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KOMPARASI ALGORITMA NAIVE BAYES, RANDOM FOREST DAN SVM UNTUK MEMPREDIKSI NIAT PEMBELANJA ONLINE Cucu Ika Agustyaningrum; Windu Gata; Ridan Nurfalah; Ummu Radiyah; Mawadatul Maulidah
Jurnal Informatika Vol 20, No 2 (2020): Jurnal Informatika
Publisher : IIB Darmajaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30873/ji.v20i2.2402

Beberapa tahun terakhir ini, penggunaan e-commerce atau toko online sangat meningkat. Bermacam-macam toko online yang bermunculan di internet, baik berskala kecil maupun yang berskala besar. Hal ini memiliki pengaruh yang sangat penting pada penggunaan waktu yang efektif dan tingkat angka penjualan. Maka dari itu e-commerce atau toko online harus mempunyai kemampuan menilai sarana yang digunakan untuk mengetahui dan mengklasifikasikan niat pembelanjaan online sehingga menghasilkan keuntungan bagi toko tersebut. Niat pembelanja online dapat dilakukan pengklasifikasian menggunakan beberapa algoritma, seperti Naive Bayes, Random Forest dan Support Vector Machine. Dalam penelitian ini perbandingan algoritma dilakukan menggunakan aplikasi WEKA dengan mengetahui nilai F1-Score, Akurasi, Kappa Statistic dan Mean Absolute Error. Terdapat perbedaan antara hasil pengujian, untuk nilai F1-Score, Akurasi, Kappa Statistic menghasilkan pengujian algoritma Random Forest-lah yang paling baik dibandingkan Naive Bayes dan Support Vector Machine. Sedangkan pada nilai Mean Absolute Error hasil pengujian algoritma Support Vector Machine merupakan nilai terbaik dari pada Naive Bayes dan Random Forest. Sehingga berdasarkan penelitian ini Algoritma Random Forest merupakan algoritma yang paling baik dan tepat untuk diterapkan sebagai pengklasifikasian niat pembelanja online, karena algoritma Random Forest yang paling mendominasi dalam mengetahui nilai kriteria seperti F1-Score, Akurasi, Kappa Statistic dan Mean Absolute Error.

Prediction Of Myers-Briggs Type Indicator Personality Using Long Short-Term Memory Mawadatul Maulidah; Hilman Ferdinandus Pardede
Jurnal Elektronika dan Telekomunikasi Vol 21, No 2 (2021)
Publisher : LIPI Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/jet.v21.104-111

Personality is defined as the mix of features and qualities that make up an individual's particular character, including thoughts, feelings, and behaviors. With the rapid development of technology, personality computing is becoming a popular research field by providing users with personalization. Many researchers have used social media data to automatically predict personality. This research uses a public dataset from Kaggle, namely the Myers-Briggs Personality Type Dataset. The purpose of this study is to predict the accuracy and F1-score values so that the performance for predicting and classifying Myers–Briggs Type Indicator (MBTI) personality can work optimally by using attributes from the MBTI dataset, namely posts and types. Predictive accuracy analysis was carried out using the Long Short-Term Memory (LSTM) algorithm with random oversampling technique with the Imblearn library for MBTI personality type prediction and comparing the performance of the method proposed in this study with other popular machine learning algorithms. Experiments show that the LSTM model using the RMSprop optimizer and learning speed of 10-3 provides higher performance in terms of accuracy while for the F1-score the LSTM model using the RMSprop Optimizer and learning speed of 10-2 gives a higher value than the proposed machine learning algorithm so that the model MBTI dataset using LSTM with random oversampling can help in identifying the MBTI personality type.

PREDICTION OF HOTEL BOOKING CANCELLATION USING DEEP NEURAL NETWORK AND LOGISTIC REGRESSION ALGORITHM Nugroho Adi Putro; Rendi Septian; Widiastuti Widiastuti; Mawadatul Maulidah; Hilman Ferdinandus Pardede
Jurnal Techno Nusa Mandiri Vol 18 No 1 (2021): Techno Nusa Mandiri : Journal of Computing and Information Technology Period of
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/techno.v18i1.2056

Booking cancellation is a key aspect of hotel revenue management as it affects the room reservation system. Booking cancellation has a significant effect on revenue which has a significant impact on demand management decisions in the hotel industry. In order to reduce the cancellation effect, the hotel applies the cancellation model as the key to addressing this problem with the machine learning-based system developed. In this study, using a data collection from the Kaggle website with the name hotel-booking-demand dataset. The research objective was to see the performance of the deep neural network method which has two classification classes, namely cancel and not. Then optimized with optimizers and learning rate. And to see which attribute has the most role in determining the level of accuracy using the Logistic Regression algorithm. The results obtained are the Encoder-Decoder Layer by adamax optimizer which is higher than that of the Decoder-Encoder by adadelta optimizer. After adding the learning rate, the adamax accuracy for the encoders and encoders decreased for a learning rate of 0.001. The results of the top three ranks of each neural network after adding the learning rate show that the smaller the learning rate, the higher the accuracy, but we don't know what the optimal value for the learning rate is. By using the Logistic Regression algorithm by eliminating several attributes, the most influential level of accuracy is the state attribute and total_of_special_requests, where accuracy increases when the state attribute is removed because there are 177 variations in these attributes

Ear Identification Using Convolution Neural Network Nadiyah Hidayati; Mawadatul Maulidah; Elin Panca Saputra
Jurnal Mantik Vol. 6 No. 1 (2022): May: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v6i1.2263

Today's identification system has become a necessity for system security. One method of identification system that has a high level of security and accuracy is biometrics. Biometrics uses parts of the human body that are considered unique and can differentiate between one individual and another. One of the new biometrics that has become a concern in the world of research on biometrics is the ear. Ears have several advantages that other biometrics do not have, one of which is that they are not affected by changes in age. The purpose of this study was to determine the accuracy of the Convolutional Neural Network (CNN) algorithm in identifying ear images. CNN is currently one of the most superior algorithms in the field of object classification and identification. In this study, the ears that will be identified are images taken from the Kaggle dataset of 780 ears from 13 individuals with 60 images for each individual. This study resulted in a training accuracy of 96,3% and a testing accuracy of 79,7%.

ALGORITMA KLASIFIKASI DECISION TREE UNTUK REKOMENDASI BUKU BERDASARKAN KATEGORI BUKU Mawadatul Maulidah; Windu Gata; Rizki Aulianita; Cucu Ika Agustyaningrum
E-Bisnis : Jurnal Ilmiah Ekonomi dan Bisnis Vol 13 No 2 (2020): Jurnal Ilmiah Ekonomi dan Bisnis
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/e-bisnis.v13i2.251

With the increasing development of technology the more variety of books circulating on the internet. As is the recommendation system on online book sites that provide books relevantly and as needed with one's preferences. One alternative is GoodReads, a social networking site that specializes in cataloging books and users can share reading book recommendations with each other by rating, reviewing, and commenting. As a large book recommendation site, it has a lot of data that can be processed by applying machine learning methods, but still not known as the most accurate model. By using the right model, we can provide more accurate recommendations. Therefore, this study will analyze the data obtained from the www.kaggle.com namely the goodreads-books dataset. This study proposed a data mining classification model to get the best model in recommending books on GoodReads. The algorithms used are Decision Tree, K-Nearest Neighbor, Naïve Bayes, Random Forest, and Support Vector Classifier, then for model evaluation using accuracy, precision, recall, f1-score, confusion matrix, AUC, and Mean Error Absolute. The test results of several classification algorithms found that Decision Tree has the highest accuracy among the methods presented by 99.95%, precision by 100%, recall by 96%, f1-score of 98% with MAE of 0.05 and AUC of 99.96%. This is proof that decision tree algorithms can be used as book recommendations based on book categories on GoodReads.

Komparasi Evaluasi Kinerja Siswa Belajar dengan Mengggunakan Algoritma Machine Learning Elin Panca Saputra; Mawadatul Maulidah; Nadiyah Hidayati; Andi Saryoko
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4786

In our current study, we are doing a comparison of several algorithms that we have tested, namely in searching for the accuracy level of learning performance in students, the problem of this research is how to get the results of excellent generalization abilities so that a higher accuracy value is obtained. Our goal is to get the best-performing accuracy level results and then to identify features that can affect student learning performance. From the results of the algorithm that we have tested, four of them are Naïve Bayes, Support Vectore Machine, Neural Network and KNN contained in machine learning. The results of the four algorithms for the Naïve Bayes algorithm have an accuracy value of 96.30%, the Support Vectore Machine algorithm has an accuracy of 98.70%, and the Naural Network algorithm has an accuracy of 99.50% and the last one with the KNN algorithm produces an accuracy of 94.80%. it can be concluded that using the Neural Network algorithm is an algorithm with the best performance than using other algorithms in evaluating student learning performance, besides that the Neural Network can be used as an excellent alternative to be used as predictions, especially in the field of education.

Analisis Quality of Service (QoS) Performa Jaringan Internet Wireless LAN PT. Bhineka Swadaya Pertama Elin Panca Saputra; Andi Saryoko; Mawadatul Maulidah; Nadiyah Hidayati; Sopiyan Dalis
Evolusi : Jurnal Sains dan Manajemen Vol 11, No 1 (2023): Jurnal Evolusi 2023
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/evolusi.v11i1.14955

In the current era of digitalization, the use of ISPs or Internet Service Providers is very commonly used in the business world because they have effective and flexible services because they can be used wirelessly or without cables to meet their needs. Currently, there are many connection service providers in Indonesia, First Media is one of several existing ISPs. First Media offers a stable and fast internet connection, unlimited internet without quota and unlimited download speed of up to 10 Mbps and 768 Kbps upload speed, and has several other features that can be used wirelessly or without cable as needed. The use of wireless-based networks in general must have a service standard known as Quality Of Service (QOS). QoS is used in measuring the performance of an internet network in order to provide even better service. In conducting internet network analysis, Quality Of Service (QOS) parameters are needed which include packet loss, delay/latency, Throughput, Jitter, it will produce information in the form of network analysis results and the results of this analysis can be used as recommendations so that in the future it can develop internet networks to be more good again and can support the addition of other services. The purpose of this research is to analyze the Quality Of Service (QOS) of the internet network at PT. Bhineka Swadaya Pertama and knowing the quality of the Wireless Lan internet network at "PT. The First Unity of Self-Help”. From the results of this study it is hoped that the quality level of the LAN network at PT. Bhineka Swadaya Pertama and can check the Quality of Service of the LAN network at PT. Bhinneka Swadaya First. Keywords: Network Performance, Quality of Service (QOS), Wireless Lan

KLASIFIKASI KEPRIBADIAN MENGGUNAKAN ALGORITMA MACHINE LEARNING Mawadatul Maulidah
Jurnal Informatika Dan Tekonologi Komputer (JITEK) Vol. 3 No. 1 (2023): Maret : Jurnal Informatika dan Tekonologi Komputer
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jitek.v3i1.1292

Myers-Briggs Personality Type (MBTI) is a popular personality metric that uses four dichotomies as indicators of personality traits. This study uses a public dataset from Kaggle, namely the Myers-Briggs Personality Type Dataset, the model tested is several machine learning classification models with the help of imlearn under-over sampling techniques for classifying MBTI personality types. This study aims to classify the Myers-Briggs Type Indicator (MBTI) personality type based on text from user posts on the social media platform Reddit. The dataset used in this study consists of around 8,000 posts collected from the MBTI subreddit. Several text processing methods such as tokenization, punctuation removal, and stemming are used to process the raw data before it is entered into the model. The experimental results show that the LSTM model using Adam's optimizer and a learning rate of 0.01 produces good performance with an accuracy of 80.73 compared to other machine learning models. In addition to the LSTM model, XG Boost is also a classification model with the highest accuracy based on 16 personality types producing an accuracy of 60.09 and Logistic Regression with the NS dimension as the best accuracy value of 87.21%.

EKSTRAKSI FITUR DENGAN COLOR HISTOGRAM DAN CLASSIFIER RANDOM FOREST PADA CITRA KUPU-KUPU Nadiyah Hidayati; Maulidah, Mawadatul
JAMI: Jurnal Ahli Muda Indonesia Vol. 4 No. 2 (2023): Desember 2023
Publisher : Akademi Komunitas Negeri Putra Sang Fajar Blitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46510/jami.v4i2.172

Objektif. Penelitian dalam pengolahan citra banyak dikembangkan dalam berbagai bidang, misalnya kesehatan, pertanian, kesenian, aneka ragam hayati dll. Salah satu penelitian yang berkembang adalah pengklasifikasian jenis serangga yaitu kupu-kupu. Kupu-kupu merupakan salah satu serangga yang menguntungkan bagi manusia, namun populasi spesies kupu-kupu di Indonesia banyak yang menurun atau terancam punah. Dengan banyaknya jenis kupu-kupu dalam berbagai bentuk, corak yang berbeda, dan keunikan diperlukan suatu teknik yang memfasilitasi pembelajaran dengan lebih efisien. Kupu-kupu dijadikan dataset karena mempunyai pola tekstur yang unik dan warna serta bentuk yang beragam. Tujuan dari penelitian ini adalah mengklasifikasikan jenis kupu-kupu dengan menggabungkan hasil ekstraksi fitur dan metode classifier. Material and Metode. Pada penelitian ini diusulkan sebuah penggabungan tiga hasil ekstraksi fitur diantaranya color histogram, haralick, dan hu-moments. Ekstraksi dilakukan terhadap 2400 citra kupu-kupu yang dibagi menjadi 2 kelas. Penggabungan hasil ketiga ekstraksi fitur tersebut selanjutnya dilakukan proses klasifikasi menggunakan metode Random Forest (RF). Hasil. Pengujian yang telah dilakukan menunjukkan nilai akurasi sebesar 75% sedangkan nilai precision sebesar 78% dan recall sebesar 69%. Kesimpulan. Algoritma classification RF (Random Forest) mempunyai nilai akurasi tertinggi dibandingkan dengan algoritma classification yang lainnya. Sedangkan hasil ekstraksi fitur terbaik pada eksperimen ekstraksi fitur color histogram.

ALGORITMA KLASIFIKASI DECISION TREE UNTUK REKOMENDASI BUKU BERDASARKAN KATEGORI BUKU Maulidah, Mawadatul; Windu Gata; Rizki Aulianita; Cucu Ika Agustyaningrum
E-Bisnis : Jurnal Ilmiah Ekonomi dan Bisnis Vol 13 No 2 (2020): Jurnal Ilmiah Ekonomi dan Bisnis
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/e-bisnis.v13i2.251

With the increasing development of technology the more variety of books circulating on the internet. As is the recommendation system on online book sites that provide books relevantly and as needed with one's preferences. One alternative is GoodReads, a social networking site that specializes in cataloging books and users can share reading book recommendations with each other by rating, reviewing, and commenting. As a large book recommendation site, it has a lot of data that can be processed by applying machine learning methods, but still not known as the most accurate model. By using the right model, we can provide more accurate recommendations. Therefore, this study will analyze the data obtained from the www.kaggle.com namely the goodreads-books dataset. This study proposed a data mining classification model to get the best model in recommending books on GoodReads. The algorithms used are Decision Tree, K-Nearest Neighbor, Naïve Bayes, Random Forest, and Support Vector Classifier, then for model evaluation using accuracy, precision, recall, f1-score, confusion matrix, AUC, and Mean Error Absolute. The test results of several classification algorithms found that Decision Tree has the highest accuracy among the methods presented by 99.95%, precision by 100%, recall by 96%, f1-score of 98% with MAE of 0.05 and AUC of 99.96%. This is proof that decision tree algorithms can be used as book recommendations based on book categories on GoodReads.

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