Controlling the temperature and humidity of oyster mushroom cultivation is done manually by spraying air on the mushroom container so it takes a lot of time and effort. This is done to meet the requirements for growing oyster mushrooms which are strongly influenced by temperature and humidity conditions so that they can grow well. In this study, a device for controlling and monitoring the temperature and humidity of oyster mushroom cultivation was made automatically based on Arduino UNO. This tool can regulate and monitor the temperature and humidity in oyster mushroom cultivation automatically so that the temperature and humidity can be maintained without having to spend a lot of time and effort. The components used in building the automatic temperature and humidity controller for mushroom cultivation based on the Arduino UNO are the dht11 sensors, Arduino UNO, L298N driver, relay, and 16x2 I2C LCD. From the results of the tests that have been carried out, it can be concluded that the temperature and humidity control and monitoring device for automatic oyster mushroom cultivation based on Arduino UNO has been able to work well in regulating and monitoring temperature and humidity as expected.
{"title":"Controlling and Monitoring of Temperature and Humidity of Oyster Mushrooms in Tropical Climates","authors":"I. G. M. N. Desnanjaya, P. Sugiartawan","doi":"10.22146/ijeis.73346","DOIUrl":"https://doi.org/10.22146/ijeis.73346","url":null,"abstract":"Controlling the temperature and humidity of oyster mushroom cultivation is done manually by spraying air on the mushroom container so it takes a lot of time and effort. This is done to meet the requirements for growing oyster mushrooms which are strongly influenced by temperature and humidity conditions so that they can grow well. In this study, a device for controlling and monitoring the temperature and humidity of oyster mushroom cultivation was made automatically based on Arduino UNO. This tool can regulate and monitor the temperature and humidity in oyster mushroom cultivation automatically so that the temperature and humidity can be maintained without having to spend a lot of time and effort. The components used in building the automatic temperature and humidity controller for mushroom cultivation based on the Arduino UNO are the dht11 sensors, Arduino UNO, L298N driver, relay, and 16x2 I2C LCD. From the results of the tests that have been carried out, it can be concluded that the temperature and humidity control and monitoring device for automatic oyster mushroom cultivation based on Arduino UNO has been able to work well in regulating and monitoring temperature and humidity as expected.","PeriodicalId":31590,"journal":{"name":"IJEIS Indonesian Journal of Electronics and Instrumentation Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42844342","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}
Negara Indonesia merupakan salah satu negara yang memiliki potensi besar terhadap terjadinya gempa bumi. Bangunan yang merupakan salah satu infrastruktur yang sangat penting bagi kehidupan manusia, merupakan sasaran utama bagi bencana alam gempa bumi yang sering terjadi dan dapat menimbulkan kerusakan yang tidak terduga. Oleh karena itu, diperlukan sebuah sistem peringatan yang dapat mengukur dan mengamati getaran yang terjadi dengan besar tertentu untuk mengetahui tingkat kerentanan bangunan tersebut.Sistem ini menggunakan metode logika fuzzy Mamdani dengan proses defuzzyfikasi centroid. Logika fuzzy tersebut digunakan pada sistem peringatan untuk menentukan tingkat bahayanya. Masukan dari sistem terdiri dari nilai resonansi bangunan dan nilai simpangan bangunan. Masukan tersebut diperoleh dari pembacaan sensor IMU MPU6050. Proses defuzzyfikasi menghasilkan nilai keluaran crisp berupa rentang keputusan alarm. Data yang diolah dari pembacaan sensor ditampilkan dalam web server sebagai antarmuka. Berdasarkan hasil pengujian sistem peringatan tingkat kerentanan pada purwarupa bangunan yang telah dilakukan, akurasi logika fuzzy mencapai 95% dari 20 kali pengambilan data. Sistem peringatan yang dirancang dapat berjalan secara real time. Secara keseluruhan proses mulai dari pembacaan sensor hingga akuisisi data dapat berjalan dengan baik.
{"title":"Sistem Peringatan Tingkat Kerentanan Bangunan Berbasis Sensor IMU dengan Metode Fuzzy","authors":"Muhammad Fikri Ahsanandi, Lukman Awaludin","doi":"10.22146/ijeis.70141","DOIUrl":"https://doi.org/10.22146/ijeis.70141","url":null,"abstract":"Negara Indonesia merupakan salah satu negara yang memiliki potensi besar terhadap terjadinya gempa bumi. Bangunan yang merupakan salah satu infrastruktur yang sangat penting bagi kehidupan manusia, merupakan sasaran utama bagi bencana alam gempa bumi yang sering terjadi dan dapat menimbulkan kerusakan yang tidak terduga. Oleh karena itu, diperlukan sebuah sistem peringatan yang dapat mengukur dan mengamati getaran yang terjadi dengan besar tertentu untuk mengetahui tingkat kerentanan bangunan tersebut.Sistem ini menggunakan metode logika fuzzy Mamdani dengan proses defuzzyfikasi centroid. Logika fuzzy tersebut digunakan pada sistem peringatan untuk menentukan tingkat bahayanya. Masukan dari sistem terdiri dari nilai resonansi bangunan dan nilai simpangan bangunan. Masukan tersebut diperoleh dari pembacaan sensor IMU MPU6050. Proses defuzzyfikasi menghasilkan nilai keluaran crisp berupa rentang keputusan alarm. Data yang diolah dari pembacaan sensor ditampilkan dalam web server sebagai antarmuka. Berdasarkan hasil pengujian sistem peringatan tingkat kerentanan pada purwarupa bangunan yang telah dilakukan, akurasi logika fuzzy mencapai 95% dari 20 kali pengambilan data. Sistem peringatan yang dirancang dapat berjalan secara real time. Secara keseluruhan proses mulai dari pembacaan sensor hingga akuisisi data dapat berjalan dengan baik. ","PeriodicalId":31590,"journal":{"name":"IJEIS Indonesian Journal of Electronics and Instrumentation Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48628750","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 mean amplitude of glycemic excursions (MAGE) is an important indicator in the assessment of glycemic variability (GV) which is used as a reference for continuous blood glucose control. In this case, quantitative considerations in monitoring blood sugar in diabetes are very important for diagnosis and then proceed with clinical treatment. This study focuses more on strengthening the training and testing data processing system and reducing the independent variables that occur during the classification process. To support this purpose, this study uses Cross Validation as a training and testing data processing with the number of K-Fold is 10 and Naïve Bayes as a classification method. The resulting accuracy is 93% which is an increase from previous studies with an RMSE value (error value) of 0.267. It was concluded that patients in the pre-diabetic and diabetic groups tend to have more varied blood glucose values than patients from the normal class.
{"title":"Prediksi Diabetes Berdasarkan Pengukuran Mean Amplitude Glycemic Excursion (MAGE) Menggunakan Naïve Bayes","authors":"Lailis Syafa’ah, M. S. Ma'arif, Amrul Faruq","doi":"10.22146/ijeis.72608","DOIUrl":"https://doi.org/10.22146/ijeis.72608","url":null,"abstract":" The mean amplitude of glycemic excursions (MAGE) is an important indicator in the assessment of glycemic variability (GV) which is used as a reference for continuous blood glucose control. In this case, quantitative considerations in monitoring blood sugar in diabetes are very important for diagnosis and then proceed with clinical treatment. This study focuses more on strengthening the training and testing data processing system and reducing the independent variables that occur during the classification process. To support this purpose, this study uses Cross Validation as a training and testing data processing with the number of K-Fold is 10 and Naïve Bayes as a classification method. The resulting accuracy is 93% which is an increase from previous studies with an RMSE value (error value) of 0.267. It was concluded that patients in the pre-diabetic and diabetic groups tend to have more varied blood glucose values than patients from the normal class.","PeriodicalId":31590,"journal":{"name":"IJEIS Indonesian Journal of Electronics and Instrumentation Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43203603","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}
Security systems are one of the crucial topics in the era of digital transformation. In the use of digital technology, security systems are used to ensure the confidentiality, integrity, and availability of data. Machine learning techniques can be applied to support the system's adaptability to the environment, so that prevention, detection and recovery can be carried out. Given the importance of these things, it is necessary to review the literature to find out how machine learning is applied to security systems. This paper presents a summary of 31 research papers to determine what machine learning techniques or methods are the most promising for prevention, detection and recovery. The research stages in this paper consist of 6 stages, namely: formulating research questions, searching for articles, documenting search strategies, selecting studies, assessing article quality, and extracting data obtained from articles. Based on the results of the study, it was found that the K-means method was the most promising for prevention, while for detection, SVM could be used, and for security recovery, machine learning could be implemented using NLP-based features.
{"title":"Pembelajaran Mesin untuk Sistem Keamanan - Literatur Review","authors":"Nuruddin Wiranda, Fal Sadikin, Wanvy Arifha Saputra","doi":"10.22146/ijeis.69022","DOIUrl":"https://doi.org/10.22146/ijeis.69022","url":null,"abstract":"Security systems are one of the crucial topics in the era of digital transformation. In the use of digital technology, security systems are used to ensure the confidentiality, integrity, and availability of data. Machine learning techniques can be applied to support the system's adaptability to the environment, so that prevention, detection and recovery can be carried out. Given the importance of these things, it is necessary to review the literature to find out how machine learning is applied to security systems. This paper presents a summary of 31 research papers to determine what machine learning techniques or methods are the most promising for prevention, detection and recovery. The research stages in this paper consist of 6 stages, namely: formulating research questions, searching for articles, documenting search strategies, selecting studies, assessing article quality, and extracting data obtained from articles. Based on the results of the study, it was found that the K-means method was the most promising for prevention, while for detection, SVM could be used, and for security recovery, machine learning could be implemented using NLP-based features.","PeriodicalId":31590,"journal":{"name":"IJEIS Indonesian Journal of Electronics and Instrumentation Systems","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42234854","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}
Tea quality can be recognized through the aroma it produces. Tea classification using e-nose generally only detects aroma using a general gas sensor. However, redundancy of sensor features can cause a decreasing in the system performance. Therefore we need a system that can select features so the classification performance becomes optimal. A software system of feature selection was built to optimize classification performance. Input data for the system is e-nose sensor response to 3 black tea qualities. The features are sensors on the e-nose instrument. Feature selection is implemented using wrapper approach, ABC algorithm is used for feature selection, then the selected features are evaluated by SVM classification. The results of the ABC-SVM system are then compared with the SVM only system. The results showed that from 12 e-nose sensors, sensors that most characterized black tea quality were TGS 2600, TGS 813, TGS 825, TGS 2602, TGS 2611, TGS 832, TGS 2612, TGS 2620 and TGS 822. Meanwhile, MQ-7, TGS 826 and TGS 2610 sensors are redundant in the system because the gas detected by the 3 sensors can be represented by other sensors. With the reduction in features to 9, the classification accuracy performance increased by 16.7%.
{"title":"Seleksi Fitur dengan Artificial Bee Colony untuk Optimasi Klasifikasi Data Teh menggunakan Support Vector Machine","authors":"Suhaila Suhaila, Danang Lelono, Yunita Sari Sari","doi":"10.22146/ijeis.63902","DOIUrl":"https://doi.org/10.22146/ijeis.63902","url":null,"abstract":"Tea quality can be recognized through the aroma it produces. Tea classification using e-nose generally only detects aroma using a general gas sensor. However, redundancy of sensor features can cause a decreasing in the system performance. Therefore we need a system that can select features so the classification performance becomes optimal. A software system of feature selection was built to optimize classification performance. Input data for the system is e-nose sensor response to 3 black tea qualities. The features are sensors on the e-nose instrument. Feature selection is implemented using wrapper approach, ABC algorithm is used for feature selection, then the selected features are evaluated by SVM classification. The results of the ABC-SVM system are then compared with the SVM only system. The results showed that from 12 e-nose sensors, sensors that most characterized black tea quality were TGS 2600, TGS 813, TGS 825, TGS 2602, TGS 2611, TGS 832, TGS 2612, TGS 2620 and TGS 822. Meanwhile, MQ-7, TGS 826 and TGS 2610 sensors are redundant in the system because the gas detected by the 3 sensors can be represented by other sensors. With the reduction in features to 9, the classification accuracy performance increased by 16.7%.","PeriodicalId":31590,"journal":{"name":"IJEIS Indonesian Journal of Electronics and Instrumentation Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42468597","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}
Learning to pronounce hijaiah letters needs to be assessed objectively, so it is necessary to form digital audio resulting from the synthesis of Harmonic Plus Residual (HPR) modeling, which conducted with two pronunciation methods, taskin and tasydid. The experiment consists data acquisition, signal cutting, framing and windowing, detection of fundamental and harmonic frequencies, synthesis of HPR, to produce synthetic signals. The results of the synthetic signals then analyzed qualitatively by signal spectrogram analysis and scoring.From the experimental results, it can be concluded that this study was ultimately unable to determine the best HPR parameters, but concluded that the tasydid method was the best method for learning pronunciation and for the HPR model synthesis. This is because the tasydid method with different parameters but all of them can produce good synthetic signal, both in terms of comparative analysis of similar signal spectrograms and from the results of scoring with an average value of 10. On the other hand, the taskin method harf shows unsatisfactory results, where the synthetic sound is mostly just noise, so the scoring results is under 9, and is reinforced by the results of the spectrogram comparison between the original and synthetic signals which visually different.
{"title":"Pemodelan Harmonik untuk Pelafalan Makhraj Huruf Hijaiah","authors":"Muhammad Fadhlullah, Catur Atmaji","doi":"10.22146/ijeis.71664","DOIUrl":"https://doi.org/10.22146/ijeis.71664","url":null,"abstract":"Learning to pronounce hijaiah letters needs to be assessed objectively, so it is necessary to form digital audio resulting from the synthesis of Harmonic Plus Residual (HPR) modeling, which conducted with two pronunciation methods, taskin and tasydid. The experiment consists data acquisition, signal cutting, framing and windowing, detection of fundamental and harmonic frequencies, synthesis of HPR, to produce synthetic signals. The results of the synthetic signals then analyzed qualitatively by signal spectrogram analysis and scoring.From the experimental results, it can be concluded that this study was ultimately unable to determine the best HPR parameters, but concluded that the tasydid method was the best method for learning pronunciation and for the HPR model synthesis. This is because the tasydid method with different parameters but all of them can produce good synthetic signal, both in terms of comparative analysis of similar signal spectrograms and from the results of scoring with an average value of 10. On the other hand, the taskin method harf shows unsatisfactory results, where the synthetic sound is mostly just noise, so the scoring results is under 9, and is reinforced by the results of the spectrogram comparison between the original and synthetic signals which visually different.","PeriodicalId":31590,"journal":{"name":"IJEIS Indonesian Journal of Electronics and Instrumentation Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49259217","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}
Pembatasan jarak fisik merupakan salah satu cara yang diterapkan untuk mencegah penyebaran virus pada tempat umum. Pelaksanaan pembatasan jarak fisik tersebut memerlukan pengawasan agar berhasil sesuai harapan. Pengawasan yang dilakukan secara manual terutama pada tempat dengan tingkat keramaian tinggi kurang efektif karena memerlukan banyak petugas di lokasi yang justru akan menambah keramaian.Pada penelitian ini dikembangkan purwarupa sistem pengawasan pembatasan jarak fisik dengan memanfaatkan kamera CCTV dengan pemrosesan citra digital berbasis computer vision dan deep learning. Metode yang digunakan adalah kombinasi pendeteksian dan pelacakan pedestrian dengan YOLOv4 dan DeepSORT. Metode trigonometri digunakan dalam proses estimasi jarak untuk mendeteksi pelanggaran pembatasan jarak oleh pedestrian. Pada penelitian ini didapatkan hasil pengujian dengan nilai terbaik recall 0,86; precision 0,69 dan mean average precision (mAP) sebesar 0,83 dengan metode pelatihan transfer learning model YOLOv4 dengan maksimum batch 6000 menggunakan 473 data latih dan 119 data validasi. Keseluruhan sistem mencapai kecepatan rata-rata proses real-time yakni pada 24 sampai 26 FPS.
{"title":"Sistem Pengawasan Physical Distancing di Tempat Umum Menggunakan Kamera Berbasis Deep Learning","authors":"Rizqy Arya Dinata, Ika Candradewi, B. Prastowo","doi":"10.22146/ijeis.70886","DOIUrl":"https://doi.org/10.22146/ijeis.70886","url":null,"abstract":"Pembatasan jarak fisik merupakan salah satu cara yang diterapkan untuk mencegah penyebaran virus pada tempat umum. Pelaksanaan pembatasan jarak fisik tersebut memerlukan pengawasan agar berhasil sesuai harapan. Pengawasan yang dilakukan secara manual terutama pada tempat dengan tingkat keramaian tinggi kurang efektif karena memerlukan banyak petugas di lokasi yang justru akan menambah keramaian.Pada penelitian ini dikembangkan purwarupa sistem pengawasan pembatasan jarak fisik dengan memanfaatkan kamera CCTV dengan pemrosesan citra digital berbasis computer vision dan deep learning. Metode yang digunakan adalah kombinasi pendeteksian dan pelacakan pedestrian dengan YOLOv4 dan DeepSORT. Metode trigonometri digunakan dalam proses estimasi jarak untuk mendeteksi pelanggaran pembatasan jarak oleh pedestrian. Pada penelitian ini didapatkan hasil pengujian dengan nilai terbaik recall 0,86; precision 0,69 dan mean average precision (mAP) sebesar 0,83 dengan metode pelatihan transfer learning model YOLOv4 dengan maksimum batch 6000 menggunakan 473 data latih dan 119 data validasi. Keseluruhan sistem mencapai kecepatan rata-rata proses real-time yakni pada 24 sampai 26 FPS.","PeriodicalId":31590,"journal":{"name":"IJEIS Indonesian Journal of Electronics and Instrumentation Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49035828","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 lungs are an important organ in the human respiratory system, which functions to exchange carbon dioxide from the blood with oxygen in the air. Detection of respiratory disorders and lung disorders can be done in various ways; view medical records, physical examination, detection by x-ray and also auscultation of breathing. Digital signal processing can be used as a method to detect lung disorders based on the sound produced. In this study, lung sounds were classified into normal, crackle, wheeze, and crackle-wheeze classes using the Mel Frequency Cepstral Coefficient (MFCC) and Convolutional Neural Network (CNN) methods.Observations were made by varying the MFCC feature extraction using MFCC 8 and 13 coefficients, the number of frames are 50 and 60, and the width of the frames used was 0,1, 0,15 and 0,2 seconds. The result of feature extraction is then applied to the CNN classification system, and the confusion matrix is used to get the accuracy and precision values. The highest accuracy and precision values were obtained at 71,85% and 65,70% on the MFCC 13 coefficient with an average of 71,18%. Based on these results, the system that has been created can classify normal lung sounds, crackle, wheeze and crackle-wheeze quite well.
{"title":"Klasifikasi Suara Paru-Paru Berdasarkan Ciri MFCC","authors":"Dody Rafiqo, Yohanes Suyanto, Catur Atmaji","doi":"10.22146/ijeis.70813","DOIUrl":"https://doi.org/10.22146/ijeis.70813","url":null,"abstract":"The lungs are an important organ in the human respiratory system, which functions to exchange carbon dioxide from the blood with oxygen in the air. Detection of respiratory disorders and lung disorders can be done in various ways; view medical records, physical examination, detection by x-ray and also auscultation of breathing. Digital signal processing can be used as a method to detect lung disorders based on the sound produced. In this study, lung sounds were classified into normal, crackle, wheeze, and crackle-wheeze classes using the Mel Frequency Cepstral Coefficient (MFCC) and Convolutional Neural Network (CNN) methods.Observations were made by varying the MFCC feature extraction using MFCC 8 and 13 coefficients, the number of frames are 50 and 60, and the width of the frames used was 0,1, 0,15 and 0,2 seconds. The result of feature extraction is then applied to the CNN classification system, and the confusion matrix is used to get the accuracy and precision values. The highest accuracy and precision values were obtained at 71,85% and 65,70% on the MFCC 13 coefficient with an average of 71,18%. Based on these results, the system that has been created can classify normal lung sounds, crackle, wheeze and crackle-wheeze quite well.","PeriodicalId":31590,"journal":{"name":"IJEIS Indonesian Journal of Electronics and Instrumentation Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49225469","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}
A GAP analysis has been conducted on the evaluation of E-Learning systems of LMS UIKA Bogor. Five (5) subjects of discussion in this study, namely include Structured Learning Methods, Unstructured Learning Methods, Population and Samples, E-learning User Activity Record, Evaluation of Results. Process of evaluating the results of a calculation of the System Usability Scale (SUS). Judging from usability or usefulness the e-learning system is feasible. With the following details: a. Based on acceptability ranges, the e-learning falls into the accepted category, b. Based on the grade scale, included in grade C where the SUS score produced is 79, c. Based on adjective ratings, the value is between a score of 70-80 which means it falls into the range of good categories. The results of the usability evaluation of LMS UIKA Bogor products stated that overall, were acceptable or feasible.
{"title":"Analisis Gap Evaluasi Kualitas Sistem E-Learning di Universitas Ibn Khaldun Bogor","authors":"R. Ritzkal, R. Rachmawati","doi":"10.22146/ijeis.72631","DOIUrl":"https://doi.org/10.22146/ijeis.72631","url":null,"abstract":"A GAP analysis has been conducted on the evaluation of E-Learning systems of LMS UIKA Bogor. Five (5) subjects of discussion in this study, namely include Structured Learning Methods, Unstructured Learning Methods, Population and Samples, E-learning User Activity Record, Evaluation of Results. Process of evaluating the results of a calculation of the System Usability Scale (SUS). Judging from usability or usefulness the e-learning system is feasible. With the following details: a. Based on acceptability ranges, the e-learning falls into the accepted category, b. Based on the grade scale, included in grade C where the SUS score produced is 79, c. Based on adjective ratings, the value is between a score of 70-80 which means it falls into the range of good categories. The results of the usability evaluation of LMS UIKA Bogor products stated that overall, were acceptable or feasible.","PeriodicalId":31590,"journal":{"name":"IJEIS Indonesian Journal of Electronics and Instrumentation Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43710318","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}
Fluid flow rate measurement is important in industries, especially determining fluid flow rate. This process requires a good level of precision and accuracy because it refers to each volumetric's price or custody transfer processor. Many devices are used to measure flow rates, but from some devices, ultrasonic flowmeters are considered, which have more advantages than others. Ultrasonic flowmeters also have some problems, especially in installation, so this research aims to simulate the position of path configuration. The method refers to the weighting process of multi-path configuration and the simulation of track performance, which includes three-factor, hydrodynamic (H), orientation sensitivity (S) and orientation range (T). Each trajectory pattern is rotated 1ᴼ at each angle. In addition, there are also parameter functions that are used to image the profile. The test uses 7 path configurations, so an ideal form is obtained to be implemented. After multiplying weighting factors, the obtained value of hydrodynamic (H) for Area weighting method (1.002), the best value 1. Orientation sensitivity (S), with Area weighting method (0.019), the best result is 0. Meanwhile, with orientation range (T) 1%, with Area weighting method (163,2), the best value is 180.
{"title":"Penempatan Posisi Transduser Ultrasonik Pada Penampang Pipa untuk Pengukuran Laju Aliran Fluida","authors":"L. F. Wiranata, I. W. Ardana","doi":"10.22146/ijeis.74151","DOIUrl":"https://doi.org/10.22146/ijeis.74151","url":null,"abstract":"Fluid flow rate measurement is important in industries, especially determining fluid flow rate. This process requires a good level of precision and accuracy because it refers to each volumetric's price or custody transfer processor. Many devices are used to measure flow rates, but from some devices, ultrasonic flowmeters are considered, which have more advantages than others. Ultrasonic flowmeters also have some problems, especially in installation, so this research aims to simulate the position of path configuration. The method refers to the weighting process of multi-path configuration and the simulation of track performance, which includes three-factor, hydrodynamic (H), orientation sensitivity (S) and orientation range (T). Each trajectory pattern is rotated 1ᴼ at each angle. In addition, there are also parameter functions that are used to image the profile. The test uses 7 path configurations, so an ideal form is obtained to be implemented. After multiplying weighting factors, the obtained value of hydrodynamic (H) for Area weighting method (1.002), the best value 1. Orientation sensitivity (S), with Area weighting method (0.019), the best result is 0. Meanwhile, with orientation range (T) 1%, with Area weighting method (163,2), the best value is 180.","PeriodicalId":31590,"journal":{"name":"IJEIS Indonesian Journal of Electronics and Instrumentation Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44370056","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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