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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Prosiding Seminar Nasional Universitas Ma Chung
Dendy, Livanty Efatania
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Penerapan CRISP-DM untuk Deteksi Eksoplanet menggunakan Algoritma Regresi Logistik dan K-Nearest Neighbors Dendy, Livanty Efatania; Irawan, Valencia Elcheiana; Tanamal, Rinabi
Prosiding Seminar Nasional Universitas Ma Chung (Informatika & Sistem Informasi Bahasa dan Seni
Publisher : Ma Chung Press

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Abstract

Studi ini bertujuan untuk mengeksplorasi penggunaan model machine learning dalam klasifikasi eksoplanet dengan memprediksi apakah objek luar angkasa adalah eksoplanet berdasarkan intensitas cahayanya. Penemuan eksoplanet merupakan salah satu perkembangan paling menarik dalam astrofisika modern, hingga kini ribuan eksoplanet telah dikonfirmasi dan akan terus bertambah. Secara khusus, studi ini mengikuti metodologi CRISP-DM, yang mencakup pemahaman bisnis, pemahaman data, persiapan data, pemodelan, dan evaluasi serta menggunakan model Regresi Logistik dan K-Nearest Neighbors untuk menganalisis data dari Teleskop Luar Angkasa Kepler NASA. Tujuan utamanya adalah meningkatkan akurasi klasifikasi eksoplanet dan membantu dalam mengidentifikasi planet yang memiliki potensi untuk mendukung kehidupan. Model Regresi Logistik menunjukkan akurasi sebesar 0.53, precision sebesar 0.98, recall sebesar 0.53, dan F1 Score sebesar 0.68, menunjukkan kinerja sedang dengan fokus pada presisi. Sebaliknya, model K-Nearest Neighbors mencapai akurasi sebesar 0.99, precision sebesar 0.98, recall sebesar 0.99, dan F1 Score sebesar 0.99, menunjukkan kinerja superior di semua metrik. Studi ini menyimpulkan bahwa model K-Nearest Neighbors jauh lebih efektif untuk deteksi eksoplanet dibandingkan dengan Regresi Logistik. Studi selanjutnya disarankan untuk mempertimbangkan penambahan variabel tambahan dan memperluas ukuran sampel untuk meningkatkan validitas hasil serta mengeksplorasi penggunaan algoritma machine learning lainnya untuk meningkatkan akurasi prediksi.
Association Rule Mining for Truck Body Damage Pattern Analysis Using Apriori and CRISP-DM Dendy, Livanty Efatania; Tanamal, Rinabi
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12134

Abstract

This study investigates damage patterns in truck body components by applying the Apriori association rule mining algorithm within the CRISP-DM framework. The analysis is based on 281 historical repair records from CV Lestari’s fleet throughout 2024. The dataset encompasses 14 attributes, including vehicle types, route categories, body materials, and load conditions. To ensure the robustness of the generated rules, parameter tuning was conducted using a grid search approach, resulting in minimum support and confidence thresholds of 15% and 60%, respectively. A total of 50 association rules were derived, with several rules demonstrating high confidence values and lift values above 1.1, indicating meaningful and non-random correlations. Notably, structural frame damage is strongly associated with mountainous routes and heavy loads, while door and hinge damage tends to occur in aluminum box-bodied trucks operating under medium loads. These patterns were aligned with practical insights from field technicians and further contextualized through technical recommendations, such as reinforcing high-stress points and adjusting inspection schedules for high-risk configurations. The findings support the formulation of predictive maintenance strategies, enabling companies to transition from reactive repairs to proactive, data-driven decision-making. By integrating rule-based insights into maintenance planning, the study contributes to reducing unexpected failures, optimizing inspection frequency, and enhancing overall fleet reliability.
Co-Authors Irawan, Valencia Elcheiana Rinabi Tanamal