No abstract

<p>Indonesia merupakan negara maritim yang lebih dari 70 % wilayahnya adalah lautan. Lautan memiliki banyak fenomena alam yang mempengaruhi kehidupan sehari-hari masyarakat bahari atau masyarakat yang hidup tergantung pada laut. Salah satu fenomena alam dari laut adalah pasang surut. Pasang surut air laut dalam hal ini tinggi memegang peranan penting pada masyarakat diberbagai aspek seperti transportasi, pariwisata dan ekonomi. Prediksi tinggi pasang surut air dapat bermanfaat untuk memudahkan segala aktifitas masyarakat bahari. Penelitian ini menggunakan metote <em>Particle Swarm Optimization</em><em> (PSO)</em> dan <em>Jaringan Syaraf Tiruan (JST) </em>untuk prediksi tinggi pasang surut air<em> laut</em>. Metode <em>Particle Swarm Optimization</em> dan <em>Neural Network</em> memiliki beberapa parameter inputan seperti, jumlah neuron input, <em>learning rate</em>, <em>swarm</em>, c1,c2 <em>inertia</em> min, <em>inertia</em> max. Data yang digunakan sebanyak 1000 yang terbagi menjadi  700 data training dan 300 data testing. Hasil pengujian menunjukkan akurasi prediksi adalah 91.56 %  dengan menggunakan 90 swarm, learning rate 0,9 dan iterasi sebanyak 20 kali.</p><p> </p><p class="Judul2"><strong><em>Abstract</em></strong></p><p class="Judul2"><em>Indonesia is a maritime country where 70% of its territory is  the ocean. Oceans have many natural phenomena that affect the daily lives of maritime communities or people who live dependent on the sea. One of the natural phenomena of the sea is tide level. Tide level plays an important role in the community both directly and indirectly such as transportation, tourism and the economy. Predictions of tide level can be useful to facilitate all marine activities. This study uses Particle Swarm Optimization (PSO) and Artificial Neural Networks (ANN) to forecast tide level. PSO is used </em>to optimize the minimum error value on the network in order to get the ideal ANN network.<em> The Particle Swarm Optimization and Neural Network methods have several input parameters such as number of input neurons, learning rate, swarm, c1, c2 inertia min, inertia max. The number of data being used in this reseach is 1000 which divided into 700 training data and 300 testing data. The test results shows the prediction accuracy level is 0. 078373 using 90 swarms, learning rate is 0.9 and iteration is 20 times.</em></p><p> </p>

Latar Belakang dan Tujuan: Stunting merupakan masalah balita yang saat ini terjadi di berbagai daerah. Terjadinya stunting pada balita sering kali tidak disadari, dan setelah dua tahun  baru  terlihat  balita  tersebut  pendek. Masalah gizi yang kronis pada balita disebabkan oleh asupan gizi yang kurang dalam waktu yang cukup lama akibat orang tua atau keluarga tidak tahu  atau  belum  sadar  untuk memberikan makanan yang sesuai dengan  kebutuhan gizi anak. Faktor penyebab dari beberapa penelitian diantaramya masalah BBLR, Gizi, infeksi dan lain lain. Penelitian ini memiliki tujuan menganalisis hubungan berat badan lahir, panjang badan lahir dan jenis kelamin  pada balita stunting. Metode: Desain penelitian korelasional, dengan populasi balita stunting yang berada diwilayah desa Suci jumlah 58 orang. Jumlah sampel pada penelitian ini 48 balita dengan teknik sampling simple random sampling. Hasil: Sebagian besar responden berjenis kelamin perempuan (54,2%), memiliki riwayat berat badal lahir normal (91,7%), memiliki panjang badan lahir kurang dari 50 (52%). Hasil analisis bivariat menunjukkan pada indikator berat badan lahir P = 0.550, panjang badan lahir P= 0,744 sedangkan pada jenis kelamin P= 0,299 denan demikian semua variable tidak memiliki hubungan yang signifikan terhadap kejadian stunting.  Simpulan: Variable yang tidak berhubungan dengan kondisi stunting balita bisa dikarenakan faktor lain yang lebih dominan misal pemenuhan ASI dan pemenuhan gizi anak pada 6 bulan kehidupan pertama, sehingga perlu penelitian lebih lanjut mengenai faktor ini.

This paper presents an efficient and simple technique on reducing the false contour problem which often occurs in the JPEG decoded image. The false contour appears on JPEG decoded image while applying small quality factor. This problem induces unpleasant visual appearance. The proposed scheme exploits the usability of Halftoning-Based Block Truncation Coding (HBTC) approach on generating visual illumination to reduce the aforementioned problem. Three HBTC techniques, namely Ordered Dither Block Truncation Coding (ODBTC), Error Diffusion Block Truncation Coding (EDBTC) and Dot Diffused Block Truncation Coding (DDBTC), modify the DC components of all Discrete Cosine Transform (DCT) processed image on JPEG encoding stage. Experimental results show the proposed method superiority on JPEG false contour reduction. To further improve the JPEG decoded image quality, the proposed method utilizes the Gaussian kernel on replacing the DDBTC diffused kernel on spreading the error term. It assumes that the adjacent image blocks have a strong correlation in which high diffused coefficient should be applied on nearest adjacent neighbor. This paper extends the HBTC usability on JPEG false contour suppression.

Background: Multidrug-resistant tuberculosis (MDR-TB) is a serious threat to global TB control programs. According to WHO, there are 23,000 cases of TB multidrug-/rifampicinresistant (MDR/RR-TB) in Indonesia. In 2017, there were 442,000 of TB cases. There were 8,600 -15,000 MDR/RR-TB cases, of which 2.4% were new cases and 13% were previously treated TB cases. This study aims to determine the factors that influence the delay in diagnosis and treatment of MDR-TB patients. Subjects and Method: This study was a cross-sectional study conducted at Dr. Moewardi hospital, from September to October 2017, Surakarta, Central Java. A sample of 73 MDR-TB patients with disabilities on medical records was selected for this study. The dependent variables were delay in diagnosis and delay in therapy of MDR-TB cases. The independent variables were age, gender, distance to health facilities, and type of health facilities. Data were collected from medical records of MDR-TB patients who were treated from March 2012 to March 2017. Data were analyzed using the chi-square model. Results: Median delay in diagnosis = 4 days. Median treatment delay = 12 days. The average patient who had delayed MDR-TB therapy (≥4 days) was 44 years old (Mean= 44.19; SD= 12.64). Delay in MDR-TB diagnosis was not significantly associated with gender (OR= 0.53; 95% CI= 0.18 to 1.57; p= 0.264), distance to health facility (OR= 1.56; 95% CI= 0.58 to 4.21; p= 0.389), and type of health facility (OR= 0.60; 95% CI= 0.26 to 1.41; p= 0.983). The average of patient who had delayed MDR-TB therapy (≥12 days) was 41 years old (Mean= 41.39; SD= 12.69). Treatment delay was not significantly related to gender (OR= 0.45; 95% CI= 0.16 to 1.26; p= 0.137), distance to health facility (OR=1.44; 95% CI= 0.55 to 3.78; p= 0.466), and type of health facility (OR= 2.31; 95% CI= 1.03 to 5.21; p= 2.967). Conclusion:There was no statistically significant relationship between gender, distance from the patient's home to health facilities, and type of health facility with the delay in diagnosis and treatment of MDR-TB patients.

No abstract

Advance development of social media application has affected to human lifestyle. Everyone can obtain information from social media easyly. Its become easier to communicate each other using social media. Twitter is one of the fastest growing social media application. Deliver good and hoax information from one user to another. Event there are alot of fake account (bot) in Twitter. This objection of this study is to detect Twitter Bot accounts on Twitter social media by using the Decission Tree classification. Experiment results show the accuracy performance of the Decision Tree model reached 88.84% and UC curve by 0.965. Its shows that the Decision Tree classification is excellent in detecting Twitter Bot accounts.

Teknologi budidaya abalon telah tersedia dan dilakukan dengan berbagai metode budidaya pendederan dan pembesaran. Namun, hingga saat ini belum berkembang di masyarakat karena kurangnya minat pengusaha/pembudidaya abalon untuk mengaplikasikan secara komersial. Hal ini disebabkan oleh kualitas dan kuantitas benih belum stabil, pertumbuhan lambat, biaya tinggi, dan memerlukan waktu pemeliharaan lebih lama. Oleh karena itu, perlu diupayakan metode yang lebih sederhana dengan biaya murah untuk pembesaran abalon. Penelitian ini bertujuan untuk membandingkan pertumbuhan dan produksi abalon dengan padat tebar berbeda pada sistem-sistem tangki air mengalir. Benih abalon dipelihara di bak beton ukuran 12 m x 0,8 m x 0,8 m; kepadatan 70% dan 80% dari luasan dasar bak. Sementara untuk menghitung kelayakan usaha, rumus yang digunakan adalah revenue cost ratio (R/C). Hasil penelitian menunjukkan bahwa pertumbuhan panjang, lebar dan bobot cangkang pada densitas 70% lebih baik dibandingkan dengan densitas 80%. Kepadatan 70% menghasilkan 8,98% peningkatan hasil biomassa dan kematian 6,51% lebih tinggi dari kepadatan 80%. Berdasarkan analisis ekonomi, sistem pembibitan ini layak secara ekonomi di mana padat tebar 70% dari total luas dasar memiliki keuntungan finansial terbaik.Breeding technology for abalone is available, and its farming can be done using different nursery and grow-out methods. However, abalone farming has not yet been commercially practiced due to the lack of interest from fish entrepreneurs/fish farmers. This is due to several factors related to the quality and quantity of seeds, such as inconsistent availability, slow growth, high cost, and long culture was period. Therefore, it is necessary to develop a simpler and inexpensive method to culture abalone. This research aimed to improve the rearing technique for abalone. Two concrete tanks of 12 m x 0.8 m x 0.8 m in size were used in which abalone seeds were stocked with stocking densities of 70% and 80% of the bottom area. The concrete tanks were equipped with a flow-through water circulation system. The business feasibility of the culture system was calculated using the revenue cost ratio (R/C) formula. The results showed that the growth in length, shell width and weight at a density of 70% was better than that of the density of 80%. The density of 70% resulted in an 8.98% increase in biomass yield and a 6.51% mortality higher than a density of 80%. Based on the economic analysis, this nursery system is economically feasible where the stocking density of 70% of the total bottom area has the best financial return.