PENDAMPINGAN MERANCANG PENELITIAN TINDAKAN KELAS DAN PENULISAN PUBLIKASI KEPADA GURU-GURU SD DI DESA SAKATIGA

Penelitian Tindakan Kelas  (PTK) merupakan penelitian yang dapat dilakukan guru dalam rangka memperbaiki proses pembelajaran untuk mencapai tujuan tertentu. Hal ini menunjukkan bahwa sangat penting bagi guru untuk melakukan PTK. Peningkatan jenjang jabatan dan golongan bagi para guru memerlukan beberapa karya ilmiah yang dipublikasikan. Karya ilmiah dapat dihasilkan dari kegiatan PTK, oleh sebab itu perlu adanya tambahan wawasan bagi para guru bagaimana menuangkan hasil PTK ke dalam makalah ilmiah. Pelaksanaan kegiatan pengabdian ini bertujuan untuk memberikan tambahan wawasan akan pentingnya PTK dalam proses pembelajaran dan membantu para guru dalam menulis serta mempublikasikan hasil PTK pada jurnal nasional. Diharapkan dari kegiatan ini, para guru dapat melakukan PTK dan menulis makalah ilmiah dari hasil PTK yang sesuai metode ilmiah. Kegiatan pengabdian dilakukan dengan dua tahap, yaitu tahap pelaksanaan dan tahap pendampingan. Tahap pelaksanaan meliputi penyampaian materi PTK dan Penulisan ilmiah. Berdasarkan hasil kuesioner diketahui bahwa terdapat peningkatan  mengenai pemahaman konsep PTK dan publikasi ilmiah antara sebelum penyampaian materi dan sesudah penyampaian materi. Tahap yang kedua adalah tahap pendampingan. Pada tahap ini dilakukan pendampingan penulisan dalam bentuk review draf artikel

PELATIHAN MERANCANG UJIAN ONLINE MENGGUNAKAN GOOGLE FORM UNTUK GURU-GURU DI WILAYAH KECAMATAN PAMPANGAN KABUPATEN OGAN KOMERING ILIR

During the pandemic, teaching and learning activities in Indonesia were carried out online. Apart from online learning activities, the learning evaluation process is also carried out online. Computer applications can be used as a tool to carry out learning evaluation activities. In this service, training is given to make online exams using google form. The training participants were 32 teachers with undergraduate educational backgrounds consist of elementary, junior high school, senior high school public and private school teachers in Pampangan Subdistrict, Ogan Komering district, South Sumatra. The training activities are carried out for one day. The activity stages consist of the preparation, implementation and evaluation stages. Based on the results at the implementation and evaluation stages, it can be concluded that participants in online exam preparation training activities can make online exams using google form. Selama masa pandemi kegiatan belajar mengajar di Indonesia dilakukan secara online atau dalam jaringan. Selain kegiatan pembelajaran online, proses evaluasi pembelajaran juga dilakukan secara online. Aplikasi komputer dapat digunakan sebagai alat bantu untuk melaksanakan kegiatan evaluasi pembelajaran. Dalam kegiatan pengabdian ini diberikan pelatihan membuat ujian online dengan menggunakan google form. Peserta pelatihan sebanyak 32 orang guru dengan latar belakang pendidikan sarjana yang terdiri dari guru sekolah dasar, sekolah menengah pertama dan sekolah menengah atas negeri dan swasta di Kecamatan Pampangan, Kabupaten Ogan Komering Ilir, Sumatera Selatan. Kegiatan pelatihan dilaksanakan selama satu hari. Tahapan kegiatan terdiri dari tahap persiapan, pelaksanaan dan evaluasi. Berdasarkan hasil pada tahap pelaksanaan dan evaluasi dapat disimpulkan bahwa peserta kegiatan pelatihan penyusunan ujian online dapat membuat ujian online menggunakan google form

ESK Pengaruh Penerapan Konsep Matematika Gasing dalam Meningkatkan Kemampuan Penjumlahan Bilangan Bulat Guru SDIT Auladi Plaju

THE EFFECT OF MATEMATIKA GASING CONCEPTS APPLICATION IN IMPROVING THE ADDITION SKILL OF SDIT AULADI PLAJU TEACHERS. The teacher is the spearhead of the mathematics learning process at the elementary school level (SD). The SD curriculum applies a thematic system, where lower-level classes, grades 1, 2, and 3 are guided by a class teacher who is responsible for delivering material for all subjects. Includes math subjects. Problems arise when the teacher does not master the material, mathematical concepts. This is due to the background of most classroom teachers not from the field of mathematics. One of the approaches used to help increase teachers' knowledge is Gasing mathematics. In accordance with the level, the mathematical concept conveyed in the training is addition. The activity method is a general lecture and demonstration using simple props and items in the school environment. The evaluation results showed an increase in the participants' abilities by 55% compared to before. This means that the top method has succeeded in increasing the teacher's ability to complete additions more quickly and correctly

Liver Segmentation Using Convolutional Neural Network Method with U-Net Architecture

Abnormalities in the liver can be used to identify the occurrence of disorders of the liver, one of which is called liver cancer. To detect abnormalities in the liver, segmentation is needed to take part of the liver that is affected. Segmentation of the liver is usually done manually with x-rays. . This manual detection is quite time consuming to get the results of the analysis. Segmentation is a technique in the image processing process that allocates images into objects and backgrounds. Deep learning applications can be used to help segment medical images. One of the deep learning methods that is widely used for segmentation is U-Net CNN. U-Net CNN has two parts encoder and decoder which are used for image segmentation. This research applies U-Net CNN to segment the liver data image. The performance results of the application of U-Net CNN on the liver image are very goodAccuracy performance obtained is 99%, sensitivity is 99%. The specificity is 99%, the F1-Score is 98%, the Jacard coefficient is 96.46% and the DSC is 98%.  The performance achieved from the application of U-Net CNN on average is above 95%, it can be concluded that the application of U-Net CNN is very good and robust in segmenting abnormalities in the liver. This study only discusses the segmentation of the liver image. The results obtained have not been applied to the classification of types of disorders that exist in the liver yet. Further research can apply the segmentation results from the application of U-Net CNN in the problem of classifying types of liver disorders

Implementasi Model Production Routing Problem With Perishable Inventory (PRPPI) dengan Kebijakan Optimize Delivery-Optimized Selling pada Produksi dan Distribusi Tempe

Industri tempe Ana adalah sebuah industri rumah tangga yang berada di Kota Palembang. Tempe produksi  Ana dikemas dalam tiga kemasan yaitu tempe daun, tempe plastik kepingan dan tempe plastik batangan, pada makalah ini diterapkan model PRPPI untuk jenis kemasan tempe daun. Tempe adalah salah satu produk yang bersifat mudah rusak (perishable). Penelitian ini bertujuan untuk meminimalkan biaya produksi, mendapatkan rute dan jumlah produksi tempe optimal menggunakan model Production Routing Problem Perishable Inventory (PRPPI) dengan kebijakan inventory Optimized Delivery–Optimized Selling (OD-OS). Model linier PRPPI diselesaikan menggunakan software Lingo 17. Diperoleh biaya produksi optimal adalah Rp 84.701. Rute optimal pendistribusian adalah Depot ? pasar Perumnas ? pasar Sekip Ujung ? pasar Kebon Semai ? Depot.  Jumlah produksi optimal adalah 27 potong

OPTIMIZATION OF RICE INVENTORY USING FUZZY INVENTORY MODEL AND LAGRANGE INTERPOLATION METHOD

Interpolation is a method to determine the value that is between two values and is known from the data. In some cases, the data obtained is incomplete due to limitations in data collection. Interpolation techniques can be used to obtain approximate data. In this study, the Lagrange interpolation method of degree 2 and degree 3 is used to interpolate the data on rice demand. A trapezoidal fuzzy number expresses the demand data obtained from the interpolation.  The other parameters are obtained from company data related to rice supplies and are expressed as trapezoidal fuzzy numbers. The interpolation accuracy rate is calculated using Mean Error Percentage (MAPE). The second-degree interpolation method produces a MAPE value of 30.76 percent, while the third-degree interpolation has a MAPE of 32.92 percent. The quantity of order  respectively  202677 kg, 384610 kg, 1012357 kg, 1447963 kg, and a Total inventory cost of Rp. 129231797951

PENERAPAN MODEL INVENTORI PROBABILISTIK FUZZY MULTIOBJEKTIF PADA SISTEM PERSEDIAAN BUAH SALAK

Inventory control is very important in production and trading activities. The purpose of inventory control is to maintain product availability. In certain cases, the products provided must be ordered from distributors outside the city and require waiting time from the time the order is placed until the product is received. The Multiobjective Probabilistic Fuzzy Inventory model can be applied to inventory optimization problems with the uncertainty of the leadtime parameter. In this study, the model was applied to the problem of supply salak fruit at one of the distributors. The first objective function is to minimize holding costs and the second is to minimize deterioration costs. The inventory model is transformed into a single objective form using a weighted method. Based on the results, the order cycle time is 3 days with the optimal total inventory of 430.1086 kg. The holding cost and deterioration costs are IDR 2,075,866 and IDR 571,034, respectively. Changes in the weight value of the objective function result in changes in the total cost value. The greater the weight for the first objective function, the smaller the total cost

ALGORITMA K-NEAREST NEIGHBOR (K-NN) DAN SINGLE LAYER PERCEPTRON (SLP) UNTUK KLASIFIKASI PENYAKIT ALZHEIMER

Alzheimer's disease is a brain disorder that causes memory loss, decreased thinking skills, communication difficulties, and behavioral changes. Early detection of this disease is very important for proper treatment and planning of medical needs. However, there is currently no drug that can cure Alzheimer's. Therefore, this study aims to develop accurate early predictions for Alzheimer's disease by comparing two algorithms: K-Nearest Neighbor (KNN) and Single Layer Perceptron (SLP) using the percentage split method. The results showed that testing using the K-NN algorithm resulted in an accuracy of 96%. The precision and recall values for class 0 (nondemented) are 93% and 100%, respectively, while for class 1 (demented) are 100% and 91%. On the other hand, testing using the SLP algorithm produces an accuracy of 99%. The precision and recall values for class 0 (nondemented) are 97% and 100% respectively, while for class 1 (demented) are 100% and 98%. Based on a comparison of the values for accuracy, precision, and recall, as well as the performance of the two classification methods, it can be concluded that the implementation of the Single Layer Perceptron algorithm provides the best prediction for early detection of Alzheimer's disease. These findings provide potential use of this algorithm in facilitating early diagnosis and timely intervention for patients with Alzheimer's

A Bootstrap-Aggregating in Random Forest Model for Classification of Corn Plant Diseases and Pests

Control of diseases and pests of maize plants is a significant challenge to ensure global food security, self-sufficiency, and sustainable agriculture. Classification or early detection of diseases and pests of corn plants is intended to assist the control process. Random forest is a classification model in tree-based statistical learning in making decisions. This approach is an ensemble method that generates many decision trees and makes classification decisions based on the majority of trees selecting the same class. However, tree-based methods are often unstable when small changes or disturbances exist in the learning data. Such instability can produce significant variances and affect model performance. This study classifies diseases and pests of the corn plant using a random forest method based on bootstrap-aggregating. It fits multiple models of a single random forest, then combines the predictions from all models and determines the final result using majority voting. The results showed that the bootstrap aggregating could improve the classification of diseases and pests of maize using a random forest if the number of trees is optimal