Penanganan Missing Values Untuk Meningkatkan Kinerja Model Machine Learning Pada Data Telemarketing

Abstrak: Telemarketing menjadi salah satu media pemasaran produk maupun jasa yang sampai saat ini masih banyak digunakan oleh berbagai perusahaan terutama di bidang perbankan, asuransi, dan telekomunikasi. Telemarketing dianggap lebih efektif dibandingkan dengan penggunaan baliho, flyer maupun brosur karena menghubungkan secara langsung antara perusahaan dengan calon pelanggan. Pada proses implementasinya, penggunaan telemarketing ini justru seringkali menemui kegagalan dan bahkan berujung pada sikap antipati calon pelanggan yang dihubungi bahkan sebelum proses komunikasi dilakukan. Salah satu penyebab terjadinya kegagalan dari proses telemarketing adalah tidak adanya proses seleksi untuk mengetahui potensi dari calon pelanggan. Machine learning menjadi salah satu alternatif yang dapat digunakan untuk membantu memilih dan menentukan calon pelanggan potensial dan sesuai dengan target perusahaan. Akan tetapi, missing values yang ada pada data telemarketing seringkali menjadi permasalahan baru yang harus ditangani agar penggunaan machine learning menjadi lebih efektif dan mendukung keberhasilah telemarketing. Pada penelitian ini, K-means digunakan sebagai salah satu cara untuk menangani adanya missing values pada data telemarketing. Hasilnya, terdapat peningkatan hingga sebesar 6% jika diukur berdasarkan nilai akurasi menggunakan model Decision Tree. Kata Kunci: Telemarketing, Machine Learning, Missing Values, K-Means, Klasifikasi

DIAGNOSIS OF CUCUMBER PLANT DISEASES USING CERTAINTY FACTOR AND FORWARD CHAINING METHODS

Cucumber plants spread and can live in tropical climates like Indonesia. The cucumber plant has many benefits and can be a beauty ingredient. Cucumbers, like other plants, can also have disease attacks, which can threaten farmers. This expert system can help farmers discover diseases that attack cucumber plants and how to control them. The certainty Factor is a method used to measure the certainty of facts to describe an expert's confidence in facing a problem. Forward Chaining is an approach method monitored by data starting from information in the form of facts and supported by rules to reach conclusions. Implementing an expert system for diagnosing cucumber diseases using certainty factor and forward chaining methods will make it easier for farmers and the public to cultivate cucumber plants and get good results. Applying the forward chaining method and factor certainty in this expert system can produce an accuracy level of 95.918%

DIAGNOSE OF MENTAL ILLNESS USING FORWARD CHAINING AND CERTAINTY FACTOR

The prevalence of mental disorders in Indonesia is increasingly significant, as seen from the 2018 Riskesdas data. Riskesdas records mental, emotional health problems (depression and anxiety) as much as 9.8%. This shows an increase when compared to the 2013 Riskesdas data of 6%. Based on these data, it can be said that many people still suffer from mental disorders. Meanwhile, the number of medical personnel, medicines and public treatment facilities for people with mental disorders is still limited. In addition, the lack of public awareness, concern and knowledge about mental health causes a lack of public interest in consulting a psychologist, so people tend to self-diagnose. One solution for self-diagnosis is to use an expert system. This study developed an expert system using the forward chaining method and certainty factor. Based on the research conducted, the results are as follows. First, the expert-based system that has been developed can help provide the results of a diagnosis that is carried out before there are complaints and will be detected early by efforts to increase awareness of the prevention of mental illness and reduce the tendency to self-diagnose. Second, applying the forward chaining method and certainty factor to this expert system can produce an accuracy rate of 95.918%. An expert has also validated these results; in this study, the expert was a psychologist at a hospital in Yogyakarta

R-Peaks Detection Method for Classifying Arrhythmia Disorder

Electrocardiography (ECG) is a non-invasive technique that is used to diagnose heart abnormalities. ECG records all heart activities and represent them using bio electric signals. Arrhythmia is one of the cardiac disorder that can be detected using ECG. Arrhythmia need to be detected early because of an early symptom of heart disease as deadly as coronary heart disease and heart failure. Arrhythmia described using the difference between the R-peaks based on QRS complex. Therefore, R-peaks detection will be an important factor that can be used to classify arrhythmia disease. One of the widely used methods to detect R-peaks is Pan-Tompkins method. Pan-Tompkins method used a threshold value approach to get all location of R-peaks point from the ECG signals. This study proposed a development based on Pan-Tompkins method by change the threshold value using normalize technique and moving windows approach to get all location of R-peaks point from the ECG signals. This study uses MIT-BIH arrhythmia dataset. This method can show the R-peaks detection with 99.83% sensitivity and 0.40% total error rate detection. Hence, this method has potential to be used for classifying arrhythmia disorder based on the R-peaks point

Extreme Gradient Boosting Algorithm to Improve Machine Learning Model Performance on Multiclass Imbalanced Dataset

Unbalanced conditions in the dataset often become a real-world problem, especially in machine learning. Class imbalance in the dataset is a condition where the number of minority classes is much smaller than the majority class, or the number is insufficient. Machine learning models tend to recognize patterns in the majority class more than in the minority class. This problem is one of the most critical challenges in machine learning research, so several methods have been developed to overcome it. However, most of these methods only focus on binary datasets, so few methods still focus on multiclass datasets. Handling unbalanced multiclass is more complex than handling unbalanced binary because it involves more classes than binary class datasets. With these problems, we need an algorithm with features that can support adjustments to the difficulties that arise in multiclass unbalanced datasets. One of the algorithms that have features for adjustment is the ensemble algorithm, namely Xtreme Gradient Boosting. Based on the research, our proposed method with Xtreme Gradient Boosting showed better results than the other classification and ensemble algorithms on eight datasets with five evaluation metrics indicators such as balanced accuracy, the geometric-mean, multiclass area under the curve, true positive rate, and true negative rate. In future research, we suggest combining methods at the data level and Xtreme Gradient Boosting. With the performance increase in Xtreme Gradient Boosting, it can be a solution and reference in the case of handling multiclass imbalanced problems. Besides, we also recommended testing with datasets in the form of categorical and continuous data

Meta-Algorithms untuk Meningkatkan Kinerja Klasifikasi dalam Keberhasilan Telemarketing Perbankan

Banyak bank dan perusahaan asuransi berinteraksi dengan pelanggan mereka menggunakan jasa telemarketing agar dapat lebih meningkatkan pendapatan bank dan mencapai target. Kompetisi yang ketat di industri perbankan membuat tenaga penjualan bank mengejar target terutama menjelang akhir tahun. Pelaku telemarketing atau biasa disebut telemarketer pada perbankan bertugas menghubungi nasabah potensial yang dilakukan secara berkala melalui sambungan telepon. Namun tidak semua nasabah mendapatkan respons yang positif, sering kali telemarketing menemui antipati dari nasabah sebelum proses komunikasi dilakukan. Pendekatan yang diusulkan untuk menemukan nasabah potensial adalah dengan cara klasifikasi data nasabah menggunakan pembelajaran mesin (machine learning). Penggunaan algoritme pembelajaran yang dilakukan secara tunggal memungkinkan untuk dikembangkan menggunakan meta-algorithms sehingga akan memaksimalkan kinerja klasifikasi dalam memprediksi target pelanggan atau nasabah yang sesuai dengan kebutuhan perusahaan agar tujuan pemasaran tercapai. Berdasarkan hasil penelitian yang diperoleh, tindakan pra-pemrosesan dan penggunaan meta-algorithms seperti bagging memberikan kontribusi yang baik untuk proses pembelajaran model decission tree pada data telemarketing dengan hasil akurasi terbaik 98,7%

Meta-Algorithms untuk Meningkatkan Kinerja Klasifikasi dalam Keberhasilan Telemarketing Perbankan

Banyak bank dan perusahaan asuransi berinteraksi dengan pelanggan mereka menggunakan jasa telemarketing agar dapat lebih meningkatkan pendapatan bank dan mencapai target. Kompetisi yang ketat di industri perbankan membuat tenaga penjualan bank mengejar target terutama menjelang akhir tahun. Pelaku telemarketing atau biasa disebut telemarketer pada perbankan bertugas menghubungi nasabah potensial yang dilakukan secara berkala melalui sambungan telepon. Namun tidak semua nasabah mendapatkan respons yang positif, sering kali telemarketing menemui antipati dari nasabah sebelum proses komunikasi dilakukan. Pendekatan yang diusulkan untuk menemukan nasabah potensial adalah dengan cara klasifikasi data nasabah menggunakan pembelajaran mesin (machine learning). Penggunaan algoritme pembelajaran yang dilakukan secara tunggal memungkinkan untuk dikembangkan menggunakan meta-algorithms sehingga akan memaksimalkan kinerja klasifikasi dalam memprediksi target pelanggan atau nasabah yang sesuai dengan kebutuhan perusahaan agar tujuan pemasaran tercapai. Berdasarkan hasil penelitian yang diperoleh, tindakan pra-pemrosesan dan penggunaan meta-algorithms seperti bagging memberikan kontribusi yang baik untuk proses pembelajaran model decission tree pada data telemarketing dengan hasil akurasi terbaik 98,7%.</jats:p