Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal J-Icon : Jurnal Komputer dan Informatika Jurnal Lembaga Pengabdian Kepada Masyarakat Undana
Efraim Kurniawan Dairo Kette
Universitas Nusa Cendana
Computer Science & IT Economics, Econometrics & Finance Education Engineering Health Professions Social Sciences

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Optimasi Hiperparameter Model Pembelajaran Mesin Untuk Klasifikasi Data Survey Langit Efraim Kurniawan Dairo Kette
J-Icon : Jurnal Komputer dan Informatika Vol 13 No 2 (2025): October 2025
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v13i2.18493

Abstract

Discovering the optimal model in today's popularity of various machine learning applications remains an essential challenge. Besides data dependency, the performance of classification models is also affected by deciding on suitable algorithm with optimal hyperparameter settings. This study conducted a hyperparameter optimization process and compared the accuracy results by applying various classification models to the observation dataset. This study obtains data from the Sloan Digital Sky Survey Data Release 18 (SDSS-DR18) and Sloan Extension for Galactic Understanding and Exploration (SEGUE-IV). The SDSS-DR18 and SEGUE-IV provide observational data of space objects, such as stellar spectra with corresponding positions and magnitudes of galaxies or stars. The SDSS-DR18 dataset contains magnitude and redshift data of celestial objects with target features of stars, Quasi Stellar Objects (QSOs), and galaxies. The SEGUE-IV dataset contains equivalent-width parameters, inline indices, and other features to the radial velocity of the corresponding star spectrum. This study utilized several machine learning models, such as k-Nearest Neighbor (KNN), Gaussian-Naive Bayes, eXtreme Gradient Boosting (XGBoost), Random Forest, Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). This study utilized Bayesian, Grid, and Random-based approaches to find the optimal hyperparameters to maximize the performance of the classification model. This study proved that some classification models have improved accuracy scores through the Bayesian-based hyperparameter optimization settings. This study discovers the XGBoost model shows the highest classification results after hyperparameters optimization compared to other models for both datasets with an average accuracy of 99.10% and 95.11%, respectively.
PELATIHAN PENGGUNAAN MAPLE DAN SPSS UNTUK GURU SMP KENCANA SAKTI HAUMENI SO’E Irvandi Gorby Pasangka; Robertus Dole Guntur; Albert Mario Kumanireng; Mira Wadu; Elisabeth Brielin Sinu; Brian Juned Septory; Arista Marlince Tamonob; Efraim Kurniawan Dairo Kette; Farly Oktriany Haning
Jurnal Pengabdian Kepada Masyarakat Undana Vol 20 No 1 (2026): JUNI 2026
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jpkmlppm.v20i1.27536

Abstract

Kegiatan pengabdian pada masyarakat ini bertujuan untuk dapat meningkatkan kualitas dan ketrampilan guru dengan memanfaatkan teknologi yang semakin berkembang khususnya dalam menggunakan program Maple dan SPSS sebagai alat bantu dalam melakukan suatu penelitian. Dengan bantuan program Maple dan SPSS, pengolahan data penelitian dapat dilakukan dengan mudah dan cepat. Materi yang disampaikan yaitu terkait dasar-dasar penggunaan program Maple dan SPSS beserta manfaatnya. Berdasarkan hasil evaluasi kegiatan ini, diketahui bahwa para guru sudah dapat menggunakan program Maple dan SPSS.
Co-Authors Albert Mario Kumanireng Arista Marlince Tamonob Brian Juned Septory Elisabeth Brielin Sinu Farly Oktriany Haning Irvandi Gorby Pasangka Mira Wadu Robertus Dole Guntur