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All Journal Dinamik Jambi Medical Journal : Jurnal Kedokteran dan Kesehatan e-Journal BUDIDAYA PERAIRAN ICON-CSE Agricore: Jurnal Agribisnis dan Sosial Ekonomi Pertanian Unpad Jurnal Sains & Teknologi Modifikasi Cuaca al-Afkar, Journal For Islamic Studies EDUVELOP (Journal of English Education and Development) Pendas : Jurnah Ilmiah Pendidikan Dasar Jurnal Kreativitas PKM Jurnal Hilirisasi IPTEKS (JHI) Jurnal Ilmu dan Teknologi Kayu Tropis Majalah Kedokteran Andalas Progresif: Jurnal Ilmiah Komputer Grouper: Jurnal Ilmiah Perikanan JURNAL EDUKASI NONFORMAL Mahaguru: Jurnal Pendidikan Guru Sekolah Dasar JURNAL ILMIAH EDUKATIF JIKEM: Jurnal Ilmu Komputer, Ekonomi dan Manajemen JITU (Jurnal Ilmiah Teknik Unida) Jurnal Multidisiplin Madani (MUDIMA) Jurnal Ekonomika: Manajemen, Akuntansi & Perbankan Syari'ah Jurnal Sains dan Teknologi Journal of Innovation Research and Knowledge Jurnal Ilmu Pendidikan dan Kearifan Lokal (JIPKL) Jurnal Pemberdayaan Sosial dan Teknologi Masyarakat Prosiding Seminar Nasional Rekayasa Teknologi Industri dan Informasi ReTII Journal of Education and Culture Jurnal Abdimas Maduma Ininnawa: Jurnal Pengabdian Masyarakat Perspektif: Jurnal Pendidikan dan Ilmu Bahasa Al-Tarbiyah: Jurnal Ilmu Pendidikan Islam Jurnal Pengabdian kepada Masyarakat Dewantara Jurnal Pendidikan dan Pembelajaran Jurnal Riset Guru Indonesia Pengertian: Jurnal Pendidikan Indonesia (PJPI) Jurnal Intelek Insan Cendikia ADM : Jurnal Abdi Dosen dan Mahasiswa Edelweiss : Journal of Innovation in Educational Research Jurnal Pustaka Cendekia Hukum dan Ilmu Sosial Menulis: Jurnal Penelitian Nusantara
Muhammad Fadli
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Journal : Progresif: Jurnal Ilmiah Komputer

 1 
Prediksi Keberhasilan Menindaklanjuti Pelanggan pada Dealer Mobil dengan Komparasi Algoritma Random Forest dan XGBoost Heidy, Helma Nopijani; Putro, Dimas Eko; Fadli, Muhammad; Susanto, Erliyan Redy
Progresif: Jurnal Ilmiah Komputer Vol 21, No 2 (2025): Agustus
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v21i2.2776

Abstract

The automotive industry is facing intense competition in boosting vehicle sales, where the follow-up process with prospective customers plays a crucial role in sales conversion. This study develops a predictive model for the success of follow-ups at car dealerships by comparing two machine learning algorithms: Random forest and XGBoost. A dataset of Honda car dealership customers from 2023 was processed through a preprocessing stage, including handling data imbalance and encoding categorical data. The models were evaluated using accuracy, precision, recall, and F1-score metrics. The results show that XGBoost outperforms with an accuracy of 91.67%, compared to Random forest's 88.89%. Both models demonstrate balanced performance across positive and negative classes, indicating a significant improvement over previous approaches. This study recommends expanding the dataset and developing a prediction-based decision support system to enhance the marketing effectiveness of car dealerships.Keywords: Machine learning; Random forest; XGBoost AbstrakIndustri otomotif menghadapi persaingan ketat dalam meningkatkan penjualan kendaraan, di mana proses tindak lanjut (Follow-up) kepada calon pelanggan menjadi faktor krusial dalam konversi penjualan. Penelitian ini mengembangkan model prediksi keberhasilan Follow-up pada dealer mobil dengan membandingkan dua algoritma machine learning, yaitu Random forest dan XGBoost. Dataset pelanggan dealer mobil Honda tahun 2023 diproses melalui tahap preprocessing, termasuk penanganan ketidakseimbangan data menggunakan encoding data kategorikal. Model dievaluasi menggunakan metrik akurasi, precision, recall, dan F1-score. Hasil menunjukkan XGBoost unggul dengan akurasi 91,67%, lebih baik dibanding Random forest dengan akurasi 88,89%. Kedua model menunjukkan performa yang seimbang pada kelas positif dan negatif, menandai peningkatan signifikan dari pendekatan sebelumnya. Penelitian merekomendasikan perluasan dataset dan pengembangan sistem pendukung keputusan berbasis prediksi untuk meningkatkan efektivitas pemasaran dealer mobil.Kata kunci: Machine learning; Random forest; XGBoost
Prediksi Risiko Kredit Nasabah Menggunakan Algoritma Data Mining: Studi Kasus pada PT Toyota Astra Finance Permadani, Icha Winadya; Sulistyo, Raka; Fadli, Muhammad; Susanto, Erliyan Redy
Progresif: Jurnal Ilmiah Komputer Vol 21, No 2 (2025): Agustus
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v21i2.2909

Abstract

This study aims to develop a credit risk prediction model for customers at PT Toyota Astra Financial Services using data mining algorithms, specifically Random Forest and XGBoost. In response to the challenge of non-performing loans (NPL), machine learning-based predictive models offer an effective solution to identify potential risks early. The research utilizes historical customer data encompassing demographic information, employment status, and loan history. After data preprocessing, the models were evaluated using accuracy, precision, recall, F1-score, and ROC-AUC metrics. The results indicate that XGBoost outperformed other models with an accuracy of 91.67% and an F1-score of 0.89 for the positive class. These findings demonstrate that applying machine learning algorithms can significantly enhance credit selection efficiency and reduce potential losses from defaulted loans.Keywords: Credit Risk; Machine learning; Random Forest; XGBoost, Data mining. AbstrakPenelitian ini bertujuan untuk membangun model prediksi risiko kredit nasabah pada PT Toyota Astra Financial Services dengan memanfaatkan algoritma data mining, khususnya Random Forest dan XGBoost. Dalam menghadapi tantangan kredit macet, model prediktif berbasis machine learning dapat memberikan solusi yang efektif untuk mengidentifikasi potensi risiko sejak dini. Penelitian ini menggunakan data historis nasabah yang mencakup informasi demografi, status pekerjaan, dan riwayat pinjaman. Setelah melalui tahap pra-pemrosesan data, model dievaluasi menggunakan metrik akurasi, presisi, recall, F1-score, dan ROC-AUC. Hasil menunjukkan bahwa XGBoost memiliki performa terbaik dengan akurasi sebesar 91,67% dan F1-score 0,89 pada kelas positif. Temuan ini menunjukkan bahwa penerapan algoritma machine learning dapat meningkatkan efisiensi seleksi kredit dan mengurangi potensi kerugian akibat kredit bermasalah.Kata kunci: Risiko Kredit; Machine learning; Random Forest; XGBoost; Data mining
Co-Authors Achmad Fahruddin Rais Adinda Hermawaty Agriqisthi 01 Agriqisthi Agriqisthi Agriqisthi Agriqisthi Ahmad Yusam Thobroni, Ahmad Yusam Ahmad, Dian Resty Aldi Helfahmi Alwina, Sakura An Nisa Andreas Lubis Anisya Nursyah Gusman Asprin Tamba Azzahra Zulfi Zulfiah Bambang Wahyudi Bambang Wijayanto Cornelia Dumarya Manik Darlan Dedi Ahmadi Devina Sari Siregar Dewi Kurniawati Diah kesumawati Dian Ayu Andrianindriani Dian Sri Purwanti Dio Ardy Azhari Marpaung Dwi Noviani Edho Dwi Tirwanda Eko Susanto ELIHAMI, ELIHAMI Ellya Anggraeni Ema Utami Endah Djuwendah Erika Meinovelia Erlina Rustam Erliyan Redy Susanto Evi Yulyantika Eviyanti Nurmalasari Fabel Fachrezzy Fahd Abdul Azis Fahmi Fahrurozi Fahrul Rizal Fani Setiawan Fernando De Napoli Marpaung Ghosy Thosan Haerawati Haerawati Hanina Hanina Hardeza Anggara Heidy, Helma Nopijani Hijrawati Ismail Holyness Nurdin Singadimedja Husnil Kadri Iin Wahyudi Jeni Alfikri Ginting Juliasman Juliasman Kartini J Khairul Bahri Kurnia Wati Kuswarini Kusno Lubis, Renni Ramadhani Luthfia Azahra Luthfil Hadi Anshari M Imam Budi Laksamana M Nasir Mardiono Mareta Fitria Wulandari Marpaung, Fernando De Napoli Marwan Lubis Ma’arif Arya Panca Meirando Rukhuz Mella Marianti Putri Muhamad Aris.M Muhammad Fachrur Rozi Muhammad Farhan Nst Muhammad Fuad Muhammad Hasan Muhammad Lathifuddin Arif Muhammad Mahfud Muhammad Surono Muhammad Surya, Muhammad Muslih, Rahmadani, Siti Makwa Nst, Aida Sari, Mahya Azizah Daulay, Ade Arga Wahyudi, Sri Hidayati Muhammad Syaiful Anam Muhammad Syukri Erwin Mustafiyanti Mustafiyanti Natalia Neni Triastuti Ninda Akilla Nur Azizah Nurhidayah Parinduri, Wina Mariana Permadani, Icha Winadya Purwanti, Bernadeta Siti Rahayu Puspita Dewi Rani Puspita Dian Agustin Putri Ayu Putri Sari Wulandari Putro, Dimas Eko Rachmawati, Diana Widhi Rahmanita Ginting Rani Saputri RATNA SARI Rezky Yunita Rifa Qidya Ardi Risqah Amaliah Kasman Rita Halim Rita Kartika Sari Rizki Hidayat Rizki Hidayat Ronal H.T Simbolon Rossi Passarella Roza Muliati Roziah Rudi Apriyanto Rumondang Safira Putri Wibowo Saptino Miro, Saptino Sarudin Sarudin Sati Simanjuntak, Vitalia Hanako Murni Sipriana S. Tumembouw SITI FATIMAH Soenardi Soenardi Stella Namira Suawaludin Suawaludin Sulistyo, Raka Supriadi Surya Khairunnisa Surya Khairunnisa Susanto, Erliyan Redy syafrizal Syahril Shobirin Syahrul, Muhammad Zulfadli Syamsul Arifin Syarifah Lulu Zannati Syawaluddin Tamaulina Br Sembiring Tamaulina Br. Sembiring Thania Dhea Fany Purba Tia Wida Ekaputri Tri Setia Ningsih Tuti Karyani Viki Sazali Wafik Azizah Wama Fima Wasrin Syafii Yusda Novianti ⁠Putri Andini