Article Per Year (5 Year)
p-Index From 2019 - 2024
0.659
P-Index
This Author published in this journals
All Journal MATAPPA: Jurnal Pengabdian Kepada Masyarakat Matrik : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer
Bobby Poerwanto
Universitas Cokroaminoto Palopo
Computer Science & IT Education

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
Pelatihan Penelitian Tindakan Kelas (PTK) Sebagai Upaya Meningkatkan Motivasi Kelompok Guru Kecamatan Bua Meneliti Bobby Poerwanto; Baso Ali
MATAPPA: Jurnal Pengabdian Kepada Masyarakat Vol 1, No 2 (2018): September
Publisher : STKIP Andi Matappa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31100/matappa.v1i2.170

Abstract

Kegiatan ini bertujuan untuk membiasakan guru-guru untuk melakukan penelitian tindakan kelas, sehingga masalah-masalah yang terjadi di kelas bisa diselesaikan dengan penelitian. Subjek kegiatan ini adalah guru dari SMPN 2 Bua dan SMPN Satap Raja Bua. Kedua sekolah ini dipilih karena di Kecamatan Bua, sekolah ini yang masih berakreditasi B. Materi kegiatan ini ada 3 yaitu kebijakan pemerintah tentang penelitian, teknis pelaksanaan PTK, dan model pembelajaran inovatif. Hasil dari kegiatan ini adalah dari 31 peserta, dan semua peserta ingin melakukan PTK setelah kegiatan ini. Kebanyakan dari para guru belum melakukan PTK karena tidak mengetahui teknis pelaksaan PTK
Implementasi Algoritma Fuzzy C-Means dalam Mengelompokkan Kecamatan di Tana Luwu Berdasarkan Produktifitas Hasil Perkebunan Bobby Poerwanto; Baso Ali
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 19 No 1 (2019)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (767.095 KB) | DOI: 10.30812/matrik.v19i1.499

Abstract

This study aims to classify sub-districts in Tana Luwu (Kabupaten Luwu, Luwu Timur, Luwu Utara, dan Palopo City ) using Fuzzy C-Means algorithm. The data used are data from BPS on the results of estate crops, namely coconut, oil palm, coffee, pepper, cocoa, clove, and land area. The results obtained from this study are from 45 sub-districts, there are 8 districts that are included into productive categories namely Burau, Sabbang, Mappedeceg, Baebunta, West Malangke, Sukamaju, Bone-Bone, and Tanalili. In addition, this study also found that matrix pseudo-partition selection can affect this algorithm in terms of the number of iterations produced.
Resilient Backpropagation Neural Network on Prediction of Poverty Levels in South Sulawesi Bobby Poerwanto; Fajriani Fajriani
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 20 No 1 (2020)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (441.726 KB) | DOI: 10.30812/matrik.v20i1.726

Abstract

Poverty is a topic that continues and is always discussed up to this time, as a benchmark indicator of how the level of welfare and prosperity in the lives of people in a country. Several attempts have been made by the central and regional governments to reduce poverty levels, including “Bantuan Langsung Tunai” (BLT) and the “Program Keluarga Harapan” (PKH). However, poverty reduction in Indonesia is still slowing down, including in South Sulawesi. Based on this, this study aims to predict poverty levels in South Sulawesi. Factors thought to influence poverty levels are the Human Development Index (HDI), the Open Unemployment Rate (TPT), and the Gross Regional Domestic Product (GRDP). The data used are data from 2010 to 2014. The method used is a backpropagation neural network with a resilient algorithm or better known as a resilient backpropagation neural network (RBNN). The results of the prediction of poverty levels using predictors of HDI, TPT, and GRDP showed that the analysis of the RBNN reached its optimum using architecture [3- 9 - 1] and reached convergence at the 81th iteration with an accuracy rate of 95.34%.
Implementation of K-Means Clustering on Poverty Indicators in Indonesia Suwardi Annas; Bobby Poerwanto; Sapriani Sapriani; Muhammad Fahmuddin S
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 21 No 2 (2022)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (411.441 KB) | DOI: 10.30812/matrik.v21i2.1289

Abstract

This study aims to cluster all districts/cities in Indonesia related to poverty indicators. The attributes used are poverty gap index and poverty severity index. The data used comes from BPS. The method used is K-Means clustering, and the results show that by using the elbow and silhouette index methods, the optimal number of clusters is 2, where for cluster 1, it can be defined as a cluster with an area with a high poverty gap index and poverty severity index compared to cluster 2. As a result, cluster 1 has 42 districts/cities, and 472 for cluster 2.
Co-Authors Ali, Baso Annas, Suwardi Fajriani Fajriani Muhammad Fahmuddin Sapriani Sapriani