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All Journal REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat Jurnal Ilmu Komputer Dan Informatika
Haffiyan Putra Pratama
Universitas Pendidikan Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Introduction of Augmented Reality to Create Interactive Learning Media for Teachers in Purwakarta Ichwan Nul Ichsan; Dewi Indriati Hadi Putri; Haffiyan Putra Pratama; Diky Zakaria; Makna A’raaf Kautsar; Diana Eka Putri
REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat Vol 4, No 1 (2023): REKA ELKOMIKA
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/rekaelkomika.v4i1.16-24

Abstract

Utilization of educational media using Augmented Reality (AR) technology can stimulate students’ mindset in thinking critically about problems and events that exist in everyday life. The use of educational media with augmented reality directly can helps students learn wherever and whenever they want. In order to facilitate knowledge sharing, a community service activity was carried out by the UPI Telecommunication Systems Study Program, Purwakarta Campus, to introduce AR technology and train teachers to be able to create interactive technology-based learning media. The results obtained show that 1) participants are very satisfied with the content of the material with a percentage of 90.3% which means good; 2) the method of delivery of the workshop material by the presenters is considered good with a percentage of 89% and 3) according to the participants the activity of making the AR program is very useful with a response percentage of 93%.
Prediksi Tingkat Pendidikan Arsitek Perangkat Lunak Menggunakan Pendekatan Machine Learning Classification Seni Agustira; Haffiyan Putra Pratama
JUKOMIKA (Jurnal Ilmu Komputer dan Informatika) Vol. 9 No. 1 (2026): July
Publisher : LPPMPP Yayasan Sejahtera Bersama Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54650/jukomika.v9i1.664

Abstract

This study builds a classification model to predict the final education level of software architects based on their usage patterns of software architectural styles in real projects. The dataset used is the Dataset of Software Architectural Styles from Kaggle, consisting of 1,002 respondents with 18 numerical features representing the frequency of usage of 18 architectural styles. The target variable has three classes: BSC (CS or SE), MS (CS or SE), and PhD (CS or SE). The main challenge is the severely imbalanced class distribution, with BSC comprising 77.4% of samples. Preprocessing steps include IQR-based outlier handling, label encoding, StandardScaler normalization, and stratified 80:20 splitting. Two models were trained and compared: Logistic Regression as a baseline and Random Forest as an ensemble model. Results show that Random Forest significantly outperforms Logistic Regression, achieving 78.1% accuracy versus 38.8% on the test set, and cross-validation means of 76.1% versus 39.8%. Feature importance analysis reveals that Blackboard, Data-Centric, and Layered architectural styles contribute most to distinguishing education levels. This study concludes that Random Forest is better suited for non-linear classification with imbalanced classes, and recommends SMOTE and hyperparameter tuning as future improvements.
Co-Authors Diana Eka Putri Diky Zakaria Hadi Putri, Dewi Indriati Ichwan Nul Ichsan Makna A’raaf Kautsar Seni Agustira