<p>Ikan Gurami (<em>Osphronemus Goramy)</em> merupakan ikan yang banyak dibudidayakan dan dikomsumsi masyarakat ini menjadi sektor unggulan di beberapa wilayah kabupaten Banyumas. Ikan gurami yang dibudidayakan oleh masyarakat Banyumas, sebenarnya bukan tanpa hambatan. Salah satu hambatan bagi peternak gurami adalah penyakit yang disebabkan oleh bakteri. Pada penelitian ini penulis membuat sistem pakar untuk mendiagnosis penyakit ikan Gurami yang disebabkan bakteri. Penelitian ini menggunakan metode<em> Case Based Reasoning</em> dan <em>Similarity</em> <em>Nearest Neighbor</em> untuk mendapatkan solusi yang terbaik dari kasus yang di identifikasi. Metode tersebut membandingkan antara kasus lama dengan kasus baru dan menghitung suatu nilai <em>similarity </em>kasus. Nilai <em>similarity</em> tertinggi dapat dijadikan kesimpulan untuk kasus yang paling mirip dengan diagnosa pakar. Sehingga dari kedua metode tersebut dapat dihasilkan sistem pakar yang dapat mendiagnosis dan menganalisis sesuai dengan nilai kemiripan gejala terhadap penyakit, serta menampilkan solusi penanganan dari penyakit yang didiagnosis. Hasil pengujian antar kasus dan sistem menggunakan perhitungan <em>similarity</em> mencapai nilai terbaik yaitu 100%. Hasil pengujian akurasi sistem untuk diagnosis yang sesuai dengan pikiran pakar, memperoleh hasil sebesar 93,33% dari 30 kasus yang diuji dengan sistem. Kesimpulan dari hasil tersebut adalah sistem dapat dikatakan layak untuk mendiagnosis penyakit Gurami yang disebabkan bakteri sesuai dengan yang dipikirkan pakar.</p><p> </p><p><em><strong>Abstract</strong></em></p><p><em>Gurami (Osphronemus Goramy) is a fish that is widely cultivated and consumed by the community. This fish is a leading sector in several regions of Banyumas district. Gouramy which is cultivated by the Banyumas people, is actually not without obstacles. One obstacle for gouramy breeders is a disease caused by bacteria. Reporting from the online news portal, circulating in February 2018 circulated that news about Gurami farmers was losing money because thousands of broodstock fish that had been raised to death were attacked by bacterial diseases, namely Aeromoniasis. Experts who handle this are limited, namely only 2 people in the Banyumas Regency.</em><em> </em><em>In this study the authors made an expert system to diagnose Gurami fish disease caused by bacteria. This study uses the Case Based Reasoning (CBR) and Nearest Neighbor methods used to get the best solution from the identified case. The CBR method compares the old case with the new case and calculates a case similarity value. The system was built with 13 symptoms and 3 Gurami diseases caused by bacteria. Each symptom each has a weight of 5, 3, and 1. The highest similarity value can be used as a conclusion for the case most similar to the expert diagnosis. So that from these two methods an expert system can be produced that can diagnose and analyze according to the similarity of symptoms to the disease, as well as display solutions to the treatment of diagnosed diseases. The test results are between cases and the system uses the similarity calculation to achieve the best value of 100%. The results of the system accuracy test for diagnoses that are in accordance with the expert's mind, obtained results of 93.33% from 30 cases tested with the system. The conclusion of these results is that the system can be said to be feasible to diagnose Gurami disease caused by bacteria according to what experts think.</em></p><p><em><strong><br /></strong></em></p>
Mobile learning is the intersection of Mobile Computing and E-Learning that provides the resources that can be accessed from anywhere, a powerful search system capabilities, rich interaction, full support for effective learning and assessment based on performance. E-learning has the characteristics are independent of place and time. Education requires an alternative model of learning has not characteristic depending on location and time. In addition to the proficiency level, an alternative model is also expected to provide knowledge sharing and visualization facilities of knowledge so that knowledge becomes more interesting and easy to understand. Mobile learning application built to run on android operating system. Android operating system was chosen because it has mastered the current android smartphone market and is an open source operating system that is easy to develop. Android versions that support this application is version 2.2 to 4.2. Using jQuery mobile framework allows users to access the M-Learning. Because besides its attractive, jQuery mobile display can also customize the display of the mobile device.
Sebuah perangkat lunak dikatakan siap untuk dipakai apabila sudah melalui tahap pengujian. Pada era pengembangan perangkat lunak dengan metodologi tradisional, pengujian dilakukan dengan cara mencoba satu persatu menu aplikasi ketika aplikasi yang dikembangkan sudah jadi. Cara pengujian tersebut akan membutuhkan waktu yang lama apabila developer mengerjakan proyek perangkat lunak dalam skala besar. Selain itu, cara tersebut juga tidak dapat menguji logika dan method dari suatu kelas. Salah satu metode pengembangan perangkat lunak yang dapat menghemat waktu pengujian, namun fungsionalitasnya tetap terjaga adalah test driven development (TDD). Pada metode TDD, pengembangan perangkat lunak dilakukan dengan membuat test case terlebih dahulu baru kemudian melakukan producing code. Pada penelitian ini, dikembangkan sebuah aplikasi web menggunakan TDD. Aplikasi web yang dikembangkan adalah berupa sistem informasi mengenai ulasan film lokal indonesia atau disebut Indonesia Movie Database (IMDB). Bahasa pemrograman web yang dipakai adalah ruby dengan menggunakan framework rails. Sedangkan alat yang dipakai untuk pengujian adalah Rspec. Hasil implementasi TDD membuktikan bahwa fungsi-fungsi dari aplikasi web yang dibangun dapat berkerja dengan baik. Selain itu kode program yang dihasilkan juga menjadi rapi dan mudah dibaca oleh pengembang lain karena menerapkan refactoring. Pengujian unit test menggunakan Rspec membantu pengembang dalam menangani kesalahan dan memudahkan menambah fitur baru dari aplikasi web.
Waste is goods / materials that have no value in the scope of production, where in some cases the waste is disposed of carelessly and can damage the environment. The Indonesian government in 2019 recorded waste reaching 66-67 million tons, which is higher than the previous year, which was 64 million tons. Waste is differentiated based on its type, namely organic and anorganic waste. In the field of computer science, the process of sensing the type waste can be done using a camera and the Convolutional Neural Networks (CNN) method, which is a type of neural network that works by receiving input in the form of images. The input will be trained using CNN architecture so that it will produce output that can recognize the object being inputted. This study optimizes the use of the CNN method to obtain accurate results in identifying types of waste. Optimization is done by adding several hyperparameters to the CNN architecture. By adding hyperparameters, the accuracy value is 91.2%. Meanwhile, if the hyperparameter is not used, the accuracy value is only 67.6%. There are three hyperparameters used to increase the accuracy value of the model. They are dropout, padding, and stride. 20% increase in dropout to increase training overfit. Whereas padding and stride are used to speed up the model training process.
Indonesia is a tropical country that has various skin diseases. Tinea versicolor, ringworm, and scabies are the most common types of skin diseases suffered by the people of Indonesia. The classification of the three skin diseases can be automatically completed by artificial intelligence and deep learning technology because the classification process using an expert will require a lot of money and time. The challenge in classifying skin diseases is in the process of collecting data. Because health data cannot be obtained freely, there must be approval from the patient or hospital. Therefore, to overcome the limited amount of data, Pre-Trained CNN is used. The Pre-Trained CNN model has many patterns from thousands of images, so we do not need many images to train the model. In this study, a comparison of five pre-trained CNN models was conducted, namely VGGNet16, MobileNetV2, InceptionResNetV2, ResNet152V2, and DenseNet201. The aim is to find out which CNN model can produce the best performance in classifying skin diseases with a limited amount of image data. The test results show that the ResNet152V2 model has the best classification ability with the highest accuracy, precision, recall, and F1 score values, namely 95.84%, 0.963, 0.96, and 0.956. As for the training execution time, the ResNet152V2 model has the fastest time to achieve 95% accuracy. That's happened because the addition of the dropout parameter is 20%.
Indonesia is a country that has a variety of cultures, one of which is wayang kulit. This typical javanese performance art must continue to be preserved so that to be known by future generations. There are many wayang figures in Indonesia, and the most famous is punakawan. Wayang punakawan consists of four character namely semar, gareng petruk, and bagong. To preserve wayang punakawan to be known by the next generation, then in this study created a system that is able to identify real-time punakawan object using deep learning technology. The method that used is Single Shot Multiple Detector (SSD) as one of the models of deep learning that has a good ability in classifying data with three-dimensional structures such as real-time video. SSD model with MobileNet layer can work in slight computation, so that it can be run in real-time system. To classify object there are two steps that must be done such as training process and testing process. Training process takes 28 hours with 100.000 steps of iteration.The result of training process is a model which used to identify object. Based on the test result obtained an accuracy to detect object was 98,86%. This prove that the system has been able to optimize object in real-time accurately.
Monitoring is the important thing for the security of an area. Based on the monitoring, the condition of the area, events, and objects can be observed. Monitoring of an area generally uses Closed Circuit Television (CCTV). But CCTV cameras only function as passive supervisor which are unable to detect the appearance of objects. Therefore, the motion detection techniques need to be applied to detect the appearance of the objects. In this paper, a Gaussian blur and accumulative frame difference method was applied to detect the appearance of the objects. The method works by comparing the reference frame as a benchmark with the target frame which is filled by the objects. Based on the results of the test, the system is able to detect the objects that appear by raising a segmentation line on the objects. Then, the time of objects appearance will be recorded in a *.csv file and the system visualizes the appearance of the objects in time-series graphs. Objects appearance examination at a distance of 1 to 10 meters can work well during a bright conditions. However, in dark environments (less than 40 lux), the system has not been able to detect the appearance of an object because it depends on the specification of camera used by the users. Then, in testing the number of the objects, the system can detect multiple objects. However, if there are several objects that are too close, those objects will be merged as one object. Intisari-Pengawasan menjadi hal yang penting untuk keamanan suatu area. Berdasarkan pengawasan tersebut, dapat diketahui kondisi area, kejadian, dan objek yang muncul. Pengawasan suatu area umumnya dilakukan menggunakan Closed Circuit Television (CCTV). Namun, kamera CCTV hanya berfungsi sebagai pengawas pasif yang tidak mampu mendeteksi kemunculan objek. Oleh karena itu, teknik deteksi gerak perlu diterapkan untuk mendeteksi kemunculan objek. Pada makalah ini diterapkan metode Gaussian blur dan accumulative frame difference untuk mendekteksi kemunculan objek. Metode tersebut bekerja dengan membandingkan frame referensi sebagai patokan dengan frame target yang terisi objek. Berdasarkan hasil pengujian, sistem mampu untuk mendeteksi objek yang muncul dengan memunculkan garis segmentasi pada objek. Kemudian, waktu kemunculan objek akan terekam dalam sebuah berkas *.csv dan sistem memvisualisasikan kemunculan objek dalam grafik fungsi waktu. Pengujian kemunculan objek pada jarak 1 sampai 10 meter dapat bekerja dengan baik saat kondisi terang. Namun, untuk deteksi objek pada lingkungan yang gelap (kurang dari 40 lux), sistem belum mampu mendeteksi kemunculan objek karena spesifikasi kamera yang dipakai. Kemudian pada pengujian jumlah objek, sistem dapat mendeteksi beberapa objek. Akan tetapi, jika terdapat objek yang sangat dekat jaraknya, maka akan digabung menjadi satu objek. Kata Kunci-frame, deteksi, objek, pengawasan.
Kegiatan diseminasi produk teknologi kepada masyarakat yang diajukan pada proposal ini bertujuan untuk memberikan alternatif solusi pemecahan masalah yang ada pada UKM Batik Blekok di Kelurahan Mangunharjo Kecamatan Tembalang Kota Semarang, yaitu dengan penerapan teknologi informasi (IT) untuk digitalisasi sistem pemasaran dan penjualannya. Permasalah utama yang ada pada UKM Batik Blekok ini adalah minimnya order, ketidakpastian jumlah order, minimnya customer, dan masih terbatasnya wilayah pemasaran dan penjualannya. Alternatif solusi yang akan diterapkan di UKM tersebut adalah penggunaan aplikasi mobile untuk promosi dan penjualan berbasis android yang terintegrasi ke media sosial seperti facebook, dan instagram. Kegiatan ini direncanakan dalam waktu 5 (lima) bulan yang terdiri dari 5 tahapan meliputi: bulan pertama untuk kegiatan observasi lapangan, diskusi dengan Mitra, dan analisis situasi untuk menetapkan permasalahan yang dihadapi Mitra. Pada bulan pertama ini pula mulai dilakukan proses desain aplikasi dan database aplikasi online berbasis android. Bulan kedua digunakan untuk pembuatan aplikasi Android dan database. Bulan ketiga digunakan untuk pelatihan SDM agar terampil dalam mengoperasikan sistem aplikasi berbasis android dan sekaligus dilakukan uji coba (trial and error) penerapan dan koreksi sistem aplikasi Android. Bulan keempat dilakukan publikasi pada media massa dan jurnal, sedangkan pada bulan kelima dilakukan pembuatan laporan akhir. Sistem aplikasi promosi dan penjualan online berbasis android ini akan diterapkan di UKM Batik Blekok sehingga diharapkan dapat membantu UKM tersebut dalam mengatasi permasalahan yang selama ini dialaminya. Sistem aplikasi promosi dan penjualan online berbasis android ini akan diterapkan di UKM Batik Blekok sehingga diharapkan dapat membantu UKM tersebut dalam mengatasi permasalahan yang selama ini dialaminya. Pada akhirnya outcome yang diharapkan dari kegiatan ini adalah dapat memperluas wilayah pemasaran, meningkatkan jumlah pelanggan, meningkatkan volume penjualan dan meningkatkan omset dan keuntungan UKM Batik Blekok. Metode yang akan diterapkan untuk menyelesaikan permasalahan yang dihadapi oleh kedua mitra ini meliputi 5 tahap/macam kegiatan yaitu:  observasi lapangan, penyediaan infrastruktur internet dan peralatan pendukung, pelatihan SDM, penerapan Sistem, aplikasi promosi dan penjualan online dan pengoperasian Sistem Aplikasi promosi dan penjualan online.
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