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All Journal Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) JiTEKH (Jurnal Ilmiah Teknologi Harapan) Journal of Computing Theories and Applications
Karyudi, Mochammad Daffa Putra
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Mechanical Engineering

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Signifikansi Pengaruh Akses Teknologi Informasi terhadap Indeks Pembangunan Manusia di Indonesia Andriyan Rizki Jatmiko; Nofrian Deny Hendrawan; Rizza Muhammad Arief; Firnanda Al Islama Achyunda Putra; Mochammad Daffa Putra Karyudi
JITEKH Vol 11 No 2 (2023): September 2023
Publisher : Universitas Harapan Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35447/jitekh.v11i2.780

Abstract

Human Development Index is an indicator of the progress of a country, Information Technology is an important supporter to measure the Human Development Index. This research can provide an overview to measure the progress of a country in terms of access to Information Technology. This study processed secondary data provided by the Central Statistics Agency from 2017-2019. Using K-Means and K-Medoids clustering methods. K-Means is a popular non-hierarchical grouping method that groups objects by distance to a central point, aiming to maximize similarity within groups. K-Medoids is a powerful algorithm that handles outliers using techniques such as CLARA and PAM. In 2017 with an average of 0, Gorontalo 294611827 was a low cluster while in 2018 and 2019 Gorontalo entered a medium cluster with an average of 0.349570215 and 0.394531648. Similar to Central Sulawesi, in 2017 with an average of 0.275848883 Central Sulawesi was included in the low cluster while in 2018 and 2019 Central Sulawesi entered the medium cluster with an average of 0.291938731 and 0.334276807 From this result, it can be ascertained, by increasing knowledge in Information Technology, the HDI in an area can increase as well.
Analisis Cluster dengan K-Means untuk Pengelompokan Kabupaten/Kota di Provinsi Jawa Timur Berdasarkan Pembangunan TIK Tahun 2021-2022 Hidayati, Rahmatina; Indana, Luthfi; Karyudi, Mochammad Daffa Putra; Sasongko, Redoti Zulfikar
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 8 No 2 (2024): APRIL-JUNE 2024
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v8i2.1815

Abstract

The National Statistics Agency has grouped the IP-TIK by province into 4 categories (high, medium, low and very low). However, the details of IP-TIK in each province have not been explained in detail by city or district. The province chosen was East Java. In this province, the Human Development Index (HDI) is at a high level, but there is inequality in the distribution. This research aims to explore information on whether there are gaps in ICT development using a data mining approach, namely grouping. This is so that the East Java Provincial government focuses more on ICT development at the lower level. The method used is k-Means with Euclidean distance calculations. The highest group results are in cluster 2, which indicates that the IP-ICT level in East Java province is on average moderate. Group inequality occurred in cluster 4 for the household having a computer. Those included in cluster 4 (low category) include Pacitan Regency, Lumajang Regency, Probolinggo Regency, Bangkalan Regency, Sampang Regency and Pamekasan Regency.
Classifying School Scope Using Deep Neural Networks Based on Students' Surrounding Living Environments Karyudi, Mochammad Daffa Putra; Zubair, Anis
Journal of Computing Theories and Applications Vol. 2 No. 2 (2024): JCTA 2(2) 2024
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.11739

Abstract

This research investigates school scope classification using Deep Neural Networks (DNN), focusing on students living environments and educational opportunities. By addressing the interplay of socioeconomic and educational factors, the study aims to develop an analytical framework for understanding how environmental contexts shape academic trajectories. The research provides a nuanced understanding of the importance of features in educational classification by developing DNN models based on Spearman's Rank Correlation Coefficient (SRCC). The methodology employs machine learning techniques, integrating data wrangling, exploratory analysis, and multiple DNN models with K-fold cross-validation. The study analyzes 677 student records from two schools. The research examined multiple model configurations. Results show that the 'All Data' model achieved 83.08% accuracy, the 'Top 5' model 81.54%, and the 'Non-Top 5' model 79.23%. The SRCC-based approach revealed that while top correlated features are important, additional variables significantly contribute to model performance. The study highlights the profound impact of family background, social environment, and educational contexts on school selection. Furthermore, it demonstrates DNN's capability to uncover intricate, non-linear relationships, offering actionable insights for policymakers to leverage machine learning's potential in developing targeted educational strategies.
Co-Authors Andriyan Rizki Jatmiko Anis Zubair, Anis Indana, Luthfi Putra, Firnanda Al Islama Achyunda Rahmatina Hidayati Rizza Muhammad Arief Sasongko, Redoti Zulfikar