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All Journal E-Bisnis : Jurnal Ilmiah Ekonomi dan Bisnis Jurnal Informatika Jurnal Teknik Komputer AMIK BSI Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Riset Informatika Jurnal Mantik Jurnal Infortech Paradigma
Cucu Ika Agustyaningrum
Universitas Bina Sarana Informatika
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science

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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

Abstract

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.
INFORMATION AND POPULATION SERVICE SYSTEM IN SLAWI WETAN VILLAGE Cucu Ika Agustyaningrum; Haryani; Yosep Tajul Arifin; Warjiyono
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

Abstract

Slawi Wetan Village really needs an information system that supports and provides satisfactory services for its citizens. For this reason, the author tries to make a final project on service and population information systems in Slawi Wetan Village, which until now has not been computerized. The existing system in Slawi Wetan Village is still done manually, starting from the service for covering letters of ID cards, family cards, and transfer letters to those relating to population services, population data errors, and making reports. It is possible that during the process there is an error in recording, less accurate information exists. The best solution to solving problems at the Slawi Wetan sub-district office is a computerized system. An effective and efficient activity can be achieved in supporting activities in the kelurahan. In this information system, the method used is waterfall, and in its implementation it uses the PHP programming language and MySQL as the database. Based on the results of the study, it can be concluded several things, namely: this software can be used to handle the population service process, information about the village, and knowing population data, this system can also provide print reports of village cover letters.
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

Abstract

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.
Perbandingan Gradient Boosting dan Light Gradient Boosting Dalam Melakukan Klasifikasi Rumah Sewa Rizka Dahlia; Cucu Ika Agustyaningrum
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 5, No 6 (2022): Desember 2022
Publisher : Program Studi Teknik Informatika, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v5i6.5460

Abstract

Abstrak— Persaingan antar perusahaan tidak akan dapat terhindarkan apalagi terkait tujuan perusahaan dalam mendapatkan omset sebesar-besarnya. Salah satu persaingan yang terjadi adalah dibidang property atau jika lebih spesifik lagi yaitu penyewaan rumah. Sebuah perusahaan harus menentukan strategi bagaimana rumah yang akan disewakan nantinya akan sebanding dengan harga pembangunan. Maka dari itu perusahaan dapat melakukan klasifikasi rumah sewa dalam menentukan hal tersebut. Penelitian ini menggunakan model Gradient Boosting dan Light Gradient Boosting. Hasil yang didapatkan adalah bahwa model Gradient Boosting adalah model yang cocok pada penelitian ini dengan mendapatkan hasil accuracy 84.38%, precision 83.33% dan recall 87.53%. Jika dilihat perbandingan dari confusion matrix, Gradient Boosting memiliki jumlah hasil prediksi data lebih besar dibanding dibanding Light  Gradient Boosting.Kata kunci: Rumah Sewa, Data Mining, Gradient Boosting, Light Gradient Boosting Abstract— Competition between companies cannot be avoided, especially regarding the company's goal of getting the maximum turnover. One of the competitions that occurs is in the property sector, or more specifically, house rental. A company must determine a strategy for how the house to be rented out will be comparable to the construction price. Therefore the company can classify rental houses in determining this. This study uses the Gradient Boosting and Light Gradient Boosting models. The results obtained are that the Gradient Boosting model is a suitable model in this study with 84.38% accuracy, 83.33% precision and 87.53% recall. If you look at the comparison of the confusion matrix, Gradient Boosting has a greater number of data prediction results than Light Gradient Boosting.Keywords : House for rent, Data Mining, Gradient Boosting, Light Gradient Boosting
Klasifikasi Penerima Bantuan Sosial Menggunakan Algoritma C 4.5 Agus Junaidi; Yunita Yunita; Sarifah Agustyani; Cucu Ika Agustyaningrum; Yoseph Tajul Arifin
Jurnal Teknik Komputer AMIK BSI Vol 9, No 1 (2023): JTK Periode Januari 2023
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/jtk.v9i1.14378

Abstract

Saat pandemi Covid-19 mulai melanda di Indonesia, banyak pembatasan yang diterapkan oleh pemerintah untuk membatasi merebaknya virus tersebut. Masyarakat dan dunia usaha secara otomatis juga mengalami ketidakstabilan dalam perekonomian karena pembatasan tersebut. Oleh karena itu pemerintah juga mulai membuat kebijakan untuk melindungi perekonomian masyarakat dengan menyalurkan bantuan sosial kepada desa atau kelurahan diseluruh Indonesia. Penelitian ini bertujuan untuk memberikan kontribusi yang nyata tentang kelayakan penerima bantuan agar penyaluran bantuan benar-benar tepat sasaran sehingga bisa mengangkat perekonomian masyarakat yang turun drastis karena pandemi ini. Metode dalam penelitian ini menggunakan metode survey lapangan dengan memberikan batasan kriteria pada pendapatan bulanan, jumlah tanggungan, jenis tempat tinggal, dan kendaraan yang dilakukan pada salah satu kelurahan di wilayah Tangerang dengan metode klasifikasi C4.5. Hasil output dari penelitian ini adalah menentukan apakah penerima bantuan yang terdaftar tersebut layak atau tidak layak untuk menerima bantuan yang dapat dijadikan rekomendasi oleh pengambil keputusan, dalam hal ini pihak kelurahan untuk menyeleksi warganya.
Comparison of Conventional Machine Learning and Deep Neural Network Algorithms in the Prediction of Monkey-Pox Cucu Ika Agustyaningrum; Rizka Dahlia; Omar Pahlevi
Jurnal Riset Informatika Vol 5 No 3 (2023): Priode of June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i2.522

Abstract

Smallpox syndrome, also known as monkeypox, is an uncommon zoonotic viral infection brought on by the monkeypox virus, which belongs to the genus orthopoxvirus and family Poxviridae. Injury-related mortality in primates ranges from 1 to 10%. Data mining is a method for analyzing data. Deep neural networks and traditional machine learning methods are both used in the data analysis process. The Python programming language is used during the comparison procedure of this research algorithm to generate values for accuracy, f1 score, precision, recall, ROC, and AUC. The test results demonstrate that using sigmoid activation function parameters, the deep neural network algorithm's accuracy is 70.08%, F1 score is 79.18%, precision is 68.59%, recall is 62.65%, and AUC is 62.65%. In comparison to using conventional machine learning algorithms, the adagrad optimizer with learning rate 0.01 and 0.2 dropout has a higher value. The conventional machine learning model algorithm has the best xgboost, F1 score, precision, recall, and AUC scores when compared to other approaches: 64.40%, 64.45%, and 78.14%. According to these numbers, the average fairness disparity between deep neural network algorithms and traditional machine learning is 5.68%, F1 score is 13.79%, precision is 4.14%, recall is 1.75%, and AUC is 1.75%.
Algoritma Klasifikasi Multilayer Perceptron Dalam Analisa Data Kebakaran Hutan Haryani Haryani; Cucu Ika Agustyaningrum; Artika Surniandari; Sucitra Sahara; Ratna Kurnia Sari
Jurnal Infortech Vol 5, No 1 (2023): JUNI 2023
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/infortech.v5i1.15792

Abstract

Kebakaran hutan atau yang sering disebut dengan wildfire merupakan salah satu isu lingkungan yang utama karena berdampak negatif terhadap kelestarian hutan, merugikan lingkungan dan ekonomi, serta merugikan masyarakat. Kebakaran hutan adalah kondisi di mana hutan terbakar, merusak hasil hutan dan menyebabkan kerusakan ekologi dan ekonomi. Tujuan dari peramalan kebakaran hutan adalah untuk mengetahui seberapa sering terjadi kebakaran hutan. Oleh karena itu, proses analisis data dilakukan dengan menggunakan teknik machine learning tradisional melalui metode Random Forest, Decision Tree, Logistic Regression, Naive Bayes dan Multilayer Perceptron. Mengetahui keakuratan dan nilai hasil F1 memungkinkan membandingkan metode ini dengan bahasa pemrograman Python. Hasil pengujian menunjukkan bahwa pendekatan Multilayer Perceptron mengungguli metode Random Forest, Decision Tree, Logistic Regression dan Nave Bayes dengan nilai akurasi masing-masing sebesar 93,35% dan F1 Score 93,69% dengan ukuran hidden layer sebesar 64,64. Dibandingkan dengan pendekatan lain yang dipelajari, nilai metode multilayer perceptron cukup signifikan. Penelitian ini dapat membantu menentukan kemungkinan kebakaran hutan.
Queue System Implementation at PT. Mulia Persada Indonesia Call Center Services Haryani Haryani; Choirunisa Iqbar Qurotaaini; Cucu Ika Agustyaningrum; Artika Surniandari; Dedi Saputra
Paradigma Vol. 25 No. 2 (2023): September 2023 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v25i2.2310

Abstract

In this modern era, call centers have become an important element in providing efficient and responsive customer service. One of the main challenges faced by call centers is how to efficiently manage customer call queues. This study aims to implement a queuing system at PT Mulia Persada Indonesia's call center service by utilizing a computer network, with the aim of increasing the efficiency and effectiveness of customer service. This research also includes an analysis of network security using a Virtual Private Network (VPN) and point-to-Point Tunneling Protocol (PPTP) as a security method for call center network connections. The results of the study show that the use of PPTP in PT Mulia Persada Indonesia's network security provides significant benefits. Call center employees can connect to the corporate network through a secure connection, even from an external location such as home. This access allows them to access the queuing system and perform call center tasks effectively without having to be in a physical office. Meanwhile, the implementation of a queue system at PT Mulia Persada Indonesia's call center service has had a positive impact, namely increasing customer satisfaction. With an effective queuing system, customer waiting time can be minimized and calls can be handled quickly.
Comparison of Conventional Machine Learning and Deep Neural Network Algorithms in the Prediction of Monkey-Pox Cucu Ika Agustyaningrum; Rizka Dahlia; Omar Pahlevi
Jurnal Riset Informatika Vol. 5 No. 3 (2023): June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i3.217

Abstract

Smallpox syndrome, or monkeypox, is an uncommon zoonotic viral infection brought on by the monkeypox virus, which belongs to the genus Orthopoxvirus and family Poxviridae. Injury-related mortality in primates ranges from 1 to 10%. Data mining is a method for analyzing data. Deep neural networks and traditional machine learning methods are used in data analysis. The Python programming language is used during the comparison procedure of this research algorithm to generate values for accuracy, f1 score, precision, recall, ROC, and AUC. The test results demonstrate that using sigmoid activation function parameters, the deep neural network algorithm's accuracy is 70.08%, F1 score is 79.18%, precision is 68.59%, recall is 62.65%, and AUC is 62.65%. Compared to conventional machine learning algorithms, the adagrad optimizer has a higher value with a learning rate of 0.01 and 0.2 dropouts. The conventional machine learning model algorithm has the best xgboost, F1 score, precision, recall, and AUC scores compared to other approaches: 64.40%, 64.45%, and 78.14%. According to these numbers, the average fairness disparity between deep neural network algorithms and traditional machine learning is 5.68%, F1 score is 13.79%, precision is 4.14%, recall is 1.75%, and AUC is 1.75%.
Co-Authors Agus Junaidi Agustiani, Sarifah Artika Surniandari, Artika Choirunisa Iqbar Qurotaaini Dedi Saputra Gata, Windu Haryani Mawadatul Maulidah Omar Pahlevi Ratna Kurnia Sari Ridan Nurfalah Rizka Dahlia Rizki Aulianita Sucitra Sahara Ummu Radiyah Warjiyono Warjiyono Windu Gata Yosep Tajul Arifin Yoseph Tajul Arifin Yunita Yunita