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<![CDATA[Analisis Sajian E-Book Fisika SMA Berdasarkan Landasan Ilmu Pendidikan ]]>
<![CDATA[Ummah, Khairul]]>;
<![CDATA[Festiyed, Festiyed]]>;
<![CDATA[Asrizal, Asrizal]]>
<![CDATA[ Jurnal Penelitian Pembelajaran Fisika Vol 5, No 1 (2019) ]]>
Publisher : <![CDATA[Universitas Negeri Padang ]]>
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DOI: 10.24036/jppf.v5i1.107424
<![CDATA[The development of science and information technology has an impact on the emergence of various sources of learning. The development of learning resources will be better if it utilizes technology (digital form), so that it is more effective and efficient in use, for example, digital books (e-books). Currently, there are still many e-books in circulation without regard to the needs of students. The basic of education has described various components of the needs of students in learning. A good e-book is a learning resource that meets the basic components of educational science. For this reason, it is necessary to find out whether the e-books that are circulating are in accordance with the various needs of students in the components of the basic of educator science. The solution to this problem is to analyze the extent to which e-books meet the basic components of educational science. This type of research is a descriptive study with a qualitative approach. The population data in this study is a physics e-book for high school. The sample in this study were eight physics e-books used in learning physics in high school. The data in this study were taken using a research instrument that has 7 components which are broken down into 22 assessment indicators and data collection techniques used are through observation. The results of this study indicate that the average suitability obtained for each component of the educational science basic in physics e-book 1 has a value of 100% with a very complete category. In e-book physics 2 has a value of 57% with enough categories. In e-book physics 3 with a value of 71% with a complete category. In physics e-book 4 with a value of 86% with a very complete category. In physics e-book 5 with a value of 43% with a very sufficient category. In physics e-book 6 with a value of 57% with a very sufficient category. Finally, in physics e-book 7 and physics e-book, 8 with the same value of 100% with a very complete category meets the basic components of educational science]]>
<![CDATA[LAYANAN INFORMASI OLEH GURU BK UNTUK MENGETAHUI PERSEPSI SISWA TENTANG PENGINFORMASIAN HASIL TES INTELIGENSI ]]>
<![CDATA[Ummah, Khairul]]>;
<![CDATA[Ilyas, Asmidir]]>;
<![CDATA[Sukma, Dina]]>
<![CDATA[ Konselor Vol 2, No 1 (2013): KONSELOR ]]>
Publisher : <![CDATA[Universitas Negeri Padang ]]>
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DOI: 10.24036/02013211182-0-00
<![CDATA[BK teachers have a very big role in providing an understanding of the different characteristics of students, which includes intelligence. Therefore, researchers feel the need to conduct research on student's perceptions about information intelligence test results through information services by BK teacher in high school Adabiah Padang. This study aimed descriptive form to describe the perceptions of students about information intelligence test results through information services by BK teacher. The population of this study were all high school students of class XI Adabiah Padang. Data collection tool was a questionnaire that reveals student's perceptions about information intelligence test results through information services by BK teacher, then the collected data were analyzed by using percentages. The findings of the study revealed that student's perceptions about information intelligence test results through information services by BK teachers belong to the category quite well. Based on the findings of the study suggested, should be able to provide more intensive information on the results of intelligence test, especially to students who do not understand the benefits and follow-up of intelligence test results obtained.]]>
<![CDATA[Bird Detection System Design at The Airport Using Artificial Intelligence ]]>
<![CDATA[Ummah, Khairul]]>;
<![CDATA[Hidayat, Muhammad Fadly]]>;
<![CDATA[Kurniawan, Denni]]>;
<![CDATA[Zulhanif, Zulhanif]]>;
<![CDATA[Sembiring, Javensius]]>
<![CDATA[ International Journal of Aviation Science and Engineering - AVIA Vol. 4, No. 2 (December 2022) ]]>
Publisher : <![CDATA[FTMD Institut Teknologi Bandung ]]>
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DOI: 10.47355/avia.v4i2.72
<![CDATA[Bird strike is a process of crashing between bird and airplane which occurs in flight phase. Based on data, there are 40 times bird strike occurs every day (FAA, 2019). There are lot of research that already conducted to decrease number of birds at the airport. But it is not given significant changes. Hence, it is needed a model that can detect bird at the airport so that we can decrease the number of birds. Study already conducted by comparing motion detection with object detection and filter which can be used to improve detection quality. Model already developed using YOLOv4 object detection with 71.89% mean average precision. It is expected that object detection can be developed to become a bird repellent system in the future]]>
<![CDATA[Deep Learning Implementation on Aerial Flood Victim Detection System ]]>
<![CDATA[Ummah, Khairul]]>;
<![CDATA[Hidayat, M Thariq]]>;
<![CDATA[Risano, A Yudi Eka]]>
<![CDATA[ International Journal of Aviation Science and Engineering - AVIA Vol. 4, No. 2 (December 2022) ]]>
Publisher : <![CDATA[FTMD Institut Teknologi Bandung ]]>
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DOI: 10.47355/avia.v4i2.73
<![CDATA[Hydrometeorological hazard such as floods are considered as a regular natural disaster in Indonesia due to its frequent occurrence. To mitigate the risk, search and rescue operations need to be carried out immediately. The sheer magnitude of floods poses a major challenge for responders, and the emerging drone technology could help to alleviate the problem due to its deployment speed and coverage. Automation in drone technology has potential to improve its effectiveness. This paper explores the idea of human detection during floods using a computer vision approach. This approach utilizes a one stage detector model as detection speed is crucial in disaster management case. The dataset used for training consists of 200 labelled and negative images taken from drone point of view. This paper conducted 3 experiments to find out the difference in performance when the model was trained on flood and non-flood dataset, as well as the effect of image input size to the model’s performance. The first experiment was trained on non-flood dataset. The second experiment was trained on flood dataset, and the third experiment is the modified version of the second model. The results show that the model trained on flood dataset performed worse than non-flood counterparts with the non-flood mAP reached 90.80% while flood mAP reached 39.15%. In addition, the experiments also conclude that increasing the input size of image during training, will increase the detection performance of the model at the cost of FPS]]>
<![CDATA[Development of Anti-UAV System Using Visual Artificial Intelligence ]]>
<![CDATA[Sembiring, Javen]]>;
<![CDATA[Prianggoro, Dimas]]>;
<![CDATA[Saputra, Rizal Adi]]>;
<![CDATA[Tarkono, Tarkono]]>;
<![CDATA[Ummah, Khairul]]>
<![CDATA[ International Journal of Aviation Science and Engineering - AVIA Vol. 5 No. 1: (June 2023) ]]>
Publisher : <![CDATA[FTMD Institut Teknologi Bandung ]]>
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DOI: 10.47355/avia.v5i1.83
<![CDATA[Unmanned Aerial Vehicles (UAV) was first developed as a tool for military purposes. Due to the rapid growth in technology, UAVs are now used in various applications including civil needs. Of course, there are consequences for this where UAVs can be misused by irresponsible parties. One example is the use of UAVs in airport fields which can disrupt the airport operations and possibly become a serious threat towards security and safety of flights in the airport. This paper will discuss the artificial intelligence (AI) modeling to detect UAVs. This AI modeling is the first step in designing counter unmanned aerial system (C-UAS). UAV detection will use deep learning using YOLOv4 (single-stage detection) for optimal detection speed and accuracy. There are a total of 500 image data processed and used in two AI modeling experiments in this study. Gaussian blur filter is used to create dataset variations so that the training can be processed more efficiently and the model can detect better. The results shows that the training dataset that has been processed with gaussian blur (filtered dataset) increases the AI model’s detection performance in rainy and clear conditions. Therefore, the model trained using filtered datasets is more suitable for use in detecting UAV objects in anti-UAV systems.]]>
<![CDATA[Aircraft Detection in Low Visibility Condition Using Artificial Intelligence ]]>
<![CDATA[Ummah, Khairul]]>;
<![CDATA[Widyosekti, M. Dhiku]]>;
<![CDATA[Arif, Yanuar Zulardiansyah]]>;
<![CDATA[Saputra, Rizal Adi]]>;
<![CDATA[Riszal, Akhmad]]>;
<![CDATA[Sembiring, Javensius]]>
<![CDATA[ International Journal of Aviation Science and Engineering - AVIA Vol. 5 No. 1: (June 2023) ]]>
Publisher : <![CDATA[FTMD Institut Teknologi Bandung ]]>
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DOI: 10.47355/avia.v5i1.84
<![CDATA[Bad weather often interferes with the functioning of the air transport system. One example is the frequent flight delays for commercial aircraft, resulting in losses for both the airline and passengers. Artificial Intelligence (AI) technology can now minimize delays caused by bad weather, especially in low visibility conditions. This paper discusses AI modeling that can detect aircraft in a low visibility weather condition, especially in the airport area. The employed method is the deep learning approach with the YOLOv4 algorithm (single-stage detection), which is regarded as one of the optimal platforms in this field. There are 600 images used in this work to create and train three different models. Image Dehazing filter is employed on the training data before it is trained to produce the detection model. The result shows that the model has a good performance in terms of performance metrices. Thus, this model is suitable to be used to detect aircraft in low visibility conditions.]]>
<![CDATA[Clustering and BiLSTM Network for Aircraft Trajectory Prediction Model ]]>
<![CDATA[Sembiring, Javen]]>;
<![CDATA[Fauzan, M Ariq]]>;
<![CDATA[Ummah, Khairul]]>;
<![CDATA[Hamdani, Fadil]]>;
<![CDATA[Djuansjah, Joy R P]]>
<![CDATA[ International Journal of Aviation Science and Engineering - AVIA Vol. 5 No. 2: (December,2023) ]]>
Publisher : <![CDATA[FTMD Institut Teknologi Bandung ]]>
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DOI: 10.47355/avia.v5i2.89
<![CDATA[The increasing demand for air travel requires the development of more accurate aircraft trajectory prediction methods to optimize airspace utilization and enhance safety. This paper presents a hybrid approach for single-flight-route trajectory prediction that employs the K-means clustering and Bidirectional Long Short-Term Memory (BiLSTM) networks. The primary objective is to develop a deep learning model that effectively predicts aircraft trajectories. Additionally, this research investigates the influence of trajectory clustering on prediction accuracy. To fulfill the objectives, a four-step methodology: data preprocessing, model construction, validation testing, and analysis is employed. Real-world historical flight data is used to train the BiLSTM model after being clustered with K-means. The model's performance is evaluated using randomized enroute flight data and various metrics like mean squared error and root mean squared error. This research is successful in accurately predicting the flight and the clustering process was proven to increase prediction accuracy by 15 percent in latitude, and 10 percent in longitude.]]>
<![CDATA[Intelligent Eyes on the Battlefield: Developing an AI-Vision Based Military Vehicle and Infantry Detection System ]]>
<![CDATA[Wibowo, Pasha R A]]>;
<![CDATA[Ummah, Khairul]]>;
<![CDATA[Arifianto, Ony]]>;
<![CDATA[Widagdo, Djarot]]>;
<![CDATA[Riszal, Akhmad]]>;
<![CDATA[Arif, Yanuar Zulardiansyah]]>;
<![CDATA[Sadono, Mahardi]]>
<![CDATA[ Jurnal Inovasi Teknologi Vol 5 No 1 (2024): April ]]>
Publisher : <![CDATA[Engineering Forum of Western Indonesian Government Universities Board (Forum Teknik, BKS-PTN Wilayah Barat) Indonesia ]]>
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<![CDATA[The importance of accurate, real-time intelligence in modern warfare is crucial, especially in reconnaissance and surveillance operations. Currently, drones are widely used for reconnaissance, but generally rely only on the operator's ability to monitor operation targets. This research is aimed at developing an AI vision assistance system to enhance the ability to detect military vehicles and infantry. The method used is computer vision trained to recognize and differentiate several military objects. The YOLO model is used to detect and distinguish objects. To improve detection capabilities, the YOLO v8 model was retrained with an additional dataset sourced from battle recordings on the battlefield. The results show a detection accuracy rate of 95% in detecting vehicles and infantry under normal visual conditions. The model from this research can be used to enhance the capabilities of reconnaissance drones and the effectiveness of monitoring operations.]]>
<![CDATA[Effect of Variation of Quenching Process Cooling Media on Hardness and Microstructure of AISI 1020 Steel Subjected to The Pack Carburizing Process Using Graphite and Eggshell Carbon Media ]]>
<![CDATA[Zulhanif, Zulhanif]]>;
<![CDATA[Supriadi, Harnowo]]>;
<![CDATA[Prihastomo, Sigiet]]>;
<![CDATA[Hanif, Muhammad]]>;
<![CDATA[Ummah, Khairul]]>
<![CDATA[ Journal of Applied Science, Engineering and Technology Vol. 4 No. 1 (2024): June 2024 ]]>
Publisher : <![CDATA[INSTEP Network ]]>
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DOI: 10.47355/jaset.v4i1.73
<![CDATA[AISI 1020 steel is a low carbon steel which has a low selling price compared to medium carbon steel, high carbon steel, and alloy steel. This material is used as a construction material. in general, it is widely used and applied to machine components and construction components such as gears and shafts with relatively small loads. The purpose of this research is to increase the value of hardness. The pack carburizing process is carried out using graphite carbon media and egg shells at a temperature of 850 ºC. This process can increase the hardness of a material. The highest hardness value was obtained with the brine cooling medium of 457,111 BHN, then the water-cooling medium of 319,345 BHN, and finally the oil cooling medium of 248,204 BHN.]]>
