Implementasi Teori Konstruktivisme Dalam Pembelajaran Matematika Di Rumah Untuk Siswa Menengah Pertama Pada Masa Pandemi Covid-19 Di Desa Huyula Kecamatan Randangan Kabupaten Pohuwato

Esensi dari para ahli mengantarkan model pembelajaran konstruktivisme menjadi pilihan yang tepat dalam menjalankan proses pembelajaran matematika di masa pandemi, hal ini dikarenakan konstruktivisme merupakan suatu model pembelajaran yang menjadikan siswa sebagai peserta didik aktif untuk membangun sendiri pengetahuannya. Melatih mahasiswa mengimplementasikan kegiatan Tridharma Perguruan Tinggi yaitu Pendidikan dan Pengajaran, Penelitian dan Pengabdian Masyarakat dalam waktu yang bersamaan. Di mana pada kegiatan ini mahasiswa adalah Tutor untuk masyarakat dalam rangka pembuatan media dan pada saat bersamaan mahasiswa melakukan penelitian serta berinteraksi dengan masyarakat melalui kegiatan pendataan dan pelatihan, melatih mahasiswa agar memiliki komitmen terhadap tujuan KKN yang ditujukan untuk memberikan pelayanan yang berkualitas pada individu, keluarga dan komunitas/masyarakat dan mendorong partisipasi mahasiswa untuk mengaplikasikan program pemerintah di masyarakat, Mendorong partisipasi masyarakat agar turut aktif dalam proses pemanfaatan bahan bekas dalam penyediaan media pembelajaran untuk mengenalkan konsep matematika awal pada masa pandemi dan meningkatkan pengetahuan dan pemahaman masyarakat tentang arti penting dari model pembelajaran dalam mengenal konsep matematika. Adapun yang mejadi hasil dari kegiatan ini adalah Luaran praktis Terimplementasinya model pembelajaran konstruktivisme dalam pembelajaran matematika pada siswa di desa Huyula Kab. Pohuwato dan Luaran wajib yaitu laporan hasil pelaksanaan KKN, catatan harian kegiatan, catatan keuangan, laporan kegiatan mahasiswa, Publikasi media massa

Pemodelan Pneumonia Berat Menggunakan Regresi Zero Inflated Negative Binomial di Gorontalo

In certain cases, the response variable has an excess zero that causes overdispersion. Therefore, to overcome overdispersion because excess zero Zero-inflated negative binomial regression can be used. The purpose of this study is to apply Zero inflated negative binomial regression to model the case of severe pneumonia in Bone Bolango and on the city of Gorontalo. Based on the result of ZINB Regression, it was found that there are 3 (three) factors that are significant that is ASI, Vitamin A, and low birth weight

METODE EXPONENTIAL SMOOTHING EVENT BASED (ESEB) DAN METODE WINTER’S EXPONENTIAL SMOOTHING (WES) UNTUK PERAMALAN JUMLAH PENUMPANG TIBA DI PELABUHAN PENYEBERANGAN GORONTALO

Forecasting the number of passengers can be a consideration for managers of Gorontalo Crossing Port regarding the provision of tickets. The number of passengers can be influenced by certain seasonal or special events. To see the special events that affect the number of passengers arriving at Gorontalo Crossing Port, the forecasting method used is the Exponential Smoothing Event Based (ESEB) method. The seasonal influences can be known through historical data patterns and using the Winter’s Exponential Smoothing (WES) method. After compared, the ESEB method is a better method of forecasting the number of arriving passengers at Gorontalo Crossing Port because it has a smaller MAPE value than the WES method

Total Rainbow Connection Number Of Shackle Product Of Antiprism Graph (〖AP〗_3)

Function if  is said to be k total rainbows in , for each pair of vertex  there is a path called  with each edge and each vertex on the path will have a different color. The total connection number is denoted by trc  defined as the minimum number of colors needed to make graph  to be total rainbow connected. Total rainbow connection numbers can also be applied to graphs that are the result of operations. The denoted shackle graph  is a graph resulting from the denoted graph  where t is number of copies of G. This research discusses rainbow connection numbers rc and total rainbow connection trc(G) using the shackle operation, where  is the antiprism graph . Based on this research, rainbow connection numbers rc shack , and total rainbow connection trc shack for .Fungsi jika c : G → {1,2,. . . , k} dikatakan k total pelangi pada G, untuk setiap pasang titik  terdapat lintasan disebut x-y dengan setiap sisi dan setiap titik pada lintasan akan memiliki warna berbeda. Bilangan terhubung total pelangi dilambangkan dengan trc(G), didefinisikan sebagai jumlah minimum warna yang diperlukan untuk membuat graf G menjadi terhubung-total pelangi. Bilangan terhubung total pelangi juga dapat diterapkan pada graf yang merupakan hasil operasi. Graf shackle yang dilambangkan (G1,G2,…,Gt) adalah graf yang dihasilkan dari graf G yang dilambangkan (G,t) dengan t adalah jumlah salinan dari  Penelitian ini membahas mengenai bilangan terhubung pelangi rc dan bilangan terhubung total pelangi trc(G)menggunakan operasi shackle, dimana G adalah graf Antiprisma (AP3)Berdasarkan penelitian ini, diperoleh bilangan terhubung pelangi rc(shack AP3,t )= t+2, dan total pelangi trc(shack AP3,t)=2t+3 untuk t ≥2

METODE SIMULASI HISTORIS UNTUK PERHITUNGAN NILAI VALUE AT RISK PADA PORTOFOLIO DENGAN MODEL MARKOWITZ

A portfolio concerns the formation of the composition of multiple assets to obtain optimum results. At the same time, Value at Risk is a technique in risk management to measure and assess parametrically (variant and co-variant), Monte-Carlo, and historical simulation. This research employed historic simulation because normal distribution is not required from returns and is a Value at Risk calculation model that is determined by the past value on produced return asset, in which this research aimed to determine the Markowitz model positive shares and Value at Risk in the portfolio by using historical simulation. The Markowitz model found eight shares with positive expected returns, which are as follows: BBCA, BBRI, BRPT, EXCL, ICBP, INDF, MNCN, and TPIA. The BBCA has the most significant exposure of all the shares with the amount of Rp 2.287.200.440.000, while the TPIA has the smallest exposure of all the shares with the amount of Rp 58.899.375.000. Further, the EXCL has the largest VaR with the amount of Rp 236.189.538.497, while the TPIA and ICBP had no VaR losses because the VaR of TPIA and ICBP is Rp 0 and Rp -1.407.719.893, respectively, along with the INDF as the share with the smallest VaR of Rp 18.513.213.620. The most significant exposure average is Rp 719.246.318.375, while the largest VaR average is Rp 76.827.608.341,3. As long as the VaR did not exceed the exposure value, the investors will be safe and have no loss

Pendekatan Goal Programming untuk Rute Pengangkutan Sampah

Saat ini masalah sampah telah menjadi masalah serius bagi semua lapisan masyarakat termasuk pemerintah daerah. Hal ini dipengaruhi oleh tingginya produktivitas manusia, pertambahan jumlah penduduk, dan ketersediaan ruang hidup manusia yang terbatas. Meningkatnya jumlah sampah yang dihasilkan dari hari-kehari dapat menimbulkan permasalahan yang serius, karena sampah sering terjadi tanpa disadari oleh penduduknya sendiri. Satu masalah dalam pengelolaan sampah adalah masalah pengangkutan sampah dari Tempat Pembuangan Sementara (TPS) ke Tempat Pembuangan Akhir (TPA). Tujuan penelitian ini adalah mengoptimalkan rute pengangkutan sampah di Kota Gorontalo.  Dalam penelitian, digunakan pendekatan Goal Programming untuk memformulasikan model dan mengoptimalkan rute dengan memperhatikan biaya, waktu, jarak, serta banyaknya pelanggan yang dapat terlayani.  Hasil penelitian menunjukan bahwa rute pengangkutan sampah dapat dioptimalkan dengan pendekatan Goal Programming. Adapun rute optimal dari masing-masing komponen yaitu biaya, waktu, jarak, serta banyaknya pelanggan yang dapat terlayani diperoleh hasil yaitu: 16 rute dengan 131 node, total biaya bahan bakar keseluruhan kendaraan Rp. 1.648.000,  yang membutuhkan waktu 128 jam per hari. Setelah menggunakan Goal Programming, node yang dapat dikunjungi sebanyak 127 node dengan total biaya bahan bakar minimum sebesar 22,10% per hari dengan waktu tempuh perjalanan 11 jam dan total jarak tempuh keseluruhan kendaraan 240,43 KM

PENDEKATAN MODEL VECTOR AUTOREGRESSIVE (VAR) UNTUK MERAMALKAN FAKTOR-FAKTOR YANG MEMPENGARUHI INFLASI DI PROVINSI GORONTALO

The vector autoregressive (VAR) model is a simultaneous equation modeling used to construct forecasting systems from interrelated time-series data. This study intends to predict factors that significantly influence inflation in the province of Gorontalo. Moreover, the data used in this study involved inflation data and factors that influence inflation every month in the province in the period of January 2009 - December 2018. The results of inflation forecasting in Gorontalo in 2019 show that at the beginning of 2019, the inflation was considered to be very low at around -0.48% to -0.40%. However, the inflation surged in March with -0.25% (the highest inflation rate). The percentage decreased to -0.30% and -0.33% in April and May. After the decline in April and May, in the middle of the year (June) inflation returned to -0.31% and did not experience a significant change until the end of the year, which was still in the range of -0.32%. The accuracy of the prediction results seen in the MAPE value from out sample data of variables Y1 to Y8 is on the average below 10%, indicating that VAR is a significant forecasting model

PERBANDINGAN METODE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE DAN METODE DOUBLE EXPONENTIAL SMOOTHING DARI HOLT DALAM MERAMALKAN NILAI IMPOR DI INDONESIA

As a form of purchased goods from other state’s imports have impacts both positive and negative to the states’s condition; therefore, prediction is required. Employing Autoregressive Integrated Moving Average (ARIMA) and Holt’s Double Exponential Smoothing (DES) methods, this study intends to identify which of the methods is the most accurate to predict Indonesia’s import value.  The ARIMA method stage involved: data ploting, data stasioneriation, temporary model identification, parameter estimation, test residual assumption, and prediction. Moreover, the Holt’s DES method involved: data plotting, initial value determination, optimal parameter identification, Level Lt and Trend Tt value quantification, andprediction. The result shows that ARIMA method is the most accurate method to predict Indonesia’s import value

Comparison of R and GeoDa Software in Case of Stunting Using Spatial Error Model

Gorontalo city is the capital of Gorontalo province which has a high incidence of stunting. This high incidence rate needs to get attention because stunting can further become one of the indicators of the low quality of human resources in Gorontalo. One method that can be used to analyze the factors that cause stunting is the spatial regression method, namely Spatial Error Model (SEM). SEM model can analyze used R and GeoDa software. The purpose of this study is to find out the factors that affect stunting in Gorontalo City and compare the results of the Spatial Error Model analysis based on the results of R and GeoDa software. The results showed that there are two variables that have a significant effect on stunting incidence, namely the variable number of Complete Basic Immunization (IDL) and the amount of proper sanitation. The R and GeoDa software comparison results showed there were several similar outputs i.e. LM test output, parameter estimation and R-square value, while the different outputs were Moran's I test output, Breusch-Pagan test, and AIC value. Although Moran's I test output and Breusch-Pagan’s test are different, but they produce the same conclusion. The AIC value produced by GeoDa is smaller than R software

Algoritma Genetika Untuk Penjadwalan Karyawan Ira Stationary

Employee scheduling is an activity plan for time sharing that contains a schedule for carrying out planned activities in the form of a table. This study aims to create an employee schedule model using a Genetic Algorithm, which is a heuristic method inspired by the process of natural selection, the strong will survive and reproduce, the stages of the Genetic Algorithm are population initialization, fitness value, selection, crossover, and mutation. The study results show an optimal model consisting of at most two shifts with a maximum of two holidays a week and not consecutively