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All Journal JURNAL PENGABDIAN KEPADA MASYARAKAT JTT (Jurnal Teknologi Terpadu) Jurnal Informatika IJCIT (Indonesian Journal on Computer and Information Technology) Jurnal Mantik Penusa JIPN (Journal of Informatics Pelita Nusantara) Martabe : Jurnal Pengabdian Kepada Masyarakat JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Jurnal Sains dan Teknologi TRIDARMA: Pengabdian Kepada Masyarakat (PkM) JATI (Jurnal Mahasiswa Teknik Informatika) INFOKUM MEANS (Media Informasi Analisa dan Sistem) Journal of Soft Computing Exploration Bulletin of Information Technology (BIT) Archive: Jurnal Pengabdian Kepada Masyarakat PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer Journal of Artificial Intelligence and Engineering Applications (JAIEA) Sasambo: Jurnal Abdimas (Journal of Community Service)
Insan Taufik
Universitas Negeri Medan
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences

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Journal : Journal of Soft Computing Exploration

 1 
Comparison of supervised machine learning methods in predicting the prevalence of stunting in north sumatra province Saragih, Vinny Ramayani; Arnita, Arnita; Indra, Zulfahmi; Taufik, Insan; Sinaga, Marlina Setia
Journal of Soft Computing Exploration Vol. 5 No. 4 (2024): December 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i4.498

Abstract

Stunting is a growth and development disorder in children caused by chronic malnutrition and repeated infections. Stunting has significant short- and long-term impacts and is one of the major health issues currently faced by Indonesia. The prevalence of stunting in North Sumatra Province is 18.9%, and the provincial government aims to reduce this prevalence to 14% by 2024. This study aims to compare the performance of several supervised machine learning methods in predicting stunting prevalence in North Sumatra Province. The data used is secondary data from 2021 to 2023, covering 33 districts/cities in the province. This study evaluates three machine learning models: Support Vector Regression (SVR), Decision Tree, and Random Forest, using evaluation metrics such as Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). The analysis results show that Random Forest provides the most accurate and consistent predictions, with lower MSE, MAE, RMSE, and MAPE values compared to the other models in most areas. Decision Tree yields good results in some regions but tends to produce higher errors in certain cases. SVR exhibits a more varied performance, with some regions showing higher prediction errors. Overall, Random Forest is the superior model for predicting district/city-level data, although model selection should be tailored to the data characteristics and application needs
Website based classification of karo uis types in north sumatra using convolutional neural network (CNN) algorithm Purba, Boy Hendrawan; Syahputra, Hermawan; Idrus, Said Iskandar Al; Taufik, Insan
Journal of Soft Computing Exploration Vol. 5 No. 4 (2024): December 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i4.500

Abstract

Indonesia is one of the largest archipelagic countries in the world. It has abundant cultural diversity including nature, tribes. One of the tribes in Indonesia is the Batak Karo tribe. Batak Karo is a tribe that inhabits the Karo plateau area, North Sumatra, Indonesia. Batak Karo has various cultures, one of which is a traditional cloth known as uis. Unfortunately, the Karo Batak community, especially the younger generation, has insufficient knowledge of the types of uis. Thus, a solution that is easily accessible both in terms of time, cost and experts in recognizing Uis is needed. This research aims to build a website-based application that can classify the types of Karo Uis. This research uses Convolution neural network (CNN) using Alex Net architecture, to get the best model this research compares several hyper parameters, namely learning rate of 10-1 to 10-4, and data division with a ratio of 70:30 and 80:20. The best model falls on a ratio of 70:30 and a learning rate of 10-4 with an accuracy of 98%, and a validation accuracy of 99%, then the model is stored in h5 format in this study successfully builds and implements the model into a web-based application.
Co-Authors Abdul Ghofur Abdul Ghofur Ahmad Landong Alfattah Atalarais Amran Sitohang Andriyani S, Rahayu Antonius Purba Arjon Samuel Sitio Arnita Arnita Arnita Arnita Chairunisah Chairunisah, Chairunisah Dede Yusuf, Dede Dinda Farahdilla Dharma Dinda Kartika Farhan Ramadhan, Haikal Fevi Rahmawati Suwanto Fitra, Awaludin Fristy Riandari Hafiz Harahap, Fauzan Hasugian , Paska Marto Hermawan Syahputra Hermawan Syahputra Ihsan Zulfahmi Irham Ramadhani Isda Pramuniati Izwita Dewi Kana Saputra S Kuraini, Atifa Nuzulul Latifah Hasibuan, Najwa Lubis, Afiq Alghazali MANSUR AS Mhd Hidayat Mhd Hidayat Mohammed Hafizh Al-Areef Muhammad Noer Fadlan Muhammad, Fachrezzy Muthmainnah, Inna Nadilla Putri, Rezkya Niska, Debi Yandra Nurliani Manurung Pane, M Iqbal Anata Pane, Yeremia Yosefan Pinem, Josua Purba, Boy Hendrawan Putri, Rezkya Nadilla Raiyan Fairozi Said . Iskandar Saragih, Vinny Ramayani Sasalia S, Putri Simamora, Elmanani Sinaga, Marlina Setia Siregar, Angginy Akhirunnisa Sri Mulyana Syawali, Yusfi Syifa Cendikia, Yolanda Tengku Ratna Soraya Tiopan Pandapotan Purba, Yeremia TONNI LIMBONG Wahabi Hasibuan, Rahman Warjaya, Angga Wete Polili, Andi Wete Polili, Andi Yulita Molliq Rangkuti Zulfahmi Indra, Zulfahmi