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Hasil Pencarian

Ditemukan 4 dokumen yang sesuai dengan query
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Nadhira Riska Maulina
Pengelompokkan pengguna mesin kasir modern berdasarkan perilaku pelanggan dengan pendekatan data mining = Data mining approach for clustering modern cash register machine's customers based on customer behavior
"Dengan adanya teknologi, pelaku usaha dapat memanfaatkannya sebagai salah satu faktor pendorong peningkatan operasional bisnis menjadi lebih efisien. Peningkatan jumlah pengguna yang beralih ke mesin kasir modern mengakibatkan kemunculan perusahaan penyedia dengan layanan dan harga yang tidak jauh berbeda. Di Indonesia perusahaan penyedia meisn kasir modern bersaing untuk dapat mempertahankan pelanggannya. Penelitian ini bertujuan untuk mendapatkan informasi mengenai karakteristik pelanggan dengan membuat segmentasi pelanggan berdasarkan perilaku penggunaan mesin kasir pada usaha yang dikelolanya. Setelah diketahui kelompok pelanggan dengan karakteristiknya, setiap kelompok akan diklasifikasikan berdasarkan status pelanggan (Aktif dan Tidak Aktif). Data untuk penelitian ini didapatkan dari salah satu perusahaan penyedia yang menjadi pelopor mesin kasir modern di Indonesia. Atribut yang dipilih yaitu Length, Recency, Frequency, dan Monetary (LRFM). Data Mining merupakan metode yang dapat digunakan untuk mengidentifikasi informasi yang berada pada set data untuk Customer Relationship Management. Metode yang digunakan untuk segmentasi pelanggan adalah metode K-Means Clustering sedangkan untuk klasifikasi status pelanggan adalah metode Decision Tree C4.5. Hasil dari penelitian ini berupa karakteristik setiap kelompok pelanggan dan model prediksi status pelanggan yang dapat digunakan sebagai dasar pembuatan usulan strategi untuk membantu perusahaan dalam penyusunan strategi retensi pelanggan.
As the development of technology becomes advanced, businessmen can utilize it as one of the factors to drive the improvement or growth of their business operations to be more efficient. The increasing number of users who switch to use the modern cash register machines has resulted in the emergence of company with services and price that are not much different. In Indonesia, modern cash register companies compete to be able to retain their customers. This study aims to get information about the characteristics of customers through customer segmentation based on the behavior. After knowing the customer segments with their characteristics, each segment will be classified based on customer status (Active and Inactive). The data for this study was obtained from one of the provider companies that became the pioneer of the modern cash register in Indonesia. The selected attributes are Length, Recency, Frequency, and Monetary (LRFM). Data Mining is a method that can be used to identify information that is in a data set for Customer Relationship Management. The method used for customer segmentation is the K-Means Clustering while the classification of customer status is the Decision Tree C4.5. The results of this study are in the form of characteristics of each customer group and customer status prediction models that can be used as the basis for making strategies to assist companies in preparing customer retention strategies."
Depok: Fakultas Teknik Universitas Indonesia, 2019
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UI - Skripsi Membership  Universitas Indonesia Library
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Harjani Rezkya Putri
Aplikasi Data Mining untuk Prediksi Kondisi Kesulitan Keuangan Perusahaan Di Bursa Efek Indonesia = Financial Distress Prediction in Indonesia Stock Exchange using Data Mining
"Tantangan dan persaingan di dunia investasi telah lama menjadi fokus besar untuk dipelajari karena keterkaitannya dengan profitabilitas. Penelitian menunjukkan bahwa penghasilan negatif substantif dari profitabilitas perusahaan sangat terkait dengan kondisi kesulitan keuangan perusahaan, suatu kondisi di mana perusahaan mengalami kesulitan dalam memenuhi kewajiban keuangannya. Penelitian sebelumnya mengembangkan model prediksi kesulitan keuangan dengan menggunakan metode statistik konvensional yang memiliki beberapa kerugian karena ketergantungannya pada beberapa asumsi restriktif. Penelitian ini menggunakan metode data mining karena keunggulannya dengan asumsi yang tidak terlalu ketat untuk memprediksi kesulitan keuangan, dilengkapi dengan variabel keuangan dan non-keuangan. Berfokus pada perusahaan-perusahaan yang terdaftar di Bursa Efek Indonesia (BEI) selama periode 5 tahun, decision tree C4.5 dan random forest dikembangkan dan dievaluasi. Model decision tree C4.5 menunjukkan model prediksi dengan kinerja terbaik, dengan tingkat kesalahan terkecil, akurasi dan recall, dengan akurasi dan recall keseluruhan masing-masing adalah 96,14%, dan 98,06%. Hasil penelitian ini juga memiliki beberapa luaran seperti ROA yang menjadi variabel yang paling penting untuk menentukan perusahaan yang mengalami kesulitan keuangan.
The challenges and competition in the investment world recently became a great focus to be studied as it is greatly linked to profitability. It has been agreed that substantive negative earning of profitability of firms greatly linked to financial distress, a condition which a firm has difficulty fulfilling its financial obligations. Previous research developed financial distress prediction model using conventional statistical methods that suffer from disadvantages as it depends largely on some restrictive assumptions. This research used data mining methods as its superiority with less restrictive assumptions to predict financial distress, with both financial and non-financial variables examined. Focused in listed firms in Indonesia Stock Exchange (IDX) for 5 years period, C4.5 decision tree and random forest are developed and evaluated. The C4.5 decision tree model demonstrated the best performing prediction model, with the smallest error rates, highest accuracy and recall, with overall accuracy and recall are 96.14%, and 98.06% respectively. The result of this research also offer several inferences such as return on asset being the most significant or important predictive variable to determine financially distressed firms"
Depok: Fakultas Teknik Universitas Indonesia, 2019
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UI - Skripsi Membership  Universitas Indonesia Library
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Naufal Allaam Aji
Perancangan Penilaian Risiko Kredit Pada Kredit Pemilikan Rumah Dengan Pendekatan Teknik Klasifkasi Data Mining = Credit Scoring Through Data Mining Approach: A Case Study of Mortgage Loan in Indonesia
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Non-performing loans has been one of the biggest problems in the banking sector. One alternative to minimize credit risk is to improve the evaluation of the applicant's credibility. Credit risk assessment methods must be improved. Credit scoring is an evaluation of the feasibility of credit requests. Poor credit can lead to an increase in non-preforming loans that may reduce bank productivity even in the event of financial crises and financial institutions bankruptcy. The number of Data-mining-based Credit scoring model has increased. The performance of classifiers in solving financial problem become the main reason why it is growing rapidly. Previously, credit scoring is based on the conventional statistics such as logistic regression and discriminant analysis. Eventhough those techniques produce a good accuracy, some of the assumptions cannot be accomplished by the data. Along the development of infromation technology, more advance approach named data mining has been developed. Therefore, this study performs Data Mining approach to solve NPL percentage problems in Bank. The classification methods that will be used is Decision Tree C4.5, Back Propagation Neural Network, and ensemble classifier algorithms. Classifier with the best accuracy is Decision Tree C4.5 with Adaboost with 98,87% The best sensitivity also performed by Decision Tree C.5 complemented by adaboost with 97,3%. It is considered as the best model in terms of prevent the type II error which could impact to the increase of non-performing loan in a bank.

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Depok: Fakultas Teknik Universitas Indonesia, 2019
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UI - Skripsi Membership  Universitas Indonesia Library
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Iffa Maula Nur Prasasti
Automobile insurance fraud detection using supervised learning classifiers = Deteksi kecurangan pada asuransi kendaraan bermotor dengan supervised learning classifiers.
"Asuransi mobil adalah produk asuransi yang banyak digunakan di Indonesia. Namun, asuransi mobil memiliki potensi untuk kecurangan klaim yang menyebabkan kerugian bagi perusahaan dan pemegang polis. Penelitian ini bertujuan untuk merancang model prediksi deteksi kecurangan asuransi mobil di Indonesia menggunakan pendekatan machine learning. Supervised classifiers adalah salah satu teknik machine learning yang memiliki kemampuan untuk memprediksi kasus-kasus anomali. Supervised classifiers yang digunakan pada penelitian ini adalah Multilayer Perceptron (MLP), Decision Tree C4.5, dan Random Forest (RF). Penelitian ini menggunakan data real-world pada perusahaan asuransi mobil di Indonesia. Dataset memiliki distribusi tidak seimbang yang sangat tinggi antara data pemegang polis yang melakukan kecurangan dan pemegang polis yang sah. Penelitian ini menangani masalah dataset yang tidak seimbang dengan menggunakan Synthetic Minority Oversampling Technique (SMOTE) dan metode undersampling. Kinerja model dievaluasi melalui confusion matrix, Kurva ROC, dan parameter seperti sensitivitas. Penelitian ini menemukan bahwa Random Forest memberikan hasil terbaik dibandingkan dengan MLP dan Decision Tree C4.5.
Automobile insurance is widely used insurance product in Indonesia. However, automobile insurance has the potential for  fraudulent claim that leads to several consequences for the company and policyholder. This research aims to design a prediction model of automobile insurance fraud detection in Indonesia using a machine learning approach. Supervised classifiers is one of machine learning techniques that has the ability to predict anomaly cases. The proposed supervised classifiers are Multilayer Perceptron (MLP), Decision Tree C4.5, and Random Forest(RF). This research used real-world data on an automobile insurance company in Indonesia. The dataset has a high imbalanced distribution between the data of policyholders who commit fraud and legitimate. This study handles the imbalanced dataset problem by using the Synthetic Minority Oversampling Technique (SMOTE) and undersampling methods. The performance of models is evaluated through the confusion matrix, ROC Curve, and parameters such as sensitivity. This research found that Random Forest outperformed the results comparing to other classifiers."
Depok: Fakultas Teknik Universitas Indonesia, 2020
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UI - Skripsi Membership  Universitas Indonesia Library