3 10 25 https://repository.horizon.ac.id/files/original/942d0bf2433303e90df657403e4a5cb5.pdf 358deca40b095063de9bd081b155868f Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 22, NO.1 2024 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Automatic detection, report, and alert of faults in 220 volts electric power distribution parts Subject The topic of the resource Cable grid Distribution transformer Electric power grid Fault detection Global system for mobile modems Global system for mobile network Description An account of the resource The electrical power distribution parts include the distribution transformer responsible for reducing high voltage (HV) 11 Kv to low voltage (LV) 416/240 volts, and the power distribution lines (PDLs), which are the source of electrical power supply for residential neighborhoods. Any malfunction of these parts, result in a power outage. Currently, any faults are reported, detected, and alert in a primitive way, and this mechanism certainly takes a lot of time and effort. In this paper, an automatic approach for detection, report and alert has been suggested. This approach process is carried out by continuously checking the status of the three output feeders (power distribution lines), which is the low voltage side of the distribution transformer, when a malfunction occurs in any one or more of these feeders, it will generate an alert signal in the form of a text message using short message service (SMS). The maintenance center is notified by receiving this SMS via SIM900 modem through the global system for mobile (GSM) communications network. The SMS text message includes feeder number, fault type and location address (street number and distribution transformer number). In this design, time and effort are shortened and the maintenance center enables fully automatic monitoring of electric power distribution parts within the sector of responsibility. Creator An entity primarily responsible for making the resource Jabbar Shatti Jahlool1, Mohammed Abdulla Abdulsada2, Majid S. Naghmash1 Source A related resource from which the described resource is derived Journal homepage: http://telkomnika.uad.ac.id Date A point or period of time associated with an event in the lifecycle of the resource Nov 15, 2023 Contributor An entity responsible for making contributions to the resource peri irawan Format The file format, physical medium, or dimensions of the resource pdf Language A language of the resource english Type The nature or genre of the resource text Cable grid Distribution transformer Electric power grid Fault detection Global system for mobile modems Global system for mobile network https://repository.horizon.ac.id/files/original/b05b90bc46995be676e3fef7745510e0.pdf 041e36177486b60987ee470bc9ddd4a8 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 22, NO.1 2024 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Neural network with k-fold cross validation for oil palm fruit ripeness prediction Subject The topic of the resource Artificial neural network Hyperspectral images K-fold cross validation Oil palm fresh fruit bunch Ripeness prediction Description An account of the resource The combination of hyperspectral imaging and artificial neural network (ANN) can predict fruit ripeness. This work investigated the application of hyperspectral imaging and ANN models with the k-fold cross-validation method for ripeness prediction of oil palm fresh fruit bunches (FFB) for in-line sorting and grading machine vision. Crude palm oil (CPO) is an exporting commodity for countries such as Indonesia and Malaysia. Oil palm FFB ripeness determines the quality of CPO. The unique shapes and colors of FFBs need innovative methods to substitute tedious and cumbersome manual sorting and grading. The oil palm FFB samples used in this study were categorized previously based on color and loosed fruits. We applied the Savitzky-Golay (SG) smoothing filter and 7-fold cross-validation for hyperspectral datasets before being used for the ANN models and a confusion matrix to find the ANN model accuracies. We obtained 72 data points after SG filter and data selection from 523 data points. The prediction results showed an average accuracy of 79.48%, in which three folds with k of 2, 5, and 7 gave the highest accuracy of 90%. The results confirmed the potential use of hyperspectral imaging, with k-fold cross-validation and ANN models for ripeness prediction of oil palm FFBs. Creator An entity primarily responsible for making the resource Minarni Shiddiq1, Feri Candra2, Barri Anand2, Mohammad Fisal Rabin1 Source A related resource from which the described resource is derived Journal homepage: http://telkomnika.uad.ac.id Date A point or period of time associated with an event in the lifecycle of the resource Sep 27, 2023 Contributor An entity responsible for making contributions to the resource peri irawan Format The file format, physical medium, or dimensions of the resource pdf Language A language of the resource english Type The nature or genre of the resource text Artificial neural network Hyperspectral images K-fold cross validation Oil palm fresh fruit bunch Ripeness prediction https://repository.horizon.ac.id/files/original/baef6acb7d8705b66dc5a7dbc1702692.pdf 036797820d1657d7310a9e5d490240af Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 22, NO.1 2024 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Towards a simple and efficient vehicular delay tolerant networks routing protocol for data collection in smart cities Subject The topic of the resource Data collection Intelligent transportation systems Routing protocol Smart city Vehicle-to-everything Vehicular delay tolerant networks Description An account of the resource Smart cities today can utilize vehicular delay tolerant networks (VDTN) to collect data from connected-objects in the environment for various delay-tolerant applications. They can take advantage of the available intelligent transportation systems (ITS) infrastructures to deliver data to the central server. The system can also exploit multiple and diverse mobility patterns found in cities, such as privately owned cars, taxis, public buses, and trams, along with their vehicle-toeverything (V2X) communications capabilities. In the envisioned convergence between the ITS and V2X, we believe that a simple and efficient routing protocol can be deployed for the delay-tolerant data delivery, contrary to the implementation of optimized solutions that might be resource-demanding and difficult to standardize. In this paper, we analyzed the performances of four baseline VDTN routing protocols, namely: direct delivery, first contact, epidemic, and spray and wait, to understand their strengths and weaknesses. Our simulation results highlighted the trade-off between distinct approaches used by those protocols and pointed out some gaps that can be refined. This study provides new interesting ideas and arguments towards developing a simple, efficient, and high-performing routing protocol for data collection in smart cities. Creator An entity primarily responsible for making the resource Ngurah Indra Er1, Kamal Deep Singh2,3, Christophe Couturier4,5, Jean-Marie Bonnin4,5 Source A related resource from which the described resource is derived Journal homepage: http://journal.uad.ac.id/index.php/TELKOMNIKA Date A point or period of time associated with an event in the lifecycle of the resource Sep 26, 2023 Contributor An entity responsible for making contributions to the resource peri irawan Format The file format, physical medium, or dimensions of the resource pdf Language A language of the resource english Type The nature or genre of the resource text Data collection Intelligent transportation systems Routing protocol Smart city Vehicle-to-everything Vehicular delay tolerant networks https://repository.horizon.ac.id/files/original/b1bd26e9fce2adb100643f307c1a1318.pdf a04c2e1c3b2c4fd013881786ab5323d3 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 22, NO.1 2024 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Classification of Solo Batik patterns using deep learning convolutional neural networks algorithm Subject The topic of the resource Accuracy Batik Solo motif Convolutional neural networks Dropout Image processing Description An account of the resource The ideology of the Solo Batik pattern has not been conveyed to the public. In addition, a lot of people are unaware that batik contains particular patterns that are also used for particular activities. This study uses a convolutional neural network model to categorize 9 different Solo Batik patterns according to their use of elaborate geometric shapes, complicated symbols, patterns, dots, and natural designs. With 1 to 4 hidden layers, we aim to select the number of hidden layers that yields the highest accuracy. A 100×100 pixel image is used as the input. The feature extraction process then makes use of 3×3 feature maps from three convolution layers. The dropout regularization is then added, with settings ranging from 0.1 to 0.9. The Adam algorithm is also used in this model to perform optimization. The 3-layered convolutional neural networks (CNN) with a dropout value of 0.2, run in 20 epochs, produced accuracy results of 97.77%, which was the highest. Additionally, it can be inferred that applying a certain number of hidden layers and adding right dropout regularization values has an impact on raising the accuracy score. Creator An entity primarily responsible for making the resource Dimas Aryo Anggoro1, Assyati Amadjida Tamimi Marzuki1, Wiwit Supriyanti2 Source A related resource from which the described resource is derived Journal homepage: http://telkomnika.uad.ac.id Date A point or period of time associated with an event in the lifecycle of the resource Aug 15, 2023 Contributor An entity responsible for making contributions to the resource peri irawan Format The file format, physical medium, or dimensions of the resource pdf Language A language of the resource english Type The nature or genre of the resource text Accuracy Batik Solo motif Convolutional neural networks Dropout Image processing https://repository.horizon.ac.id/files/original/f86dec38182dd3faeba7959dfd1f8b4d.pdf 808f758f83a6e599d0b40ad2f327ae82 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 22, NO.1 2024 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Internet of things system for lime planting in Maha Sarakham community Subject The topic of the resource Internet of things Lime planting Maha Sarakham Smart farm Thailand Description An account of the resource The objectives of this research were: i) to study components of the internet of things (IoT) system to support community lime planting (CLP); in Maha Sarakham Province; ii) to develop IoT-CLP; and iii) to transfer IoT-CLP to the target community in Maha Sarakham Province. The research tools were: i) IoT-CLP system suitability assessment form; ii) IoT-CLP efficacy evaluation form; iii) a questionnaire to assess the effectiveness of the community lime planting; and iv) a survey of community satisfaction towards IoT-CLP. The research results showed that i) components of IoT-CLP consist of: (1.1) data receivers: soil moisture, temperature, soil pH, fertilizer/nutrient; (1.2) control units: water supply, water spraying, soil pH control, and processing, fertilizer/nutrients; (1.3) operating systems: water supply, water spraying, soil pH communication and distribution system of fertilizer/soil nutrients water; (1.4) mobile application; ii) the effectiveness of IoT-CLP was evaluated at the highest level; and iii) the results of lime cultivation using IoT-CLP showed a higher yield than the traditional practices in terms of the amount of lime, size, and weight. The community was satisfied with IoT-CLP at the highest level. Creator An entity primarily responsible for making the resource Tharach Arreerard1, Woraphapa Arreerard1, Naphattanon Thongpan1, Niraj Ruangsan2 Source A related resource from which the described resource is derived Journal homepage: http://telkomnika.uad.ac.id Date A point or period of time associated with an event in the lifecycle of the resource Aug 30, 2023 Contributor An entity responsible for making contributions to the resource peri irawan Format The file format, physical medium, or dimensions of the resource pdf Language A language of the resource english Type The nature or genre of the resource text Internet of things Lime planting Maha Sarakham Smart farm Thailand