Andreas Febrillianto Primawan, Yohannes Suyanto, Catur Atmaji
Gamelan is traditional Indonesian musical instruments that is often used in traditional events and parties.The community's need for the gamelan has actually increased, but unfortunately the price of gamelan is very expensive and the gamelan itself is difficult to move from one place to another place. Besides that, the limited tones that can be played by gamelan reduce the level of public interest in playing this instrument. Current technological developments make it possible to perform voice synthesis with several methods. One method that can be used is pitch shifting.This study aims to generate a synthetic saron sound based on plucking a guitar string. Analysis of the saron sound signal in the frequency domain is carried out to obtain the semitone values needed in the synthesis process. Synthetic saron signal generation is done by calling synthetic saron sounds that are stored in soundfont form, with reference data in the form of high and low pitch obtained from the guitar input pitch detection. Onset detection of guitar strokes is used as the initial trigger for calling out synthetic saron tones. The test was carried out by looking for similarities between the sound data of the original saron and synthetic saron using the cross-correlation method. The test results obtained a similarity accuracy rate of 91.6%. On the results of testing the guitar strum signal with the generation output, the average delay time for each strum is 0.152 seconds. From the results obtained, the system is classified as fast and accurate enough to be implemented in everyday life.
{"title":"Implementasi Sintesis Suara Saron Menggunakan Petikan Senar Gitar Dengan Metode Pitch Shifting","authors":"Andreas Febrillianto Primawan, Yohannes Suyanto, Catur Atmaji","doi":"10.22146/ijeis.80818","DOIUrl":"https://doi.org/10.22146/ijeis.80818","url":null,"abstract":" Gamelan is traditional Indonesian musical instruments that is often used in traditional events and parties.The community's need for the gamelan has actually increased, but unfortunately the price of gamelan is very expensive and the gamelan itself is difficult to move from one place to another place. Besides that, the limited tones that can be played by gamelan reduce the level of public interest in playing this instrument. Current technological developments make it possible to perform voice synthesis with several methods. One method that can be used is pitch shifting.This study aims to generate a synthetic saron sound based on plucking a guitar string. Analysis of the saron sound signal in the frequency domain is carried out to obtain the semitone values needed in the synthesis process. Synthetic saron signal generation is done by calling synthetic saron sounds that are stored in soundfont form, with reference data in the form of high and low pitch obtained from the guitar input pitch detection. Onset detection of guitar strokes is used as the initial trigger for calling out synthetic saron tones. The test was carried out by looking for similarities between the sound data of the original saron and synthetic saron using the cross-correlation method. The test results obtained a similarity accuracy rate of 91.6%. On the results of testing the guitar strum signal with the generation output, the average delay time for each strum is 0.152 seconds. From the results obtained, the system is classified as fast and accurate enough to be implemented in everyday life.","PeriodicalId":31590,"journal":{"name":"IJEIS Indonesian Journal of Electronics and Instrumentation Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49007616","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}
Floods are natural disasters where there is an excessive volume of water that dampens the land. If left unchecked, flooding can bring disease, cause environmental damage and hinder people's mobility. Therefore, we need a system that can provide early warning to the community before a flood occurs. As technology develops, we can monitor the water level in an area to anticipate flooding. This concept is called the Early Warning System (EWS). IoT can help with real-time and continuous monitoring and warning of floods. IoT can also monitor air levels remotely. The MQTT protocol research also uses water level and rainfall sensors as data for flood detection. This system will detect water level and rainfall in real time. If the water level and rainfall reach a certain limit, the system will provide a warning to local residents via chatbot. There are 3 types of alerts in this system. Monitoring results are also displayed via the dashboard and sensor readings will be stored in the cloud database. The evaluation results show that the designed system can work well in providing flood early warning. Bots on Telegram have also sent notifications with an average delay of 0.561 seconds.
{"title":"Monitoring Ketinggian Air dan Curah Hujan Dalam Early Warning System Bencana Banjir Berbasis IoT","authors":"Ivander Achmad Wandi, A. Ashari","doi":"10.22146/ijeis.83569","DOIUrl":"https://doi.org/10.22146/ijeis.83569","url":null,"abstract":" Floods are natural disasters where there is an excessive volume of water that dampens the land. If left unchecked, flooding can bring disease, cause environmental damage and hinder people's mobility. Therefore, we need a system that can provide early warning to the community before a flood occurs. As technology develops, we can monitor the water level in an area to anticipate flooding. This concept is called the Early Warning System (EWS). IoT can help with real-time and continuous monitoring and warning of floods. IoT can also monitor air levels remotely. The MQTT protocol research also uses water level and rainfall sensors as data for flood detection. This system will detect water level and rainfall in real time. If the water level and rainfall reach a certain limit, the system will provide a warning to local residents via chatbot. There are 3 types of alerts in this system. Monitoring results are also displayed via the dashboard and sensor readings will be stored in the cloud database. The evaluation results show that the designed system can work well in providing flood early warning. Bots on Telegram have also sent notifications with an average delay of 0.561 seconds.","PeriodicalId":31590,"journal":{"name":"IJEIS Indonesian Journal of Electronics and Instrumentation Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49264471","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}
Forest has an important role on earth. The need to monitor forest from illegal activities and the types of animals in there is needed to keep the forest in good condition. However, the condition of the vast forest and limited resource make direct forest monitoring by officer (human) is limited. In this case, sound with digital signal processing can be used as a tool for forest monitoring. In this study, a system was implemented to classify sound on the Raspberry Pi 3B+ using mel-spectrogram. Sounds that classified are the sound of chainsaw, gunshot, and the sound of 8 species of bird. This study also compared pretrained VGG-16 and MobileNetV3 as feature extractor, and several classification methods, namely Random Forest, SVM, KNN, and MLP. To vary and increase the number of training data, we used several types of data augmentation, namely add noise, time stretch, time shift, and pitch shift. Based on the result of this study, it was found that the MobileNetV3-Small + MLP model with combined training data from time stretch and time shift augmentation provide the best performance to be implemented in this system, with an inference duration of 0.8 seconds; 93.96% accuracy; and 94.1% precision.
{"title":"Klasifikasi Suara Untuk Memonitori Hutan Berbasis Convolutional Neural Network","authors":"Rizqi Fathin Fadhillah, R. Sumiharto","doi":"10.22146/ijeis.79536","DOIUrl":"https://doi.org/10.22146/ijeis.79536","url":null,"abstract":"Forest has an important role on earth. The need to monitor forest from illegal activities and the types of animals in there is needed to keep the forest in good condition. However, the condition of the vast forest and limited resource make direct forest monitoring by officer (human) is limited. In this case, sound with digital signal processing can be used as a tool for forest monitoring. In this study, a system was implemented to classify sound on the Raspberry Pi 3B+ using mel-spectrogram. Sounds that classified are the sound of chainsaw, gunshot, and the sound of 8 species of bird. This study also compared pretrained VGG-16 and MobileNetV3 as feature extractor, and several classification methods, namely Random Forest, SVM, KNN, and MLP. To vary and increase the number of training data, we used several types of data augmentation, namely add noise, time stretch, time shift, and pitch shift. Based on the result of this study, it was found that the MobileNetV3-Small + MLP model with combined training data from time stretch and time shift augmentation provide the best performance to be implemented in this system, with an inference duration of 0.8 seconds; 93.96% accuracy; and 94.1% precision.","PeriodicalId":31590,"journal":{"name":"IJEIS Indonesian Journal of Electronics and Instrumentation Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44460727","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}
Thalassemia blood disorder is a condition that can affect the blood's ability to function normally and can lead to erythropoiesis. In this blood disorder, there are nine types of abnormal erythrocytes, namely elliptocytes, pencils, teardrops, acanthocytes, stomatocytes, targets, spherocytes, hypochromic and normal. At present, thalassemia examination is carried out using Hb electrophoresis and is done manually so it will be subjective and take a long time. Therefore, this research implements the Convolutional Neural Network (CNN) and Multi-Layer Perceptron (MLP) algorithms. This study aims to determine the performance of convolution features as image feature extraction and MLP as an image classification method and then implemented on NVIDIA Jetson Nano. The convolution features used in this study apply the CNN VGG16 architecture. Then model learning is carried out on 7245 data by configuring hyperparameters. The best accuracy with the hyperparameter configuration is a batch that is 16, the epoch is 400, the learning rate is 0.0001, the dropout1 layer is 0.1 and the dropout2 layer is 0.1. From this configuration it produces optimal accuracy at 96.175%. In the following, the model that has been made is then implemented on the NVIDIA Jetson Nano as a mobile media to be applied to the medical world resulting in an average prediction speed for each class of 48.330 seconds. The obtained performance time and accuracy are suitable for use by medical personnel to predict the class of abnormal erythrocytes.
{"title":"Klasifikasi Eritrosit Pada Thalasemia Minor Menggunakan Fitur Konvolusi dan Multi-Layer Perceptron","authors":"Zuhrufun Nufusy Nugroho, Agus Harjoko, M. Auzan","doi":"10.22146/ijeis.83473","DOIUrl":"https://doi.org/10.22146/ijeis.83473","url":null,"abstract":" Thalassemia blood disorder is a condition that can affect the blood's ability to function normally and can lead to erythropoiesis. In this blood disorder, there are nine types of abnormal erythrocytes, namely elliptocytes, pencils, teardrops, acanthocytes, stomatocytes, targets, spherocytes, hypochromic and normal. At present, thalassemia examination is carried out using Hb electrophoresis and is done manually so it will be subjective and take a long time. Therefore, this research implements the Convolutional Neural Network (CNN) and Multi-Layer Perceptron (MLP) algorithms. This study aims to determine the performance of convolution features as image feature extraction and MLP as an image classification method and then implemented on NVIDIA Jetson Nano. The convolution features used in this study apply the CNN VGG16 architecture. Then model learning is carried out on 7245 data by configuring hyperparameters. The best accuracy with the hyperparameter configuration is a batch that is 16, the epoch is 400, the learning rate is 0.0001, the dropout1 layer is 0.1 and the dropout2 layer is 0.1. From this configuration it produces optimal accuracy at 96.175%. In the following, the model that has been made is then implemented on the NVIDIA Jetson Nano as a mobile media to be applied to the medical world resulting in an average prediction speed for each class of 48.330 seconds. The obtained performance time and accuracy are suitable for use by medical personnel to predict the class of abnormal erythrocytes.","PeriodicalId":31590,"journal":{"name":"IJEIS Indonesian Journal of Electronics and Instrumentation Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49651673","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}
Damar Buana Murti, Danang Lelono, Roghib Muhammad Hujja
Indoor Positioning System (IPS) is an object tracking technology that utilizes networks such as Wireless Fidelity (Wi-Fi) to determine the location of an object. IPS is closely related to the implementation of the Internet of Things (IoT) to carry out an order in a smart home. However, the weakness of IPS is the attenuation of the signal received when the tag or target moves to a room that borders another room, causing errors in tracking. The IPS implementation will be carried out based on the 2.4 GHz Wi-Fi signal emitted from the ESP32.The research will use the trilateration method which requires three sink nodes to receive signal strength, then a machine learning algorithm, namely Support Vector Machine (SVM), to classify rooms in three different scenarios, namely when the target is stationary, moving between rooms, and is on the edge room adjacent to another room.The results of the test show that the three scenarios provide different levels of accuracy. The accuracy of the system on the target scenario while still in the room reaches 100%, on the target moving room scenario reaches 86.15%, and on the target scenario that is at the edge of the room adjacent to another room reaches 80%.
{"title":"Rancang Bangun Sistem Deteksi Posisi Objek dalam Rumah dengan Metode Support Vector Machine Berdasar Kekuatan Sinyal Wi-Fi","authors":"Damar Buana Murti, Danang Lelono, Roghib Muhammad Hujja","doi":"10.22146/ijeis.80736","DOIUrl":"https://doi.org/10.22146/ijeis.80736","url":null,"abstract":" Indoor Positioning System (IPS) is an object tracking technology that utilizes networks such as Wireless Fidelity (Wi-Fi) to determine the location of an object. IPS is closely related to the implementation of the Internet of Things (IoT) to carry out an order in a smart home. However, the weakness of IPS is the attenuation of the signal received when the tag or target moves to a room that borders another room, causing errors in tracking. The IPS implementation will be carried out based on the 2.4 GHz Wi-Fi signal emitted from the ESP32.The research will use the trilateration method which requires three sink nodes to receive signal strength, then a machine learning algorithm, namely Support Vector Machine (SVM), to classify rooms in three different scenarios, namely when the target is stationary, moving between rooms, and is on the edge room adjacent to another room.The results of the test show that the three scenarios provide different levels of accuracy. The accuracy of the system on the target scenario while still in the room reaches 100%, on the target moving room scenario reaches 86.15%, and on the target scenario that is at the edge of the room adjacent to another room reaches 80%.","PeriodicalId":31590,"journal":{"name":"IJEIS Indonesian Journal of Electronics and Instrumentation Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45750733","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}
Gamelan consists of various kinds of instruments that have different characteristics. Each has characteristics in terms of the basic frequency, amplitude, signal envelope, and different ways of playing it, resulting in differences in the sustain power of the signal. These characteristics cause the problem of vanishing gradient in the Elman Network model which was used in previous studies in studying the onset detection in the Saron instrument signal which has an average interval of more than 0.6 seconds. This study uses BLSTM (Bidirectional Long Short Term Memory) as a model for training and Wavelet Packet Transformation to design a psychoacoustic critical bandwidth as a model for feature extraction. For the peak picking method, this study uses a fixed threshold method with a value of 0.25. The use of the BLSTM model supported by the Wavelet Packet Transform is expected to overcome the vanishing gradient that exists in a simple RNN architecture. The model was tested based on 3 evaluation parameters, namely precision, recall and F-Measure. Based on the test scenario carried out, the model can overcome the vanishing gradient problem on the Saron instrument which has an average interval between onset of 600 ms. Out of a total of 428 onsets on the Saron instrument, the model successfully detected 426 correctly, with 4 incorrectly detected onsets and 2 undetected onsets. A thorough evaluation for each of the precision, recall, and F1-Measure algorithms obtained 0.975, 0.945 and 0.960.
{"title":"Deteksi Onset Gamelan Bebasis DWPT dan BLSTM","authors":"Hisyam Mustofa, A. E. Putra","doi":"10.22146/ijeis.79534","DOIUrl":"https://doi.org/10.22146/ijeis.79534","url":null,"abstract":"Gamelan consists of various kinds of instruments that have different characteristics. Each has characteristics in terms of the basic frequency, amplitude, signal envelope, and different ways of playing it, resulting in differences in the sustain power of the signal. These characteristics cause the problem of vanishing gradient in the Elman Network model which was used in previous studies in studying the onset detection in the Saron instrument signal which has an average interval of more than 0.6 seconds. This study uses BLSTM (Bidirectional Long Short Term Memory) as a model for training and Wavelet Packet Transformation to design a psychoacoustic critical bandwidth as a model for feature extraction. For the peak picking method, this study uses a fixed threshold method with a value of 0.25. The use of the BLSTM model supported by the Wavelet Packet Transform is expected to overcome the vanishing gradient that exists in a simple RNN architecture. The model was tested based on 3 evaluation parameters, namely precision, recall and F-Measure. Based on the test scenario carried out, the model can overcome the vanishing gradient problem on the Saron instrument which has an average interval between onset of 600 ms. Out of a total of 428 onsets on the Saron instrument, the model successfully detected 426 correctly, with 4 incorrectly detected onsets and 2 undetected onsets. A thorough evaluation for each of the precision, recall, and F1-Measure algorithms obtained 0.975, 0.945 and 0.960.","PeriodicalId":31590,"journal":{"name":"IJEIS Indonesian Journal of Electronics and Instrumentation Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48341240","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}
Leonard Fidelcristo Supit, Tri Wahyu Supardi, T. Widodo
Data Acquisition Device (DAQ) is an electronic component used in formula student vehicles. To optimize the performance of the formula student vehicle and its driver, it is necessary to analyze and monitor the data acquisition system. Parameters acquired on the car include the position of the brake pedal/throttole and wheel speed.DAQ system has 5 input channels namely 3 analog input pins and 2 digital input pins, and 3 output channels, which is the controller pin, fault pin, and brake light pin. The DAQ system in this research is designed and made using Teensy 3.6, a signal conditioning circuit consisting of an RC low pass filter, voltage follower, non-inverting amplifier, and logic level shifter. DAQ system uses CANBUS to read and process sensor data. DAQ system can acquire data from the KTC Linear Motion Position sensor PZ-12-A-50P with an accuracy value of 99,91%; Hall-effect Rotary Position sensor RTY120LVNAX with an accuracy value of 99,94% for both the first and second sensors; and Proximity sensor LJ12A3-4-Z/BX with an accuracy value of 99,58% for the first sensor and 99,46% for the second sensor. DAQ is able to run controller signal processing, detect faults, and activate brake light signal according to FSAE rules.
{"title":"Perancangan dan Pembuatan Data Acquisition Device Sebagai Sistem Akuisisi Data untuk Kendali Mobil Formula Student","authors":"Leonard Fidelcristo Supit, Tri Wahyu Supardi, T. Widodo","doi":"10.22146/ijeis.83395","DOIUrl":"https://doi.org/10.22146/ijeis.83395","url":null,"abstract":"Data Acquisition Device (DAQ) is an electronic component used in formula student vehicles. To optimize the performance of the formula student vehicle and its driver, it is necessary to analyze and monitor the data acquisition system. Parameters acquired on the car include the position of the brake pedal/throttole and wheel speed.DAQ system has 5 input channels namely 3 analog input pins and 2 digital input pins, and 3 output channels, which is the controller pin, fault pin, and brake light pin. The DAQ system in this research is designed and made using Teensy 3.6, a signal conditioning circuit consisting of an RC low pass filter, voltage follower, non-inverting amplifier, and logic level shifter. DAQ system uses CANBUS to read and process sensor data. DAQ system can acquire data from the KTC Linear Motion Position sensor PZ-12-A-50P with an accuracy value of 99,91%; Hall-effect Rotary Position sensor RTY120LVNAX with an accuracy value of 99,94% for both the first and second sensors; and Proximity sensor LJ12A3-4-Z/BX with an accuracy value of 99,58% for the first sensor and 99,46% for the second sensor. DAQ is able to run controller signal processing, detect faults, and activate brake light signal according to FSAE rules.","PeriodicalId":31590,"journal":{"name":"IJEIS Indonesian Journal of Electronics and Instrumentation Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45931457","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}
Hidayat Eko Saputro, Nur Achmad Sulistyo Putro, S. Hartati, Ilona Usuman
The basic thing to consider when building a quadruped robot is the issue of balance. These factors greatly determine the success of the quadruped robot in carrying out movements such as stabilizing the body on an inclined plane, walking movements and others. Conventional feedback control methods by performing mathematical modeling can be used to balance the robot. However, this method still has weaknesses. The application of conventional feedback control methods often results in an inaccurate controller, so it must be manually tuned for its application. In this study, reinforcement learning methods were used using Q-Learning algorithms. The use of reinforcement learning methods was chosen because no mathematical calculations are needed to control the balance of quadruped robots. The process of learning the system to train the agent's abilities is carried out using a Gazebo simulator. The learning results show that the system could run well as evidenced by the higher value of sum rewards per episode.
{"title":"Implementasi Sistem Kendali Keseimbangan Statis Pada Robot Quadruped Menggunakan Reinforcement Learning","authors":"Hidayat Eko Saputro, Nur Achmad Sulistyo Putro, S. Hartati, Ilona Usuman","doi":"10.22146/ijeis.73865","DOIUrl":"https://doi.org/10.22146/ijeis.73865","url":null,"abstract":"The basic thing to consider when building a quadruped robot is the issue of balance. These factors greatly determine the success of the quadruped robot in carrying out movements such as stabilizing the body on an inclined plane, walking movements and others. Conventional feedback control methods by performing mathematical modeling can be used to balance the robot. However, this method still has weaknesses. The application of conventional feedback control methods often results in an inaccurate controller, so it must be manually tuned for its application. In this study, reinforcement learning methods were used using Q-Learning algorithms. The use of reinforcement learning methods was chosen because no mathematical calculations are needed to control the balance of quadruped robots. The process of learning the system to train the agent's abilities is carried out using a Gazebo simulator. The learning results show that the system could run well as evidenced by the higher value of sum rewards per episode.","PeriodicalId":31590,"journal":{"name":"IJEIS Indonesian Journal of Electronics and Instrumentation Systems","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68323149","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}
Theresia Prima Ari Setiyani, Adrian Kristoforus, Yashinta Maretyana
The process of packing food products manually raises many problems, including inefficiency, quality of hygiene, changing the composition according to market demand, and monitoring, that are difficult to do remotely in real-time. This research builds SCADA for filling and packing Multi-grain rice packages containing 2-3 kinds of grains.The composition of the grain be filled in the package through the HMI. The conveyor moves to carry the packing bags and stops at each grain container. The rotary vane feeder of the container will rotate so that the packaging bag will be filled with grains and then glued together. The position of the packaging bag is detected by 3 pairs of photosensors. The actuator control uses a Schneider M221 PLC via ethernet port. The communication system between the I/O devices and the PLC uses a cable, while the communication between the PLC and the HMI is via the internet network.The system is capable of filling the material according to the desired composition, sealing, and packing to the number of packages. HMI feeds composition input to the system, displays animations correctly, raises an alarm when the material in the tub is running low, and record production as a daily report.
{"title":"SCADA Sistem Pengisian dan Pengepakan Kemasan Multigrain Rice Berisi 2-3 Macam Biji-bijian","authors":"Theresia Prima Ari Setiyani, Adrian Kristoforus, Yashinta Maretyana","doi":"10.22146/ijeis.72708","DOIUrl":"https://doi.org/10.22146/ijeis.72708","url":null,"abstract":"The process of packing food products manually raises many problems, including inefficiency, quality of hygiene, changing the composition according to market demand, and monitoring, that are difficult to do remotely in real-time. This research builds SCADA for filling and packing Multi-grain rice packages containing 2-3 kinds of grains.The composition of the grain be filled in the package through the HMI. The conveyor moves to carry the packing bags and stops at each grain container. The rotary vane feeder of the container will rotate so that the packaging bag will be filled with grains and then glued together. The position of the packaging bag is detected by 3 pairs of photosensors. The actuator control uses a Schneider M221 PLC via ethernet port. The communication system between the I/O devices and the PLC uses a cable, while the communication between the PLC and the HMI is via the internet network.The system is capable of filling the material according to the desired composition, sealing, and packing to the number of packages. HMI feeds composition input to the system, displays animations correctly, raises an alarm when the material in the tub is running low, and record production as a daily report.","PeriodicalId":31590,"journal":{"name":"IJEIS Indonesian Journal of Electronics and Instrumentation Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43861822","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}
Insufficient use of fertilizers results in non-optimal plant growth, while excessive use is wasteful and results in pollution. Sensor usage is one method for measuring the nutrient content in the soil so that the fertilization process can be carried out precisely. Accuracy, precision, and error are commonly used in analyzing sensor quality. But for chemical measurements, the specificity also needs to be analyzed. This is because the soil contains various types of nutrients, so it is necessary to know whether the readings from the sensor only come from the desired nutrients or the others. In this writing, the elements measured were N, P, and K which are nutrients needed by plants in large quantities. The sensors were tested using compounds containing N, P, or/and K elements and NPK fertilizers with different NPK ratios. Results of this study prove that the sensor is not specific as the readings are known to use the electroconductivity method as proved by the regression results between the two variables which have R² ≈ 1 correlation. In addition, the sensor also produces readings for elements/compounds that should not be read and cannot distinguish the ratio of types of compound NPK fertilizer with different types.
{"title":"Analisis Spesifisitas Terhadap Sensor NPK","authors":"Ivan Lionel, Abd Ro’uf, Bakhtiar Alldino","doi":"10.22146/ijeis.79672","DOIUrl":"https://doi.org/10.22146/ijeis.79672","url":null,"abstract":"Insufficient use of fertilizers results in non-optimal plant growth, while excessive use is wasteful and results in pollution. Sensor usage is one method for measuring the nutrient content in the soil so that the fertilization process can be carried out precisely. Accuracy, precision, and error are commonly used in analyzing sensor quality. But for chemical measurements, the specificity also needs to be analyzed. This is because the soil contains various types of nutrients, so it is necessary to know whether the readings from the sensor only come from the desired nutrients or the others. In this writing, the elements measured were N, P, and K which are nutrients needed by plants in large quantities. The sensors were tested using compounds containing N, P, or/and K elements and NPK fertilizers with different NPK ratios. Results of this study prove that the sensor is not specific as the readings are known to use the electroconductivity method as proved by the regression results between the two variables which have R² ≈ 1 correlation. In addition, the sensor also produces readings for elements/compounds that should not be read and cannot distinguish the ratio of types of compound NPK fertilizer with different types.","PeriodicalId":31590,"journal":{"name":"IJEIS Indonesian Journal of Electronics and Instrumentation Systems","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42217767","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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