FAKTOR-FAKTOR YANG BERHUBUNGAN DENGAN KINERJA PERAWAT DALAM PELAKSANAAN PENDIDIKAN PASIEN DAN KELUARGA DI RS JIWA DAERAH PROVINSI JAMBI TAHUN 2018

Secara global angka kekambuhan pasien gangguan jiwa mencapai 50-92%. Tingginya angka kekambuhan tersebut salah satu penyebabnya adalah karena kurang pahamnya pasien dan keluarga dalam merawat pasien di rumah setelah pulang dari rumah sakit jiwa. Pemahaman tersebut didapat melalui proses pendidikan yang diberikan tenaga kesehatan, salah satunya adalah perawat. Pendidikan kesehatan pada pasien dan keluarga sangat diperlukan untuk kemandirian pasien setelah pulang kerumah, terutama pada pasien dengan gangguan jiwa.. Penelitian ini bertujuan untuk mengetahui hubungan kinerja perawat dalam pelaksanaan pendidikan kesehatan pasien gangguan jiwa yang di rawat inap rumah sakit jiwa. Penelitian menggunakan metode kuantitatif dengan pendekatan cross sectional. Jumlah sampel 117 orang perawat. Teknik pengambilan sampel menggunakan teknik propotional random sampling dan analisis data menggunakan Chi square. Penelitian ini dilaksanakan dari bulan juni 2017- Juli 2018. Tempat penelitian di ruang Rawat Inap RS Jiwa Provinsi Jambi. Hasil penelitian diketahui bahwa variabel yang memiliki hubungan dengan kinerja perawat dalam pelaksanaan pendidikan pasien dan keluarga adalah variabel tingkat pendidikan (p-value : 0,025), motivasi (0,03) dan variabel imbalan (0,003). Faktor yang paling berhubungan adalah faktor imbalan (0,003). Diharapkan bahwa perawat sebagai ujung tombak pelayanan di RSJD Provinsi Jambi meningkatkan peran sebagai supervisor pelaksanaan asuhan keperawatan khususnya dalam pendidikan pasien dan keluarga bukan hanya sebatas instrument standar akreditasi RS dan penanggung jawabnya saja, namun supervisi langsung saat pemberian asuhan seperti saat melakukan assasmen dan pemberian edukasi pada pasien Kepala ruangan membuat jadwal pelaksanaan supervisi, melakukan bimbingan dan arahan kepada staf keperawatan terhadap pelaksanaan edukasi apabila belum berjalan sesuai standar. Kata Kunci: Kinerja, Perawat, Pendidikan pasien dan Keluarga Daftar Pustaka : 110 (2004-2017

XgBoost Hyper-Parameter Tuning Using Particle Swarm Optimization for Stock Price Forecasting

Investment in the capital market has become a lifestyle for millennials in Indonesia as seen from the increasing number of SID (Single Investor Identification) from 2.4 million in 2019 to 10.3 million in December 2022. The increase is due to various reasons, starting from the Covid-19 pandemic, which limited the space for social interaction and the easy way to invest in the capital market through various e-commerce platforms. These investors generally use fundamental and technical analysis to maximize profits and minimize the risk of loss in stock investment. These methods may lead to problem where subjectivity and different interpretation may appear in the process. Additionally, these methods are time consuming due to the need in the deep research on the financial statements, economic conditions and company reports. Machine learning by utilizing historical stock price data which is time-series data is one of the methods that can be used for the stock price forecasting. This paper proposed XGBoost optimized by Particle Swarm Optimization (PSO) for stock price forecasting. XGBoost is known for its ability to make predictions accurately and efficiently. PSO is used to optimize the hyper-parameter values of XGBoost. The results of optimizing the hyper-parameter of the XGBoost algorithm using the Particle Swarm Optimization (PSO) method achieved the best performance when compared with standard XGBoost, Long Short-Term Memory (LSTM), Support Vector Regression (SVR) and Random Forest. The results in RSME, MAE and MAPE shows the lowest values in the proposed method, which are, 0.0011, 0.0008, and 0.0772%, respectively. Meanwhile, the  reaches the highest value. It is seen that the PSO-optimized XGBoost is able to predict the stock price with a low error rate, and can be a promising model to be implemented for the stock price forecasting. This result shows the contribution of the proposed method

APLIKASI PEMBERIAN AROMATERAPI LAVENDER TERHADAP NYERI AKUT DISMENOREA PADA REMAJA DI WILAYAH KERJA PUSKESMAS CILAKU

When adolescent girls experience menstruation, dysmenorrhea usually occurs which causes acute pain in the abdomen so that it can inhibit daily activities and requires non-pharmacological measures, one of which is using lavender aromatherapy techniques. The purpose of this action is to understand and apply the action of lavender aromatherapy to reduce the acute pain of dysmenorrhea in adolescents. The results of this study before the client experienced moderate pain with a scale of 6 (0-10), after the lavender aromatherapy action the pain decreased to mild pain with a scale of 0 (0-10) measured by the Numeric Rating Scale (NRS). It can be concluded that the administration of lavender aromatherapy is effective against the acute pain of dysmenorrhea. The action of lavender aromatherapy can be an alternative to reduce pain. Suggestions for further researchers, researchers can be used as information material for lavender aromatherapy techniques to reduce menstrual pain (dysmenorrhea)

Automated-tuned hyper-parameter deep neural network by using arithmetic optimization algorithm for Lorenz chaotic system

Deep neural networks (DNNs) are very dependent on their parameterization and require experts to determine which method to implement and modify the hyper-parameters value. This study proposes an automated-tuned hyper-parameter for DNN using a metaheuristic optimization algorithm, arithmetic optimization algorithm (AOA). AOA makes use of the distribution properties of mathematics’ primary arithmetic operators, including multiplication, division, addition, and subtraction. AOA is mathematically modeled and implemented to optimize processes across a broad range of search spaces. The performance of AOA is evaluated against 29 benchmark functions, and several real-world engineering design problems are to demonstrate AOA’s applicability. The hyper-parameter tuning framework consists of a set of Lorenz chaotic system datasets, hybrid DNN architecture, and AOA that works automatically. As a result, AOA produced the highest accuracy in the test dataset with a combination of optimized hyper-parameters for DNN architecture. The boxplot analysis also produced the ten AOA particles that are the most accurately chosen. Hence, AOA with ten particles had the smallest size of boxplot for all hyper-parameters, which concluded the best solution. In particular, the result for the proposed system is outperformed compared to the architecture tested with particle swarm optimization

MAKNA SIMBOLIK SUNDA WIWITAN DALAM TRADISI MEUNGKEUT BUMI

This research is motivated by problems from culture in the beliefs of Sunda Wiwitan which require the existence of the "Meungkeut Bumi" tradition in the pre-marital process. The research method used in this research is descriptive research method with a qualitative approach and data collection is done through participant observation, in-depth interviews, literature studies and documentation. The theory used in this study is the ethnographic theory by Prof. Engkus Kusworno, communication ethnography is a theory that examines the role of language in communicative behavior in different societies. The theoretical assumptions include speech communities, communication activities, communication components, communication competencies and language varieties. The results of this study explain that "Sunda Wiwitan's Symbolic Meaning in the Mengkeut Bumi Tradition" is a procession which means uniting two women and men by tying a yellow and white shawl which also means that they have made promises to each other to step into marriage. The color on the yellow shawl means wind and the white shawl means water. Sunda Wiwitan is a sect adhered to by the indigenous Sundanese people or the original Sundanese religion. The Sunda Wiwitan community is spread across West Java, one of which is in Kampung Pasir, Cintakarya Village, Samarang District, Garut Regency

PERAN SATPOL PP DALAM PENEGAKKAN PERDA NO 2 TAHUN 2014 TENTANG PEMBERANTASAN PELACUR DAN PERBUATAN ASUSILA BERKEDOK KOS-KOSAN KASUS DI KOTA JAMBI

Skripsi ini bertujuan untuk mengetahui tugas dan fungsi Satpol PP (Satuan Polisi Pamong Praja) Kota Jambi dalam Penegakkan Perda No 2 Tahun 2014 Tentang pemberantasan pelacuran dan perbuatan asusila berkedok Kost-kostan, dan untuk mengetahui kendala yang dihadapi dan peran Satuan Polisi Pamong Praja Kota Jambi dalam Penegakkan Peraturan Daerah No 2 Tahun 2014 tentang pemberantasan pelacuran dan perbuatan asusila berkedok Kost-kostan. Skripsi ini menggunakan pendekatan kualitatif dengan metode pengumpulan data melalui observasi, wawancara, dan dokumentasi. Berdasarkan penelitian yang dilakukan diperoleh hasil dan kesimpulan sebagai berikut: Pertama, Tugas dan fungsi Satpol PP sebagaimana yang tercantum dalam Perda nomor 2 Tahun 2014 pasal 11 ayat 1 yaitu (a) melakukan pengawasan dan penindakan terhadap tindak pidana pelacuran dan tindak pidana kesusilaan; (b) menindak lanjuti setiap laporan dari masyarakat tentang kejadian tindak pidana pelacuran dan tindak pidana kesusilaan serta memberikan perlindungan kepada pelapor; (c) berkoordinasi dengan penyidik Polri. Kedua, Kendala yang dihadapi Satpol PP adalah adanya kebocoran informasi saat melakukan razia, anggaran/Dana, oknum TNI atau Polri melindungi tempat kos-kosan, selain itu adalah kendala internal dan kendala eksternal. Ketiga, Peran Satpol PP Kota Jambi tercantum dalam pasal 10 ayat 2 huruf e dan f yang berbunyi: (a) Melakukan kerjasama dengan Polri, Tentara Nasional Indonesia, Kejaksaan Negeri dan Pengadilan Negeri. (b) Melakukan kerja sama antar daerah dan dengan pihak swasta, perguruan tinggi dan lembaga swadaya masyarakat

Best lecturer decision support system using method Analytical Hierarchy Process (AHP) and simple adaptive weighting (SAW)

Data processing as one of information technology area has wide range of applications including the selection of best lecturer in universities. Best Lecturer Assessment is required by the university to identify qualified human resources and in the future this process will give motivations to the lectures to be more productive and increase the quality of doing their jobs. In Buana Perjuangan University, Karawang, the selection of best lecturer is still conducted manually, where giving result to be subjective and questionable. Additionally, decision support system method has not been applied in determining the best lecturer. In this study, we proposed hybrid method which combines Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW) to determine the best lecturer by using five criteria, namely attendance at lecture meetings, academic rank, number of publications, additional assignments, and education levels. The data sample used were obtained using a random sampling technique on lecturer data, provided by the University Data and Information Center. The study involves two phases. Phase 1 involves in developing a questionnaire that contains a list of criteria that a best lecturer must have. In Phase 2, an AHP decision matrix is developed, and the candidates will be ranked based on the selected criteria obtained from Phase 1 and their weight values. The result will then be ranked by using SAW to obtain even more accurate decision of best lecturer. The results obtained indicate that the value of the Consistency Index (CI) for all respondents is consistent, 0.8 in average. Then, the CI result is used to determine the Consistency Ratio (CR), where the result is also consistent for all of the respondents, -0.7 in average. These result shows that the proposed method has the same output with the expert (respondents) decisions

Parameter Prediction for Lorenz Attractor by using Deep Neural Network

Nowadays, most modern deep learning models are based on artificial neural networks. This research presents Deep Neural Network to learn the database, which consists of high precision, a strange Lorenz attractor. Lorenz system is one of the simple chaotic systems, which is a nonlinear and characterized by an unstable dynamic behavior. The research aims to predict the parameter of a strange Lorenz attractor either yes or not. The primary method implemented in this paper is the Deep Neural Network by using Phyton Keras library. For the neural network, the different number of hidden layers are used to compare the accuracy of the system prediction. A set of data is used as the input of the neural network, while for the output part, the accuracy of prediction data is expected. As a result, the accuracy of the testing result shows that 100% correct prediction can be achieved when using the training data. Meanwhile, only 60% correct prediction is achieved for the new random data

Waypoint navigation of quad-rotor MAV

Quad-rotor Micro Aerial Vehicle (MAV) is a multi-rotor MAV with 4 propellers which propel the MAV up to the air and move around. It has high maneuverability to move around, such as roll, pitch and yaw movements. However, line of sight and radio control effective range are the major limitation for the MAVs which significantly shorten the travel distance. Therefore, we proposed a waypoint navigation quad-rotor MAV based on PID controller in this paper. User can set mission with multiple waypoint and the PID controller to control MAV autonomously moving along the waypoint to the desired position without remotely controlled by radio control and guidance of pilot. The results show PID controller is capable to control MAV to move to the desired position with high accuracy. As the conclusion, the result of real flight experiment shows that the %OS of designed PID controller for x is 13% while y is 11.89% and z is 2.34%. Meanwhile, steadystate error for all axis are 0%. This shows that the performance of PID controller is satisfied. Hence, the quadrotor MAV could move to the desired location via waypoint navigation without guidance of pilot

PID controller design for mobile robot using Bat Algorithm with Mutation (BAM)

By definition, a mobile robot is a type of robot that has capability to move in a certain kind of environment and generally used to accomplish certain tasks with some degrees of freedom (DoF). Applications of mobile robots cover both industrial and domestic area. It may help to reduce risk to human being and to the environment. Mobile robot is expected to operate safely where it must stay away from hazards such as obstacles. Therefore, a controller needs to be designed to make the system robust and adaptive. In this study, PID controller is chosen to control a mobile robot. PID is considered as simple yet powerful controller for many kind of applications. In designing PID, user needs to set appropriate controller gain to achieve a desired performance of the control system, in terms of time response and its steady state error. Here, an optimization algorithm called Bat Algorithm with Mutation (BAM) is proposed to optimize the value of PID controller gain for mobile robot. This algorithm is compared with a wellknown optimization algorithm, Particle Swarm Optimization (PSO). The result shows that BAM has better performance compared to PSO in term of overshoot percentage and steady state error. BAM gives 2.29% of overshoot and 2.94% of steady state error. Meanwhile, PSO gives 3.07% of overshoot and 3.72% of steady state error