1 10 27 https://repository.horizon.ac.id/files/original/813a1f45f8649eca490473a5ac58f0bb.pdf 2b19baa5bed9e998aecc32a43ed26655 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.5 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 Navigating the digital shift: a service blueprint for coopetition technology-enabled networks Subject The topic of the resource Coopetition Networks SD logic Service blueprint Service science Description An account of the resource This study addresses the gap in applying traditional service blueprint methodologies to technology-enabled coopetition networks characterized by simultaneous collaboration and competition among actors. Leveraging service science insights, it proposes an enhanced service blueprint framework designed for the complexities of digital coopetition. This framework introduces the cyber. Frontstage lane, physical backstage lane, and support stage lane aim to provide a holistic view of interactions, value co-creation processes, and resource allocations. Empirical validation within the Portuguese stone sector-a key player in the national economy-demonstrates the framework’s effectiveness in identifying network dysfunctions and its ease of use by industry professionals. Feedback confirms its relevance in capturing today’s coopetition environments’ multifaceted engagements and digital nuances. The study emphasizes adapting service blueprint methodology to better manage and innovate service processes in digital ecosystems. Future research should extend this framework’s application across various sectors and explore the integration of emerging technologies to optimize service delivery and value co-creation. Creator An entity primarily responsible for making the resource Agostinho da Silva1,2, Antonio J. Marques Cardoso1 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 Jul 12, 2024 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 https://repository.horizon.ac.id/files/original/cc583db31b7c5479e4d5171293df90f3.pdf ee3e783fc5eb5fc4109b52d518dc3fe1 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.5 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 MDI and PI XGBoost regression-based methods: regional best pricing prediction for logistics services Subject The topic of the resource Explainable artificial intelligence Extreme gradient boosting Mean decrease in impurity Permutation importance Retail price prediction Description An account of the resource The logistics industry in Indonesia, with PT Pos Indonesia as the dominant player, is confronted with intense price competition. The challenge lies in establishing the most favorable price for regional logistics services in every region, with the aim of gaining a competitive edge and augmenting revenue. This intricate task encompasses local market conditions, competition, customer preferences, operational costs, and economic factors. To address this complexity, this study proposes the utilization of machine learning for price prediction. The price prediction model devised incorporates the extreme gradient boosting regression (XGBR), support vector machine (SVM), random forest, and logistics regression algorithms. This research contributes to the field by employing mean decrease in impurity (MDI) and permutation importance (PI) to elucidate how machine learning models facilitate optimal price predictions. The findings of this study can assist company management in enhancing their comprehension of how to make informed pricing decisions. The test results demonstrate values of 0.001, 0.005, 0.458, 0.009, and 0.9998. By employing machine learning techniques and explanatory models, PT Pos Indonesia can more accurately determine optimal prices in each region, bolster profits, and effectively compete in the expanding regional market. Creator An entity primarily responsible for making the resource Agus Purnomo1, Aji Gautama Putrada2, Roni Habibi3, Syafrianita4 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 May 26, 2024 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 Explainable artificial intelligence Extreme gradient boosting Mean decrease in impurity Permutation importance Retail price prediction https://repository.horizon.ac.id/files/original/361a80b737b1e0d203aec5c48636cbcb.pdf 694d30b0eb6aa6cbd6c7dc62fe9a24b6 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.5 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 Feature selection to improve distributed denial of service detection accuracy using hybrid N-Gram heuristic techniques Subject The topic of the resource Chi-square distance Distributed denial of service Malware N-Grams Payload Description An account of the resource Distributed denial of service (DDoS) attacks servers and computers in various ways, such as flooding traffic. There are three DDoS detection methods, namely anomaly-based, pattern-based and heuristic-based. However, pattern-based methods cannot detect recent attacks, while anomaly-based methods have low accuracy and relatively high false positives. This research proposes increasing accuracy using a heuristic-based DDoS detection method and a new feature. The combination of CSDPayload+N-Gram and CSPayload+N-Gram features is called hybrid N-Gram, which is analysed on four datasets: CIC2017, CIC2019, MIB-2016, and H2NPayload. Next, calculate Chi-square distance (CSD) and cosine similarity (CS) using the N-Gram frequency value results. Subsequently, compute Pearson Chi-square using the N-Gram frequency value results. Compare the CSDPayload+N-Gram and CSPayload+N-Gram, along with the Pearson Chi-square value, to classify it as either DDoS or not. Finally, feature selection based on weight correlation and payload classification employs machine learning algorithms: support vector machine (SVM), K-nearest neighbors (KNN), and neural network (NN). The average accuracy rate for detecting DDoS attacks across four datasets, utilising the CSDPayload+4-Gram and CSPayload+4-Gram features with the SVM algorithm, is 99.71%, which surpasses the accuracy achieved by using KNN (96.22%) and NNs (99.50%) imitation. Thus, the best algorithm for detecting DDoS is SVM with hybrid 4-Gram. Creator An entity primarily responsible for making the resource Andi Maslan1, Abdul Hamid2, Dedy Fitriawan3, Anggia Dasa Putri1, Tukino1 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 May 26, 2024 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 https://repository.horizon.ac.id/files/original/448676fa5ba2c0343052ab3c6318dedb.pdf 3a93fb22d44caa02014055712e312efc 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.5 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 The causal loop diagram model of traceability system rental equipment in oil and gas supporting companies Subject The topic of the resource Causal loop diagram Oil and gas support company Rental equipment System thinking Traceability Description An account of the resource Traceability in equipment rental systems enhances security, reliability, and operational transparency by providing the ability to accurately track leased equipment. Challenges in implementing traceability include difficulties in collecting accurate data, the absence of standardized recording practices, and the complexities of integrating technology to ensure complete tracking. This research aims to identify variables affecting the traceability system thinking to improve its efficiency in ongoing business processes. A qualitative descriptive approach is used to offer comprehensive insights into implementing traceability in equipment rental systems, focusing on oil and gas support companies. The study employs the causal loop diagram (CLD) method to dynamically map and identify traceability process variables. Findings show that traceability enables more precise tracking of equipment movement and usage, enhancing inventory management and streamlining maintenance. The CLD method reveals the dynamic relationships between system variables such as equipment availability, maintenance needs, and customer satisfaction, which guide continuous improvement. These results provide stakeholders with valuable insights for optimizing efficiency and service quality in equipment rental operations, particularly in oil and gas support companies. Enhanced traceability can significantly boost operational effectiveness and customer satisfaction. Creator An entity primarily responsible for making the resource Asep Endih Nurhidayat1,2, Rina Fitriana2,3,4, Didien Suhardini2,3,4, Asri Nugrahanti5 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 Jul 12, 2024 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 Causal loop diagram Oil and gas support company Rental equipment System thinking Traceability https://repository.horizon.ac.id/files/original/66bf0f5f5c81e159cf77fc893dec3775.pdf 0f6bd1d084ed6cf5d2f324b3b0b96465 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.5 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 Performance analysis of circular polarization X-band microstrip patch antenna combined with conical horn Subject The topic of the resource Circular polarization Conical horn Microstrip antenna Parabolic reflector Satellite X-band Description An account of the resource A ground station antenna system features a conical horn attached to a microstrip antenna as an alternate receiving feed antenna. The proposed microstrip antenna has circular polarization and operates in the X-band. The far-field parameters of the antenna are measured after developing and simulating the combination of a conical horn and a microstrip antenna. The modeling results for a microstrip antenna with a conical horn offer a working bandwidth of 930 MHz, spanning frequencies 7.44 GHz to 8.37 GHz. The axial ratio measurements for a microstrip antenna joined with a conical horn produce a working bandwidth of 150 MHz, covering frequencies ranging from 7.72 GHz to 7.87 GHz. The conical horn is attached to a microstrip antenna and has a gain of 15.1 dB. The proposed antenna is designed to meet ground station antenna requirements by pairing it with a parabolic reflector with a diameter of 3.7 meters. The primary focus antenna of the proposed design was chosen. The feed antenna and parabolic reflector should be positioned at a focus point distance of 1110 mm for a total gain of 38.9 dB at 7.8 GHz frequency. The proposed antenna can be applied on X-band remote sensing ground stations as a receiving antenna. Creator An entity primarily responsible for making the resource B. Pratiknyo Adi Mahatmanto1, Dedi Irawadi1, Hidayat Gunawan1, Nugroho Widi Jatmiko1, Dinari Nikken Sulastrie Sirin1, Supriyono1, Suhermanto1, Bambang Dewandaru2, Catur Apriono3 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 May 26, 2024 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 https://repository.horizon.ac.id/files/original/651f49c79c34a4e252b6f8bdb1c5e287.pdf 7d52c21b65906dc0f4a3db646db738ae 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.5 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 Analysis comparison, calibration, and application of low-cost soil moisture in smart agriculture based on internet of things Subject The topic of the resource Calibration Capacitive sensor Internet of things Resistive sensor Soil moisture Description An account of the resource The gravimetric method is one of the most accurate for determining soil water content (SWC). Several low-cost sensors have been developed to simplify measuring water content in soil by measuring soil moisture. However, the sensor must be calibrated to determine soil moisture parameters accurately. In this research, comparative analysis, and calibration of resistive and capacitive low-cost sensors were carried out. The calibration method for each sensor uses the gravimetric water content (GWC) and volumetric water content (VWC) methods. Measuring changes in SWC using sensors is performed in real time based on internet of things (IoT). Based on the measurements of the capacitive, resistive type 1, and resistive type 2 sensors with three repetitions, the linear regression R2 values were obtained at 0.980, 0.827, and 0.942, respectively. Furthermore, a stability test is carried out to see how stable the sensor is when making measurements over a long period. The result is that the capacitive, resistive type 1, and resistive type 2 sensors have errors 1.971×10-4, 7.001×10-4, and 6.270×10-4. Based on the results obtained, capacitive sensors have the highest level of accuracy and stability. Furthermore, capacitive sensors are applied to IoT-based agriculture with long range (LoRa) as communication data. Creator An entity primarily responsible for making the resource Beny Agustirandi1, Inayatul Inayah1,2, Nina Siti Aminah1, Maman Budiman1 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 May 26, 2024 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 Calibration Capacitive sensor Internet of things Resistive sensor Soil moisture https://repository.horizon.ac.id/files/original/89cd16c503b35f087c5f41c621233444.pdf 10cc90a655ad834366207c9a0c8c5291 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.5 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 Smart hydroponic agriculture using genetic algorithm based k-nearest neighbors Subject The topic of the resource Genetic algorithms Internet of things K-nearest neighbor Machine learning Raspberry Pi Description An account of the resource In this research, researcher has implemented supervised machine learning, namely k-nearest neighbor (k-NN) which is optimized using genetic algorithms, and the internet of things (IoT) on the nutrient film technique (NFT) hydroponic system. The aim of this research is to improve the accuracy of classification of nutrient and light conditions in NFT system, and evaluating the harvest of hydroponic farming. The dataset was obtained by observing and recording nutritional and light conditions using sensors for 35 days during the growing period of lettuce in the NFT system, thus obtaining 1,680 data. Then, a training dataset is created based on that dataset. The system architecture is divided into 3 parts, namely the sensor system, data processing, and actuator system. The conclusion of this research is the IoT can be used to monitor the nutritional and light conditions of NFT system in real time and automatic control actions can be carried out using actuators controlled by the Raspberry Pi, the impact of applying the k-NN algorithm and the genetic algorithms is the accuracy of classifying nutritional and light conditions is 92%, the lettuce in a NFT system controlled by the system grow better than the lettuce in a NFT system controlled manually. Creator An entity primarily responsible for making the resource Budi Sutrisno1, Nico Surantha1,2 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 May 26, 2024 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 Genetic algorithms Internet of things K-nearest neighbor Machine learning Raspberry Pi https://repository.horizon.ac.id/files/original/fa3008cc2df9681371d32f6f06c9e40a.pdf eaf175d874dfe2fc3e7834fa866fd64c 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.5 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 Video semantic segmentation with low latency Subject The topic of the resource Convolutional neural network Decision network FlowNet Latency Object detection SegNet Semantic segmentation Description An account of the resource Recent advances in computer vision and deep learning algorithms have yielded intriguing results. It can perform tasks previously requiring human eyes and brains. Semantic video segmentation for autonomous cars is difficult due to the high cost, low latency, and performance requirements of convolutional neural networks (CNNs). Deep learning architectures like SegNet and FlowNet 2.0 on the Cambridge-driving labeled video database (CamVid) dataset enable low-latency pixel-wise semantic segmentation of video features. Because it uses SegNet and FlowNet topologies, it is ideal for practical applications. The decision network chooses an optical flow or segmentation network for an image frame based on the expected confidence score. Combining this decision-making method with adaptive scheduling of the key frame approach can speed up the process. ResNet50 SegNet has a “54.27%” mean intersection over union (MIoU) and a “19.57” average FPS. In addition to decision network and adaptive key frame sequencing, FlowNet2.0 increased graphics processing unit (GPU) frame processing per second to “30.19” with a MIoU of “47.65%”. The GPU is used “47.65%” of the time. This performance gain illustrates that the video semantic segmentation network is faster without sacrificing quality. Creator An entity primarily responsible for making the resource Channappa Gowda D. V., Kanagavalli R. 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 Mar 26, 2024 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 Convolutional neural network Decision network FlowNet Latency Object detection SegNet Semantic segmentation https://repository.horizon.ac.id/files/original/b55be8f9f40a1204ee647340310b83a1.pdf 2b19baa5bed9e998aecc32a43ed26655 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.5 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 Augmented reality in customer experience: systematic review Subject The topic of the resource Attitude Augmented reality Consumer experience Customer experience Customer satisfaction Interactivity Systematic review Description An account of the resource Augmented reality (AR) is an emerging technology that offers the opportunity to explore a new way of shopping in the customer experience, showcasing its benefits, such as the superimposition of virtual elements in a physical environment or the high degree of interactivity provided by this technology. Despite its great potential to satisfy customer needs, the evaluation of the customer experience has not been fully studied. The main of this study is to identify the constructs that influence customer experience using the systematic review technique. A total of 88 studies published between 2016 and 2021, which relate to customer experience, were identified. Relevant information, such as the definitions of AR and customer experience, and the constructs that various authors use to assess customer experience, was extracted. The results of the review indicate that five fundamental constructs–attitude, interactivity, customer satisfaction, purchase intention, and hedonic value–are used to assess customer experience. These results contribute to a better understanding of the customer experience with AR. Creator An entity primarily responsible for making the resource Christian Cervantes1, Sussy Bayona-Oré2, Nicolás Pintado Torre1 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 May 26, 2024 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 Attitude Augmented reality Consumer experience Customer experience Customer satisfaction Interactivity Systematic review https://repository.horizon.ac.id/files/original/fcac93a3c41141d9a0e6fb344678c031.pdf 5c582d0a68652a872627b9131c9c8185 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.5 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 Class-G series audio power amplifier for subwoofer Subject The topic of the resource Audio power amplifier Class-AB Class-G Power efficiency Subwoofer Description An account of the resource An audio power amplifier is an electronic device used to amplify a small signal source power at the input into a large signal power at the output that is speaker. In general, audio power amplifiers use class-AB amplifiers. In the process of amplifying the signal power, there will be power losses in the amplifiers which results in relatively lower amplifier power efficiency because there is a difference between the supply and output voltage levels. In this paper a class-G series audio power amplifier for subwoofers is designed to minimize these power losses and increase the power efficiency of the amplifier. The designed amplifier voltage supply is 40 V with a maximum output power of 80 W at an 8 Ω load. The amplifier has frequency response from 20–200 Hz and gain twice. Realization of the power amplifier and measurements were carried out using the circuit maker simulator, and the measurement results of the class-G power amplifier were compared with the class-AB power amplifier. The measurement results show that both power amplifiers meet the design specifications. The proposed class-G amplifier has 0.14% larger total harmonic distortion (THD) but has 10.4% greater power efficiency advantages over the class-AB amplifier. Creator An entity primarily responsible for making the resource Class-G series audio power amplifier for subwoofer 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 May 26, 2024 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 Audio power amplifier Class-AB Class-G Power efficiency Subwoofer