Penetapan Strategi Penjualan Menggunakan Association Rules dalam Konteks CRM

Persaingan usaha telah memaksa perusahaan perdagangan untuk lebih selektif dalam menerapkan strategi pemasarannya. Customer Relationship Management (CRM) hadir sebagai sebagai sebuah solusi. Bagian CRM seperti cross/up selling, product affinity analysis, dan product bundling dapat digunakan sebagai sebuah solusi alternatif. Transaksi penjualan dimungkinkan dapat ditingkatkan melalui penjualan additional products dari produk utama dimana pelanggan telah berkomitmen untuk membelinya. Paper ini akan melakukan analisis terhadap data transaksi penjualan sebuah perusahaan ritel umum yang bergerak di bidang fotografi, fotokopi, medical imaging, printing, dan telekomunikasi yang memiliki variasi produk yang sangat beragam. Keanekaragaman produk ini menghasilkan kemungkinan kombinasi produk yang lebih beragam pula. Hasil penelitian ini menunjukkan bahwa penetapan strategi penjualan dengan menggunakan data pada tengah semester pertama, tren penjualan dari masing-masing kombinasi produk pada semester kedua mengalami peningkatan yang signifikan

Penetapan Strategi Penjualan Menggunakan Association Rules dalam Konteks CRM

Persaingan usaha telah memaksa perusahaan perdagangan untuk lebih selektif dalam menerapkan strategi pemasarannya.  Customer Relationship Management (CRM)hadir sebagai sebagai sebuah solusi. Bagian CRM seperti cross/up selling, product affinity analysis, dan product bundlingdapat digunakan sebagai sebuah solusi alternatif. Transaksi penjualan dimungkinkan dapat ditingkatkan melalui penjualan additional products dari produk utama dimana pelanggan telah berkomitmen untuk membelinya. Paper ini akan melakukan analisis terhadap data transaksi penjualan sebuah perusahaan ritel umum yang bergerak di bidang fotografi, fotokopi, medical imaging,  printing, dan telekomunikasi yang memiliki variasi produk yang sangat beragam. Keanekaragaman produk ini menghasilkan kemungkinan kombinasi produk yang  lebih beragam pula. Hasil penelitian ini menunjukkan bahwa penetapan strategi penjualan dengan menggunakan data pada tengah semester pertama, tren penjualan dari masing-masing kombinasi produk pada semester kedua mengalami peningkatan yang signifikan

TSE-IDS: A Two-Stage Classifier Ensemble for Intelligent Anomaly-based Intrusion Detection System

Intrusion detection systems (IDS) play a pivotal role in computer security by discovering and repealing malicious activities in computer networks. Anomaly-based IDS, in particular, rely on classification models trained using historical data to discover such malicious activities. In this paper, an improved IDS based on hybrid feature selection and two-level classifier ensembles is proposed. An hybrid feature selection technique comprising three methods, i.e. particle swarm optimization, ant colony algorithm, and genetic algorithm, is utilized to reduce the feature size of the training datasets (NSL-KDD and UNSW-NB15 are considered in this paper). Features are selected based on the classification performance of a reduced error pruning tree (REPT) classifier. Then, a two-level classifier ensembles based on two meta learners, i.e., rotation forest and bagging, is proposed. On the NSL-KDD dataset, the proposed classifier shows 85.8% accuracy, 86.8% sensitivity, and 88.0% detection rate, which remarkably outperform other classification techniques recently proposed in the literature. Results regarding the UNSW-NB15 dataset also improve the ones achieved by several state of the art techniques. Finally, to verify the results, a two-step statistical significance test is conducted. This is not usually considered by IDS research thus far and, therefore, adds value to the experimental results achieved by the proposed classifier

Analisis Kepuasan Konsumen Terhadap Restoran Cepat Saji Melalui Pendekatan Data Mining: Studi Kasus XYZ

Berbagai cara dilakukan oleh masing-masing restoran cepat saji untuk memenangkan persaingan, antara lain yakni dengan meningkatkan kepuasan konsumen. Salah satu restoran cepat saji yang berkomitmen dalam meningkatkan kepuasan konsumennya adalah Restoran XYZ. Penelitian ini bertujuan untuk menganalisis kepuasan konsumen Restoran XYZ menggunakan data mining dengan algoritma C4.5. Attribut masukan kepuasan konsumen dalam penelitian ini, mencakup produk, pelayanan, fasilitas, harga dan lokasi yang berasal dari data hasil survei. Dalam penelitian ini, didapatkan bahwa rules yang dibangkitkan dari beberapa atribut masukan menghasilkan hubungan sebab-akibat dalam mengklasifikasikan konsumen puas dan tidak puas. Penelitian ini diharapkan dapat membantu pihak manejemen dalam meningkatkan kepuasan konsumen untuk mempertahankan konsumen dan meningkatkan laba mereka

Analisis Kepuasan Konsumen Terhadap Restoran Cepat Saji Melalui Pendekatan Data Mining: Studi Kasus XYZ

Berbagai cara dilakukan oleh masing-masing restoran cepat saji untuk memenangkan persaingan, antara lain yakni dengan meningkatkan kepuasan konsumen. Salah satu restoran cepat saji yang berkomitmen dalam meningkatkan kepuasan konsumennya adalah Restoran XYZ. Penelitian ini bertujuan untuk menganalisis kepuasan konsumen Restoran XYZ menggunakan  data mining  dengan algoritma C4.5. Attribut masukan  kepuasan konsumen dalam penelitian ini, mencakup produk, pelayanan, fasilitas, harga dan lokasi yang berasal dari data hasil survei. Dalam penelitian ini, didapatkan bahwa rules yang dibangkitkan dari beberapa atribut masukan menghasilkan hubungan sebab-akibat  dalam mengklasifikasikan konsumen puas dan tidak puas. Penelitian ini diharapkan dapat membantu pihak manejemen dalammeningkatkan kepuasan konsumen untuk mempertahankankonsumen dan meningkatkan laba mereka

Ensemble learning for intrusion detection systems: A systematic mapping study and cross-benchmark evaluation

Intrusion detection systems (IDSs) are intrinsically linked to a comprehensive solution of cyberattacks prevention instruments. To achieve a higher detection rate, the ability to design an improved detection framework is sought after, particularly when utilizing ensemble learners. Designing an ensemble often lies in two main challenges such as the choice of available base classifiers and combiner methods. This paper performs an overview of how ensemble learners are exploited in IDSs by means of systematic mapping study. We collected and analyzed 124 prominent publications from the existing literature. The selected publications were then mapped into several categories such as years of publications, publication venues, datasets used, ensemble methods, and IDS techniques. Furthermore, this study reports and analyzes an empirical investigation of a new classifier ensemble approach, called stack of ensemble (SoE) for anomaly-based IDS. The SoE is an ensemble classifier that adopts parallel architecture to combine three individual ensemble learners such as random forest, gradient boosting machine, and extreme gradient boosting machine in a homogeneous manner. The performance significance among classification algorithms is statistically examined in terms of their Matthews correlation coefficients, accuracies, false positive rates, and area under ROC curve metrics. Our study fills the gap in current literature concerning an up-to-date systematic mapping study, not to mention an extensive empirical evaluation of the recent advances of ensemble learning techniques applied to IDSs. (C) 2020 Elsevier Inc. All rights reserved

STRATEGI PEMILIHAN KONTRAKTOR PERANGKAT LUNAK DENGAN MEMANFAATKAN PENGETAHUAN TERHADAP CAPABILITY MATURITY MODEL INTEGRATION FOR DEVELOPMENT (CMMI FOR DEV)

Given out a same software development to different contractors will produce a certain different software result. This principle can be used by project manager in selecting software contractors. Therefore, a strategy in choosing the best candidate is needed. To support this activity, CMMI for Dev can be used as guidance or reference for its reader, especially for project managers and their teams about how to assess quality and productivity of activity process in a software company. This knowledge can be applied in selecting the candidate in order to work together for developing the required software. Also, the process area of CMMI for Dev promotes strategy in selecting the candidate thatwill develop the software in an agreement. However, due to the limitation the writer have, not all aspect can be discover and worth for further research in the future. Kata kunci : Kontraktor perangkat lunak, strategi-strategi, Capability Maturity ModelIntegration for Development (CMMI for Dev), Process Area (PA)

A Comparative Performance Evaluation of Classification Algorithms for Clinical Decision Support Systems

Classification algorithms are widely taken into account for clinical decision support systems. However, it is not always straightforward to understand the behavior of such algorithms on a multiple disease prediction task. When a new classifier is introduced, we, in most cases, will ask ourselves whether the classifier performs well on a particular clinical dataset or not. The decision to utilize classifiers mostly relies upon the type of data and classification task, thus making it often made arbitrarily. In this study, a comparative evaluation of a wide-array classifier pertaining to six different families, i.e., tree, ensemble, neural, probability, discriminant, and rule-based classifiers are dealt with. A number of real-world publicly datasets ranging from different diseases are taken into account in the experiment in order to demonstrate the generalizability of the classifiers in multiple disease prediction. A total of 25 classifiers, 14 datasets, and three different resampling techniques are explored. This study reveals that the classifier that is likely to become the best performer is the conditional inference tree forest (cforest), followed by linear discriminant analysis, generalize linear model, random forest, and Gaussian process classifier. This work contributes to existing literature regarding a thorough benchmark of classification algorithms for multiple diseases prediction

Leveraging a Heterogeneous Ensemble Learning for Outcome-Based Predictive Monitoring Using Business Process Event Logs

Outcome-based predictive process monitoring concerns predicting the outcome of a running process case using historical events stored as so-called process event logs. This prediction problem has been approached using different learning models in the literature. Ensemble learners have been shown to be particularly effective in outcome-based business process predictive monitoring, even when compared with learners exploiting complex deep learning architectures. However, the ensemble learners that have been used in the literature rely on weak base learners, such as decision trees. In this article, an advanced stacking ensemble technique for outcome-based predictive monitoring is introduced. The proposed stacking ensemble employs strong learners as base classifiers, i.e., other ensembles. More specifically, we consider stacking of random forests, extreme gradient boosting machines, and gradient boosting machines to train a process outcome prediction model. We evaluate the proposed approach using publicly available event logs. The results show that the proposed model is a promising approach for the outcome-based prediction task. We extensively compare the performance differences among the proposed methods and the base strong learners, using also statistical tests to prove the generalizability of the results obtained

An Early Detection Method of Type-2 Diabetes Mellitus in Public Hospital

Diabetes is a chronic disease and major problem of morbidity and mortality in developing countries. The International Diabetes Federation estimates that 285 million people around the world have diabetes. This total is expected to rise to 438 million within 20 years. Type-2 diabetes mellitus (T2DM) is the most common type of diabetes and accounts for 90-95% of all diabetes. Detection of T2DM from various factors or symptoms became an issue which was not free from false presumptions accompanied by unpredictable effects. According to this context, data mining and machine learning could be used as an alternative way help us in knowledge discovery from data. We applied several learning methods, such as instance based learners, naive bayes, decision tree, support vector machines, and boosted algorithm acquire information from historical data of patient’s medical records of Mohammad Hoesin public hospital in Southern Sumatera. Rules are extracted from Decision tree to offer decision-making support through early detection of T2DM for clinicians.