Lestari DA, Fiqa AP, Fauziah, Budiharta S. 2019. Growth evaluation of native tree species planted on post coal mining reclamation site in East Kalimantan, Indonesia. Biodiversitas 20: 134-143. Mining activities affect environmental qualities including the loss of vegetation cover and the damages of physical, chemical and biological properties of soil. As such, regulations enacted by the Indonesian Government state a legal obligation for mining companies to carry out reclamation on post coal mining site. Reclamation is an activity carried out to organize, restore and improve of environmental quality after mining operations to enhance the highly disturbed ecosystem of mined land into ecologically usable state. Yet, there is little body of knowledge on how to monitor the effectiveness of reclamation in improving environmental quality of ex-mining land. Aims of this study are to evaluate of the growth of native tree species planted on various types of reclamation sites in a mining concession in East Kalimantan, and to analyze the most influencing factor of their growth. Growth parameters measured in this research were plant height, stem diameter and branch-free stem height of the planted species. Micro-climatic factors and diversity of understorey plants were also measured as environmental parameters. PCA (Principal Component Analysis) analysis was conducted using PAST 4.0. statistical program. The results shows that reclamation area of post coal mining in the study site which is most suitable for local plant species, especially Shorea balangeran, has a sloping terrain. PCA shows that factor having maximum influence on growth of planted species on the reclamation site is pH of soil. The higher is the soil pH (i.e. less acid), the better is the species growth because the soil conditions in the post-coal mining area tend to be acidic. This research suggests that in post coal mining reclamation using native trees two key factors to enhance growth performance of planted species are sloping terrain so as water is not inundated and soil pH so as it is not too acid.

Perkembangan industri telekomunikasi saat ini sangat pesat karena telekomunikasi sudah menjadi kebutuhan utama bagi masyarakat sehingga banyak perusahaan yang bergerak di industry telekomunikasi. Banyaknya industry Telekomunikasi menuntut para pengembang untuk menemukan strategi atau suatu pola yang dapat meningkatkan penjualan dan pemasaran produk, salah satu strateginya adalah dengan memanfaatkan data transaksi. Paket data merupakan produk dibidang telekomunikasi. Proses Clustering saat ini masih di lakukan secara manual sehingga membutuhkan waktu, proses perhitungan dan ketelitian yang tinggi. Pada penelitian ini dibuat aplikasi berbasis website dengan tujuan untuk mempermudah Clustering data sehingga dapat digunakan sebagai referensi dalam perencanaan promosi produk telkomsel ke berbagai daerah. Metode yang digunakan untuk mengatasi permasalahan tersebut yaitu metode Clustering dengan menggunakan Algoritma K-Means. Algoritma K-Means merupakan algoritma pengelompokkan sejumlah data menjadi menjadi kelompok-kelompok data tertentu. Pada penelitian ini data penjualan dikelompokkan menjadi 3 yaitu data penjualan rendah, data penjualan sedang dan data penjualan tinggi. Pengujian clustering dengan algoritma K-Means pada aplikasi terhadap data transaksi penjualan paket telkomsel diperoleh persentase kesesuaian yaitu 100% dibandingkan dengan clustering manual.

Humans are facing a non-natural disaster that threatens the entire human population on Earth. Non-natural disaster is called Corona Virus Desease (COVID-19), which is a large family of viruses that can attack humans and animals that are currently a global pandemic. Humans usually cause respiratory infections, ranging from the common cold to serious illnesses such as MERS and SARS. COVID-19 itself is a new type of coronavirus found in humans and in the Wuhan area, Hubei Province, China in 2019. To assist medical staff in early detecting symptoms experienced by patients and facilitate the administration of hospital records, one of them was made an expert system that could detect this COVID-19 early with the Certainty Factor (CF) method. This expert system mimics similar symptoms experienced by COVID-19 patients and will be grouped into several patient statuses. Patients who experience serious symptoms will be grouped into Patients Under Supervision (PDP) and patients who are considered to have milder symptoms will be grouped into Insider Oversight status (ODP) while those who experience symptoms that are outside of the main symptoms will be classified into Non Suspect (NON) status . From 152 patient data inputted in this study, 114 ODP results with an average CF value of 91.38%, 36 PDP with an average CF value of 98.25% and 2 NONs with an average CF value of 40%. CF with system calculation experiments that represent patients get a CF value of 0.998848 or 99.88% to PDP. This expert system can be used to make decisions that can help medical personnel perform actions and administer better before conducting a through test in the laboratory to ensure positive or negative patients COVID-19

In theory of language and automata explain about abstract machine which in there is finite state automata which can be implemented into vending machine. Vending machine for a city like Jakarta is not a difficult subject to find it. Operational way is already widely known procedures, but about how it works in the machine, still not many know it, through this simulation is expected the user also better understand it.Abstrak-Pada teori bahasa dan automata menjelaskan tentang mesin-mesin abstrak yang mana didalamnya terdapat finite state automata yang bisa diimplementasikan kedalam vending machine. Vending machine untuk kota seperti Jakarta sudah bukan perihal sulit untuk menemukannya. Cara kerja secara operasional sudah banyak diketahui prosedurnya, namun perihal bagaimana cara kerjanya didalam mesin tersebut, masih belum banyak yang mengetahuinya, melalui simulasi ini diharapkan pengguna juga lebih paham akan hal itu. Kata Kunci : Vending Machine, Simulasi, Finite State, Bahasa dan Automata.

Water yam (Dioscorea alata L.) is an important tuber crop containing essential nutrition as an alternative food source. This study aimed to analyze the biochemical composition and nutritional value of fresh tuber of fifteen local accessions of water yam from East Java and followed by the accession selection for the recommendation of accession with the best nutritional value. Results showed that the nutritional value of proximates including moisture, carbohydrate and fiber were significantly different; except in protein, fat, and ash, with the range of moisture (65.47-82.46%), carbohydrate (17.10-29.37%), protein (1.29-3.00%), fat (0.00-0.29%), fiber (6.70-11.62%) and ash (0.85-1.44%). The ranges of mineral contents (mg/100g) were K (224.54-483.21), Ca (15.63-61.97), Mg (16.75-43.06), Fe (1.40-13.40), Zn (0.43-2.83) and P (329.37-699.91); Na mostly not detected. The anti-nutritions contents (mg/100g) comprised tannin (63.36-167.68) and oxalic acid (12.73-44.92). Analysis through HCA showed three clusters with a range of similarity from 67% to 96%; through PCA scatter biplot clearly illustrated the variation and correlation pattern among local accessions. Six promising accessions are recommended for further breeding and development i.e. Uwi Perti/30, Uwi Bangkulit/36, Uwi Bangkulit/42, Uwi Bangkulit/43, Uwi Biru/58 and Uwi Legi/66, based on their highest carbohydrates and protein content, and the lowest fat content.