Feby Amanda, S. Samsugi, Styawati Styawati, Syahirul Alim
In Indonesia there are often natural disasters, one of which is flooding. Flooding is a natural disaster that is marked by the overflowing of river water irrigation channels in urban areas, one is the river Irrigation that exists at the Technokrat University of Indonesia. Therefore, the study aims to develop a flood early detection tool using LoRa (Long Range) technology to monitor potential flooding in Kalibalau, Indonesian Technocratic University, Bandar Lampung. The research method involves installing an ultrasonic sensor in the Kalibalau River and connecting it to the Heltec Wifi LoRa 32 V2 microcontroller. Test results show that the LoRa transmitter and receiver operate as planned. This tool does not require an internet connection because it uses the Heltec Wifi LoRa 32 V2. The status of the river is categorized into four: Safe, Alert 1, Alert 2, and Danger, with appropriate warnings. The test showed a delay of 5 seconds on the water height reading. At safe (water height 44 cm), the buzzer does not sound. At morning 1 (water altitude 82 cm), it sounds once with a 1 minute delay. The device has a communication capacity of up to 400 meters. Thus, the tool is effective in monitoring the Kalibalau river and giving early warning of potential floods. This research has contributed to the development of flood monitoring technology to increase public alertness and safety in flood-prone areas
{"title":"Teknologi Deteksi Dini Banjir Daerah Aliran Sungai menggunakan Heltec Wifi LoRa 32 V2","authors":"Feby Amanda, S. Samsugi, Styawati Styawati, Syahirul Alim","doi":"10.30591/jpit.v9i1.5892","DOIUrl":"https://doi.org/10.30591/jpit.v9i1.5892","url":null,"abstract":"In Indonesia there are often natural disasters, one of which is flooding. Flooding is a natural disaster that is marked by the overflowing of river water irrigation channels in urban areas, one is the river Irrigation that exists at the Technokrat University of Indonesia. Therefore, the study aims to develop a flood early detection tool using LoRa (Long Range) technology to monitor potential flooding in Kalibalau, Indonesian Technocratic University, Bandar Lampung. The research method involves installing an ultrasonic sensor in the Kalibalau River and connecting it to the Heltec Wifi LoRa 32 V2 microcontroller. Test results show that the LoRa transmitter and receiver operate as planned. This tool does not require an internet connection because it uses the Heltec Wifi LoRa 32 V2. The status of the river is categorized into four: Safe, Alert 1, Alert 2, and Danger, with appropriate warnings. The test showed a delay of 5 seconds on the water height reading. At safe (water height 44 cm), the buzzer does not sound. At morning 1 (water altitude 82 cm), it sounds once with a 1 minute delay. The device has a communication capacity of up to 400 meters. Thus, the tool is effective in monitoring the Kalibalau river and giving early warning of potential floods. This research has contributed to the development of flood monitoring technology to increase public alertness and safety in flood-prone areas","PeriodicalId":503683,"journal":{"name":"Jurnal Informatika: Jurnal Pengembangan IT","volume":"21 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140504025","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}
Penyakit Hati atau liver merupakan penyakit yang menyerang organ hati pada manusia dimana organ hati berfungsi dalam pengelolaan kolesterol atau lemak pada tubuh. Dampak yang diberikan oleh penyakit liver ini berbeda-beda tergantung pada tingkat keparahan dan respons pengobatan yang dilakukan oleh individu. Oleh karena itu, pengembangan sistem prediksi penyakit liver menjadi relevan dan bermanfaat dalam membantu dokter dan tenaga medis untuk mengambil tindakan yang tepat secara lebih cepat. Untuk dapat mengembangkan sistem ini maka dapat dilakukan dengan menggunakan metode Artificial Neural Network (ANN). Tujuan dilakukan klasifikasi ini adalah untuk membantu mengetahui keakuratan model ANN dalam mengklasifikasi dataset penyakit liver. Menggunakan metode tersebut dataset dibagi menjadi 3 tahapan yaitu preprocessing data, pemrosesan data, dan evaluasi data. Preprocessing data dilakukan perbaikan terhadap dataset dan melakukan split data sehingga dihasilkan dataset baru. Pada pemrosesan data dilakukan penentuan hidden layer, model aktivasi, dan normalisasi pada model. Pada tahap terakhir yaitu evaluasi dataset, terdapat nilai akurasi, confusion matrix, dan classification report. Pada model ini didapatkan sebuah prediksi true negatif 70, true positif 14, false negatif 16, dan false positif 17. Dengan menggunakan model ini didapatkan hasil akurasi 71,79% yang menandakan bahwa model baik dalam melakukan klasifikasi pada dataset.
{"title":"Sistem Diagnosa Penyakit Liver Menggunakan Metode Artificial Neural Network: Studi Berdasarkan Dataset Indian Liver Patient Dataset","authors":"Ashri Shabrina Afrah","doi":"10.30591/jpit.v8i3.5346","DOIUrl":"https://doi.org/10.30591/jpit.v8i3.5346","url":null,"abstract":"Penyakit Hati atau liver merupakan penyakit yang menyerang organ hati pada manusia dimana organ hati berfungsi dalam pengelolaan kolesterol atau lemak pada tubuh. Dampak yang diberikan oleh penyakit liver ini berbeda-beda tergantung pada tingkat keparahan dan respons pengobatan yang dilakukan oleh individu. Oleh karena itu, pengembangan sistem prediksi penyakit liver menjadi relevan dan bermanfaat dalam membantu dokter dan tenaga medis untuk mengambil tindakan yang tepat secara lebih cepat. Untuk dapat mengembangkan sistem ini maka dapat dilakukan dengan menggunakan metode Artificial Neural Network (ANN). Tujuan dilakukan klasifikasi ini adalah untuk membantu mengetahui keakuratan model ANN dalam mengklasifikasi dataset penyakit liver. Menggunakan metode tersebut dataset dibagi menjadi 3 tahapan yaitu preprocessing data, pemrosesan data, dan evaluasi data. Preprocessing data dilakukan perbaikan terhadap dataset dan melakukan split data sehingga dihasilkan dataset baru. Pada pemrosesan data dilakukan penentuan hidden layer, model aktivasi, dan normalisasi pada model. Pada tahap terakhir yaitu evaluasi dataset, terdapat nilai akurasi, confusion matrix, dan classification report. Pada model ini didapatkan sebuah prediksi true negatif 70, true positif 14, false negatif 16, dan false positif 17. Dengan menggunakan model ini didapatkan hasil akurasi 71,79% yang menandakan bahwa model baik dalam melakukan klasifikasi pada dataset.","PeriodicalId":503683,"journal":{"name":"Jurnal Informatika: Jurnal Pengembangan IT","volume":"59 30","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139180362","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}
Artikel ini menyelaraskan dengan penyesuaian kerangka kerja model ketahanan siber dari NIST Cybersecurity Framework (NIST-CSF) dengan penambahan aspek kesadaran dan kewaspadaan yang terhubung melalui aspek resistensi untuk mencapai ketahanan siber bagi UMKM yang saat ini rentan terhadap berbagai serangan siber. Khususnya, dalam proses tranformasi digital bisnis proses usahanya. Metodologi penelitian ini bersifat analisis deskriptif berbasis kualitatif, guna mengeksplorasi hasil secara intuitif dengan struktur sistematik dalam merekonstruksi pandangan inovatif guna menjawab tantangan pengembangannya. Hasil dalam pembahasan kajian ini adalah rekonstruksi model keamanan siber yang merupakan sebuah tema besar dengan prinsip-prinsip strategis dalam upaya harmonisasi resistensi serangan dengan ketahan Siber. Hal ini dapat membantu organisasi seperti UMKM untuk mengidentifikasi, menilai, dan mengurangi ancaman dalam dunia siber secara komprehensif dan berkelanjutan.
{"title":"Penyesuaian Model Ketahanan Siber Umkm Di Indonesia Dengan Nist Cybersecurity Framework","authors":"Sabri Balafif","doi":"10.30591/jpit.v8i3.5662","DOIUrl":"https://doi.org/10.30591/jpit.v8i3.5662","url":null,"abstract":"Artikel ini menyelaraskan dengan penyesuaian kerangka kerja model ketahanan siber dari NIST Cybersecurity Framework (NIST-CSF) dengan penambahan aspek kesadaran dan kewaspadaan yang terhubung melalui aspek resistensi untuk mencapai ketahanan siber bagi UMKM yang saat ini rentan terhadap berbagai serangan siber. Khususnya, dalam proses tranformasi digital bisnis proses usahanya. Metodologi penelitian ini bersifat analisis deskriptif berbasis kualitatif, guna mengeksplorasi hasil secara intuitif dengan struktur sistematik dalam merekonstruksi pandangan inovatif guna menjawab tantangan pengembangannya. Hasil dalam pembahasan kajian ini adalah rekonstruksi model keamanan siber yang merupakan sebuah tema besar dengan prinsip-prinsip strategis dalam upaya harmonisasi resistensi serangan dengan ketahan Siber. Hal ini dapat membantu organisasi seperti UMKM untuk mengidentifikasi, menilai, dan mengurangi ancaman dalam dunia siber secara komprehensif dan berkelanjutan.","PeriodicalId":503683,"journal":{"name":"Jurnal Informatika: Jurnal Pengembangan IT","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139281501","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}
Tinara Nathania Wiryonoputro, Theresia Ratih Dewi Saputri
Pregnancy is a significant and transformative period for women, both physically and emotionally. During this time, it is crucial for expectant mothers to prioritize their own health and well-being to create a healthy environment for their growing baby. One of the physical changes that many breastfeeding mothers experience after childbirth is weight gain. Factors contributing to this include increased caloric needs, lack of sleep, reduced physical activity, and feelings of stress and fatigue due to caring for a newborn. Maintaining a healthy weight is vital to reduce the risk of various health issues and ensure the quality of breast milk for the baby. However, it is important to note that mothers should not engage in strict dieting during the postpartum period, or the puerperium, which lasts up to 40 days after delivery. During this time, mothers should gradually resume normal activities and movement. To support breastfeeding mothers in maintaining their health after childbirth, a structured and monitored approach that provides tailored information according to each stage of development is necessary. The Laav application, available for iOS, is designed to calculate and record the caloric intake of breastfeeding mothers, helping them achieve proper nutrition while maintaining an ideal weight. The application is built using the User-Centered Design (UCD) methodology and uses the Mifflin-St Jeor algorithm to calculate calories. The application is programmed in SwiftUI, a language optimized for the iOS platform
{"title":"Rancang Bangun Aplikasi Diet untuk Ibu Menyusui Pasca Persalinan dengan Algoritma Mifflin-St Jeor","authors":"Tinara Nathania Wiryonoputro, Theresia Ratih Dewi Saputri","doi":"10.30591/jpit.v8i3.5733","DOIUrl":"https://doi.org/10.30591/jpit.v8i3.5733","url":null,"abstract":"Pregnancy is a significant and transformative period for women, both physically and emotionally. During this time, it is crucial for expectant mothers to prioritize their own health and well-being to create a healthy environment for their growing baby. One of the physical changes that many breastfeeding mothers experience after childbirth is weight gain. Factors contributing to this include increased caloric needs, lack of sleep, reduced physical activity, and feelings of stress and fatigue due to caring for a newborn. Maintaining a healthy weight is vital to reduce the risk of various health issues and ensure the quality of breast milk for the baby. However, it is important to note that mothers should not engage in strict dieting during the postpartum period, or the puerperium, which lasts up to 40 days after delivery. During this time, mothers should gradually resume normal activities and movement. To support breastfeeding mothers in maintaining their health after childbirth, a structured and monitored approach that provides tailored information according to each stage of development is necessary. The Laav application, available for iOS, is designed to calculate and record the caloric intake of breastfeeding mothers, helping them achieve proper nutrition while maintaining an ideal weight. The application is built using the User-Centered Design (UCD) methodology and uses the Mifflin-St Jeor algorithm to calculate calories. The application is programmed in SwiftUI, a language optimized for the iOS platform","PeriodicalId":503683,"journal":{"name":"Jurnal Informatika: Jurnal Pengembangan IT","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139281435","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}
The rapid advancements in Narrowband IoT (NB-IoT) technology present significant opportunities for creating innovative products that can be implemented in daily life. One of these innovative products is the utilization of NB-IoT for monitoring cage conditions, maintenance, and boosting livestock productivity under challenging conditions that are difficult to manually control. This study aims to design an automated system capable of maintaining ideal cage conditions, including temperature, humidity, levels of ammonia (NH3 and CO2), as well as providing feed/water to livestock automatically and periodically. The research methodology involves the integration of various sensors mounted on a microcontroller, such as temperature sensors, humidity sensors, ammonia sensors, water level sensors, and pH sensors. The program executed by this microcontroller is connected to a control panel, and through the internet network, control and monitoring can be carried out using mobile and desktop devices. The test results indicate that this system is capable of providing ease in controlling the chicken coop for owners and workers, maintaining poultry health, and increasing livestock product yields from 97.17% of harvested poultry to 98.263%, with a decrease in the mortality rate from 2.830% to 1.737%. Overall, the utilization of NB-IoT technology in this research provides a positive impact on livestock management, offering an automated solution that enhances efficiency and productivity in the agricultural sector.
{"title":"Pemanfaatan Narrowband IoT (NB-IoT) dalam Peningkatan Produktivitas Peternakan melalui Monitoring Otomatis","authors":"Arif Rakhman, Achmad Sutanto, Rudi Hernowo","doi":"10.30591/jpit.v8i3.5824","DOIUrl":"https://doi.org/10.30591/jpit.v8i3.5824","url":null,"abstract":"The rapid advancements in Narrowband IoT (NB-IoT) technology present significant opportunities for creating innovative products that can be implemented in daily life. One of these innovative products is the utilization of NB-IoT for monitoring cage conditions, maintenance, and boosting livestock productivity under challenging conditions that are difficult to manually control. This study aims to design an automated system capable of maintaining ideal cage conditions, including temperature, humidity, levels of ammonia (NH3 and CO2), as well as providing feed/water to livestock automatically and periodically. The research methodology involves the integration of various sensors mounted on a microcontroller, such as temperature sensors, humidity sensors, ammonia sensors, water level sensors, and pH sensors. The program executed by this microcontroller is connected to a control panel, and through the internet network, control and monitoring can be carried out using mobile and desktop devices. The test results indicate that this system is capable of providing ease in controlling the chicken coop for owners and workers, maintaining poultry health, and increasing livestock product yields from 97.17% of harvested poultry to 98.263%, with a decrease in the mortality rate from 2.830% to 1.737%. Overall, the utilization of NB-IoT technology in this research provides a positive impact on livestock management, offering an automated solution that enhances efficiency and productivity in the agricultural sector.","PeriodicalId":503683,"journal":{"name":"Jurnal Informatika: Jurnal Pengembangan IT","volume":"111 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139335907","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}
In the era of business and economics that are interconnected with each other and competition between companies in seeking market share so that there will be an increase, especially in the number of customers, especially deposit customers, financial institutions and other companies are increasingly realizing the importance of understanding and identifying potential customers correctly to get potential customers. customers subscribe to deposits. Potential customer classification is a strategic approach that allows financial institutions to identify potential customers who have the potential to subscribe to deposits. With a deeper understanding of the characteristics and needs of potential customers, financial institutions can direct marketing resources more effectively, increase marketing efforts, and increase the conversion of potential customers to active customers. The aim of this research is to develop and test the Ensemble Least Square Support Vector Machine model with AdaBoost in classifying potential customers which can increase accuracy in identifying potential customers who have the potential to subscribe to deposits. The research results showed that this method achieved an accuracy of 95.15%, a sensitivity of 92.93%, and a specificity of 97.61%. In comparison with single Support Vector Machine and Least Squares Support Vector Machine models, the Ensemble Least Squares Support Vector Machine outperforms both in terms of accuracy.
{"title":"Klasifikasi Nasabah Potensial menggunakan Algoritma Ensemble Least Square Support Vector Machine dengan AdaBoost","authors":"Firman Aziz, Benny Leonard Enrico Panggabean","doi":"10.30591/jpit.v8i3.5675","DOIUrl":"https://doi.org/10.30591/jpit.v8i3.5675","url":null,"abstract":"In the era of business and economics that are interconnected with each other and competition between companies in seeking market share so that there will be an increase, especially in the number of customers, especially deposit customers, financial institutions and other companies are increasingly realizing the importance of understanding and identifying potential customers correctly to get potential customers. customers subscribe to deposits. Potential customer classification is a strategic approach that allows financial institutions to identify potential customers who have the potential to subscribe to deposits. With a deeper understanding of the characteristics and needs of potential customers, financial institutions can direct marketing resources more effectively, increase marketing efforts, and increase the conversion of potential customers to active customers. The aim of this research is to develop and test the Ensemble Least Square Support Vector Machine model with AdaBoost in classifying potential customers which can increase accuracy in identifying potential customers who have the potential to subscribe to deposits. The research results showed that this method achieved an accuracy of 95.15%, a sensitivity of 92.93%, and a specificity of 97.61%. In comparison with single Support Vector Machine and Least Squares Support Vector Machine models, the Ensemble Least Squares Support Vector Machine outperforms both in terms of accuracy.","PeriodicalId":503683,"journal":{"name":"Jurnal Informatika: Jurnal Pengembangan IT","volume":"110 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139338242","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}
Shallots are one of the most widely produced crops in Enrekang Regency. The obstacle in cultivation is the presence of disease in the plant which can reduce production yields. We can recognize this disease from the spots on the leaves because these spots have unique color and texture characteristics. The aim of this research is to determine the results of the classification of shallot plant diseases which focuses on purple spot and moler disease. The classification algorithms used are CNN and SVM with RBF, linear, sigmoid and polynomial kernels. The feature extraction method used is Gray Level Co-occurance Matrix (GLCM). The analysis was carried out using 320 datasets with 2 classes, namely, purple spot disease and moler disease, each class has 160 datasets. The test results show that the CNN and SVM methods with RBF, linear and polynomial kernels get accuracy, precision, recall and F1 scores of 100% respectively. Meanwhile, the SVM method on the sigmoid kernel using texture feature extraction with the GLCM method states that the accuracy value is 75%, precision 75%, recall 73% and F1-Score 74%. So these results state that the Sigmoid method using GLCM feature extraction has the lowest value among other methods
{"title":"Klasifikasi Penyakit Tanaman Bawang Merah Menggunakan Metode SVM dan CNN","authors":"Alya Zalvadila","doi":"10.30591/jpit.v8i3.5341","DOIUrl":"https://doi.org/10.30591/jpit.v8i3.5341","url":null,"abstract":"Shallots are one of the most widely produced crops in Enrekang Regency. The obstacle in cultivation is the presence of disease in the plant which can reduce production yields. We can recognize this disease from the spots on the leaves because these spots have unique color and texture characteristics. The aim of this research is to determine the results of the classification of shallot plant diseases which focuses on purple spot and moler disease. The classification algorithms used are CNN and SVM with RBF, linear, sigmoid and polynomial kernels. The feature extraction method used is Gray Level Co-occurance Matrix (GLCM). The analysis was carried out using 320 datasets with 2 classes, namely, purple spot disease and moler disease, each class has 160 datasets. The test results show that the CNN and SVM methods with RBF, linear and polynomial kernels get accuracy, precision, recall and F1 scores of 100% respectively. Meanwhile, the SVM method on the sigmoid kernel using texture feature extraction with the GLCM method states that the accuracy value is 75%, precision 75%, recall 73% and F1-Score 74%. So these results state that the Sigmoid method using GLCM feature extraction has the lowest value among other methods","PeriodicalId":503683,"journal":{"name":"Jurnal Informatika: Jurnal Pengembangan IT","volume":"100 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139338701","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}
The government's decision to discuss the RUU Omnibus law on health has become a controversial topic in society, especially among users of the Twitter social media platform. Users express their opinions regarding their stance on the RUU Omnibus law through tweets on Twitter. With diverse comments from users, it is essential to classify and visualize them into useful information about the positive and negative sentiments towards the RUU on health. This is crucial to understand the public's response to this policy. A total of 2406 sentiment data from Twitter users were collected using the RapidMiner software. Before analyzing the data using the K-Nearest Neighbors (KNN) algorithm, data preprocessing was carried out. After preprocessing, 2.406 data points were obtained, which were then divided into 1.684 tweets for testing and 722 tweets for training. The data was then processed using the KNN algorithm model executed in the RapidMiner software. The results of the data processing were presented in the form of tables, graphs, and word clouds, aligning with the research objective of providing clear and easily understandable visualizations about the RUU on health. This facilitates understanding for stakeholders without technical backgrounds to grasp the meaning and sentiments expressed. The research results indicate that the testing of K-Nearest Neighbors (KNN) yielded a high accuracy value, making it well-visualized at 84.58%. This indicates that the KNN model is highly successful in analyzing Twitter users' opinions on the Health Omnibus Law based on the data used and its ability to visualize effectively
{"title":"Visualisasi Data Analisa Sentimen RUU Omnibus Law Kesehatan Menggunakan KNN dengan Software RapidMiner","authors":"Tupari Tupari, Syaukani Abdullah, Chairani Chairani","doi":"10.30591/jpit.v8i3.5641","DOIUrl":"https://doi.org/10.30591/jpit.v8i3.5641","url":null,"abstract":"The government's decision to discuss the RUU Omnibus law on health has become a controversial topic in society, especially among users of the Twitter social media platform. Users express their opinions regarding their stance on the RUU Omnibus law through tweets on Twitter. With diverse comments from users, it is essential to classify and visualize them into useful information about the positive and negative sentiments towards the RUU on health. This is crucial to understand the public's response to this policy. A total of 2406 sentiment data from Twitter users were collected using the RapidMiner software. Before analyzing the data using the K-Nearest Neighbors (KNN) algorithm, data preprocessing was carried out. After preprocessing, 2.406 data points were obtained, which were then divided into 1.684 tweets for testing and 722 tweets for training. The data was then processed using the KNN algorithm model executed in the RapidMiner software. The results of the data processing were presented in the form of tables, graphs, and word clouds, aligning with the research objective of providing clear and easily understandable visualizations about the RUU on health. This facilitates understanding for stakeholders without technical backgrounds to grasp the meaning and sentiments expressed. The research results indicate that the testing of K-Nearest Neighbors (KNN) yielded a high accuracy value, making it well-visualized at 84.58%. This indicates that the KNN model is highly successful in analyzing Twitter users' opinions on the Health Omnibus Law based on the data used and its ability to visualize effectively","PeriodicalId":503683,"journal":{"name":"Jurnal Informatika: Jurnal Pengembangan IT","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139338938","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}
Indonesia's exchange rate can rise due to foreign tourist visits, which can also benefit the local economy. The provincial capital. South Sulawesi is Makassar which is one of the locations for tourist visits. There are 11 main tourist attractions in Prov. South Sulawesi according to sulselprov 1) Maritime Tourism, 2) Losari Beach, 3) Rotterdam Fort, 4) Somba opu Fort, 5) Takabonerate Marine Park, 6) Bantimurung National Park, 7) Malino, 8) Tanjung Bira Beach, 9) Kesu Tourism, 10) Londa Tourism, 11) Pallawa Tourism. The purpose of this study is to analyze the application of data mining in classifying the number of foreign tourists visiting the prefecture. South Sulawesi uses k-means. The data used comes from BPS Prov. South Sulawesi. The data is grouped into two clusters. That is, the most tourists as C1 with results from Malaysia, and low tourist arrivals as C0 with results from Singapore, Japan, South Korea, Taiwan, China, India, the Philippines, Hong Kong, Thailand, Australia, USA, UK, Netherlands, Germany, France, Russia, Saudi Arabia, Egypt, United Arab Emirates, Pearl of the Persian Gulf, and Switzerland then I use and process this data again with SVM to look for precision, precision and recall values and get 100.00% accuracy in the RapidMiner application.
{"title":"Penerapan Data Mining Dalam Mengelompokkan Kunjungan Wisatawan Mancanegara Di Prov. Sulawesi Selatan Dengan K-Means Dan SVM","authors":"Nero Caesar Gosari, Rismayani Rismayani","doi":"10.30591/jpit.v8i3.4554","DOIUrl":"https://doi.org/10.30591/jpit.v8i3.4554","url":null,"abstract":"Indonesia's exchange rate can rise due to foreign tourist visits, which can also benefit the local economy. The provincial capital. South Sulawesi is Makassar which is one of the locations for tourist visits. There are 11 main tourist attractions in Prov. South Sulawesi according to sulselprov 1) Maritime Tourism, 2) Losari Beach, 3) Rotterdam Fort, 4) Somba opu Fort, 5) Takabonerate Marine Park, 6) Bantimurung National Park, 7) Malino, 8) Tanjung Bira Beach, 9) Kesu Tourism, 10) Londa Tourism, 11) Pallawa Tourism. The purpose of this study is to analyze the application of data mining in classifying the number of foreign tourists visiting the prefecture. South Sulawesi uses k-means. The data used comes from BPS Prov. South Sulawesi. The data is grouped into two clusters. That is, the most tourists as C1 with results from Malaysia, and low tourist arrivals as C0 with results from Singapore, Japan, South Korea, Taiwan, China, India, the Philippines, Hong Kong, Thailand, Australia, USA, UK, Netherlands, Germany, France, Russia, Saudi Arabia, Egypt, United Arab Emirates, Pearl of the Persian Gulf, and Switzerland then I use and process this data again with SVM to look for precision, precision and recall values and get 100.00% accuracy in the RapidMiner application.","PeriodicalId":503683,"journal":{"name":"Jurnal Informatika: Jurnal Pengembangan IT","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139339330","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}
Rapid technological developments have resulted in various innovative techniques that help humans, including object detection which functions to identify each element in an image. Object detection is often used to overcome problems that occur because of its ability to identify each element in the image. One of the problems that is often encountered is a decrease in agricultural income due to disease in chili plants. The maintenance of chili plants has various obstacles including the impact of weather which causes the development of diseases and pests so that chili production has decreased. By implementing the object detection, farmers can easily identify diseases that attack chili plants through pictures so that chili disease can be treated more quickly. This study uses the YOLOv5 algorithm to test the performance of the model in identifying diseases in chili plants. Pictures were taken using a cellphone camera with dimensions of 3472x3472 pixels. The amount of image data used is 430 data. Image data is divided into 3 parts, namely train data, validation data, and test data. To get the best model, this study also conducted three experiments with different distribution of data. Experiment 1 with a division of 70:20:10, experiment 2 with a division of 75:15:10, and experiment 3 with a division of 80:10:10. From the experiments carried out, the best results were obtained, namely in experiment 3 with an average value obtained in the test of 0.947 with a translation of the precision, recall, and mAP values, namely 0.946, 0.936, and 0.959 respectively.
{"title":"Deteksi Penyakit Tanaman Cabai Menggunakan Algoritma YOLOv5 Dengan Variasi Pembagian Data","authors":"Laurenza Setiana Riva, Jayanta Jayanta","doi":"10.30591/jpit.v8i3.5679","DOIUrl":"https://doi.org/10.30591/jpit.v8i3.5679","url":null,"abstract":"Rapid technological developments have resulted in various innovative techniques that help humans, including object detection which functions to identify each element in an image. Object detection is often used to overcome problems that occur because of its ability to identify each element in the image. One of the problems that is often encountered is a decrease in agricultural income due to disease in chili plants. The maintenance of chili plants has various obstacles including the impact of weather which causes the development of diseases and pests so that chili production has decreased. By implementing the object detection, farmers can easily identify diseases that attack chili plants through pictures so that chili disease can be treated more quickly. This study uses the YOLOv5 algorithm to test the performance of the model in identifying diseases in chili plants. Pictures were taken using a cellphone camera with dimensions of 3472x3472 pixels. The amount of image data used is 430 data. Image data is divided into 3 parts, namely train data, validation data, and test data. To get the best model, this study also conducted three experiments with different distribution of data. Experiment 1 with a division of 70:20:10, experiment 2 with a division of 75:15:10, and experiment 3 with a division of 80:10:10. From the experiments carried out, the best results were obtained, namely in experiment 3 with an average value obtained in the test of 0.947 with a translation of the precision, recall, and mAP values, namely 0.946, 0.936, and 0.959 respectively.","PeriodicalId":503683,"journal":{"name":"Jurnal Informatika: Jurnal Pengembangan IT","volume":"36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139339350","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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