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All Journal OPERATION EXCELLENCE: Journal of Applied Industrial Engineering Malcom: Indonesian Journal of Machine Learning and Computer Science JURNAL KARYA ILMIAH MULTIDISIPLIN Indonesian Journal of Statistics and Its Applications J-KOMA : Jurnal Ilmu Komputer dan Aplikasi
Qur’ania, Arie
Unknown Affiliation
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Mathematics Other

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Klasifikasi Penyakit Cacar Monyet Menggunakan Support Vector Machine (SVM): Classification of Monkeypox Disease Using Support Vector Machine (SVM) Wijaya, Rohmatullah Sony; Qur’ania, Arie; Anggraeni, Irma
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 4 (2024): MALCOM October 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i4.1417

Abstract

Penyakit cacar monyet menjadi sebuah wabah di beberapa negara non endemik pada bulan Juli tahun 2022. Oleh karena itu, tindakan pencegahan atau pengobatan yang tepat perlu dilakukan secara dini dengan cara melakukan identifikasi penyakit menggunakan suatu metode klasifikasi. Klasifikasi dilakukan menggunakan metode knowledge discovery in database (KDD) dengan algoritma support vector machine (SVM) yang menggunakan 4 kernel yaitu linear, RBF, sigmoid, dan polynomial dengan pengaturan parameternya pada masing masing kernel. Algoritma SVM dipilih karena penggunaan berbagai kernelnya memungkinkan eksplorasi bentuk-bentuk keputusan yang berbeda dalam ruang fitur yang lebih tinggi untuk mengangkap pola pola yang tidak linear. Hasil terbaik didapatkan oleh kernel polynomial dengan tingkat akurasi sebesar 75%, sementara kernel linear sebesar 70,5%, RBF sebesar 66%, dan sigmoid sebesar 45%. Kemudian nilai grafik kurva receiver operating characteristic area under control (ROC AUC) untuk kernel polynomial sebesar 0.81. Hal tersebut menunjukkan bahwa model klasifikasi yang dibuat sudah baik dan dapat dikembangkan ke penelitian tahap selanjutnya.
Application of gaussian filter and extraction features for quality control of fruit raw materials in the puree industry Sulistyo, Soma; Thaheer, Hermawan; Qur’ania, Arie
Operations Excellence: Journal of Applied Industrial Engineering Vol. 15, No. 3, (2023): OE November 2023
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/oe.2023.v15.i3.087

Abstract

The purpose of research in using Android-based raw material quality control applications in fruit puree factories is to enhance the industry's precision, uniformity, and efficiency. This application is developed to implement digital image processing utilizing two distinct methods: Gaussian filters and feature extraction. The implemented application captures guava data using the camera of an Android phone and subsequently resizes the image to dimensions of 600x600 pixels. Subsequently, the image colors are recovered by employing a Gaussian filter that operates on normalized Red-Green-Blue values. A minimum of 120 photos of red guava fruit from the raw material of one of the puree companies were subjected to image processing procedures. The application solely focuses on the color and texture of the fruit's skin, ensuring that the sample remains undamaged while adhering to hygienic guidelines. The ripeness degree of guava fruit is determined by employing an image classification algorithm with the K-nearest neighbor method. The application validation using K-fold cross-validation achieved an accuracy of 90.0% and a precision of 90.27% when applied to color imagery. When feature extraction was used, the accuracy was 83.3%, with a precision of 83.4%. Color extraction provides a more precise method for identifying ripe guava. The utilization of guava fruit ripeness detection in the quality control of raw materials for the puree sector has been simplified and made more user-friendly through the development of an Android-based application. Officers are not obligated to possess specialized expertise regarding the quality of raw materials. 
Pengembangan Aplikasi Simulasi Investasi Reksadana Dengan Algoritma Machine Learning Prophet Halimah, Siti; Qur’ania, Arie; Anggraeni, Irma; Rama Putra, Gustian
Jurnal Karya Ilmiah Multidisiplin (JURKIM) Vol. 4 No. 3 (2024): Jurnal Karya Ilmiah Multidisiplin (Jurkim)
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/jurkim.v4i3.23550

Abstract

Investment is a crucial foundation in financial management that opens opportunities to achieve long-term financial goals. One mutual fund investment platform at a PT is innovating to create a platform tailored to the needs of Generation Z users. However, the platform is currently still under improvement and/or renewal due to functional deficiencies such as the frontend and backend display, as well as the lack of an investment simulation feature that allows investors to predict mutual fund investments. This research focuses on adding an investment simulation feature using a Machine Learning algorithm that can automate the detection of trends and seasonality in mutual fund products. The method used in the website development is the Agile method in the System Development Life Cycle (SDLC). In conducting this research, an analysis of the platform's weaknesses and deficiencies was carried out to compare the website platform to be developed with another platform. Based on the analysis that has been carried out, the development of the platform's functionality and the addition of features were made, namely, updating the latest display on each page and the investment simulation feature. This research uses the PHP programming language and the Laravel framework as well as Firebase as its database. The programming implementation results in a platform that can predict mutual fund product simulations that can be used by investors, thus easily attracting Generation Z to invest in the website platform.
Classification of Yogyakarta Batik Using the K-Nearest Neighbor (KNN) and Gray Level Co-occurrence Matrix (GLCM) Methods Qur’ania, Arie; Dias Saharani, Ananda; Handini, Riri
J-KOMA : Jurnal Ilmu Komputer dan Aplikasi Vol 7 No 2 (2024): J-KOMA : Jurnal Ilmu Komputer dan Aplikasi
Publisher : Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/jkoma.v7i2.02

Abstract

The preservation of Yogyakarta batik motifs as part of Indonesia’s cultural heritage can be supported through digital image classification technology. This study aims to develop an automatic classification system for Yogyakarta batik motifs using the Gray Level Co-occurrence Matrix (GLCM) method for texture feature extraction and the K-Nearest Neighbor (KNN) algorithm for the classification process. The dataset consists of 1,350 digital images of six different batik motif types, sourced from Kaggle. The system was developed and tested on the Google Colab platform through several stages, including preprocessing, feature extraction, model training, and performance evaluation using accuracy, precision, recall, and F1-score metrics. The evaluation results show that the model achieved an accuracy of 60%, with the best performance on the batik-ceplok motif (F1-score of 77%) and the lowest on the batik-kawung motif (F1-score of 46%). The system was then implemented as a web application using the Streamlit framework, allowing users to upload images and receive classification results in real time. This implementation not only contributes to the field of image processing but also aids in cultural preservation through digitization and easy access to batik motif classification  
Multi-Objective Optimization by Ratio Analysis (MOORA) Method for Decision Support System in Selecting the Best Electric Car: Metode Multi-Objective Optimization by Rasio Analysis (MOORA) Untuk Sistem Pendukung Keputusan Dalam Pemilihan Mobil Listrik Terbaik Humaira, Zakiyah; Ariandi, M. Irfan; Qur’ania, Arie; Puja Negara, Teguh
Indonesian Journal of Statistics and Applications Vol 8 No 2 (2024)
Publisher : Statistics and Data Science Program Study, IPB University, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v8i2p129-131

Abstract

Implementasi metode Multi-Objective Optimization by Ratio Analysis (MOORA) telah berhasil diterapkan untuk memilih mobil listrik terbaik. Hasil penelitian menunjukkan bahwa implementasi Metode MOORA berhasil merangking untuk 10 jenis mobil listrik dengan 8 jenis kriteria, yaitu: kapasitas baterai, kecepatan pengisian baterai, fitur kenyamanan, fitur keselamatan, jarak tempuh, kecepatan maksimum, harga, dan tenaga. Penerapan algoritma Moora didasarkan pada 4 tahapan, yaitu: penentuan nilai kriteria, penyusunan matriks keputusan, normalisasi dan optimasi atribut, dan penentuan rangking. Hasil penerapan metode MOORA merangking 10 jenis mobil listrik dengan urutan: Toyota BZ 4X, Hyundai ionic 5 2022, Cherry omodo E5 2024, Wuling cloud EV, Vinvost VF5, Nissan leaf 2021, Kia EV5 2023, BYD Dolphin, Wuling binguo EV, Wuling air EV 2022. Ketika terjadi penambahan dan pengurangan kriteria terjadi perubahan perangkingan. Hasil perangkingan mobil listrik terbaik ditampilkan dalam website dengan pemrograman Javascript dan PHP yang memuat tampilan halaman dashboard, halaman kriteria, halaman data, dan halaman perangkingan. Perhitungan pada sistem website telah divalidasi dengan aplikasi Excell menghasilkan akurasi 100%.
Co-Authors Anggraeni, Ade Irma Ariandi, M. Irfan Dias Saharani, Ananda Handini, Riri Hermawan Thaheer Humaira, Zakiyah Puja Negara, Teguh Rama Putra, Gustian Siti Halimah Sulistyo, Soma Wijaya, Rohmatullah Sony