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All Journal Media Jurnal Informatika Informatics and Software Engineering (ISE) Jurnal Komputasi
Agung Pambudi
Universitas Lampung
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Application of the SAW Method in a Decision Support System for Determining Non-Academic Achievement Students at XYZ High School Muhaqiqin Muhaqiqin; Ridho Sholehurrohman; Agung Pambudi
Informatics and Software Engineering Vol. 4 No. 1 (2026): June
Publisher : SAN Scientific

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58777/ise.v4i1.613

Abstract

The Simple Additive Weighting (SAW) method was applied in a Decision Support System (DSS) to identify non-academic high-achieving students at SMA XYZ, Central Lampung. The assessment includes four main criteria: competition achievements, organizational involvement, discipline and attendance and ethics and social behavior. This method used weighting, data normalization, and a final score calculation to rank the students objectively. The results showed that SAW effectively reduced subjectivity and produced fair and structured rankings. Among the ten students evaluated, Student 2 achieved the highest score of 9.4. The implementation of SAW in this DSS provided a more accountable basis for decision-making. It can serve as a data-driven evaluation model for non-academic performance in educational institutions.
Implementasi UI/UX Sistem Informasi Dinas dan Surat (SIDORA) Berbasis Website di Dinas Tenaga Kerja Provinsi Lampung Muhaqiqin; Wartariyus; Igit Sabda Ilman; Ridho Sholehurrohman; Agung Pambudi; Rofif Ramadhan Khoirullah Sowija
Jurnal Komputasi Vol. 14 No. 1 (2026)
Publisher : Jurusan Ilmu Komputer Fakultas MIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v14i1.359

Abstract

Penelitian ini mengimplementasikan antarmuka pengguna (UI) dan pengalaman pengguna (UX) pada Sistem Informasi Dinas dan Surat (SIDORA) berbasis website di Dinas Tenaga Kerja Provinsi Lampung. Laporan ini bertujuan untuk menerapkan ilmu yang diperoleh selama perkuliahan, memahami alur kerja instansi, serta mengembangkan solusi digital guna mempermudah proses administrasi surat yang sebelumnya masih dilakukan secara manual. Metodologi yang digunakan adalah System Development Life Cycle (SDLC) dengan tahapan perencanaan, analisis, perancangan, implementasi, dan pemeliharaan. Sistem SIDORA dirancang untuk mendukung digitalisasi proses surat-menyurat, mulai dari pembuatan, pengajuan, persetujuan, hingga penandatanganan digital, dengan melibatkan tiga peran pengguna utama: Pegawai, Pimpinan, dan Super Admin. Solusi ini mengatasi masalah keterlambatan, risiko kehilangan data, dan duplikasi informasi yang sering terjadi pada proses manual. Implementasi sistem menggunakan framework Laravel menghasilkan antarmuka yang sederhana dan mudah digunakan.Untuk mengukur kelayakan sistem, dilakukan pengujian usability menggunakan metode System Usability Scale (SUS). Hasil pengujian terhadap lima partisipan menunjukkan skor rata-rata SUS sebesar 89,5, yang termasuk dalam kategori Sangat Baik (Excellent). Nilai ini menunjukkan bahwa sistem SIDORA sangat mudah digunakan, memiliki navigasi yang jelas, dan diterima dengan baik oleh pengguna akhir. Secara keseluruhan, proyek ini berhasil menyediakan solusi teknologi yang efektif dan efisien untuk meningkatkan kualitas pelayanan publik di Dinas Tenaga Kerja Provinsi Lampung
Machine Learning Regression Model: Exploring Regression Algorithms for Mercedes-Benz Price Prediction Ridho Sholehurrohman; Muhaqiqin; Igit Sabda Ilman; Agung Pambudi; Wartariyus; Joko Triloka; Handoyo Widi Nugroho
Media Jurnal Informatika Vol 18 No 1 (2026): Media Jurnal Informatika
Publisher : Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v18i1.6476

Abstract

Predicting luxury car prices, such as Mercedes-Benz, remains challenging due to multiple interacting variables, including model, ratings, and market conditions. This study compares six regression algorithms, Linear Regression, Random Forest, Gradient Boosting, XGBoost, K-Nearest Neighbors, and AdaBoost, to identify the most effective model for Mercedes-Benz price prediction. A Kaggle dataset of 10,432 records was preprocessed through cleaning, removal of missing values (resulting in 10,307 records), One-Hot Encoding for categorical variables, and standardization of numerical features using StandardScaler, then split into 80% training and 20% testing data. Model performance was evaluated using MSE, RMSE, and R². Random Forest achieved the best performance (R² = 0.97; RMSE: $3,917), followed closely by Gradient Boosting (R² = 0.96; RMSE: $4,359) and XGBoost (R² = 0.96; RMSE: $4,305). Linear Regression achieved a similar R² (0.96) but higher errors (RMSE: $4,767), while AdaBoost (R² = 0.95; RMSE: $4,897) and KNN (R² = 0.90; RMSE: $5,657) showed lower performance. These findings confirm that ensemble methods, particularly Random Forest, significantly outperform traditional and distance-based approaches for luxury car price prediction. This study provides a comprehensive comparative framework for automotive pricing analytics, with future research directions including additional features, hyperparameter tuning, and integration of external market factors to further enhance prediction accuracy.
Co-Authors Handoyo Widi Nugroho Igit Sabda Ilman Igit Sabda Ilman Joko Triloka Muhaqiqin Muhaqiqin Muhaqiqin Ridho Sholehurrohman Ridho Sholehurrohman Rofif Ramadhan Khoirullah Sowija Wartariyus Wartariyus