One of the villages in Jayapura City is Kampung Kayo Batu, which provides a variety of MSME products. The marketing and sales of MSME products are still performed in the traditional way. According to this, MSME performers still receive a limited amount of income. Knowing how information and communication technology (ICT) is used is important for expanding product marketing and sales. This community service activity aims to help MSME actors use ICT in promoting and marketing their products. The method applied in this activity includes socialization and training on the use of social media and e-commerce for promoting MSME products in Kayo Batu Village. The result obtained from the program is the actors receive a new understanding which encourages to start promoting their products online through e-commerce and social media.

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Pandemi COVID-19 pada awal tahun 2020 memberikan dampak yang sangat besar di berbagai sektor di seluruh dunia. Sektor pendidikan sangat terlihat pergeserannya, dimana biasanya siswa/siswi yang melakukan pembelajaran di Sekolah, namun karena pandemic harus melakukan pembelajaran dari rumah. Kampung Holtekamp adalah salah satu kampung di pinggir kota yang masih termasuk dalam wilayah administrasi ibu kota Provinsi Papua yaitu Kota Jayapura. Proses pembelajaran tidak dilakukan secara online karena berbagai factor, yakni keterbatasan fasilitas dan pengetahuan tentang teknologi. Oleh karena itu kegiatan pengabdian yang dilakukan adalah memberikan pelatihan kepada siswa/siswi Sekolah Dasar dan Menengah untuk memanfaatkan teknologi sehingga dapat menggunakan fasilitas seperti handphone dan jaringan internet untuk membantu proses pembelajaran. Setelah kegiatan pengabdian dilakukan ada peningkatan pemahaman dan kemampuan siswa/siswi dalam memanfaatkan teknologi untuk membantu proses pembelajaran. Siswa/siswi juga mampu menggunakan berbagai aplikasi yang diperkenalkan seperti zoom, google meet, pics art dan tiktok untuk mengerjakan tugas-tugas yang diberikan dari sekolah.   Kata kunci:Pandemi COVID-19, Pembelajaran Daring, Kampung Holtekamp.

The mechanized ability to specify the way surface type is a piece of key enlightenment for autonomous transportation machine navigation like wheelchairs and smart cars. In the present work, the extracted features from the object are getting based on structure and surface evidence using Gray Level Co-occurrence Matrix (GLCM). Furthermore, K-Nearest Neighbor (K-NN) Classifier was built to classify the road surface image into three classes, asphalt, gravel, and pavement. A comparison of KNN and Naïve Bayes (NB) was used in present study. We have constructed a road image dataset of 450 samples from real-world road images in the asphalt, gravel, and pavement. Experiment result that the classification accuracy using the K-NN classifier is 78%, which is better as compared to Naïve Bayes classifier which has a classification accuracy of 72%. The paving class has the smallest accuracy in both classifier methods. The two classifiers have nearly the same computing time, 3.459 seconds for the KNN Classifier and 3.464 seconds for the Naive Bayes Classifier.