Articles
<![CDATA[Sentiment Analysis of Public Comments on Coldplay Concerts on Twitter Using the Naïve Bayes Method ]]>
<![CDATA[Dwisyahputra, Achmad Adbillah]]>;
<![CDATA[Kurniawan, Rakhmat]]>
<![CDATA[ Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024 ]]>
Publisher : <![CDATA[Information Technology and Science (ITScience) ]]>
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DOI: 10.47709/cnahpc.v6i3.4202
<![CDATA[Social media platform Twitter had become one of the most popular platforms for communication and information sharing. In the context of entertainment events such as music concerts, Twitter became a bustling place with various comments and opinions from the public regarding their experiences attending a concert. Many fans shared their experiences about Coldplay concerts on Twitter. These comments were highly varied and required a thorough understanding to interpret the overall public sentiment. Event organizers and Coldplay's band managers needed to understand public feelings about their concerts. This information was crucial for the evaluation and improvement of future events. Comments on Twitter were often brief and diverse, making manual data processing inefficient and necessitating automated tools to understand the sentiment within them. Sentiment analysis, or opinion mining, was the process used to understand, extract, and process text data automatically to gather information about the sentiment contained in opinion sentences. Research on sentiment analysis frequently focused on opinions that contained positive or negative sentiments. To classify these positive and negative sentiments, the Naive Bayes (NB) classification method was employed. The purpose of this study was to analyze the sentiment of public comments about Coldplay concerts on Twitter using the Naive Bayes method. The expected outcome was to provide insights into public sentiment towards Coldplay concerts, which would be valuable for event organizers and the band's managers in evaluating and improving future events.]]>
<![CDATA[Analysis Of Opinion Sentiment Towards Electric Vehicle Tax On Social Media X Using The Support Vector Machine (SVM) Method ]]>
<![CDATA[Jusli, Dara Taqa Assajidah]]>;
<![CDATA[Kurniawan, Rakhmat]]>
<![CDATA[ Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 4 (2024): Articles Research October 2024 ]]>
Publisher : <![CDATA[Information Technology and Science (ITScience) ]]>
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DOI: 10.47709/cnahpc.v6i4.4739
<![CDATA[Electric vehicle tax is increasingly becoming an important issue related to environmental and fiscal policies. Electric vehicles are considered an environmentally friendly solution to reduce greenhouse gas emissions and dependence on fossil fuels. However, public perception of electric vehicle tax is still mixed. This study aims to analyze public sentiment about electric vehicle tax based on data from social media platform X, using the Support Vector Machine (SVM) method. The data used was taken through a crawling technique with a total of 1,014 valid data. The data was then classified into positive and negative classes with a transformer. In this analysis, the data was divided with a ratio of 8:2 between training data and test data. 811 were used as training data and 203 as test data. The research stages involved data preprocessing, sentiment labeling, data separation into training and test data, and weighting using TF-IDF. After that, SVM was applied to classify tweets into positive and negative sentiments. The test results showed that the SVM algorithm had an accuracy of 79%, precision of 85%, recall of 89%, and F1-score of 87%. Based on the results of this study, some people feel unsure about the government's policy regarding electric vehicle tax, because it is considered unfair to the lower middle class. Electric vehicles are considered more expensive than fuel-powered vehicles, so this policy is considered unprofitable.]]>
<![CDATA[Measuring Water Content in Hydroponic Plants Based on PH Values and Nutriens Using Fuzzy Logic Microcontroller Based Tsukamoto ]]>
<![CDATA[Julianti, Miranda]]>;
<![CDATA[Rakhmat Kurniawan R]]>
<![CDATA[ Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 4 (2024): Articles Research October 2024 ]]>
Publisher : <![CDATA[Information Technology and Science (ITScience) ]]>
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DOI: 10.47709/cnahpc.v6i4.4764
<![CDATA[Hydroponic cultivation is a method of planting without soil by utilizing water containing nutrients and oxygen at certain levels. Regulation and monitoring of pH, nutrients (TDS), and water temperature are crucial factors in the success of a hydroponic system. Inaccuracies in nutrient water management can significantly affect plant growth. This study aims to design an automation system capable of monitoring pH and water nutrient levels using the Fuzzy Tsukamoto method based on the Nodemcu ESP32 microcontroller. The sensors used in this study are the MSP340 pH Module sensor to measure acidity (pH) and the Df Robot Module TDS sensor to detect nutrient levels in water. The Fuzzy Tsukamoto method is applied to make fuzzy logic-based decision-making, where the input values of pH and nutrients are converted into linguistic variables. The fuzzyfication process is carried out to determine the level of plant fertility, while the inference method is used to produce output based on previously set rules. This monitoring system also utilizes the Nutrient Film Technique (NFT) technique with a linear regression method to optimize the use of water pumps, making it more energy efficient. With the design of this system, hydroponic farmers can monitor water conditions automatically and in real-time, increasing efficiency and reducing human error in nutrient water management. The results of this study are expected to provide innovative solutions for the development of more efficient and sustainable hydroponic systems.]]>
<![CDATA[Analysis of Drug Sales Patterns in the Belawan Naval Hospital Pharmacy Using Apriori Algorithm ]]>
<![CDATA[Bahari, Mhd Raja Doly]]>;
<![CDATA[Kurniawan, Rakhmat]]>
<![CDATA[ Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 4 (2024): Articles Research October 2024 ]]>
Publisher : <![CDATA[Information Technology and Science (ITScience) ]]>
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DOI: 10.47709/cnahpc.v6i4.4805
<![CDATA[Hospital pharmacy plays an important role in ensuring drug availability and effective stock management. With the increasing number of drug redemptions, manual data management becomes inefficient and can lead to understocking or overstocking. Therefore, a method is needed that is able to automatically analyze drug sales patterns to improve stock management efficiency. One approach that can be used is the Apriori algorithm, an effective data mining technique for finding patterns in drug redemptions. This study aims to analyze drug redemption patterns at the Belawan Navy Hospital Pharmacy using the Apriori algorithm. The data used is drug redemption data. The Apriori algorithm is applied to find relationships between drug items that are often purchased together, so that it can provide useful insights in drug stock management. The results of the study showed that the Apriori algorithm successfully identified several significant drug redemption patterns. These patterns can be used to improve the efficiency of drug stock management and ensure timely drug availability, as well as reduce the risk of understocking or overstocking. The results of the study used logistic regression to predict discrete (binary) values from a column based on values from other columns and the accuracy obtained was 1.0 or 100%. This study concludes that the application of data mining with the Apriori algorithm can provide significant benefits in optimizing the management of drug stock redemption in hospital pharmacies.]]>
<![CDATA[Prediction of the Number of Patient Visits in a Psychiatric Hospital Prof. Dr. M. Ildrem Using Naive Bayesian Algorithm ]]>
<![CDATA[Syahputra, Zidhane]]>;
<![CDATA[Kurniawan, Rakhmat]]>
<![CDATA[ Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 1 (2025): Article Research January 2025 ]]>
Publisher : <![CDATA[Information Technology and Science (ITScience) ]]>
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DOI: 10.47709/cnahpc.v7i1.5145
<![CDATA[This study was conducted to predict the number of patient visits at Prof. Dr. M. Ildrem Mental Hospital using the Naive Bayes algorithm, which is relevant given the increasing need for global mental health care. The main problem of this study is the difficulty in managing hospital resources efficiently due to unpredictable fluctuations in the number of patient visits. The research aims to apply the Naive Bayes algorithm to predict the number of patient visits and evaluate their performance. The method used is a naïve Bayes algorithm with systematic steps including historical data collection, data preprocessing using LabelEncoder, and dividing the dataset into training data and test data (80:20) where the training data totals 1331 data and the test data has 333 data. The Naive Bayes model is built and tested with metrics such as accuracy, precision, recall, and F1-score. The results of the study based on confusion matrix analysis, the model achieved an accuracy of 0.8108108108108109 or 81%, a precision of 0.8206686930091185 or 82.07%, a recall value of 0.9926470588235294 or 99.26%, and an F1-score of 0.90 or 90%, which shows that this model is quite effective in predicting service units with the dominance of adolescent category patient data where it is concluded that this prediction model is able to provide accurate estimates of patient visits, supporting the management of hospital resources, and improving the operational efficiency of mental health services. This research is expected to help hospitals in planning facilities and workforce more effectively.]]>
<![CDATA[Sentiment Analysis of Public Comments on Coldplay Concerts on Twitter Using the Naïve Bayes Method ]]>
<![CDATA[Dwisyahputra, Achmad Adbillah]]>;
<![CDATA[Kurniawan, Rakhmat]]>
<![CDATA[ Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024 ]]>
Publisher : <![CDATA[Information Technology and Science (ITScience) ]]>
Show Abstract
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Download Original
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Original Source
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Check in Google Scholar
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DOI: 10.47709/cnahpc.v6i3.4202
<![CDATA[Social media platform Twitter had become one of the most popular platforms for communication and information sharing. In the context of entertainment events such as music concerts, Twitter became a bustling place with various comments and opinions from the public regarding their experiences attending a concert. Many fans shared their experiences about Coldplay concerts on Twitter. These comments were highly varied and required a thorough understanding to interpret the overall public sentiment. Event organizers and Coldplay's band managers needed to understand public feelings about their concerts. This information was crucial for the evaluation and improvement of future events. Comments on Twitter were often brief and diverse, making manual data processing inefficient and necessitating automated tools to understand the sentiment within them. Sentiment analysis, or opinion mining, was the process used to understand, extract, and process text data automatically to gather information about the sentiment contained in opinion sentences. Research on sentiment analysis frequently focused on opinions that contained positive or negative sentiments. To classify these positive and negative sentiments, the Naive Bayes (NB) classification method was employed. The purpose of this study was to analyze the sentiment of public comments about Coldplay concerts on Twitter using the Naive Bayes method. The expected outcome was to provide insights into public sentiment towards Coldplay concerts, which would be valuable for event organizers and the band's managers in evaluating and improving future events.]]>
<![CDATA[Analysis Of Opinion Sentiment Towards Electric Vehicle Tax On Social Media X Using The Support Vector Machine (SVM) Method ]]>
<![CDATA[Jusli, Dara Taqa Assajidah]]>;
<![CDATA[Kurniawan, Rakhmat]]>
<![CDATA[ Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 4 (2024): Articles Research October 2024 ]]>
Publisher : <![CDATA[Information Technology and Science (ITScience) ]]>
Show Abstract
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Download Original
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Original Source
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Check in Google Scholar
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DOI: 10.47709/cnahpc.v6i4.4739
<![CDATA[Electric vehicle tax is increasingly becoming an important issue related to environmental and fiscal policies. Electric vehicles are considered an environmentally friendly solution to reduce greenhouse gas emissions and dependence on fossil fuels. However, public perception of electric vehicle tax is still mixed. This study aims to analyze public sentiment about electric vehicle tax based on data from social media platform X, using the Support Vector Machine (SVM) method. The data used was taken through a crawling technique with a total of 1,014 valid data. The data was then classified into positive and negative classes with a transformer. In this analysis, the data was divided with a ratio of 8:2 between training data and test data. 811 were used as training data and 203 as test data. The research stages involved data preprocessing, sentiment labeling, data separation into training and test data, and weighting using TF-IDF. After that, SVM was applied to classify tweets into positive and negative sentiments. The test results showed that the SVM algorithm had an accuracy of 79%, precision of 85%, recall of 89%, and F1-score of 87%. Based on the results of this study, some people feel unsure about the government's policy regarding electric vehicle tax, because it is considered unfair to the lower middle class. Electric vehicles are considered more expensive than fuel-powered vehicles, so this policy is considered unprofitable.]]>
<![CDATA[Measuring Water Content in Hydroponic Plants Based on PH Values and Nutriens Using Fuzzy Logic Microcontroller Based Tsukamoto ]]>
<![CDATA[Julianti, Miranda]]>;
<![CDATA[Rakhmat Kurniawan R]]>
<![CDATA[ Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 4 (2024): Articles Research October 2024 ]]>
Publisher : <![CDATA[Information Technology and Science (ITScience) ]]>
Show Abstract
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Download Original
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Original Source
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Check in Google Scholar
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DOI: 10.47709/cnahpc.v6i4.4764
<![CDATA[Hydroponic cultivation is a method of planting without soil by utilizing water containing nutrients and oxygen at certain levels. Regulation and monitoring of pH, nutrients (TDS), and water temperature are crucial factors in the success of a hydroponic system. Inaccuracies in nutrient water management can significantly affect plant growth. This study aims to design an automation system capable of monitoring pH and water nutrient levels using the Fuzzy Tsukamoto method based on the Nodemcu ESP32 microcontroller. The sensors used in this study are the MSP340 pH Module sensor to measure acidity (pH) and the Df Robot Module TDS sensor to detect nutrient levels in water. The Fuzzy Tsukamoto method is applied to make fuzzy logic-based decision-making, where the input values of pH and nutrients are converted into linguistic variables. The fuzzyfication process is carried out to determine the level of plant fertility, while the inference method is used to produce output based on previously set rules. This monitoring system also utilizes the Nutrient Film Technique (NFT) technique with a linear regression method to optimize the use of water pumps, making it more energy efficient. With the design of this system, hydroponic farmers can monitor water conditions automatically and in real-time, increasing efficiency and reducing human error in nutrient water management. The results of this study are expected to provide innovative solutions for the development of more efficient and sustainable hydroponic systems.]]>
<![CDATA[Analysis of Drug Sales Patterns in the Belawan Naval Hospital Pharmacy Using Apriori Algorithm ]]>
<![CDATA[Bahari, Mhd Raja Doly]]>;
<![CDATA[Kurniawan, Rakhmat]]>
<![CDATA[ Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 4 (2024): Articles Research October 2024 ]]>
Publisher : <![CDATA[Information Technology and Science (ITScience) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
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DOI: 10.47709/cnahpc.v6i4.4805
<![CDATA[Hospital pharmacy plays an important role in ensuring drug availability and effective stock management. With the increasing number of drug redemptions, manual data management becomes inefficient and can lead to understocking or overstocking. Therefore, a method is needed that is able to automatically analyze drug sales patterns to improve stock management efficiency. One approach that can be used is the Apriori algorithm, an effective data mining technique for finding patterns in drug redemptions. This study aims to analyze drug redemption patterns at the Belawan Navy Hospital Pharmacy using the Apriori algorithm. The data used is drug redemption data. The Apriori algorithm is applied to find relationships between drug items that are often purchased together, so that it can provide useful insights in drug stock management. The results of the study showed that the Apriori algorithm successfully identified several significant drug redemption patterns. These patterns can be used to improve the efficiency of drug stock management and ensure timely drug availability, as well as reduce the risk of understocking or overstocking. The results of the study used logistic regression to predict discrete (binary) values from a column based on values from other columns and the accuracy obtained was 1.0 or 100%. This study concludes that the application of data mining with the Apriori algorithm can provide significant benefits in optimizing the management of drug stock redemption in hospital pharmacies.]]>
<![CDATA[Prediction of the Number of Patient Visits in a Psychiatric Hospital Prof. Dr. M. Ildrem Using Naive Bayesian Algorithm ]]>
<![CDATA[Syahputra, Zidhane]]>;
<![CDATA[Kurniawan, Rakhmat]]>
<![CDATA[ Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 1 (2025): Article Research January 2025 ]]>
Publisher : <![CDATA[Information Technology and Science (ITScience) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.47709/cnahpc.v7i1.5145
<![CDATA[This study was conducted to predict the number of patient visits at Prof. Dr. M. Ildrem Mental Hospital using the Naive Bayes algorithm, which is relevant given the increasing need for global mental health care. The main problem of this study is the difficulty in managing hospital resources efficiently due to unpredictable fluctuations in the number of patient visits. The research aims to apply the Naive Bayes algorithm to predict the number of patient visits and evaluate their performance. The method used is a naïve Bayes algorithm with systematic steps including historical data collection, data preprocessing using LabelEncoder, and dividing the dataset into training data and test data (80:20) where the training data totals 1331 data and the test data has 333 data. The Naive Bayes model is built and tested with metrics such as accuracy, precision, recall, and F1-score. The results of the study based on confusion matrix analysis, the model achieved an accuracy of 0.8108108108108109 or 81%, a precision of 0.8206686930091185 or 82.07%, a recall value of 0.9926470588235294 or 99.26%, and an F1-score of 0.90 or 90%, which shows that this model is quite effective in predicting service units with the dominance of adolescent category patient data where it is concluded that this prediction model is able to provide accurate estimates of patient visits, supporting the management of hospital resources, and improving the operational efficiency of mental health services. This research is expected to help hospitals in planning facilities and workforce more effectively.]]>
