Geofence untuk Deteksi Virtual Perimeter pada Aset Daerah Irigasi

Irrigation Area is a region consisting of an irrigation network aimed to improve agricultural products' effectivity and efficiency. In every development and refinement phases of the irrigation network, monitoring and preservation proceeds for every irrigation asset is a must. Floodgates, natural rivers, primary canals, secondary canals, tertiary canals, and collector canals are the irrigation assets. One of the essential data for the asset irrigation surveyors while conducting assets monitor and maintain is the geolocation data. For each irrigation areas and their assets are in multiple areas with difficult to accessed and observed. Hence, the geolocation data for every irrigation area's border and the asset's location point obtained while neglecting accurate. Therefore, monitoring and maintaining asset also obstructed as inaccurate geolocation data. Geofence is a virtual perimeter technology constitute geolocation data point with radius in a circle on the digital map. This research proposed a virtual perimeter detection system for irrigation asset locations with geofence technology. The irrigation asset data we used are from Kubu Raya Regency, West Borneo Province. The system runs in the Android operating system as the surveyor could use the geolocation data directly on the field

Pemodelan dan Verifikasi Formal Protokol EE-OLSR dengan UPPAAL CORA

Information and Communication Technology systems is a most important part of society.  These systems are becoming more and more complex and are massively encroaching on daily life via the Internet and all kinds of embedded systems. Communication protocols are one of the ICT systems used by Internet users. OLSR protocol is a wireless network communication protocol with proactive, and based on link-state algorithm. EE-OLSR protocol is a variant of OLSR that is able to prolong the network lifetime without losses of performance.Protocol verification process generally be done by simulation and testing. However, these processes unable to verify there are no subtle error or design flaw in protocol. Model Checking is an algorithmic method runs in fully automatic to verify a system. UPPAAL is a model checker tool to model, verify, and simulate a system in Timed Automata.UPPAAL CORA is model checker tool to verify EE-OLSR protocol modelled in Linearly Priced Timed Automata, if the protocol satisfy the energy efficient property formulated by formal specification language in Weighted Computation Tree Logic syntax. Model Checking Technique to verify the protocols results in the protocol is satisfy the energy efficient property only when the packet transmission traffic happens

Pemodelan dan Verifikasi Formal Protokol EE-OLSR dengan UPPAAL CORA

Information and Communication Technology systems is a most important part of society.  These systems are becoming more and more complex and are massively encroaching on daily life via the Internet and all kinds of embedded systems. Communication protocols are one of the ICT systems used by Internet users. OLSR protocol is a wireless network communication protocol with proactive, and based on link-state algorithm. EE-OLSR protocol is a variant of OLSR that is able to prolong the network lifetime without losses of performance. Protocol verification process generally be done by simulation and testing. However, these processes unable to verify there are no subtle error or design flaw in protocol. Model Checking is an algorithmic method runs in fully automatic to verify a system. UPPAAL is a model checker tool to model, verify, and simulate a system in Timed Automata. UPPAAL CORA is model checker tool to verify EE-OLSR protocol modelled in Linearly Priced Timed Automata, if the protocol satisfy the energy efficient property formulated by formal specification language in Weighted Computation Tree Logic syntax. Model Checking Technique to verify the protocols results in the protocol is satisfy the energy efficient property only when the packet transmission traffic happens

Comparison of CNN Models With Transfer Learning in the Classification of Insect Pests

Insect pests are an important problem to overcome in agriculture. The purpose of this research is to classify insect pests with the IP-102 dataset using several CNN pre-trained models and choose which model is best for classifying insect pest data. The method used is the transfer learning method with a fine-tuning approach. Transfer learning was chosen because this technique can use the features and weights that have been obtained during the previous training process. Thus, computation time can be reduced and accuracy can be increased. The models used include Xception, MobileNetV3L, MobileNetV2, DenseNet-201, and InceptionV3. Fine-tuning and freeze layer techniques are also used to improve the quality of the resulting model, making it more accurate and better suited to the problem at hand. This study uses 75,222 image data with 102 classes. The results of this study are the DenseNet-201 model with fine-tuning produces an accuracy value of 70%, MobileNetV2 66%, MobileNetV3L 68%, InceptionV3 67%, Xception 69%. The conclusion of this study is that the transfer learning method with the fine-tuning approach produces the highest accuracy value of 70% in the DenseNet-201 model