PERLINDUNGAN HUKUM TERHADAP PROFESI TENAGA KESEHATAN DALAM JASA PELAYANAN KESEHATAN STUDI KASUS PUSKESMAS TELUK LEBAN KECAMATAN MARO SEBO ULU KABUPATEN BATANGHARI

Dalam kehidupan sehari-hari banyak pekerja perempuan yang bekerja untuk memenuhi kebutuhan hidup dan keluarga yang diantaranya termasuk dibidang kesehatan. Pasal 5 Undang-Undang No.13 Tahun 2003 tentang ketenagakerjaan menjelaskan bahwa setiap tenaga kerja memiliki kesempatan yang sama tanpa diskriminasi untuk memperoleh pekerjaan. Ketentuan dalam pasal ini memberi peluang terhadap Tenaga kesehatan untuk bekerja sesuai dengan batas kemampuannya. Oleh karena itu penulis tertarik melakukan penelitian untuk menulis skripsi dengan judul “Perlindungan Hukum Terhadap Profesi Tenaga Kesehatan Dalam Jasa Pelayanan Kesehatan (Studi Kasus Puskemas Teluk Leban Kecamatan Maro Sebo Ulu Kabupaten Batanghari)”. Permasalahan dalam penelitian ini adalah : Bagaimana kronologis kasus terhadap tenaga kesehatan puskesmas teluk leban, Bagaimana bentuk perlindungan hukum bagi tenaga kesehatan puskesmas Teluk Leban. Penelitian ini menggunakan metode kualitatif dengan pendekatan yuridis empiris studi kasus. Data primer berupa wawancara, observasi dan dokumentasi. Desa Teluk Leban sendiri ternyata belum ada kasus yang sampai ke meja hijau terkait dengan dugaan malpraktek maka dari itu untuk melindungi dokter agar tidak terseret sampai ke meja hijau ialah dengan upaya menyelesaikan suatu masalah dengan jalur mediasi yang juga merupakan amanat dari undang-undang kesehatan. Kesimpulan dari penelitian yang saya lakukan bahwa penelitian ini tidak sampai ke pengadilan atau ke meja hijau kasus ini sudah terselesaikan melalui mediasi oleh kedua belah pihak. Kata Kunci: Perlindungan Hukum,Tenaga Kesehatan, dokter

Using satellite-measured relative humidity for prediction of Metisa plana’s population in oil palm plantations: a comparative assessment of regression and artificial neural network models

Metisa plana (Walker) is a leaf defoliating pest that is able to cause staggering economical losses to oil palm cultivation. Considering the economic devastation that the pest could bring, an early warning system to predict its outbreak is crucial. The state of art of satellite technologies are now able to derive environmental factors such as relative humidity (RH) that may influence pest population’s fluctuations in rapid, harmless, and cost-effective manners. This study examined the relationship between the presence of Metisa plana at different time lags and remote sensing (RS) derived RH by using statistical and machine learning approaches. Metisa plana census data of cumulated larvae instar 1, 2, 3, and 4 were collected biweekly in 2014 and 2015 in an oil palm plantation in Muadzam Shah, Pahang, Malaysia. Relative humidity values derived from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images were apportioned to 6 time lags; 1 week (T1), 2 weeks (T2), 3 week (T3), 4 weeks (T4), 5 week (T5) and 6 weeks (T6) and paired with the respective census data. Pearson’s correlation was carried out to analyse the relationship between Metisa plana and RH at different time lags. Regression analyses and artificial neural network (ANN) were also conducted to develop the best prediction model of Metisa plana’s outbreak. The results showed relatively high correlations, positively or negatively, between the presences of Metisa plana with RH ranging from 0.46 to 0.99. ANN was found to be superior to regression models with the adjusted coefficient of determination (R2) between the actual and predicted Metisa plana values ranging from 0.06 to 0.57 versus 0.00 to 0.05. The analysis on the best time lags illustrated that the multiple time lags were more influential on the Metisa plana population than the individual time lags. The best Metisa plana prediction model was derived from T1, T2 and T3 multiple time lags modelled using the ANN algorithm with R2 value of 0.57, errors below 1.14 and accuracies above 93%. Based on the result of this study, the elucidation of Metisa plana’s landscape ecology was possible with the utilization of RH as the predictor variable in consideration of the time lag effects of RH on the pest’s population

Remote sensing derivation of land surface temperature for insect pest monitoring

Temperature has major influence in insect development and outbreak. At present, the common method of collecting temperature information mainly relies on ground weather stations. However, this method is unfeasible for a large-scale area as weather stations distributions are sparse. This, however, can be compensated by the temperature measured through remote sensing satellites known as Land Surface Temperature (LST). Hence, this paper reviews the advantages and disadvantages of Thermal Infrared (TIR) and Microwave (MW) sensors for the acquisition of LST. This review will focus on the availability, suitability and adaptability of those sensors in providing LST for insect pest monitoring with the comparison being concentrated on their spatial and temporal characteristics, along with their accuracies

Development of geospatial model for Metisa plana (Walker) outbreak and outbreak prediction in oil palm plantations in Malaysia

Metisa plana (Walker) is a leaves defoliating insect that is able to cause complete skeletonization and death of the oil palm’s fronds. This insect can cause a loss of USD 2.32 billion for two consecutive years given only 10% of the 5 million hectares of oil palms being infested. Hence, efficient, rigorous control methods should be properly planned. In order to do this, the role of environmental factors on the pests’ population’s fluctuations should be well understood. Nonetheless, the current practices are still leaning towards the conventional approaches that are highly dependent on ineffective, time-consuming in-situ data collection. On the other hand, the utilization of geospatial technologies can be used to obtain data in rapid, harmless, and cost-effective manners. This study utilized the geospatial technologies to i) examine spatial and temporal climatic stresses that cause the outbreak of Metisa plana, ii) to construct the relationship between the geospatial data and Metisa plana outbreak, and iii) to predict the outbreak of Metisa plana in oil palm plantation. Metisa plana census data of larvae instar 1, 2, 3, and 4 were collected approximately biweekly over the period of 2014 and 2015. Moderate Resolution Imaging Spectroradiometer (MODIS) and The Tropical Rainfall Measuring Mission (TRMM) satellite images providing values of land surface temperature (LST), rainfall (RF), relative humidity (RH), and Normalized Difference Vegetation Index (NDVI) were extracted and apportioned to 6 time lags; 1 week (T1), 2 weeks (T2), 3 week (T3), 4 weeks (T4), 5 week (T5) and 6 weeks (T6). Linear relationship between Metisa plana with LST, RF, RH, and NDVI were carried out using the Pearson’s correlation, multiple linear regression (MLR) and multiple polynomial regression analysis (MPR). Artificial neural network (ANN) was then used to develop the best prediction model of Metisa plana’s outbreak. Presence of Metisa plana was influenced by LST, RF and RH, but not NDVI. The LST between 24oC and 28oC showed a strong relationship with Metisa plana, whereby its presence started to decline with LST from 28oC and above. However, the effect of time lag on the presence of Metisa plana was not prominent. The best MLR model was obtained with LST, RF and RH at T4 to T6 with an adjusted R2 = 0.29. The MPR model of LST at T4 to T6 depicted the best fit line with an adjusted R2 = 0.50. The highest accuracy of 95.29% was achieved by models generated by ANN utilizing the relative humidity at T1 to T3. The model generated by combined variables, LST, RF and RH at T4 to T6 was able to predict the presence of Metisa plana with the accuracy by up to 89.95%. Based on the result of this study, the elucidation of Metisa plana’s landscape ecology was possible with the utilization of geospatial technology

Remotely sensed relative humidity for predicting Metisa plana's population oil palm plantations

Metisa plana (Walker) is leaves defoliating insect that is able to cause a staggering loss of USD 2.32 billion within two years to Malaysian oil palm industry. Therefore, an early warning system to predict the outbreak of Metisa plana that is cost, time, and energy effective is crucial. In order to do this, the role of environmental factors such as relative humidity (RH) on the pests’ population’s fluctuations should be well understood. Hence, this study utilized the geospatial technologies to i) to construct the relationship between the geospatially derived relative humidity and Metisa plana outbreak, and ii) to predict the outbreak of Metisa plana in oil palm plantation. Metisa plana census data of larvae instar 1, 2, 3, and 4 were collected approximately biweekly over the period of 2014 and 2015. Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images providing values of RH were extracted and apportioned to 6 time lags; 1 week (T1), 2 weeks (T2), 3 week (T3), 4 weeks (T4), 5 week (T5) and 6 weeks (T6) prior to census date. Pearson’s correlation, multiple linear regression (MLR) and multiple polynomial regression analysis (MPR) were carried out to analyse the linear relationship between Metisa plana and RH. Artificial neural network (ANN) was then used to develop the best prediction model of Metisa plana’s outbreak. Results show that there are correlations between the presence of Metisa plana with RH, however, the time lag effect was not prominent. MPR was able to produce model with higher R2 in comparison to MLR with the highest R2 for both analysis were 0.48 and 0.15 respectively at T4 to T6. Model with the highest accuracy was achieved by ANN that utilized the RH at T1 to T3 at 95.29%. Based on the result of this study, the prediction of Metisa plana’s landscape ecology was possible with the utilization of geospatial technology and RH as the predictor parameter

Tunable Q-switched erbium-doped fiber laser in the C-band region using nanoparticles (TiO2)

A tunable, passively Q-switched fiber laser using titanium dioxide (TiO2 ) as saturable absorber (SA) is proposed and demonstrated. The TiO2 SA has an insertion loss and modulation depth of 2.6 dB and 19.1 % respectively. The generated laser output has a repetition rate of 83.3 kHz and pulse-width of 0.08 μs at a maximum pump power of 270.0 mW. On the other hand, tuning the operational wavelength from 1534.0 nm to 1570 nm while fixing the pump power at 70.0 mW varies the repetition rate and pulse width from 50.5 to 22.2 kHz and 1.3 to 2.7 μs respectively. The results indicate that TiO2 could be an inexpensive and efficient SA for generating passively Q-switched fiber laser

S-band Q-switched fiber laser using MoSe 2 saturable absorber

A passively Q-switched S-band fiber laser using Molybdenum Diselenide (MoSe2) saturable absorber (SA) is proposed and demonstrated. The SA is fabricated by depositing MoSe2 onto two fiber ferrules using the drop-cast method before heating and connecting the two fiber ferrules to form the SA. The passively Q-switched fiber laser designed using the MoSe2 SA has an operational range of 1491.0–1502.0 nm. The output pulse train has a pulse-width ranging from 2.0 μs to 1.0 μs and corresponding repetition rate of between 34.5 kHz and 90 kHz with increasing pump powers, as well as a signal-to-noise of about 35.97 dB. The peak performance of the proposed laser is between 1480.0 and 1490.0 nm, corresponding to the first peak gain region with the S-band