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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Informatics (IJEEI) JITK (Jurnal Ilmu Pengetahuan dan Komputer) INTECOMS: Journal of Information Technology and Computer Science Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JSAI (Journal Scientific and Applied Informatics) Jurnal Ilmu Teknik dan Komputer Journal of Computer Science and Engineering (JCSE) Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Sistem Informasi Dan E-Bisnis Jurnal Ilmu Komputer Dan Informatika Dedikasi Nusantara: Jurnal Pengabdian Masyarakat Pendidikan Dasar Sinergi: Jurnal Pengabdian Kepada Masyarakat JPMTT (Jurnal Pengabdian Masyarakat Teknologi Terbarukan) JCOSIS (Journal Computer Science and Information Systems) International Journal of Computing Science and Applied Mathematics-IJCSAM
Ayumi, Vina
Universitas Mercu Buana
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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Klasifikasi Teks Umpan Balik Kompetensi Kepribadian di Perguruan Tinggi Menggunakan Ekstraksi Fitur TF-IDF dan Algoritma Logistic Regression Ayumi, Vina; Purba, Mariana; Mailana, Siska
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 2 (2025): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i2.8764

Abstract

This study aimed to develop and evaluate a text classification model to identify sentiment in feedback on lecturers’ personality competencies at a university using TF-IDF feature extraction and Logistic Regression (LR) algorithms. The data originated from student evaluations of lecturers’ personality competencies at Universitas Sjakhyakirti, consisting of a total of 6,112 texts labeled as positive sentiment (3,700) and negative sentiment (2,412). The dataset was then divided into three parts: training (70%), validation (10%), and testing (20%). The research stages included text preprocessing, which involved data cleaning, letter normalization, and the removal of common words, followed by term weighting using the TF-IDF method and classification using the LR model to categorize texts as positive or negative sentiment. The model was evaluated using accuracy, precision, recall metrics, and a confusion matrix. Experimental results showed that at the 50th epoch, the model achieved a training accuracy of 81.90% and a validation accuracy of 78.30%, while on the testing data, the TF-IDF-LR model reached an accuracy of 75.1%.
Application of Random Contrast and Brightness Range Methods on Phytomedicine Leaf Image Dataset Purba, Mariana; Ayumi, Vina; Rahayu, Sarwati; Salamah, Umniy; Handriani, Inge; Farida, Ida
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 2 (2025): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i2.8766

Abstract

This study aimed to enhance the performance of deep learning models in detecting and classifying medicinal plant leaf images by applying two data augmentation techniques, namely Random Contrast Augmentation (RCA) and Brightness Range Augmentation (BRA). The RCA technique randomly adjusted the contrast of images by calculating the pixel average and modifying each pixel value based on a contrast factor, thereby increasing the variation in image lighting. Meanwhile, BRA randomly altered the brightness of the images to simulate varying lighting conditions. The research process began with the collection of medicinal plant leaf image datasets, which were then divided into three parts: training data, validation data, and testing data. The dataset was then pre-processed to prepare the images before applying the augmentation. Augmentation techniques were employed to enrich the dataset by generating modified copies of images using RCA and BRA techniques. The application of both augmentation techniques resulted in a training dataset of 2,400 images, 300 validation images, and 300 testing images.
Penerapan Metode Augmentasi pada Dataset Farmakognosi Menggunakan Teknik Flip Secara Horizontal dan Vertikal Purba, Mariana; Ayumi, Vina; Ani, Nur
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 2 (2025): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i2.8769

Abstract

This study aimed to apply image augmentation techniques, namely horizontal flip and vertical flip, to a pharmacognosy dataset to increase the diversity of training data in a pharmacognosy image recognition system. By applying these two techniques, this study focused on finalizing a pharmacognosy image dataset that could be used to train machine learning models. The application of these augmentation techniques improved the accuracy and generalization ability of the model in recognizing pharmacognosy images taken from various viewpoints and orientations. This study used two image augmentation techniques, vertical flip augmentation (VFA) and horizontal flip augmentation (HFA), to expand the pharmacognosy image dataset. Each augmentation technique produced four times the number of modified images from the original images with more and more diverse data variations. With the application of the vertical flip augmentation technique, the training dataset consisted of 2,400 images, a validation dataset of 300 images, and a testing dataset of 300 images, for a total of 3,000 data sets. Similarly, the horizontal flip augmentation technique yielded the same amount of data: 2,400 data points for training, 300 data points for validation, and 300 data points for testing. These two techniques increased the total number of training and testing data points to 3,000.
Implementasi Dataset Augmentation pada Citra Etnofimedisin Menggunakan Teknik Rotation dan Channel Shift Purba, Mariana; Ayumi, Vina; Haji, Wachyu Hari
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 2 (2025): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i2.8776

Abstract

This study aimed to increase the quantity and variety of ethnopharmacological image datasets using image augmentation techniques, specifically rotation range augmentation (RRA) and channel shift range augmentation (CSA). The dataset augmentation was conducted to enrich the training data for the development of machine learning models used to recognize medicinal plant images. The RRA technique rotated images by random angles, providing variations in object orientation, while CSA altered the color channel values to simulate changes in lighting and the natural colors of plants. The research process included dataset collection, data preprocessing, application of both augmentation techniques, and division of the dataset into training, validation, and testing data. The results showed that the CSA technique produced 2,400 training data, 300 validation data, and 300 testing data, while the RRA technique produced the same amount of data. Therefore, the total data generated from both augmentation techniques amounted to 6,000 images, which could improve the accuracy and performance of deep learning models in recognizing ethnopharmacological images.
Pemodelan Feature Modelling dan User Interface Untuk Sistem Manajemen Data Etnofarmakologi Purba, Mariana; Ayumi, Vina; Ratnasari, Anita
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 2 (2025): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i2.8777

Abstract

This research aimed to develop software for a pharmacognosy data management system by utilizing a feature modeling approach to map and categorize application features based on user requirements. The feature grouping consisted of mandatory and optional features designed to meet the diverse needs of users. The research methods used included literature study, data collection, requirement analysis, feature modeling, user interface modeling, and evaluation. The results of this study led to the development of a pharmacognosy data management system to manage data on medicinal plants, users, and reviews. The main features developed included user management and plant management, both of which were mandatory features for managing users and medicinal plants. Under Plant Management, there were optional features like plant part management and mandatory features like Plant Usage Management to support the management of medicinal plant usage information. Additionally, image data management was added as an alternative feature for managing images of medicinal plants. Thus, the system was expected to serve as a reference for software development that can adapt to user needs and the ongoing technological advancements.
Studi Pendahuluan: Analisis Metode Deep Learning Untuk Klasifikasi Citra Daun Tanaman Fitomedisin Purba, Mariana; Rusmawan, Uus; Ayumi, Vina
JUKOMIKA (Jurnal Ilmu Komputer dan Informatika) Vol. 8 No. 1 (2025): Juli
Publisher : LPPMPP Yayasan Sejahtera Bersama Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54650/jukomika.v8i1.608

Abstract

Penelitian ini melakukan tinjauan sistematis terhadap penggunaan deep learning untuk klasifikasi citra daun tanaman fitomedisin. Tujuan penelitian ini adalah untuk mengidentifikasi berbagai algoritma deep learning yang diterapkan dalam identifikasi spesies tanaman fitomedisin dan mengevaluasi kinerja masing-masing algoritma. Metode yang digunakan adalah PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) dengan melakukan analisis studi yang relevan pada database IEEE Xplore, Springer, dan ScienceDirect. Studi yang dipilih kemudian dianalisis berdasarkan algoritma yang digunakan, dataset yang diterapkan, dan hasil akurasi yang diperoleh. Hasil penelitian menunjukkan bahwa algoritma CNN memberikan akurasi yang cukup baik, namun keduanya menghadapi masalah waktu komputasi yang tinggi. Penelitian yang menggunakan ResNet mencapai akurasi tertinggi, yaitu 95,5%, meskipun terdapat masalah dengan latar belakang citra daun. Algoritma Xception, meskipun memberikan akurasi yang lebih rendah, dapat dioptimalkan lebih lanjut dengan meningkatkan ukuran dataset dan teknik yang digunakan. Dalam penelitian ini dibahas penggunaan model MobileNet yang efisien dalam hal komputasi dan dapat mengatasi tantangan-tantangan klasifikasi citra daun tanaman fitomedisin. Model MobileNet (MNET) digunakan untuk klasifikasi daun tanaman fitomedisin karena efisiensinya dalam memproses data besar dengan mengurangi beban komputasi. Perbedaan utama MNET dibandingkan CNN terletak pada penggunaan depthwise separable convolution, yang membagi konvolusi menjadi dua bagian yaitu depthwise convolution dan pointwise convolution. Metode ini dapat mengurangi jumlah parameter, menghasilkan jaringan saraf dalam yang lebih ringan. Dengan arsitektur yang lebih kecil dan latensi rendah, MNET dapat diterapkan untuk klasifikasi citra daun tanaman fitomedisin
Implementasi Metode Color Blob Detection Pada Objek Daun Sawi Wahyu Adianto; Dwika Putra, Erwin; Handrie Noprison; Vina Ayumi; Marissa utami; Mariana Purba
JCOSIS (Journal Computer Science and Information Systems) Vol. 1 No. 1 (2024): Mei
Publisher : Institute for Research and Community Service

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61567/jcosis.v1i1.176

Abstract

Tujuan : Sistem aplikasi Mendeteksi Objek Daun Sawi dengan menerapakan Metode Color Blob Detection menggunakan Bahasa pemrograman matlab. Mekanisme pembuatan aplikasi dimulai dari pembuatan rancangan aplikasi, GUI (Grafik User Interface) sampai dengan pembuatan coding. Metode/Design/Pendekatan: model deteksi Objek Daun Sawi dengan menerapakan Metode Color Blob Detection menggunakan Bahasa pemrograman matlab Hasil/Temuan: Hasil pengujian yang dilakukan dengan memiliki tingkat keakuratan paling tinggi yaitu dengan jarak pengambilan sampel objek dengan jarak 50 cm, dan tingkat keakuratan paling rendah dengan jarak pengambilan sampel objek dengan jarak 20 cm. Kebaharuan/Originalitas/Nilai: Tingkat akurasi deteksi daun sawi maka dapat disimpulkan berhasil dengan tingkat keberhasil akurasi 67.7% Keywords: Color Blob Detection, Image Processing, Matlab
Best Selling Product Sales Prediction Using K-Nearest Neighbors (KNN) Algorithm Marissa Utami; Vina Ayumi
JCOSIS (Journal Computer Science and Information Systems) Vol. 1 No. 2 (2024): Oktober
Publisher : Institute for Research and Community Service

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61567/jcosis.v1i2.214

Abstract

Purpose: This research aims to evaluate the performance of the K-Nearest Neighbor (KNN) algorithm in predicting product sales based on historical data. The data includes product price information, sales amount in the previous month, product category, promotion, and seasonal factors. This research involves several experiments with various K values and the application of data normalization to optimize prediction accuracy. Methods/Study design/approach: The KNN algorithm was chosen for its simplicity and ability to handle multivariate data. Results/Findings: The results show that the value of K=7 is the optimal parameter for this dataset, with a Root Mean Squared Error (RMSE) value of 110. Normalizing the data is proven to improve the model's accuracy, reducing the RMSE value by about 10% compared to the unnormalized data. Product price, previous sales amount, and promotion features significantly contribute to sales prediction. Novelty/Originality/Value: This research is expected to provide insight for companies that want to use machine learning to predict product sales and support business decision-making.
STRATEGI OPTIMALISASI DIGITAL MARKETING BAGI USAHA MIKRO, KECIL DAN MENENGAH (UMKM) UNTUK MENINGKATKAN JANGKAUAN PASAR DI ERA INDUSTRI 4.0 Anita Ratnasari; Wachyu Hari Haji; Vina Ayumi; Sri Dianing Asri
JURNAL SINERGI Vol. 6 No. 2 (2024): SINERGI
Publisher : FT-USNI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59134/sinergi.v6i2.667

Abstract

Optimalisasi digital marketing bagi Usaha Mikro, Kecil, dan Menengah (UMKM) di Jakarta Barat menjadi solusi penting untuk meningkatkan daya saing dan keberlanjutan usaha dalam menghadapi persaingan di era Industri 4.0. Banyak UMKM masih terjebak dalam pemasaran konvensional dan menghadapi kendala seperti kurangnya pemahaman teknologi, keterbatasan sumber daya, dan sulitnya mengikuti tren digital yang terus berkembang. Strategi yang ditawarkan meliputi pelatihan dan pendampingan dalam penerapan teknik Search Engine Optimization (SEO), termasuk penelitian kata kunci, pengoptimalan konten, meta tags, serta pemanfaatan alat analisis seperti Google Analytics. Kegiatan ini bertujuan meningkatkan visibilitas online UMKM, menjangkau konsumen yang lebih luas, dan memperluas pasar secara berkelanjutan. Selain memberikan dampak langsung pada peningkatan keterampilan digital pelaku UMKM, program ini juga berkontribusi pada pencapaian Indikator Kinerja Utama (IKU) kedua dan ketiga, di mana mahasiswa dan dosen terlibat aktif dalam memberikan pelatihan dan pengalaman praktis di dunia nyata.
PEMANFAATAN TEKNOLOGI VIRTUAL REALITY (VR) UNTUK MENINGKATKAN KUALITAS HIDUP LANSIA MELALUI SIMULASI INTERAKTIF Anita Ratnasari; Vina Ayumi; Sri Dianing Asri
JURNAL SINERGI Vol. 6 No. 2 (2024): SINERGI
Publisher : FT-USNI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59134/sinergi.v6i2.671

Abstract

Penggunaan teknologi Virtual Reality (VR) dalam program Mitra Indonesia Ramah Lansia (IRL) menawarkan solusi inovatif untuk meningkatkan motivasi dan kualitas hidup lansia. Lansia sering menghadapi tantangan berupa keterbatasan mobilitas, rasa kesepian, dan keterbatasan akses terhadap aktivitas sosial. Teknologi VR memungkinkan lansia untuk terlibat dalam aktivitas fisik, mental, dan sosial secara virtual, seperti wisata virtual, pelatihan keterampilan, dan interaksi sosial, tanpa meninggalkan rumah. Selain itu, VR membantu menjaga keterampilan kognitif dan fisik lansia melalui simulasi interaktif yang menyenangkan. Pelatihan ini tidak hanya memberikan manfaat langsung kepada lansia tetapi juga melibatkan mahasiswa dan dosen dalam mendukung kebutuhan komunitas, sejalan dengan pencapaian Indikator Kinerja Utama (IKU) kedua dan ketiga. Program ini diharapkan mampu meningkatkan kemandirian lansia dalam memanfaatkan teknologi, memperkaya hubungan sosial, serta menciptakan pengalaman belajar yang bermakna bagi seluruh pihak yang terlibat. Dengan pendekatan berbasis teknologi dan kolaborasi, VR memiliki potensi besar untuk menjadi alat yang efektif dalam meningkatkan kesejahteraan lansia secara berkelanjutan.
Co-Authors Abd Rahman Abrar, Muhammad Fauzan Angga, Tyas Dwi Anita Ratnasari Anzalna, Akbarakah Bagus Priambodo Cahyo Pambudi Darmo Desi Ramayanti Desmon, Johanes Devi Fitrianah Dhamanti, Inge Dianing Asri, Sri ferdi Fitri Dwi Astuti, Fitri Dwi Fitri Yakub Hadiguna Setiawan Handrie Noprison Handrie Noprisson Hong Wei Ida Farida Ida Nurhaida Ida Nurhaida Ida Nurhaida Inge Handriani, Inge Iryani, Lemi Jumaryadi, Yuwan Kananga, Meylani Mentari Laksono Trisnantoro Luhyana, Nahdah Mailana, Siska Mariana Purba Mariana Purba Marissa Utami Naufal, Muhamad Fahri Nengsiana, Nengsiana Noprisson, Handrie Nur Ani Pangrepti, Diah Ayu Purba, Mariana Putra, Erwin Dwika Remmy A. M. Zen Salamah, Umniy Sarwati Rahayu Sri Dianing Asri Syamsiar, Syamsiar Tjahjono, Benny Uswatun Hasanah Uus Rusmawan, Uus Wachyu Hari Haji Wachyu Hari Haji Wahyu Adianto Wina Firly, Tiara Yogga Herlambang, Helwy