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Pemilihan Pemasok Bahan Makanan Kafetaria Universitas XYZ menggunakan Best Worst Method Tangka, George Morris William; Lompoliu, Erienika
KERNEL: Jurnal Riset Inovasi Bidang Informatika dan Pendidikan Informatika Vol 6, No 1 (2025)
Publisher : Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.kernel.2025.v6i1.7914

Abstract

Ketidakstabilan mutu bahan dan keterlambatan pasok—tercermin pada food-waste ±8 % serta denda pengiriman Rp 12 juta per tahun—menjadi persoalan utama rantai pasok kafetaria Universitas XYZ. Penelitian ini mengatasi masalah tersebut dengan menyusun model seleksi pemasok berbasis Best Worst Method (BWM). Tahapan meliputi: (1) penetapan lima kriteria kunci bersama tiga pengambil-keputusan; (2) penyusunan vektor Best→Others dan Others→Worst skala 1–9; (3) pemrograman linier untuk memperoleh bobot optimal dan memverifikasi konsistensi (CR = 0,08 ≤ 0,10); serta (4) perhitungan utilitas empat calon pemasok dan uji sensitivitas ±10 %. Hasilnya menunjukkan Kualitas Kesegaran (0,35), Keandalan Pengiriman (0,25), dan Kepatuhan Keamanan Pangan (0,20) sebagai determinan dominan, sementara pemasok Alpha menempati peringkat pertama dengan skor utilitas ternormalisasi 0,825; urutan tidak berubah pada variasi bobot, menegaskan robust­ness keputusan. Temuan ini menegaskan BWM sebagai kerangka pengambilan keputusan yang ringkas, konsisten, dan efektif untuk menyelesaikan masalah seleksi pemasok multikriteria di lingkungan layanan pangan kampus.
Perancangan Desain UI/UX Aplikasi Manajemen Keuangan Menggunakan Metode Design Thinking Tangka, George William Morris; Lompoliu, Erienika Meiling
Journal of Information System Research (JOSH) Vol 5 No 4 (2024): Juli 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i4.5680

Abstract

Money is widely accepted and trusted by society as a medium of payment or transaction, facilitating the exchange of goods and services. In everyday life, individuals face daily expenses that need effective management. The lack of awareness and knowledge about the importance of financial planning and management among students and young adults leads to significant financial problems. This study aims to design a financial management application with a focus on user-friendly and enjoyable UI/UX design. The application will help users manage their income, expenses, and savings efficiently. Key features include daily transaction records, bill payment reminders, expense analysis, and financial tips to achieve financial goals. The goal is to enable users to handle their finances more wisely. By applying the Design Thinking method, the research addresses complex financial management issues innovatively and comprehensively. Results from usability testing indicate that screens like the Select Category achieved a perfect usability score of 100, while others like the Expense Pages showed recurring issues with high misclick rates, highlighting areas for improvement. Overall, the average usability score was 75, suggesting room for enhancement.
FORECASTING HEALTH INSURANCE PAYER INCOME: A COMPARATIVE ANALYSIS OF DECISION TREE AND SVR ALGORITHMS Wilsen Grivin Mokodaser; Tonny Irianto Soewignyo; George Morris William Tangka; Fanny Soewignyo
Jurnal Riset Informatika Vol. 7 No. 3 (2025): Juni 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2466.493 KB) | DOI: 10.34288/jri.v7i3.369

Abstract

An insurance company is a type of non-bank financial institution that protects clients from risks and collects premiums over a certain period, these facts provide an overview of the insurance business and highlight its role in the economy, this study evaluated the performance difference between the Decision Tree Regressor and Support Vector Regression (SVR) in predicting insurance payer income. The Decision Tree model demonstrated strong predictive accuracy, achieving a Mean Absolute Error (MAE) of approximately 57 million and an R-squared (R²) value of 0.896, meaning it could explain around 89.6% of the variance in the data. Additionally, the model maintained high consistency, as evidenced by 5-fold cross-validation scores ranging from 0.908 to 0.967, indicating strong generalization and low risk of overfitting. In contrast, the SVR model significantly underperformed. It recorded a much higher MAE of over 237 million and a large Mean Squared Error (MSE), reflecting substantial deviations from the actual values. Its R² score of -0.299 suggests that SVR performed worse than a naive mean predictor, failing to identify meaningful patterns. This poor performance was consistent across all cross-validation folds, which also produced negative R² scores. The SVR model’s inadequacy is likely due to the large scale of the income data and the lack of proper preprocessing, such as normalization, or parameter tuning. Overall, these findings clearly demonstrate that the Decision Tree Regressor is a more suitable, accurate, and stable model for predicting insurance payer income.
Pemilihan Pemasok Bahan Makanan pada Kafetaria Universitas XYZ: Penerapan Metode MARCOS untuk Keputusan Multikriteria George Morris William Tangka; Raissa Camila Maringka; Erienika Meiling Lompoliu
Journal Of Business, Finance, and Economics (JBFE) Vol 6 No 2 (2025): Desember : Journal Of Business, Finance, and Economics (JBFE)
Publisher : Universitas Veteran Bangun Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32585/jbfe.v6i2.7520

Abstract

The cafeteria of XYZ University serves as an essential service hub for students, especially dormitory residents, so the selection of food suppliers must systematically consider quality, food safety, delivery reliability, service, and cost. This study aims to formulate a transparent and replicable multi-criteria decision-making model for selecting food suppliers for a university cafeteria. Five criteria are used in the evaluation, namely quality and freshness (C1), delivery reliability (C2), compliance with food safety standards (C3), service responsiveness (C4), and price (C5), applied to four supplier alternatives (Alpha, Beta, Gamma, Delta). The criteria weights are determined based on expert judgments and normalized so that their sum equals one. The MARCOS method is implemented through six main stages: construction of the decision matrix, determination of ideal and anti-ideal alternatives, normalization according to criteria type, weighting, calculation of aggregate scores, and computation of utility degrees relative to the ideal and anti-ideal conditions. The results show the final ranking Alpha > Gamma > Beta > Delta, with Alpha having the highest utility value and Delta the lowest. Sensitivity analysis with moderate variations in the weights indicates that the ranking remains stable, suggesting that the decision is robust against reasonable changes in policy preferences. In practical terms, the proposed model provides a numerical framework that is easy to audit and communicate to non-technical stakeholders, and can serve as a basis for procurement policy formulation and periodic evaluation of supplier performance in university cafeteria settings.