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All Journal Statistika Matematika: Jurnal Teori dan Terapan Jurnal Riset Matematika Bandung Conference Series: Mathematics Matematika Jurnal Teologi Islam
Erwin Harahap
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Religion Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Industrial & Manufacturing Engineering Mathematics Social Sciences Transportation Other

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Journal : Bandung Conference Series: Mathematics

 1 
Implementasi Convolutional Neural Network dalam Mengenali Image Angka Tulisan Tangan Andara Najla Jilan; M. Yusuf Fajar; Erwin Harahap
Bandung Conference Series: Mathematics Vol. 4 No. 1 (2024): Bandung Conference Series: Mathematics
Publisher : UNISBA Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/bcsm.v4i1.15334

Abstract

Abstract. Advances in information technology and artificial intelligence, particularly in the field of machine learning, have had a significant impact on various aspects of daily life. Machine learning's ability to learn from data and experience, identify patterns, make decisions, and even perform tasks previously only possible for humans, has revolutionized numerous industries. Within the realm of machine learning, deep learning stands out as a prominent approach. Deep learning employs artificial neural networks with intricate structures to comprehend and process data. One popular deep learning algorithm is the Convolutional Neural Network (CNN). CNNs have found extensive applications, especially in image recognition tasks. By leveraging CNNs, computers can accurately identify objects and patterns within images. The deep learning process underlying CNNs involves complex mathematical computations. It begins with feature extraction to maximize the significance of features from images, which are transformed into matrices. Subsequently, the available data is trained to develop a highly accurate CNN model. This research delves into the mathematical underpinnings of how deep learning, specifically using CNN algorithms, can recognize handwritten digit images. The employed CNN algorithm achieves an impressive accuracy of 99% in recognizing handwritten digit images. Abstrak. Perkembangan teknologi informasi dan kecerdasan buatan, terutama dalam bidang machine learning, telah memiliki dampak signifikan pada berbagai aspek kehidupan sehari-hari. Kemampuan machine learning memungkinkan komputer untuk belajar dari data dan pengalaman, mengidentifikasi pola, membuat keputusan, dan bahkan melakukan tugas-tugas yang sebelumnya hanya bisa dilakukan oleh manusia.Dalam dunia machine learning, terdapat salah satu pendekatan yaitu deep learning. Deep learning adalah artificial intelligence yang menggunakan neural networks dengan struktur yang lebih kompleks untuk memahami dan memproses data. Salah satu algoritma deep learning yang populer adalah Convolutional Neural Network (CNN). CNN telah digunakan dalam berbagai permasalahan, terutama dalam permasalahan mengenai pengenalan image. Penggunaan CNN memungkinkan komputer untuk mengenali objek dan pola dalam gambar dengan akurasi yang tinggi. Proses deep learning yang terjadi di dalam algoritma CNN merupakan perhitungan matematika. Diawali dari proses feature extraction untuk memaksimalkan fitur-fitur penting dari image yang sudah diubah menjadi matriks dan melatih data yang ada hingga didapatkan model dengan algoritma CNN berakurasi tinggi. Penelitian ini berfokus pada bagaimana deep learning dengan menggunakan algoritma CNN dapat mengenali image angka tulisan tangan. Algoritma CNN yang digunakan dapat menghasilkan model berakurasi 99% dalam mengenali image angka tulisan tangan.
Segmentasi Pelanggan Menggunakan K-Means Clustering Berbasis Analisis RFM (Studi Kasus: Data E-Commerce PD XYZ) 10060221020, Jumidah; Suhaedi, Didi; Erwin Harahap
Bandung Conference Series: Mathematics Vol. 6 No. 1 (2026): Vol. 6 No. 1 (2026): Bandung Conference Series: Mathematics
Publisher : UNISBA Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/bcsm.v6i1.22978

Abstract

Abstract. Increasing business competition requires companies to gain a deeper understanding of customer behavior in order to maintain loyalty and minimize the risk of customer churn. This study aims to perform customer segmentation and identify customers who have the potential to churn using the K-Means Clustering method based on Recency, Frequency, and Monetary (RFM) values. The transaction data used in this study were obtained from PD XYZ, a store operating on an e-commerce platform in Indonesia. The data were processed through preprocessing and transformation stages, including a scaling process using RobustScaler to adjust the data scale and reduce the influence of outliers. The determination of the number of clusters was carried out using the Elbow method and evaluated using the Silhouette Coefficient and Davies–Bouldin Index. The evaluation results show that the number of clusters provides the best clustering quality, with a Silhouette Coefficient value of 0.771495 and a Davies–Bouldin Index value of 0.547348. The segmentation produced three customer groups: non-loyal customers with low activity and low transaction value, loyal customers with high activity and high transaction value, and moderate customers with medium activity and transaction value. Overall, 3,840 customers were identified as having churn potential, while 236 customers were classified as non-churn. The results of this study indicate that an RFM-based clustering approach is effective in understanding customer characteristics and can be used as a foundation for developing more targeted retention and marketing strategies. Abstrak. Peningkatan persaingan usaha menuntut pelaku usaha untuk memahami perilaku pelanggan secara lebih mendalam guna mempertahankan loyalitas dan meminimalkan risiko kehilangan pelanggan (customer churn). Penelitian ini bertujuan melakukan segmentasi pelanggan serta mengidentifikasi pelanggan yang berpotensi churn menggunakan metode K-Means Clustering berbasis nilai Recency, Frequency, dan Monetary (RFM). Data transaksi yang digunakan berasal dari PD XYZ, yaitu sebuah toko yang beroperasi pada platform e-commerce di Indonesia. Data diproses melalui tahap pra-pemrosesan dan transformasi, termasuk proses scaling menggunakan RobustScaler untuk menyesuaikan skala data dan mengurangi pengaruh outlier. Penentuan jumlah klaster dilakukan dengan metode Elbow, serta dievaluasi menggunakan Silhouette Coefficient dan Davies-Bouldin Index. Hasil evaluasi menunjukkan bahwa jumlah klaster memberikan kualitas klaster terbaik dengan nilai Silhouette Coefficient sebesar 0,771495 dan Davies-Bouldin Index sebesar 0,547348. Segmentasi menghasilkan tiga kelompok pelanggan, yaitu pelanggan tidak loyal dengan aktivitas rendah dan nilai transaksi rendah, pelanggan loyal dengan aktivitas tinggi dan nilai transaksi tinggi, serta pelanggan moderate dengan aktivitas dan nilai transaksi menengah. Secara keseluruhan diperoleh 3.840 pelanggan berpotensi churn dan 236 pelanggan tidak churn. Hasil penelitian ini menunjukkan bahwa pendekatan RFM berbasis klastering efektif dalam memahami karakteristik pelanggan serta dapat digunakan sebagai dasar penyusunan strategi retensi dan pemasaran yang lebih tepat sasaran.
Co-Authors 10060221020, Jumidah Aditia Rahmat Tulloh Andara Najla Jilan Andri Suryadi Anggie Irfhan Refhiansyah Anissa Pujiarti Asep K. Supriatna Deni Darmawan Didi Suhaedi DWI PUSPITASARI Erwin Prayoga Ali Eti Kurniati Fadilatil Zikri Fadli Azis Farid H Badruzzaman Farid H. Badruzzaman Farid Hirji Badruzzaman Farid Risqullah Fargiana Gilang Nitiasya Hana Kartini Hana Mumtaz Hazna Insita Sahla Herlina Napitupulu Icih Sukarsih Indah Linda Nur'aini Ira Sumiati Ismi Fadhillah Jelita, Ade Kalijunjung Hasibuan Limah Olivia Alviani M Yusuf Fajar M. Yusuf Fajar Mahdiyah Intan Noer Fahmi Mahmudin Hasibuan Marlina, Emas Muhammad Deni Johansyah Muty Nurvia Nurul Hani Putra, Maurino R Ridwan Raesa Novandika Rafly Anugerah Rakhmat Ceha Resqy Sellva Budianti Sajidul Fajri Salsabila Lintang Veliani Sarah Fitria Sidiq Purnama Syifa Mardliyah Taufik Maulana, Taufik Tia Febrianti Yurika Permanasari