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All Journal Jurnal Kesehatan Masyarakat (JKM) CENDEKIA UTAMA Jurnal Kedokteran dan Kesehatan Jurnal SOLMA JITK (Jurnal Ilmu Pengetahuan dan Komputer) Journal of Public Health for Tropical and Coastal Region Jurnal Manajemen Informasi dan Administrasi Kesehatan Journal of Epidemiology and Public Health Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JSAI (Journal Scientific and Applied Informatics) Jurnal Abdi Masyarakat (JAM) PREPOTIF : Jurnal Kesehatan Masyarakat Jurnal Ilmu Kesehatan Masyarakat Berkala IJECS: Indonesian Journal of Empowerment and Community Services MAHESA : Malahayati Health Student Journal Jurnal Pengabdian Masyarakat (ABDIRA) Media Publikasi Promosi Kesehatan Indonesia (MPPKI) Sehat Rakyat: Jurnal Kesehatan Masyarakat Journal Research of Social Science, Economics, and Management Pro Health Jurnal Ilmiah Kesehatan Jurnal Pengabdian Masyarakat Nasional Jurnal Kesehatan The Journal of Educational Development International Journal Of Public Health Jurnal Sains dan Kesehatan JCOSIS (Journal Computer Science and Information Systems)
Nur Ani
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Journal : JSAI (Journal Scientific and Applied Informatics)

 1 
Evaluasi Usability pada Portal Basis Data Tanaman Obat Indonesia Menggunakan Metode System Usability Scale (SUS) Wachyu Hari Haji; Anita Ratnasari; Vina Ayumi; Handrie Noprisson; Nur Ani
JSAI (Journal Scientific and Applied Informatics) Vol 6 No 3 (2023): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v6i3.6263

Abstract

Previous research discussed valuable recommendations for the development of an Indonesian medicinal plant database portal. However, previous research has not discussed usability evaluation on the Indonesian medicinal plant database portal. One usability evaluation technique that is quite popular is the system usability scale (SUS). This study aims to analyze the portal database of medicinal plants using the usability scale (SUS) system to find out the next portal improvement. The SUS method allows researchers to collect data from users through surveys and calculate usability scores, providing recommendations for improving the design and functionality of web-based systems. From the experimental results in the form of calculation results using SUS measurement, it is known that the implementation of the medicinal plant database portal received an assessment of 72.14. This value if interpreted using the measurement level of the final value of SUS can be said that the implementation of the medicinal plant database portal can be accepted (acceptable) with a good category (good).
Optimasi Kinerja Artificial Neural Network Menggunakan Ekstraksi Fitur Gray Level Co-occurrence Matrix (GLCM) Untuk Monitoring Gerakan Lansia Nur Ani
JSAI (Journal Scientific and Applied Informatics) Vol 7 No 2 (2024): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v7i2.6424

Abstract

Machine learning methods are used to detect elderly accidents while image processing is used to support machine learning performance so that detection performance can be better. This study used two methods used simultaneously, namely GLCM and ANN. The study consisted of preparation of human gesture datasets, preprocessing stage, application of GLCM, analysis of feature extraction results, classification using ANN and analysis of motion class detection results. Overall, the GLCM method with homogeneity parameters and ANN as a classifier obtained an accuracy of 24.32%. The GLCM method with contrast parameters and ANN as a classifier gets an accuracy of 99.84%. The GLCM method with mean parameters and ANN as the classifier gets an accuracy of 99.99%. The GLCM method with dissimilarity parameters and ANN as a classifier to classify hand movement images gets the best accuracy of 100%.
Aplikasi Pemantauan Lalu Lintas Kapal di Perairan Laut dengan Menggunakan Metode Haversine Formula Ani, Nur; Catra Pratama, Suga; Aziz, Faisal; Fardiansyah, Dwiki
JSAI (Journal Scientific and Applied Informatics) Vol 6 No 3 (2023): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v6i3.6443

Abstract

By leveraging spatial data, new approaches to vessel traffic safety risk assessment can be developed, enabling a better understanding of vessel trajectories and improving overall maritime safety in congested and hazardous aquatic environments. This study aims to design an application for monitoring ship movement traffic in marine waters by applying the Haversine Formula algorithm. This method is used for calculating the distance between the nearest ports from the position of the ship, this aims to facilitate decision making in case of emergencies. From the experimental results, the comparison of values with Google Maps gets an average of 10.3 with the smallest difference of 2 and the largest 34, this still indicates that the calculation of the estimated distance of the ship to the nearest port using the haversine formula in the ship traffic monitoring dashboard system has slightly better accuracy than using the Google Maps application. The design of the system prototype has been successfully carried out based on the design of use case diagrams, activity diagrams, and class diagrams as well as the design of the interface display, has modules including; traffic module, ship module, voyage module, history module, port module.
Penerapan Algoritma Term Frequency-Inverse Document Frequency (TF-IDF) Untuk Fitur Pencarian Dokumen Standar Nasional Indonesia Ani, Nur; Yosephine Sinaga, Desi; Junior, Nickolas; Doni Munggaran, Muhamad
JSAI (Journal Scientific and Applied Informatics) Vol 6 No 3 (2023): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v6i3.6504

Abstract

Information about the Indonesian National Standard (SNI) is already available on the Ministry of Industry's Pustand website and also the BSN website. The SNI search process that is less effective and must be done on different websites will make it difficult for users and business actors. Therefore, the SNI search website using keywords is a solution that will facilitate the SNI search process. SNI search with keywords where users enter the words to be searched on the website and with the TF-IDF algorithm the website will appear any SNI that matches the keyword. In its application, the keywords entered by users on the SNI search website will go through preprocessing first, namely tokenizing, filtering, then stamming, and the TF-IDF algorithm will combine two methods, namely the concept of the frequency of the appearance of terms in an SNI document and inverse back documents that have the same meaning from the keywords entered by users into the system. This application will make it easier for business actors who want to find out about SNI for the product to be produced so that when the product already exists, business actors only need to register the product to be standardized in accordance with the applicable SNI according to the product description.
Analisis Faktor Kepercayaan dan Kepuasan Pengguna Website Marketplace: Studi Empiris pada E-Commerce Lazada Hari Haji, Wachyu; Ratnasari, Anita; Ayumi, Vina; Noprisson, Handrie; Ani, Nur
JSAI (Journal Scientific and Applied Informatics) Vol 7 No 3 (2024): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v7i3.7476

Abstract

This study aims to identify the factors influencing trust and user satisfaction in online marketplaces by applying the DeLone & McLean information system success model. Data were collected through an online questionnaire distributed to Lazada marketplace buyers in Indonesia. The empirical results indicate that trust is a key predictor in determining the quality of sellers and their ability to provide the best services. Statistically, the first hypothesis (H1) shows a significant influence of website reputation on user trust (**T-Stat = 8.50; Sig = *). The second hypothesis (H2), regarding the influence of perceived website size on trust, is not significant (T-Stat = 1.42; Sig = NS). The third hypothesis (H3) demonstrates a significant positive relationship between trust and user satisfaction with the website (**T-Stat = 5.62; Sig = *). The fourth hypothesis (H4) confirms a highly significant positive relationship between trust and perceived website quality (**T-Stat = 14.59; Sig = *). This study recommends that online marketplaces enhance the prestige of sellers and maintain customer trust, as these factors play a critical role in improving user satisfaction when shopping on online marketplaces.
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.
Penerapan Metode Gamma Correction dan MobileNet Untuk Klasifikasi Citra Daun Purba, Mariana; Ayumi, Vina; Rahayu, Sarwati; Salamah, Umniy; Handriani, Inge; Ani, Nur
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 3 (2025): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i3.9459

Abstract

This study proposed an enhanced leaf image classification model by integrating gamma correction as a preprocessing technique with the MobileNet (MNET) architecture to improve visual feature extraction. The dataset consisted of 750 images representing five classes of medicinal plants, namely Psidium guajava, Syzygium polyanthum, Piper betle, Annona muricata, and Andrographis paniculata, obtained from personal documentation, online sources, and public datasets. Gamma correction was applied to adjust illumination and enhance leaf texture clarity, followed by resizing and normalization processes. Data augmentation was performed using rotation, contrast adjustment, horizontal and vertical flipping, brightness adjustment, and channel shifting to increase training data variation. The MobileNet architecture was expanded with additional layers, including global average pooling, flatten, Dense–ReLU, and Dense–softmax, enabling it to function as an efficient feature extractor and classifier. Experiments were conducted using a batch size of 32, 50 epochs, the Adam optimizer, and a learning rate of 0.0001. The combined MNET and gamma correction model achieved a training accuracy of 99.00%, a validation accuracy of 87.50%, and a testing accuracy of 84.16%.
Co-Authors Aminuddin, Arief Andi Nugroho, Andi Anisa , Nur Anita Ratnasari ARDIANSYAH ARDIANSYAH Arham Hanif Febria Ariansyah Arif Wibawa, Prasetya Arista Ririn Yulianti Asriati, Yul Awusi, Riati Yulia Emanuel Ayudia Johar, Syefira Ayumi, Vina Aziz, Faisal Bagus Priambodo Bakhtiar, Bakhtiar. Beknazar, Achmad Hait Aliyumni Catra Pratama, Suga Desi Ramayanti Dewi Puspito Sari, Dewi Puspito Doni Munggaran, Muhamad Elissa Maharani, Nine Fardiansyah, Dwiki Fiqi Nurbaya Iik Sartika Iik, Sartika Indhun, Qomariah Inge Handriani, Inge Irawan, Bibit Julia, Pertiwi Jumaryadi, Yuwan Junaidi Junaidi Junior, Nickolas Kameswara, Gilang Mahesa Khoirunisa, Asahra Kurniawan Kusnendar, Yustinus Krisna Lemi Iryani Lutfiana Kusumawati, Lutfiana Maharani, Nine Elissa Mariana Purba Marissa Utami Maulia, Putri Maysyanda, Charista mdf, sulisti Nia Umilizah Noprisson, Handrie Nugraho, Farid Setyo Nugroho, Farid Setyo Nur Ismawati, Nur Paskanita, Maria Paulitan, Alsih Priskila Gani Puji Hastuti Purba, Mariana Puspito Sari, Dewi Putra, Erwin Dwika Rahardjo, Budhi Rinto Priambodo Rosyidah, Marlia Yulianti Salamah, Umniy Sari, Noviana Ristika Sartika , Iik Sarwati Rahayu Sholihah, Imrok Atus Siti Maesaroh Susanti, Ika Yani Susi Purnama Afrilia Syamsiar, Syamsiar Syefira Ayudia Johar Titik Haryanti Titik, Haryanti Trapsilo, Firdaus Yuli Ulfah, Hana Rosian Utami, Amrih Gancar Wachyu Hari Haji Wachyu Hari Haji Wartini Wartini Wartini Wartini Wartini, Wartini Widyatama, Fer Dila Yosephine Sinaga, Desi