GESTURE RECOGNITION FOR PENCAK SILAT TAPAK SUCI REAL-TIME ANIMATION

The main target in this research is a design of a virtual martial arts training system in real-time and as a tool in learning martial arts independently using genetic algorithm methods and dynamic time warping. In this paper, it is still in the initial stages, which is focused on taking data sets of martial arts warriors using 3D animation and the Kinect sensor cameras, there are 2 warriors x 8 moves x 596 cases/gesture = 9,536 cases. Gesture Recognition Studies are usually distinguished: body gesture and hand and arm gesture, head and face gesture, and, all three can be studied simultaneously in martial arts pencak silat, using martial arts stance detection with scoring methods. Silat movement data is recorded in the form of oni files using the OpenNI ™ (OFW) framework and BVH (Bio Vision Hierarchical) files as well as plug-in support software on Mocap devices. Responsiveness is a measure of time responding to interruptions, and is critical because the system must be able to meet the demand

Pemanfaatan Sampah Rumah Tangga dan Pasar sebagai Upaya Peningkatan Kesejahteraan Keluarga

Jakarta memproduksi sekitar 7.700 ton sampah setiap harinya. Dari jumlah tersebut, sekitar 4.900 hingga 5.000 ton merupakan sampah organik. Rumah tangga dan pasar tradisional menjadi penghasil limbah yang produktif. Sampah organik yang dihasilkan dapat dikurangi dengan pemanfaatan menjadi pupuk organik cair(POC) yang bernilai jual tinggi. Pembentukan kelompok wirausaha mandiri bertujuan agar aktif membantu dalam mengurangi masalah sampah dengan dijadikan POC yang benilai jual tinggi serta peningkatan kesejahteraan keluarga. Mitra yang terlibat dalam kegiatan pengabdian kepada masyarakat (abdimas) ini yaitu kelompok ibu-ibu wirausaha mandiri warga RW 01 Tegal Alur Jakarta Barat. Metode yang digunakan yaitu Partisipatory Rural Apprasial (PRA), sebuah metode pada proses peningkatan partisipasi dan pemberdayaan masyarakat, dalam hal ini masyarakat ikut terlibat aktif pada seluruh kegiatan. Hasil dari program abdimas yaitu: 1)Meningkatnya pemahaman mitra tentang pembuatan POC dan kompos; 2)adanya hasil POC dan kompos; 3)meningkatkan pengetahuan dan kemampuan kelompok mitra dalam penerapan teknologi dan pengetahuan pada pengolahan sampah menjadi POC dan kompos; 4)mitra mampu memasarkan pupuk organik cair dan kompos baik secara offline maupun online; dan 5) meningkatnya pendapatan mitra dari hasil penjualan pupuk organik cair dan kompos; serta 6) meningkatnya kesadaran tentang dampak sampah yang timbul tanpa melalui proses daur ulang

SEBERAPA EFEKTIFKAH PENGELOLAAN KEUANGAN DAERAH UNTUK MENUNJANG INOVASI OTONOMI DAERAH? ANALISIS KOTA BOGOR

The purpose of this research is more directed to the analysis of regional financial capacity by looking at the degree of decentralization and the ratio of regional financial dependence and the ratio of regional financial independence. These three forms of analysis will show whether an autonomous region is able to stand fully by relying on its own regional financial capacity. Type of research is secondary data analysis, horizontal and vertical analysis methods are used, the research location was selected purposively, namely the city of Bogor. The data used are institutional administrative data, namely the Bogor City Government, consisting of the Regional Budget and Revenue Expenditure reports and the 2015-2019 Regional Government Implementation Report, as well as regional financial data contained in the report of the Bogor City Central Statistics Agency for 2015-2019. The results of this study; (a) the calculation of the ratio of the degree of decentralization is categorized as moderate, (b) the dependence of regional finances for 5 years is above 50%, this means that the city of Bogor in terms of high dependence on the central government, (c) the city of Bogor can be categorized as moderate in its independence rati

OPTIMASI CONVOLUTION NEURAL NETWORK UNTUK DETEKSI COVID-19

Abstrak: Optimasi Convolution Neural Network Untuk Deteksi Covid-19. Kondisi pandemi seperti sekarang ini diperlukan sebuah algoritma pembelajaran mesin untuk mendeteksi covid-19 secara otomatis berdasarkan pada gambar rontgen dada guna memudahkan dalam mambantu pengambil keputusan. Penelitian ini ingin membandingkan arsitektur CNN AlexNet dan MobileNetV2 untuk mendeteksi (a) covid-19, (b) lung opacity, (c) normal, (d) viral pneumonia. Data himpunan rontgen dada yang digunakan sejumlah 4000 yang berasal dari kaggle.com, 0.8 data dibagi untuk pelatihan sedangkan 0.2 nya digunakan untuk pengujian. Optimizer yang digunakan yaitu keras SGD momentum, dengan nilai learning rate 0.005 dan momentum 0.9, serta epoch 50. Ukuran gambar untuk input yaitu 224x224 serta ukuran batch 32. Hasil optimasi dari kedua algoritma tersebut yaitu, MobileNetV2 lebih baik untuk mendeteksi covid-19 dengan nilai akurasi presisi mencapai 99%. Penelitian selanjutnya dapat membandingkan algoritma CNN yang lainnya serta data himpunan yang lebih banyak. Kata kunci: CNN; AlexNet; MobileNetV2; Covid-19 Abstract: Convolution Neural Network Optimization for Covid-19 Detection. In the current pandemic conditions, a machine learning algorithm is needed to detect COVID-19 automatically based on chest X-ray images to make it easier to assist decision makers. Aim study be disposed for compare the architecture of CNN AlexNet and MobileNetV2 to detect (a) covid-19, (b) lung opacity, (c) normal, (d) viral pneumonia. The data set of chest X-rays used are 4000 from kaggle.com, 0.8 of the data is shared for training while 0.2 is used for testing. The optimizer used is hard SGD momentum, with a value of leaning rate 0.005 and momentum 0.9, and epoch 50. The image size for the input is 224x224 and the batch size is 32. The optimization results from the two algorithms are, MobileNetV2 is better for detecting covid-19 with an accuracy value The precision reaches 99%. Future research can compare other CNN algorithms and larger data sets. Keywords: CNN; AlexNet; MobileNetV2; Covid-1

Daur Ulang Air Leri Dalam Mengurangi Limbah Rumah Tangga

This community service aims to educate the community in Bambu Apus Pamulang to be more concerned about waste. Households are productive producers of waste, household waste water can be used as liquid organic fertilizer for hydroponic plants, the water content of leri is nitrogen, phosphorus, potassium, calcium, magnesium, sulfur, iron, and vitamin B1. Hydroponics is considered suitable for urban communities, because it does not require a large place, is relatively safe from insects and the harvest period can be controlled, the results are expected to be able to meet the nutritional needs of households with organic vegetables. These community service activities are carried out so that the community is aware of the importance of protecting the environment, starting with simple matters, providing information that households are able to process and utilize leri's waste water, and finally teaching the community members how to grow hydroponic vegetables to ensure the nutrition of families with organic vegetables independently

MEMBANGUN KULTUR ZERO WASTE DI SEKOLAH

Abstrak: Sekolah merupakan salah satu sumber penghasil sampah. Untuk itu diperlukan sosialisai dan edukasi sejak dini mengenai sampah sebagai langkah membangun kultur zero waste di sekolah. Pengabdian masyakarat dilaksanakan di SMP dan SMK Faradisa Islamic School berlokasi di Bambu Apus, Pamulang, Tangerang Selatan . Tujuan kegiatan ini adalah mengedukasi sampah sejak dini. Sekolah dipilih sebagai mitra karena merupakan instansi tempat belajar siswa. Dengan adanya sosialasi mengkampanyekan sekolah tanpa sampah diharapkan bisa mereduksi sampah dilingkungan sekitar. Penggunaan metode pada pengabdian ini berupa penyuluhan dan praktik. Metode penyuluhan dengan memberikan materi disatu waktu, sedangkan praktik dilakukan dengan memberikan tumbler gratis kepada peserta. Dengan adanya pembagian tumbler gratis diharapkan memberikan budaya dan kebiasan tidak menggunakan botol plastik sekali pakai. Pengabdian masyakarat dilaksanakan di SMP dan SMK Faradisa Islamic School berlokasi di Bambu Apus, Pamulang, Tangerang Selatan. Pengabdian masyarakat ini dihadiri oleh 50 peserta yang merupakan siswa SMK Faradisa Islamic school. Capaian pengabdian masyarakat ini yaitu peningkatan softskill berupa pengetahuan mengenai sampah dan dampak terhadap lingkungan. Sedangkan peningkatan hardskill berupa tindakan konkrit berupa penggunaan tumbler sebagai kampanye sekolah bebas tanpa sampah. Berdasarkan monitoring dan evaluasi kegiatan, sebesar 98,8% sangat setuju dan setuju bahwa kegiatan ini memberikan manfaat terhadap mitra.Abstract: Schools are one source of waste production. For this reason, early socialization and education regarding waste is needed as a step to build a zero waste culture in schools.Community service is carried out at Faradisa Islamic Middle School and Vocational School located in Bambu Apus, Pamulang, South Tangerang. The aim of this activity is to educate waste from an early age. The school was chosen as a partner because it is an institution where students study. With the outreach campaigning for schools without waste, it can reduce waste in the surrounding environment. The methods in this community service are counseling and practicing. The counseling method is by providing material at one time, while practice is carried out by giving free tumblers to participants. With the distribution of free tumblers, and it will provide a culture and habit of not using single-use plastic bottles. Community service is carried out at SMP and SMK Faradisa Islamic School located in Bambu Apus, Pamulang, South Tangerang. This community service was attended by 50 participants who were students of Faradisa Islamic School Vocational School. The achievement of this community service is the increase in soft skills in the form of knowledge about waste and its impact on the environment. While increasing hard skills in concrete actions in using a tumbler as a free school campaign without waste. Based on activity monitoring and evaluation, 98.8% strongly agreed and agreed that this activity provided benefits to partners

Perbandingan Jumlah dan Ukuran Stomata Daun Pisang Klutuk (Musa balbisiana Colla) dan Pisang Ambon (Musa Paradisiaca L)

ABSTRACT Banana Musa balbisiana (klutuk banana) Colla is an banana cultivers that’s still pure endemic and have superior immune than other banana. Banana Musa paradisiaca L (ambon banana) is an cultivers banana a lot of plant by farmer to get fruits and have vulnerable immune toward disease and abiotic stress. Stomata is an place for exchange of CO2 and O2 evaporation in the process of transpiration and photosynthesis. This research is to find out amount comparison and the size of stomata between klutuk banana and ambon banana. This research using non experiment method. The results of this research is amount stomata on the upper klutuk banana is very little (9,6 stomata) just then ambon banana (144,8 stomata). On the buttom leaf, the amount is not too different, klutuk banana (505,8 stomata) and ambon banana (470,6 stomata). The size of stomata banana leaf on the upper more narrow just then on the bottom. But ambon banana have size of the leaf which is almost same

Optimasi Deep Learning untuk Prediksi Saham di Masa Pandemi Covid-19

Penelitian ini bertujuan untuk meningkatkan akurasi dengan menurunkan tingkat kesalahan prediksi dari 5 data saham blue chip di Indonesia. Dengan cara mengkombinasikan desain 4 hidden layer neural nework menggunakan Long Short Term Memory (LSTM) dan Gated Recurrent Unit (GRU). Dari tiap data saham akan dihasilkan grafik rmse-epoch yang dapat menunjukan kombinasi layer dengan akurasi terbaik, sebagai berikut; (a) BBCA dengan layer LSTM-GRU-LSTM-GRU (RMSE=1120,651, e=15), (b) BBRI dengan layer LSTM-GRU-LSTM-GRU (RMSE =110,331, e=25), (c) INDF dengan layer GRU-GRU-GRU-GRU (RMSE =156,297, e=35 ), (d) ASII dengan layer GRU-GRU-GRU-GRU (RMSE =134,551, e=20 ), (e) TLKM dengan layer GRU-LSTM-GRU-LSTM (RMSE =71,658, e=35 ). Tantangan dalam mengolah data Deep Learning (DL) adalah menentukan nilai parameter epoch untuk menghasilkan prediksi akurasi yang tinggi

Comparison of EfficientNet B5-B6 for Detection of 29 Diseases of Fruit Plants

In initiatives to meet food needs and enhance the wellbeing of farmers and society at large, crop production performance is essential. For early attempts to be made for quick handling to prevent crop failure, farmers must be able to readily and quickly receive information in order to detect plant illnesses. In this study, two Convolutional Neural Network (CNN) architectures namely, EfficientNet versions B5 and B6 are used to develop a classification model for plant disease using Deep Learning (DL). The 66,556 visuals in the dataset, which is from Kaggle.com, are used. To create a model, the training method uses 57,067 images data and 3,170 image data for validation. The EfficientNet architecture versions B5 and B6 received very good accuracy scores for the total test results, namely 0.9905 and 0.9927. The model testing phase was carried out through testing phases utilising 3.171 images data. Future analysis can compare CNN architectures and try it with different datasets

Classification of cervical spine fractures using 8 variants EfficientNet with transfer learning

A part of the nerves that govern the human body are found in the spinal cord, and a fracture of the upper cervical spine (segment C1) can cause major injury, paralysis, and even death. The early detection of a cervical spine fracture in segment C1 is critical to the patient’s life. Imaging the spine using contemporary medical equipment, on the other hand, is time-consuming, costly, private, and often not available in mainstream medicine. To improve diagnosis speed, efficiency, and accuracy, a computer-assisted diagnostics system is necessary. A deep neural network (DNN) model was employed in this study to recognize and categorize pictures of cervical spine fractures in segment C1. We used EfficientNet from version B0 to B7 to detect the location of the fracture and assess whether a fracture in the C1 region of the cervical spine exists. The patient data group with over 350 picture slices developed the most accurate model utilizing the EfficientNet architecture version B6, according to the findings of this experiment. Validation accuracy is 99.4%, whereas training accuracy is 98.25%. In the testing method using test data, the accuracy value is 99.25%, the precision value is 94.3%, the recall value is 98%, and the F1-score value is 96%