskap Keamanan SIber Indonesia 2022, there were 311 data leakage incidents that occurred in Indonesia. Many tools can be used to evaluate information security, one of which is the Information Security Index (ISI). KAMI is a tool to assess the level of information security readiness of an organization based on the SNI ISO / IEC 27001 standard. PT Kano Teknologi Utama is a company that uses information technology in its daily operations. Therefore, researchers conducted an information technology security evaluation to determine the condition of information security and can evaluate it so that security becomes better. The evaluation is carried out by conducting observations and interviews at the company. The data that has been obtained will then be assessed according to OUR Index. Based on the results of the Our Index assessment, the electronic system category score obtained is 19 and is included in the high category. While the final evaluation result is \"Good Enough\" with a final score of 674 and the level of completeness of implementation based on the ISO 27001 standard is at levels II to IV.","PeriodicalId":359182,"journal":{"name":"Jurnal SAINTEKOM","volume":"24 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140359886","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 : 2024-03-31DOI: 10.33020/saintekom.v14i1.610
Mohammad Afdhal Jauhari, Bheta Agus Wardijono, Ega Hegarini
This research evaluates the cybersecurity maturity of a technology information company in Jakarta, using the CIS Controls framework that encompasses all controls within Implementation Group 1 (IG1). The company has not conducted formal measurements regarding cybersecurity maturity, leading to uncertainty about the effectiveness of security efforts. The aim of this study is to measure, assess, and provide recommendations to enhance cybersecurity within the company. The research methodology involves an assessment of CIS Controls implementation and maturity level measurements. The measurement results indicate a low level of maturity, with an overall score of 0.41. The company needs to make significant improvement efforts in the cybersecurity aspect. Recommendations derived from this analysis emphasize the need for policy enhancements, control improvements, and increased employee training, serving as a guide for the company to strengthen weak cybersecurity aspects. The company should adopt a sustainable approach with management commitment and active engagement of all stakeholders.
{"title":"Pengukuran Kematangan Keamanan Siber pada Perusahaan Teknologi Informasi dengan Framework Center for Internet Security Controls","authors":"Mohammad Afdhal Jauhari, Bheta Agus Wardijono, Ega Hegarini","doi":"10.33020/saintekom.v14i1.610","DOIUrl":"https://doi.org/10.33020/saintekom.v14i1.610","url":null,"abstract":"This research evaluates the cybersecurity maturity of a technology information company in Jakarta, using the CIS Controls framework that encompasses all controls within Implementation Group 1 (IG1). The company has not conducted formal measurements regarding cybersecurity maturity, leading to uncertainty about the effectiveness of security efforts. The aim of this study is to measure, assess, and provide recommendations to enhance cybersecurity within the company. The research methodology involves an assessment of CIS Controls implementation and maturity level measurements. The measurement results indicate a low level of maturity, with an overall score of 0.41. The company needs to make significant improvement efforts in the cybersecurity aspect. Recommendations derived from this analysis emphasize the need for policy enhancements, control improvements, and increased employee training, serving as a guide for the company to strengthen weak cybersecurity aspects. The company should adopt a sustainable approach with management commitment and active engagement of all stakeholders.","PeriodicalId":359182,"journal":{"name":"Jurnal SAINTEKOM","volume":"13 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140358861","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}
Terbuka University is a leading institution that implements the optimization of digital transformation, especially in distance learning systems. To improve the quality of service to students and stakeholders, Terbuka University has developed the Terbuka University Digital Learning Materials application. This application offers several learning modules that can be accessed through the Google Play Store. This research aims to classify data using different labels related to reviews of the Terbuka University Digital Learning Materials application using the Long Short-Term Memory classification algorithm. Evaluation is conducted to find accuracy, f1-score, precision, and recall values. The research results show that classification with Long Short-Term Memory achieves an accuracy of 76.72% with the Vader label, and the accuracy with the TextBlob label reaches 74.21%. Confusion matrix evaluation shows precision results of 0.91 and recall of 0.78, with an f1-score of 0.84 for the Vader label. For the TextBlob label, the precision is 0.96, recall is 0.45, and the f1-score is 0.61. This research contributes positively to understanding the evaluation and classification of reviews of the Terbuka University Digital Learning application. Implementing the Long Short-Term Memory algorithm with the Vader label can be an effective choice to improve service and learning quality through the application.
寺库卡大学是实施数字化转型优化的领先机构，尤其是在远程学习系统方面。为了提高对学生和利益相关者的服务质量，寺库卡大学开发了寺库卡大学数字学习材料应用程序。该应用程序提供多个学习模块，可通过 Google Play 商店访问。本研究旨在利用长短期记忆分类算法，使用与寺库卡大学数字学习材料应用程序评论相关的不同标签对数据进行分类。评估的目的是找出准确率、f1 分数、精确度和召回值。研究结果表明，使用长短期记忆分类法对 Vader 标签进行分类的准确率达到 76.72%，对 TextBlob 标签进行分类的准确率达到 74.21%。混淆矩阵评估结果显示，Vader 标签的精确度为 0.91，召回率为 0.78，f1 分数为 0.84。对于 TextBlob 标签，精确度为 0.96，召回率为 0.45，f1 分数为 0.61。这项研究对理解 Terbuka 大学数字学习应用程序的评论评估和分类做出了积极贡献。使用带有 Vader 标签的长短期记忆算法可以有效提高应用程序的服务和学习质量。
{"title":"Klasifikasi Sentimen Terhadap Kualitas Aplikasi Bahan Ajar Digital Akademik Universitas Terbuka di Google Play","authors":"Rhini Fatmasari, Windu Gata, Nia Kusuma Wardhani, Kurnia Prayogi, Modesta Binti Husna","doi":"10.33020/saintekom.v14i1.591","DOIUrl":"https://doi.org/10.33020/saintekom.v14i1.591","url":null,"abstract":"Terbuka University is a leading institution that implements the optimization of digital transformation, especially in distance learning systems. To improve the quality of service to students and stakeholders, Terbuka University has developed the Terbuka University Digital Learning Materials application. This application offers several learning modules that can be accessed through the Google Play Store. This research aims to classify data using different labels related to reviews of the Terbuka University Digital Learning Materials application using the Long Short-Term Memory classification algorithm. Evaluation is conducted to find accuracy, f1-score, precision, and recall values. The research results show that classification with Long Short-Term Memory achieves an accuracy of 76.72% with the Vader label, and the accuracy with the TextBlob label reaches 74.21%. Confusion matrix evaluation shows precision results of 0.91 and recall of 0.78, with an f1-score of 0.84 for the Vader label. For the TextBlob label, the precision is 0.96, recall is 0.45, and the f1-score is 0.61. This research contributes positively to understanding the evaluation and classification of reviews of the Terbuka University Digital Learning application. Implementing the Long Short-Term Memory algorithm with the Vader label can be an effective choice to improve service and learning quality through the application.","PeriodicalId":359182,"journal":{"name":"Jurnal SAINTEKOM","volume":"11 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140358981","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}
Klinik Gigi drg. Ema Medika so far still provides services to patients manually and keeps medical records using paper documents. This causes data invalidity and sometimes the confidentiality of patient data cannot be maintained properly. To increase efficiency, this research designed an application Prototype for Klinik Gigi drg. Ema Medika. The Design Thinking method is used as an approach in Prototype development, which consists of the stages of knowing user problems and needs, identifying user problems, determining solution ideas, creating a Prototype, and conducting testing. Through the designed Prototype, users can interact with the user interface and experience the user experience. Testing was carried out using User Acceptance Testing (UAT) and produced a value of 97.37. Thus, it can be concluded that the Prototype design of the Klinik Gigi drg. Ema Medika application can meet user needs very well. The expectation is that the outcomes of this study can play a role in the adoption of information technology solutions that are both effective and efficient within the dental clinic setting, leading to an enhancement in the level of service provided to patients.
{"title":"Perancangan User Interface dan User Experience Aplikasi Klinik Gigi Menggunakan Metode Design Thinking","authors":"Dhea Putri Salsabila, Risqy Siwi Pradini, Ahsanun Naseh Khudori","doi":"10.33020/saintekom.v14i1.601","DOIUrl":"https://doi.org/10.33020/saintekom.v14i1.601","url":null,"abstract":"Klinik Gigi drg. Ema Medika so far still provides services to patients manually and keeps medical records using paper documents. This causes data invalidity and sometimes the confidentiality of patient data cannot be maintained properly. To increase efficiency, this research designed an application Prototype for Klinik Gigi drg. Ema Medika. The Design Thinking method is used as an approach in Prototype development, which consists of the stages of knowing user problems and needs, identifying user problems, determining solution ideas, creating a Prototype, and conducting testing. Through the designed Prototype, users can interact with the user interface and experience the user experience. Testing was carried out using User Acceptance Testing (UAT) and produced a value of 97.37. Thus, it can be concluded that the Prototype design of the Klinik Gigi drg. Ema Medika application can meet user needs very well. The expectation is that the outcomes of this study can play a role in the adoption of information technology solutions that are both effective and efficient within the dental clinic setting, leading to an enhancement in the level of service provided to patients.","PeriodicalId":359182,"journal":{"name":"Jurnal SAINTEKOM","volume":"95 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140360304","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 : 2024-03-31DOI: 10.33020/saintekom.v14i1.526
Diana Nurfitriana, Taufik Ridwan, A. Voutama
In the current era of technological advancement and the internet, people can easily access various information. This technological advancement brings innovation in the mental health field, such as services in the form of apps. This research conducts sentiment analysis using the Naïve Bayes and Random Forest algorithms. The study aims to analyze Twitter users’ opinions regarding the Riliv apps and compare the results of classification using Naïve Bayes and Random Forest. This research methodology uses the AI Project Cycle method. The data used is tweet data from Twitter with the keyword 'aplikasi riliv’. The dataset consisted of 1035 data, which was processed to produce 273 positive, 273 neutral, and 39 negative sentiments data. The Naïve Bayes and Random Forest algorithms were applied to compare the classification results of the two. The most optimal classification results are Naïve Bayes with SMOTE with the division of 90% training data and 10% testing data, which results in an accuracy value of 82.72%, a value of precision is 82.89% and a value of recall is 82.72%. Based on the results of the distribution of sentiment data, most users gave positive reviews and were knowledgeable about the Riliv application, while only a few were disappointed
{"title":"Analisis Opini Terhadap Aplikasi Riliv di Twitter Menggunakan Algoritma Naïve Bayes dan Random Forest","authors":"Diana Nurfitriana, Taufik Ridwan, A. Voutama","doi":"10.33020/saintekom.v14i1.526","DOIUrl":"https://doi.org/10.33020/saintekom.v14i1.526","url":null,"abstract":"In the current era of technological advancement and the internet, people can easily access various information. This technological advancement brings innovation in the mental health field, such as services in the form of apps. This research conducts sentiment analysis using the Naïve Bayes and Random Forest algorithms. The study aims to analyze Twitter users’ opinions regarding the Riliv apps and compare the results of classification using Naïve Bayes and Random Forest. This research methodology uses the AI Project Cycle method. The data used is tweet data from Twitter with the keyword 'aplikasi riliv’. The dataset consisted of 1035 data, which was processed to produce 273 positive, 273 neutral, and 39 negative sentiments data. The Naïve Bayes and Random Forest algorithms were applied to compare the classification results of the two. The most optimal classification results are Naïve Bayes with SMOTE with the division of 90% training data and 10% testing data, which results in an accuracy value of 82.72%, a value of precision is 82.89% and a value of recall is 82.72%. Based on the results of the distribution of sentiment data, most users gave positive reviews and were knowledgeable about the Riliv application, while only a few were disappointed","PeriodicalId":359182,"journal":{"name":"Jurnal SAINTEKOM","volume":"11 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140360869","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 : 2023-03-31DOI: 10.33020/saintekom.v13i1.337
Zain Ahmad Taufik, S. Supriyanto
Academic Frequently Asked Questions (FAQ) service is a service designed to answer academic questions that are often asked by students. A survey conducted on April 12, 2022, to 33 students of the Ahmad Dahlan University Informatics study program showed that 39.4% of students rarely open the FAQ menu on the portal application. Students more often ask questions to administrators and lecturers, but 30.3% of students receive answers that last more than 10 minutes. Chatbot is an artificial intelligence system used to interact with users in real-time to provide information. The use of dialog flow in the system helps manage information and can provide an answer quickly and precisely to users. This research uses the waterfall method, which is a simple classic model with a linear flow system. The User Experience Questionnaire (UEQ) survey results get a score of 1,536 for attractiveness, 1,714 for clarity, 1,375 for efficiency, 1,357 for fixity, 1,536 for stimulation, and 0.964 for novelty. Based on these results, it shows that the application level is above average on the UEQ scale.
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Pub Date : 2023-03-31DOI: 10.33020/saintekom.v13i1.356
Lili Rusdiana, V. Hardita
K-Means algorithm as a method of grouping a set of data. The purpose of this study is to find out the use of the K-Means algorithm for outgoing mail data. The method used in this study focuses on the K-Means method. The grouping data used is 284 outgoing mail data at the STMIK Palangkaraya Informatics Engineering Study Program. Outgoing mail data is grouped into two groups, namely staffing and academic administration. Grouping based on letter number, status of recipient of letter, subject of letter. Steps in the K-Means algorithm result two iterations because there is a group movement that occurs, namely there is a movement from one group to another because the value of the object function changes. The iteration can be stopped in 2nd iteration because the object function change value is below the given threshold value, which is 0.1 and there is no group movement in the data used. Two iterations that have occurred, it shows a decrease in the value of changes in object function and data transfer in group locations.
{"title":"Algoritma K-Means dalam Pengelompokan Surat Keluar pada Program Studi Teknik Informatika STMIK Palangkaraya","authors":"Lili Rusdiana, V. Hardita","doi":"10.33020/saintekom.v13i1.356","DOIUrl":"https://doi.org/10.33020/saintekom.v13i1.356","url":null,"abstract":"K-Means algorithm as a method of grouping a set of data. The purpose of this study is to find out the use of the K-Means algorithm for outgoing mail data. The method used in this study focuses on the K-Means method. The grouping data used is 284 outgoing mail data at the STMIK Palangkaraya Informatics Engineering Study Program. Outgoing mail data is grouped into two groups, namely staffing and academic administration. Grouping based on letter number, status of recipient of letter, subject of letter. Steps in the K-Means algorithm result two iterations because there is a group movement that occurs, namely there is a movement from one group to another because the value of the object function changes. The iteration can be stopped in 2nd iteration because the object function change value is below the given threshold value, which is 0.1 and there is no group movement in the data used. Two iterations that have occurred, it shows a decrease in the value of changes in object function and data transfer in group locations.","PeriodicalId":359182,"journal":{"name":"Jurnal SAINTEKOM","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117142380","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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