Comparative Study of Classification Method on Customer Candidate Data to Predict its Potential Risk

Leasing vehicles are a company engaged in the field of vehicle loans. Purchase by way of credit becomes a mainstay because it can attract potential customers to generate more profit. But if there is a mistake in approving a customer candidate, the risk of stalled credit payments can happen. To minimize the risk, it can be applied the certain data mining technique to predict the future behavior of the customers. In this study, it is explored in some data mining techniques such as C4.5 and Naive Bayes for this purpose. The customer attributes used in this study are: salary, age, marital status, other installments and worthiness. The experiments are performed by using the Weka software. Based on evaluation criteria, i.e. accuracy, C4.5 algorithm outperforms compared to Naive Bayes. The percentage split experiment scenarios provide the precision value of 89.16% and the accuracy value of 83.33% wheres the cross validation experiment scenarios give the higher accuracy values of all used k-fold. The C4.5 experiment results also confirm that the most influential instant data attribute in this research is the salary

The Implementation of E-Learning System Governance to Deal with User Need, Institution Objective, and Regulation Compliance

In this digital era, it has been proven that the proper of e-learning system implementation provides various advantages and huge benefits. But to achieve the proper implementation is not an easy way since there are many obstacles have to be addressed. Beside the benefits and advantages, such as the other IT based system, e-learning also bring many risks that come from its environment or embedded in. Although many methods or approaches proposed to tackle those obstacles and risks, but the study that tackle those problems from IT Governance view is still limited. The study presents the report of the IT Governance approach to address some of the risks of eLearning system implementation such as: miss alignment with the enterprise goal and strategies, uncomplianceness with the government regulation, and unmatched with the stakeholder needs. The governance of eLearning system proposed has been implemented in the private university situated in Jakarta, Indonesia for two semesters. Based on the general observation, the University can get some benefits such as their succeed in maintaining its institution as the university that comply with government regulatory

Load balancing clustering on moodle LMS to overcome performance issue of e-learning system

In dealing with the rapid growth of digitalization, the e-learning system has become a mandatory component of any Higher Education (HE) to serve academic processes requests. Along with the increasing number of users, the need for service availability and capabilities of eLearning are increasing day by day. The organization should always look for strategies to keep the eLearning always able to meet these demands. This report presents the implementation of Load Balancing Clustering (LBC) mechanism applied to Moodle LMS in an HE Institution to deal with the poor performance issues. By utilizing existing tools such as HAProxy and keepalived, the implemented LBC configuration delivers a qualified e-learning system performance. Both qualitative and quantitative parameters convince better performance than before. In four months of the operation there is no user complaint received. Meanwhile, in the current semester has been running for two months, the up-time is 99.8 % of 52.685 minutes operational time

Analisis Permasalahan Perangkat Pencetak Menggunakan Metode Algoritma K-Means dan K-Medoids

Amido Makmor Tulus Sejati merupakan perusahaan distributor multifunction printer merek Kyocera di Indonesia. Evaluasi kinerja teknisi diperlukan untuk mempertahankan kepuasan customer terhadap penggunaan multifunction printer Kyocera. Proses penilaian kinerja teknisi masih dilakukan secara manual yang mengakibatkan hasil evaluasi kinerja teknisi yang diberikan kurang akurat atau kurang maksimal, sehingga perlu dilakukan suatu teknik pengolahan data secara cepat dan lebih akurat. Salah satunya dengan mempergunakan teknik data mining dengan menggunakan metode algoritma clustering. Metode algoritma clustering dipergunakan untuk mengelompokkan problem yang sering terjadi berdasarkan tipe mesin multifunction printer Kyocera. Pada penelitian ini diterapkan algoritma clustering K-Means dan K-Medoids, yang kemudian dilakukan uji clustering yang optimal dengan mempergunakan Metode Elbow dan Silhouette Score. Data yang dipergunakan dalam penelitian ini sebanyak 1.620 instan yang merupakan Data Kuantitatif. Proses untuk mencari nilai clustering yang optimal dilakukan dengan mencari rata-rata Silhouette Score dan Nilai Kemurnian dengan sisi luar dari algoritma K-Means dan K-Medoids. Hasil penelitian ini menunjukkan bahwa jumlah cluster optimal adalah 2 (dua) untuk algoritma K-Means dengan nilai Silhouette Score 0,606 dan jumlah cluster optimal 4 (empat) untuk algoritma K-Medoids dengan nilai Silhouette Score 0,240

Analisis Sentimen Twitter terhadap Tokoh Publik dengan Algoritma Naive Bayes dan Support Vector Machine

Salah satu media sosial yang berkembang adalah Twitter. Media sosial Twitter mempermudah masyarakat untuk bebas berpendapat melalui cuitan atau biasa disebut dengan tweets. Netizen dengan bebas menyampaikan opini pribadinya untuk topik apapun, termasuk persepsi terhadap tokoh publik. Artikel ini menyajikan hasil penelitian dan analisis sentimen masyarakat (netizen) terhadap tokoh publik, Nadiem Makariem sebagai Menteri Kementerian Pendidikan dan Kebudayaan baru. Penelitian ini menggunakan teknik data mining yang bertujuan untuk membandingkan hasil klasifikasi dari opini masyarakat yang dituliskan di Twitter. Dataset yang digunakan berasal dari tweets dengan kata kunci ”nadiem makariem”, ”kemendikbud” dan ”pak nadiem”. Tools RapidMiner digunakan untuk membantu tahap pre-processing dan klasifikasi menggunakan dua metode yaitu, Naive Bayes dan Support Vector Machine dengan evaluasi k-fold cross-validation. Dari hasil ujicoba diketahui bahwa untuk kasus yang diteliti, metode Naive Bayes menghasilkan kinerja yang lebih baik dengan accuracy 91.48%,  precision 89.28%  dan recall 91.58%

A New Data Representation Based on Training Data Characteristics to Extract Drug Name Entity in Medical Text

One essential task in information extraction from the medical corpus is drug name recognition. Compared with text sources come from other domains, the medical text mining poses more challenges, for example, more unstructured text, the fast growing of new terms addition, a wide range of name variation for the same drug, the lack of labeled dataset sources and external knowledge, and the multiple token representations for a single drug name. Although many approaches have been proposed to overwhelm the task, some problems remained with poor F-score performance (less than 0.75). This paper presents a new treatment in data representation techniques to overcome some of those challenges. We propose three data representation techniques based on the characteristics of word distribution and word similarities as a result of word embedding training. The first technique is evaluated with the standard NN model, that is, MLP. The second technique involves two deep network classifiers, that is, DBN and SAE. The third technique represents the sentence as a sequence that is evaluated with a recurrent NN model, that is, LSTM. In extracting the drug name entities, the third technique gives the best F-score performance compared to the state of the art, with its average F-score being 0.8645

Improving the MSMEs data quality assurance comprehensive framework with deep learning technique

In the year of 2022 the ministry of cooperatives and small and medium enterprises (SMEs) executed a complete data collection program for the cooperatives and micro small and medium enterprises (MSMEs) profile. As the complexity of the process and the uniqueness of the data characteristics, plenty of risks must be mitigated. The most challenging risk is the possibility of reduced data quality. This study is performed to validate the proposed comprehensive framework to ensure the quality data of cooperatives and MSME. The proposed framework aims to prevent, detect, repair, and recover dirty data to achieve the required data quality minimum standard. We investigated many techniques namely rule-based, selection-based, and deep learning-based. By applying the framework, 6,850,000 missing values are found and corrected, whereas the number of instant data containing attribute values that do not follow the domain constraints or integrity rule is 4,082,630. The first deep learning task applied in the framework is MSME activity image description (image captioning) generated by the convolutional neural network-recurrent neural network (CNN-RNN) model. By using 1000 MSME images as data training, the model’s performance is quite good, achieving the average BLEU score of Culinary 0,3149, Fashion 0,4868, and creative products 0,5086. So far, the proposed framework can contribute to supporting MSME one data as the Indonesian government program

Analisis Sentimen Transportasi Online pada Twitter Menggunakan Metode Klasifikasi Naïve Bayes dan Support Vector Machine

Transportasi online  merupakan salah satu pilihan bagi masyarakat untuk berkegiatan sehari-hari baik saat bekerja, bepergian dan melakukan aktivitas lain. Salah satu cara untuk mengetahui persepsi masyarakat terhadap layanan transportasi online adalah dengan analisis sentimen seperti yang dilakukan pada penelitian ini. Data yang digunakan merupakan data valid dari sosial media Twitter untuk Transportasi online GrabId dan GojekIndonesia. Teknik analisis sentimen yang digunakan adalah  Naïve Bayes Classifier dan metode Support Vector Machine (SVM). Keduanya digunakan untuk membandingkan tanggapan masyarakat dari analisis sentimen data tweet yang telah diklasifikasikan menjadi positif dan negatif. Berdasarkan penelitian ini didapatkan bahwa GrabId menggunakan metode SVM memberikan hasil class precision positif dan negatif yaitu 86.47% dan 46.67%, class recall positif dan negatif yaitu 96.21% dan 18.06%, accuracy 84.08%. Sedangkan untuk GojekIndonesia, metode SVM memberikan hasil yaitu class precision positif dan negatif yaitu 73.90% dan 35.65%, class recall positif dan negatif yaitu 89.84% dan 15.07%, accuracy 69.50%. Dari akurasi yang dihasilkan, metode SVM  menghasilkan kinerja terbaik

Mining Relation Extraction Based on Pattern Learning Approach

Semantically, objects in unstructured document are related each other to perform a certain entity relation. This certain entity relation such: drug-drug interaction through their compounds, buyer-seller relationship through the goods or services, etc. Motivated by those kind of interaction, this study proposes a method to extract those objects and their interactions. It is presented a general framework of object-interaction mining of large corpora. The framework is started with the initial step in extracting a single object in the unstructured document. In this study, the initial step is a pattern learning method that is applied to drug-label documents to extract drug-names. We utilize an existing external knowledge to identify a certain regular expressions surrounding the targeted object and the probabilities of those regular expression, to perform the pattern learning process. The performance of this pattern learning approach is promising to apply in this relation extraction area. As presented in the results of this study, the best f-score performance of this method is 0.78 f-score. With adjusting of some parameters and or improving the method, the performance can be potentially improved

SISTEM PENDUKUNG PENGAMBILAN KEPUTUSAN PRIORITAS PERBAIKAN MOLD PT. BIGGY CEMERLANG DENGAN MENGGUNAKAN METODE SAW (SIMPLE ADDITIVE WEIGHTING)

PT. Biggy Cemerlang adalah salah satu supplier produk plastik terbaik di Indonesia yang memproduksi peralatan rumah tangga dengan menggunakan berbagai macam mold (cetakan). Mold merupakan komponen yang sangat penting dalam mencapai target produksi yang telah ditentukan oleh perusahaan. Salah satu aktivitas yang dilakukan untuk menjaga tingkat kesiapan mold supaya hasil tetap terjamin adalah perbaikan mold. Proses penentuan perbaikan sampai dengan saat ini masih dilakukan secara manual. Sistem manual mengandung kelemahan antara lain: kesulitan akses data dan kesalahan penentuan waktu perbaikan akibat kesalahan penentuan prioritas. Pada penelitian ini dibuat sistem pendukung pengambilan keputusan penentuan prioritas perbaikan mold menggunakan metode Simple Additive Weighting (SAW) yang dilakukan dengan mencari nilai bobot untuk setiap kriteria, dan kemudian membuat proses peringkat yang akan menentukan alternatif yang optimal. Hasil dari penelitian ini adalah sistem pendukung pengambilan keputusan penentuan prioritas perbaikan mold yang dapat membantu teknisi melakukan perbaikan mold berdasarkan prioritas