UPAYA PEMERINTAH DESA DALAM PENYELESAIAN KONFLIK LAHAN PT.SAWIT ASAHAN INDAH DENGAN MASYARAKAT DI DESA LUBUK BILANG KECAMATAN RAMBAH SAMO KABUPATEN ROKAN HULU PROVINSI RIAU

Penelitian ini dilakukan dengan tujuan untuk mengetahui Upaya pemerintah desa dalam penyelesaian konflik lahan antara PT.Sawit Asahan Indah dengan masyarakat dan hambatan-hambatan didalam kegiatan penyelesaian konflik. Jenis penelitian ini yaitu penelitian kualitatif dengan mengunakan pendekatan deskriptif. Teknik pengumpulan data yang digunakan dalam penelitian ini yaitu observasi, wawancara, dan dokumentasi. Dan informan didalam penelitian ini adalah sebanyak 10 orang informan. Penyelesaian konflik lahan merupakan kegiatan yang dilakukan masyarakat di Desa Lubuk Bilang, terdapat beberapa tahapan di dalam kegiatan penyelesaian konflik lahan ini yaitu dengan cara mediasi dan musyawarah. Di dalam penyelesaian konflik lahan ini juga terdapat unsur adat, unsur pemerintah, unsur ulama dan unsur pemuda. Pada penyelesaian konflik lahan juga tidak terlepas dari upaya yang dilakukan antara pemerintah desa dan PT.Sawit asahan indah. upaya ini bertujuan untuk penyelesaian konflik yang ada serta untuk menjadikan Desa Lubuk Bilang lebih baik lagi. Hasil penelitian menunjukan bahwa proses kolaborasi dalam kegiatan penyelesaian konflik lahan belum berjalan dengan efektif dikarenakan masih adanya hambatan-hambatan yang terjadi dalam penyelesaian konflik. Hal ini membuat pelaksanaan dalam penyelesaian konflik lahan belum maksimal. Yang mana didalam penyelesaian hambatan-hambatan tersebut dilakukan dengan cara musyawarah

Clustered Hierarchical Entropy-Scaling Search of Astronomical and Biological Data

Both astronomy and biology are experiencing explosive growth of data, resulting in a “big data” problem that stands in the way of a “big data” opportunity for discovery. One common question asked of such data is that of approximate search (ρ–nearest neighbors search). We present a hierarchical search algorithm for such data sets that takes advantage of particular geometric properties apparent in both astronomical and biological data sets, namely the metric entropy and fractal dimensionality of the data. We present CHESS (Clustered Hierarchical Entropy-Scaling Search), a search tool with virtually no loss in specificity or sensitivity, demonstrating a 13.6 × speedup over linear search on the Sloan Digital Sky Survey’s APOGEE data set and a 68 × speedup on the GreenGenes 16S metagenomic data set, as well as asymptotically fewer distance comparisons on APOGEE when compared to the FALCONN locality-sensitive hashing library. CHESS demonstrates an asymptotic complexity not directly dependent on data set size, and is in practice at least an order of magnitude faster than linear search by performing fewer distance comparisons. Unlike locality-sensitive hashing approaches, CHESS can work with any user-defined distance function. CHESS also allows for implicit data compression, which we demonstrate on the APOGEE data set. We also discuss an extension allowing for efficient k-nearest neighbors search

CLUSTERED HIERARCHICAL ANOMALY AND OUTLIER DETECTION ALGORITHMS

Anomaly and outlier detection is a long-standing problem in machine learning. In some cases, anomaly detection is easy, such as when data are drawn from well-characterized distributions such as the Gaussian. However, when data occupy high-dimensional spaces, anomaly detection becomes more difficult. We present CLAM (Clustered Learning of Approximate Manifolds), a manifold mapping technique in any metric space. CLAM begins with a fast hierarchical clustering technique and then induces a graph from the cluster tree, based on overlapping clusters as selected using several geometric and topological features. Using these graphs, we implement CHAODA (Clustered Hierarchical Anomaly and Outlier Detection Algorithms), exploring various properties of the graphs and their constituent clusters to find outliers. CHAODA employs a form of transfer learning based on a training set of datasets, and applies this knowledge to a separate test set of datasets of different cardinalities, dimensionalities, and domains. On 24 publicly available datasets, we compare CHAODA (by measure of ROC AUC) to a variety of state-of-the-art unsupervised anomaly-detection algorithms. Six of the datasets are used for training. CHAODA outperforms other approaches on 16 of the remaining 18 datasets. CLAM and CHAODA scale to large, high-dimensional “big data” anomalydetection problems, and generalize across datasets and distance functions. Source code to CLAM and CHAODA are freely available on GitHub1

CLAM-Accelerated K-Nearest Neighbors Entropy-Scaling Search of Large High-Dimensional Datasets via an Actualization of the Manifold Hypothesis

Many fields are experiencing a Big Data explosion, with data collection rates outpacing the rate of computing performance improvements predicted by Moore's Law. Researchers are often interested in similarity search on such data. We present CAKES (CLAM-Accelerated $K$-NN Entropy Scaling Search), a novel algorithm for $k$-nearest-neighbor ($k$-NN) search which leverages geometric and topological properties inherent in large datasets. CAKES assumes the manifold hypothesis and performs best when data occupy a low dimensional manifold, even if the data occupy a very high dimensional embedding space. We demonstrate performance improvements ranging from hundreds to tens of thousands of times faster when compared to state-of-the-art approaches such as FAISS and HNSW, when benchmarked on 5 standard datasets. Unlike locality-sensitive hashing approaches, CAKES can work with any user-defined distance function. When data occupy a metric space, CAKES exhibits perfect recall.Comment: As submitted to IEEE Big Data 202

Pengembangan UMKM Sabun Cuci Piring “Mama Meudhen” Melalui KKN Melayu Serumpun di Aceh Jaya

Usaha Mikro, Kecil dan Menengah (UMKM) memainkan peran yang sangat penting dalam pembangunan ataupun pertumbuhan ekonomi. Utamanya, keberadaan UMKM di tingkat desa sangat berkontribusi terhadap tingkat perekonomian keluarga. Namun, masyarakat Gampong Meudheun belum menjadikan UMKM sebagai salah satu pilihan usaha walaupun banyak kelebihan yang ditawarkan. Berdasarkan kondisi tersebut peneliti yang terdiri dari mahasiswa/i KKN Melayu Serumpun di Gampong Meudheun, Aceh Jaya melakukan upaya pendampingan produksi sabun cuci piring. Sabun cuci piring ini dapat diproduksi dengan modal yang murah dan diharapkan dapat menjadi salah satu produk unggulan Gampong Meudheun yang diberi nama "Mama Meudheun". Metode yang digunakan dalam kegiatan ini adalah parcipatory action research. Upaya pengumpulan data dilakukan melalui transect mapping, diskusi dengan pihak terkait dan masyarakat serta studi literatur. Hasil kegiatan ini menunjukkan bahwa produksi sabun cuci piring dapat menjadi salah satu produk UMKM yang prospeknya menjanjikan. Produksi sabun cuci piring ini akan terus dilakukan dibawah pengawasan Pemberdayaan Kesejahteraan Keluarga (PKK)

STUDENTS' CREATIVE THINKING ANALYSIS OF SPLTV MATERIALS THROUGH VIRTUAL LEARNING REVIEWED FROM BEGINNING MATHEMATICS ABILITY

Mathematical creative thinking ability is certainly different for each student based on their initial mathematical abilities. Virtual learning has become the main learning process during a pandemic. The purpose of this study was to analyze how students' creative thinking skills on SPLTV material through virtual learning were based on students' initial mathematical abilities. The subjects in this study were students of class X SMA Malahayati Islamic School totaling 149 students, which were then taken 3 students based on their initial mathematical abilities. This study used the descriptive qualitative method. Data analysis in this study is to describe the results of student interviews and answers. Checking the validity of the data using data triangulation techniques. The results showed that students with high and moderate initial abilities had mathematical creative thinking skills from the ground up and needed to be honed again. Meanwhile, students with low initial ability cannot think creatively at all. Learning in virtual classes greatly affects the learning process, especially student learning outcomes. So that mathematical creative thinking skills are strongly influenced by students' initial mathematical abilities and student learning processes.