Article Per Year (5 Year)
p-Index From 2021 - 2026
2.679
P-Index
This Author published in this journals
All Journal Techno.Com: Jurnal Teknologi Informasi Swabumi (Suara Wawasan Sukabumi) : Ilmu Komputer, Manajemen, dan Sosial IJCIT (Indonesian Journal on Computer and Information Technology) Jurnal Riset Informatika Abditeknika - Jurnal Pengabdian Kepada Masyarakat Computer Science (CO-SCIENCE) Jurnal Pengabdian Masyarakat Bhinneka Justika : Jurnal Sistem Informasi Akuntansi Ladang Artikel Ilmu Komputer Jurnal Ilmu Komputer dan Sistem Informasi
Ita Yulianti
Universitas Bina Sarana Informatika
Agriculture, Biological Sciences & Forestry Computer Science & IT Economics, Econometrics & Finance Education Engineering Library & Information Science Public Health Social Sciences Other

Published : 18 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 18 Documents
Search

 1   2 
Implementation of the Saw Method to Discover the Optimum Internet Service Recommendations for Online Gaming Gunawan Gunawan; Ita Yulianti; Ami Rahmawati; Tati Mardiana; Nanang Ruhyana
Jurnal Riset Informatika Vol. 5 No. 3 (2023): June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (759.546 KB) | DOI: 10.34288/jri.v5i3.232

Abstract

Currently, the development and use of the Internet have a more complex function so that it can change the paradigm of people's lives, including in aspects of entertainment, especially games. With the rise of numerous ISPs in Indonesia, different internet service packages are now available, particularly for gamers, such as Indihome, Biznet, First Media, and My Republic. The variety of services makes it difficult for users to choose an internet package that suits their needs. Therefore, this research aims to build a decision support system that can facilitate users in choosing the ideal internet service for gamers based on five criteria: quota, network speed, connection, cost, and the number of users using the SAW method. The data collection methods used are observation, questionnaires, and interviews. The research results obtained from data processing using the SAW method through Microsoft Excel are then implemented into a website-based program. With this program, it is hoped that it can be a tool for users in determining the service package to be purchased.
Penerapan API WhatsApp pada Sistem Pengolahan Data Tabungan Sekolah Menggunakan Model Extreme Programming Rahmawati, Ami; Yulianti, Ita; Nurajizah, Siti
JUSTIKA : Jurnal Sistem Informasi Akuntansi Vol 2 No 2 (2022): JUSTIKA : Jurnal Sistem Informasi Akuntansi
Publisher : Program Studi Sistem Informasi Akuntansi Kampus Kota Sukabumi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/justika.v2i2.1649

Abstract

Peranan teknologi menempati posisi yang sangat penting terhadap munculnya inovasi yang positif bagi kemajuan suatu institusi terutama lembaga pendidikan. DTA Almuta'alim merupakan suatu lembaga pendidikan yang memiliki program tabungan sekolah, dimana program ini berkaitan dengan pendidikan dan ekonomi yang dilakukan sebagai upaya untuk melatih dan mendidik para siswa agar mampu mengontrol diri dalam menggunakan uang secara bijak. Dalam prosesnya, pengolahan data tabungan pada DTA ini masih dilakukan secara konvensional sehingga semakin banyaknya data yang tersimpan menyebabkan sulitnya pencarian data dan tingkat resiko kesalahan pencatatan/perhitungan menjadi lebih tinggi. Oleh karena itu, untuk mengatasi permasalahan tersebut, maka diperlukan pemanfaatan teknologi dengan membangun sebuah sistem yang dapat menunjang kegiatan program ini. Pada penelitian ini, sistem yang akan dibangun dibuat berbasis web dengan menggunakan bahasa pemrograman PHP MySQLi dan diterapkan melalui pendekatan model Extreme Programming yang terdiri dari 4 tahapan meliputi planning, design, coding dan testing. Selain itu, implementasi API WhatsApss pada sistem juga dilakukan dalam memunculkan fitur notifikasi otomatis untuk informasi transparasi saldo tabungan bagi orangtua/wali. Dengan dibuatnya sistem ini, diharapkan dapat memberikan kontribusi terhadap keberlangsungan program tabungan sekolah melalui kemudahan pengolahan data sehingga proses pengelolaannya menjadi lebih baik.
Analisis Sentimen Ulasan Aplikasi Gopay Menggunakan Naive Bayes dengan Teknik Oversampling Yulianti, Ita; Ramdhani, Lis Saumi; Koeswara, Tya Septiani Nurfauzia
IJCIT (Indonesian Journal on Computer and Information Technology) Vol 9, No 2 (2024): November 2024
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/ijcit.v9i2.23443

Abstract

Aplikasi Gopay merupakan salah satu dari aplikasi fintech yang paling banyak digunakan dibandingkan dengan platform lainnya dan tercatat memiliki ulasan sebanyak 207.000 di kolom komentar pada Play Store. Dengan memanfaatkan data tersebut, penelitian ini bertujuan untuk mengekstraksi dan menganalisis secara komputasional menjadi sentimen positif dan negatif menggunakan algoritma Naive Bayes yang dikombinasikan dengan teknik oversampling. Adapun untuk dataset yang digunakan bersumber dari Google Play Store. Hasil penelitian menunjukkan bahwa pemodelan yang diterapkan mampu menghasilkan nilai akurasi yang mencapai 92,89%. Hal ini menjadi temuan penting dikarenakan adanya peningkatan akurasi yang signifikan jika dibandingkan dengan pemodelan Naive Bayes tanpa menggunakan oversampling yang hanya 89,19% saja. Dengan demikian, penerapan teknik oversampling dalam model Naive Bayes ini dapat menjadi solusi efektif dalam mengatasi ketidakseimbangan kelas pada analisis sentimen karena mampu meningkatkan performa model klasifikasi. The Gopay application is one of the most widely used fintech applications compared to other platforms and has recorded 207,000 reviews in the comments column on the Play Store. By utilizing this data, this research aims to extract and computationally analyze positive and negative sentiment using the Naive Bayes algorithm combined with oversampling techniques. The dataset used is sourced from the Google Play Store. The research results showed that the modeling applied was able to produce an accuracy value of 92.89%. This is an important finding because there is a significant increase in accuracy when compared to Naive Bayes modeling without using oversampling which is only 89.19%. Thus, the application of oversampling techniques in the Naive Bayes model can be an effective solution in overcoming class imbalance in sentiment analysis because it can improve the performance of the classification model.
Sistem Informasi Akuntansi Penjualan Tunai Pada Toko Grosir Mukti Garut Sukabumi Yulianti, Ita; Saumi Ramdhani, Lis; Farlina, Yusti; Nurajizah, Siti; Ramdiani Azzahra, Dinda; Fitrika, Sonia
JUSTIKA : Jurnal Sistem Informasi Akuntansi Vol 4 No 2 (2024): Desember 2024
Publisher : Program Studi Sistem Informasi Akuntansi Kampus Kota Sukabumi

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Toko Grosir Mukti Garut adalah perusahaan yang bergerak dalam bidang penjualan sembako, dimana sistem pada toko tersebut masih dilakukan secara manual, mulai dari pencatatan customer, penyimpanan data yang berhubungan dengan proses penjualan hingga pembuatan laporan. Hal ini seringkali menimbulkan terjadinya kesalahan dalam pencatatan, keterlambatan dalam pencarian data ketika diperlukan dan kesulitan dalam pelaporan keuangan. Tujuan dari penelitian ini yaitu untuk membangun sistem informasi akuntansi untuk mendukung pengelolaan transaksi penjualan tunai pada toko grosir tersebut. Adapun metode pengumpulan data dilakukan meliputi observasi, wawancara, dan studi kepustakaan. Untuk mendukung pengembangan sistem pada penelitian ini digunakan metode pemodelan desain yaitu UML (Unified Modeling language) yang diimplementasikan menjadi aplikasi berbasis web dengan Database MySQL. Dengan dibangunnya sistem ini diharapkan dapat menjadi solusi yang terbaik khususnya dalam mempermudah pengelolaan data transaksi penjualan secara tunai.
Analisis Performa Model ResNet-50 Pada Diagnosis Pneumonia Balita Berdasarkan Citra Radiografi Thorax Rahmawati, Ami; Yulianti, Ita; Nurajizah, Siti; Hidayatulloh, Taufik; Sari, Ani Oktarini
Computer Science (CO-SCIENCE) Vol. 5 No. 1 (2025): Januari 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v5i1.7618

Abstract

One of the most serious complications of ARI is pneumonia, where this disease causes sufferers to experience pain when breathing and limited oxygen intake. According to the World Health Organization (WHO), pneumonia is classified as a life-threatening disease due to the high mortality rate caused. To be able to diagnose this disease, patients usually undergo various medical examination methods, one of which is through chest radiography. However, the challenge in diagnosing pneumonia generally lies in the complexity and uncertainty in interpreting the results of these methods. Therefore, this study was conducted with the aim of building an image classification model based on the Chest radiography dataset from toddler patients using the ResNet-50 architecture, which is a variant of the Convolutional Neural Networks (CNN) algorithm. The combination of the two methods is applied to analyze and process images and obtain pattern recognition with high accuracy. The research methods used include the application of data augmentation, CNN architecture design, model training, and performance evaluation. The evaluation results show that the model has quite good performance with an accuracy of 85%, which indicates the model's ability to classify images with a fairly high level of accuracy, and has the potential to help the pneumonia diagnosis process more efficiently and accurately.
Inovasi Sistem Penjualan Hasil Pangan Indo Natural Farm sebagai Upaya Digitalisasi Desa Agrowisata Koeswara, Tya Septiani Nurfauzia; Yulianti, Ita; Marusanti, Eva; Yulistria, Resti
Jurnal Pengabdian Masyarakat Bhinneka Vol. 3 No. 3 (2025): Bulan Februari
Publisher : Bhinneka Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58266/jpmb.v3i3.115

Abstract

Indo Natural Farm merupakan anakan dari PT. Indo Spices Trading yang berkecimpung di perdagangan rempah internasional. Awal mulanya, lahan yang sekarang dijadikan farm ini adalah tempat penjemuran rempah-rempah, seperti: teh, cengkeh, kapulaga dan pala. Selain mengelola dan menjual rempah, sudah ada kambing perah juga yang ditempatkan di beberapa kandang. Seiring berjalannya waktu, pemilik berinisiatif untuk memperluas area kandang kambing di tempat penjemuran rempah sekaligus menambah area untuk bertani dan bercocok tanam beragam tanaman. Melihat adanya potensi dari kambing tersebut, banyak sekolah-sekolah terdekat yang berkunjung yang menjadikan objek kambing sebagai media edukasi di sekolah, sehingga diberdayakanlah kambing tersebut dengan di pelihara, dikembangkan dan dirawat serta di perah susunya untuk dijual. Selain menawarkan produk susu kambing, Indo Natural Farm juga menjual hasil pangannya berupa sayuran dan buah-buahan segar hasil olahan petani lokal. Sistem penjualan hasil pangan di Indo Natural Farm masih dilakukan dengan metode konvensiaonal, konsumen hanya bisa membeli produk hasil pangan langsung di lokasi, atau melakukan pemesanan terlebih dahulu ke owner melalui whatsapp ataupun email. Tentunya langkah tersebut kurang efisien, mengingat keterbatasan waktu transakasi, maka diperlukan sebuah inovasi sistem berbasis website yang memudahkan konsumen atau pelanggan melakukan pemesanan produk sekaligus membantu owner mengelola penjualan, memperluas pemasaran sekaligus memudahkan dalam pembuatan laporan penjualannya.
ENHANCING SLEEP QUALITY PREDICTION THROUGH SMOTE-BASED DATA BALANCING AND HYBRID MACHINE LEARNING MODELS Ami Rahmawati; Ita Yulianti; Ani Oktarini Sari; Siti Nurajizah; Hikmatulloh
Jurnal Riset Informatika Vol. 8 No. 1 (2025): Desember 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i1.456

Abstract

Sleep is a vital aspect in maintaining a person's physical and psychological balance. Poor sleep quality can reduce physical and cognitive performance, increasing the risk of various health problems. This study aims to develop a predictive model for sleep quality based on factors such as lifestyle, stress, daily activities, and caffeine consumption, using XGBoost combined with Recursive Feature Elimination (RFE). XGBoost was chosen for its ability to handle imbalanced datasets and heterogeneous features, while RFE helps simplify the model without losing important information. In the data pre-processing stage, a class imbalance was found, so the Synthetic Minority Over-sampling Technique (SMOTE) process was carried out to balance the proportion of the minority class. The dataset in this study was divided into two parts, namely 80% as training data and 20% as testing data, and validated using cross-validation to ensure generalization. The results show very high model performance with an accuracy of 99.79% on training data, 99.63% on cross-validation, and 99.10% on testing data. This model was then developed into a web application for practical use in analyzing sleep quality prediction. This study emphasizes the methodological contribution of a SMOTE-based hybrid machine learning model and its ready-to-use application implementation, while also opening opportunities for further testing on more diverse datasets and evaluating biases caused by synthetic data.
Penerapan Arsitektur U-Net pada Segmentasi Cacat Biji Kopi untuk Optimalisasi Inspeksi Kualitas Ami Rahmawati; Ita Yulianti; Ani Oktarini Sari; Siti Nurajizah
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 5 No. 2 (2025): Mei 2026
Publisher : LKP Unity Academy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70340/jirsi.v5i2.365

Abstract

Manual visual inspection of coffee bean defects remains prone to subjectivity and inconsistency, necessitating a more accurate and efficient approach. This study proposes a deep learning-based coffee bean image segmentation method using the U-Net architecture to detect the presence of defects in coffee beans using a binary segmentation approach. The dataset consists of 300 coffee bean images evenly divided into 150 images of black coffee and 150 images of insect damage. Annotation was performed using a semi-automatic pseudo-labeling method based on Gaussian filtering, absolute difference, and thresholding to generate ground truth in binary mask format. Training data was enriched through augmentation techniques including horizontal flip, vertical flip, rotation, and brightness-contrast adjustment. The model was trained using a combined loss function of Dice Loss and Binary Cross-Entropy with the Adam optimizer over 15 epochs with an early stopping mechanism. Evaluation results demonstrate excellent performance with a Mean IoU of 0.9240, Precision of 0.9707, Recall of 0.9495, and F1 Score of 0.9600, with an overall correct prediction rate of 97.45% based on pixel-level confusion matrix analysis. These results indicate that the U-Net architecture is capable of segmenting defective coffee bean areas accurately and consistently, making it a promising foundation for the development of an automated coffee quality inspection system.
Co-Authors A.Gunawan Ami Rahmawati Ami Rahmawati Ami Rahmawati Ani Oktarini Sari ANI OKTARINI SARI, ANI OKTARINI Fitrika, Sonia Hafifah Bella Novitasari Hikmatulloh Lis Saumi Ramdhani Marusanti, Eva Monikka Nur Winnarto Muhamad Abdul Ghani Muhammad Sukrisno Mardiyanto Nanang Ruhyana Nurhadianto Nurhadianto Rahmawati, Ami Ramdiani Azzahra, Dinda Resti Yulistria Rizal Amegia Saputra Siti Nurajizah Tati Mardiana Taufik Hidayatulloh Tya Septiani Nurfauzia K Tya Septiani Nurfauzia Koeswara Veithzal Rivai Zainal Yuliani, Yuri Yusti Farlina