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All Journal Jurnal Sosio Ekonomika Bisnis Syntax Jurnal Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Informatika Universitas Pamulang Conference on Innovation and Application of Science and Technology (CIASTECH) MIND (Multimedia Artificial Intelligent Networking Database) Journal Jambura Journal of Informatics Journal of Electronics, Electromedical Engineering, and Medical Informatics INTI Nusa Mandiri BRITech : Jurnal Ilmiah Ilmu Komputer, Sains dan Teknologi Terapan TIN: TERAPAN INFORMATIKA NUSANTARA Abdi Kami : Jurnal Pengabdian Kepada Masyarakat Jurnal Teknik Informatika (JUTIF) Infotech: Jurnal Informatika & Teknologi Computer Science (CO-SCIENCE) Jurnal Mandiri IT Scientific Student Journal for Information, Technology and Science Jurnal Algoritma Technologica International Journal of Management Research and Economics Journal of Computer Science Contributions (Jucosco) ABDI KAMI: Jurnal Pengabdian Kepada Masyarakat
Fitri Nur Masruriyah, Anis
Universitas Buana Perjuangan Karawang
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Refining Diabetes Diagnosis Models: The Impact of SMOTE on SVM, Logistic Regression, and Naïve Bayes Wibowo, Arief; Masruriyah, Anis Fitri Nur; Rahmawati, Selly
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 1 (2025): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i1.596

Abstract

Accurate diabetes classification is a significant challenge in medical diagnostics, especially in imbalanced datasets. This study addresses this issue by introducing A New Modified Weighted SMOTE (ANMWS), integrated with Priority of Attribute by Expert Judgement (PAEJ) framework, to enhance the performance of machine learning models for imbalanced data. PAEJ categorizes attributes into three levels—high, medium and low priority—based on expert knowledge, while ANMWS applies weighted oversampling using these priority levels to generate synthetic data more representative of real-world cases. The proposed method was evaluated using three algorithms: Support Vector Machine (SVM), Logistic Regression, and Naïve Bayes. Results indicate that applying ANMWS algorithm with PAEJ framework significantly improved predictive performance, with AUC values increasing to 0.995 for SVM, 0.993 for Logistic Regression, and 0.990 for Naïve Bayes, compared to 0.980, 0.978, and 0.975, respectively, using standard SMOTE. Additionally, precision and recall for SVM improved by 5% and 7%, respectively. These findings demonstrate the critical role of ANMWS algorithm and PAEJ framework in addressing class imbalance, providing a reliable method for early diabetes diagnosis and informed clinical decision-making.
Deteksi Penggunaan Masker Menggunakan Algoritma CNN dengan Arsitektur YOLOv5 Prasetyo Ajie; Ahmad Fauzi; Anis Fitri Nur Masruriyah
Scientific Student Journal for Information, Technology and Science Vol. 6 No. 1 (2025): Scientific Student Journal for Information, Technology and Science
Publisher : Scientific Student Journal for Information, Technology and Science

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Abstract

Coronavirus Disease 2019 (COVID-19) menyebabkan berbagai negara mengalami kerugian, terutama di sektor kesehatan. WHO menghimbau untuk mengendalikan COVID-19 dengan menerapkan protokol kesehatan, salah satunya adalah penggunaan masker. Masker dapat mengurangi risiko penularan COVID-19, namun masih banyak masyarakat yang mengabaikan protokol tersebut. Oleh karena itu, dibuat sebuah sistem untuk mendeteksi penggunaan masker dengan tepat menggunakan arsitektur YOLOv5. Sistem ini bertujuan untuk membantu mengatur penggunaan masker di area umum atau tempat terbuka. Penelitian ini dimulai dengan pengumpulan data berupa citra, yang kemudian dijadikan dataset untuk proses pelatihan model menggunakan YOLOv5s. Hasil penelitian menunjukkan bahwa model yang dikembangkan mampu mencapai akurasi sebesar 90,37%.
Aplikasi Berbasis Android untuk Mendeteksi Kulit Kucing Berdasarkan Model CNN Riyandi Aditya Fitrah; Anis Fitri Nur Masruriyah; Ayu Ratna Juwita
Scientific Student Journal for Information, Technology and Science Vol. 6 No. 1 (2025): Scientific Student Journal for Information, Technology and Science
Publisher : Scientific Student Journal for Information, Technology and Science

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Abstract

Penyakit kulit pada kucing dapat memberikan dampak negatif baik bagi pemilik kucing maupun hewan tersebut. Penyakit kulit seperti scabies atau kudis, serta ringworm, bersifat menular melalui sentuhan langsung dengan kucing yang terjangkit penyakit tersebut. Tungau telinga pada kucing umumnya berada di bawah rongga telinga. Kucing yang terjangkit penyakit kulit dapat mengalami kerusakan pada tubuhnya. Penelitian ini bertujuan untuk mengembangkan aplikasi mobile berbasis Android yang dapat mendeteksi penyakit kulit pada kucing. Aplikasi ini dirancang untuk mengenali jenis penyakit kulit yang dialami kucing, seperti kudis, ringworm, dan tungau. Dalam penelitian ini, digunakan model deteksi objek berbasis deep learning dengan algoritma Convolutional Neural Network (CNN) dan TensorFlow Lite. Arsitektur yang digunakan adalah MobileNetV2 FPN Lite untuk memproses pelatihan model deteksi objek menggunakan dataset yang besar, sehingga model dapat diterapkan pada aplikasi mobile berbasis Android. Hasil penelitian menunjukkan bahwa model yang dirancang menghasilkan nilai mean Average Precision (mAP) sebesar 42% dan mean Average Recall (mAR) sebesar 23%. Evaluasi sistem dan validasi dari para ahli menghasilkan nilai sebesar 73%, menunjukkan bahwa aplikasi ini memiliki potensi untuk mendeteksi penyakit kulit pada kucing dengan akurasi yang cukup baik.
Klasifikasi Penggunaan Masker selama Pandemik Menggunakan Algoritma CNN dengan Notifikasi Suara Ryan Gusti Nugraha; Ahmad Fauzi; Anis Fitri Nur Masruriyah
Scientific Student Journal for Information, Technology and Science Vol. 6 No. 1 (2025): Scientific Student Journal for Information, Technology and Science
Publisher : Scientific Student Journal for Information, Technology and Science

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Abstract

Berbagai teknologi diciptakan untuk pencegahan ancaman virus Covid-19 yang telah menyebar di banyak negara, termasuk Indonesia. Salah satunya adalah penggunaan masker di tempat publik. Penelitian ini bertujuan untuk melakukan deteksi terhadap objek wajah dalam rangka memverifikasi penggunaan masker. Berdasarkan dataset dari Kaggle, objek yang digunakan untuk penelitian adalah wajah manusia dalam bentuk 2D. Penelitian ini terdiri dari dua tahapan: pertama, membuat dan menguji model deteksi. Model ini dirancang untuk mengidentifikasi dan mengklasifikasikan wajah dengan masker, masker yang tidak tepat, dan tanpa masker. Kemudian, model diuji untuk mengukur tingkat akurasinya. Hasil dari tiga puluh kali percobaan menunjukkan bahwa model memiliki akurasi sebesar 99%, yang diuji menggunakan webcam secara real-time. Model ini juga dilengkapi dengan indikator suara yang memberikan notifikasi setiap kali wajah terdeteksi, menggunakan metode algoritma Convolutional Neural Network (CNN).
Evaluasi Kinerja Algoritma AdaBoost dan XGBoost Menggunakan Dataset Penyakit Obesitas Pada Populasi Dewasa Sukmawati, Cici Emilia; Nur Masruriyah, Anis Fitri; Juwita, Ayu Ratna; Tejayanda, Rigger Damaiarta; Nurmayanti, Trisya
Jambura Journal of Informatics VOL 6, N0 2: OKTOBER 2024
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jji.v6i2.27342

Abstract

Penelitian ini membahas terkait evaluasi kinerja AdaBoost dan XGBoost pada penyakit obesitas . Penelitian tersebut menggunakan dataset yang diperoleh dari sumber kaggle dengan jumlah data 2111 dengan 17 atribut. Selanjutnya, data tersebut dilakukkan preprocessing data sehingga berkurang menjadi 591 data. Kemudian, data tersebut dilakukan split data dengan perbandingan 70:30 dengan rincian data uji 119 dan data training sebanyak 472. Pengujian dilakukan menggunakan accuracy, precision dan recall. Berdasarkan hasil penelitian yang telah dilakukan, bahwa metode XGBoost terbukti lebih unggul dibandingkan dengan AdaBoost. Adapun accuracy, precision dan recall sebesar 92%. Sedangkan untuk accuracy dan recall untuk metode AdaBoost sebesar 40% sertaa precision 39%.
Studi Komparatif Algoritma K-Means dan K-Medoids untuk Segmentasi Informasi Kesehatan Ananda, Muhammad Dwi; Mardiah, Mardiah; Masruriyah, Anis Fitri Nur; Malik, Karenina Nurmelita
Computer Science (CO-SCIENCE) Vol. 5 No. 2 (2025): Juli 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v5i2.9207

Abstract

In analyzing medical data to support clinical decisions, segmentation of health information plays a crucial role. This study presents a comparative analysis of K-Means and K-Medoids algorithms in clustering Medical Examination data. This evaluation is conducted using two main internal approaches, namely Silhouette Score and Davies-Bouldin Index in measuring the quality of separation as well as cohesion between clusters. The experiment involved varying the number of clusters to determine the optimal configuration of each algorithm. The results show that K-Means provides representative performance and is more stable against data complexity, compared to the K-Medoids algorithm which is only optimal in a small number of clusters. Statistical analysis using one-way ANOVA was applied to test the significance of performance differences between algorithms based on the average Silhouette Score value, yielding an F-value of 4.8594 with a P-value of 0.0447. This indicates that the performance difference between the two algorithms is statistically significant at 5% significance rate. This research confirms the K-Means algorithm for segmenting health data with diverse distributions and is expected to serve as a foundation for the development of more efficient health data classification systems in the future.
Impact of Digital Marketing Features on Consumer's Purchase Decision in High End Brand "ButtonScarves": Brand Image as a Mediator Dexi Triadinda; Anis Fitri Nur Masruriyah
International Journal of Management Research and Economics Vol. 2 No. 1 (2024): February : International Journal of Management Research and Economics
Publisher : Institut Teknologi dan Bisnis (ITB) Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/ijmre-itb.v2i1.1477

Abstract

The main objective of this research is to determine the influence of digital marketing on purchasing decisions. This research also aims to determine whether brand image mediates the relationship between digital marketing and purchasing decisions. The required data was obtained from a questionnaire via an online survey on Google Form. After going through responses from 45 respondents using Buttonscarves products who had made purchases on the Buttonscarves website, the data was analyzed using SEM PLS 3.0. The result is that there is a direct influence of digital marketing on brand image, but there is no significant influence of digital marketing on purchasing decisions. The results further state that brand image has a direct influence on purchasing decisions. Brand image is also able to mediate the indirect relationship between digital marketing and purchasing decisions. Theoretically, this research confirms that consumers do not immediately make purchasing decisions with digital marketing in a company, without knowledge about the brand image of the product they want to buy.
Enhancing application design for integrated evaluation through user-centered prototyping with figma Tiana, Ade Hikma; Prasetyo, Rizky Tito; Yulistiawan, Bambang Saras; Masruriyah, Anis Fitri Nur
Jurnal Mandiri IT Vol. 14 No. 2 (2025): October: Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i2.462

Abstract

This study developed an integrated evaluation application using a User-Centered Design (UCD) approach combined with user-centered prototyping via Figma, aiming to improve academic service management in educational institutions. The application focused on four main features: service request submission, complaint reporting, lecturer evaluation, and a knowledge base for self-service solutions. Requirements were gathered through questionnaires and interviews with students, lecturers, and faculty leaders to ensure the design met user needs. The prototype was iteratively refined based on quantitative and qualitative evaluations using Likert scale questionnaires. Results showed high user satisfaction with an average score of 4.41, indicating excellent usability, visual appeal, and feature relevance. Thematic analysis highlighted key themes of ease of use, interface consistency, and system security. The integrated UCD and Figma prototyping approach proved effective in producing an adaptive, interactive, and user-friendly application design that supports continuous improvement in academic services.
Optimasi Metode Support Vector Machine Menggunakan Seleksi Fitur Recursive Feature Elimination dan Forward Selection untuk Klasifikasi Kanker Payudara Septiany, Eva Senia; Handayani, Hanny Hikmayanti; Mudzakir, Tohirin Al; Masruriyah, Anis Fitri Nur
TIN: Terapan Informatika Nusantara Vol 5 No 2 (2024): July 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v5i2.5324

Abstract

Cancer, the leading cause of global death, results from abnormal cell proliferation that spreads beyond the boundaries of normal tissue. Breast cancer is one of the most common types of cancer, with approximately 2.26 million cases reported in 2020. This research aims to develop a more effective Support Vector Machine (SVM) algorithm for breast cancer classification through efficient feature selection techniques. Previous research has used various algorithms such as K-Nearest Neighbor and Logistic Regression for breast cancer identification. This research focuses on improving accuracy by using alternative feature selection methods such as Recursive Feature Elimination (RFE) and Forward Selection. The dataset used consists of 569 instances with 32 features sourced from the UCI Machine Learning Repository, and classified into benign and malignant categories. Data pre-processing methods, including data cleaning, coding, and feature selection, were applied to the dataset. RFE and Forward Selection techniques were used to identify the most important features for model training. Evaluation of the improved SVM model shows a training accuracy of nearly 100% and a Cross Validation accuracy of 97%, demonstrating the effectiveness of the proposed approach in the context of breast cancer. In addition, the Learning Curve and testing showed the stability of the SVM model with no signs of overfitting or underfitting. Thus, this study developed an SVM algorithm with a feature selection method that produces better accuracy results in breast cancer classification.
PENERAPAN ALGORITMA CNN MENGGUNAKAN FRAMEWORK YOLO UNTUK DETEKSI OBJEK PRODUK DI PERUSAHAAN MANUFAKTUR Maulana, Asep; Suherman, Maman; Masruriyah, Anis Fitri Nur; Novita, Hilda Yulia
INTI Nusa Mandiri Vol. 18 No. 2 (2024): INTI Periode Februari 2024
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v18i2.5028

Abstract

Component products used for manufacturing a machine in manufacturing companies have two types of products, type A and B. The problem that often occurs in the industry is product sorting errors due to the traditional sorting process, using human labor. The disadvantages are limited human labor so that fatigue can occur, causing errors in sorting products and losses for the company. Many studies discuss object detection, Industrial problems in the checking process can be approached with the help of this technology. Object detection works to analyze frames with the method of finding objects. There are methods in digital image processing, CNN algorithms which include methods in computer vision. The growing framework makes the CNN algorithm more powerful. YOLO includes a framework based on the CNN algorithm. YOLOv5 detects objects by taking into account the object's confidence value, the output of the detected object is a bounding box on the object. The problem in the industry in the checking process can be approached with the help of this technology. For this reason, this research aims to create a model for product object detection in manufacturing companies. The process carried out is data collection, image annotation, training, testing, evaluation. The images collected were 137 for training data and 34 for validation data totaling 171 image data. The results of the model using YOLOv5 with epoch 1000 get a precision value of 100%, recall 100% and mAP 99%, the product detection results get an average value of 100%.
Co-Authors Ahmad Fauzi Alvionika, Nindy Amril Siregar ANANDA, MUHAMMAD DWI Anton Junior Anton Romadoni Junior Ardika, Sanggi Bayu Arief Wibowo ARIF, SITI NOVIANTI NURAINI Asep Maulana, Asep Awal, Elsa Elvira Bambang Saras Yulistiawan dasmadi, dasmadi Deden Wahiddin Deny Maulana Dexi Triadinda Dwi Vina Wijaya Elsa Elvira Awal Elsa Elvira Awal Emilia Sukmawati, Cici Emilia Sukmawati Faisal, Sutan Hanny Handayani Hanny Hikmayanti Handayani Hastari, Nazwa Putri Indra, Jamaludin Juwita, Ayu Ratna MALIK, KARENINA NURMELITA Maman Suherman Mardiah Mardiah Mudzakir, Tohirin Al Muhammad Cesar Afriansyah Arief Nita, yustina Novita, Hilda Yulia Nurmayanti, Trisya Permana, Tedi Prakoso, Bobby Suryo Prasetyo Ajie Prasetyo, Rizky Tito Rahmat Hidayat Rahmat Rahmat RAMADHAN, ANGGA RAMDA Risky Pratama, Adi Riyandi Aditya Fitrah Rohana, Tatang Ryan Gusti Nugraha Selly Rahmawati Septiany, Eva Senia Siregar, Amril Mutoi Siti Robiah Sukmawati, Cici Emilia Syahrul Azis Tania Puspa Rahayu Sanjaya Tejayanda, Rigger Damaiarta Tiana, Ade Hikma Tri Denda Ulfa Amelia Wahiddin, Deden Widiharto, Banani Yana Cahyana Yogi Alfiansah Yogi Firman Alfiansyah