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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) International Journal of Computing Science and Applied Mathematics 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)
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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Perbandingan Kinerja Activation Function pada Algoritma Resnet untuk Klasifikasi Varietas Beras Ayumi, Vina
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.v7i2.6421

Abstract

Quality checking of rice seed varieties (Oryza sativa) is an important procedure for quality assessment in the agricultural sector. The application of transfer learning algorithms has shown good results in image recognition tasks, so this algorithm is suitable for classifying rice variety images automatically. The data classes to be analyzed are Arborio, Basmati, Ipsala, Jasmine and Karacadag based on morphological, shape and color features analysis using the ResNet algorithm. The experiment used three types of models, namely ResNet-TopHat-ReLU, ResNet-TopHat-LeakyReLU and ResNet-TopHat-eLU. The ResNet-TopHat-eLU model is the best model with training accuracy of 96.61%, validation accuracy of 95.12% and testing accuracy of 78.17%.
Classification of Text Datasets of Public Complaints Against the Government on Social Media Using Logistic Regression Purba, Mariana; Dianing Asri, Sri; Ayumi, Vina; Salamah, Umniy; Iryani, Lemi
JSAI (Journal Scientific and Applied Informatics) Vol 7 No 1 (2024): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v7i1.6447

Abstract

Di era teknologi saat ini, salah satu media sosial yang banyak digunakan dalam berinteraksi dan memberikan opini, pengaduan masyarakat, serta saran adalah Twitter. Dalam bidang pemerintahan, tweet yang mengandung opini atau pengaduan masyarakat terhadap suatu layanan atau program organisasi dapat digunakan sebagai umpan balik untuk memperbaiki atau meningkatkan kualitas layanan. Penelitian ini berfokus pada klasifikasi tweet untuk membedakan tweet yang tergolong pengaduan masyarakat atau non-pengaduan masyarakat dengan menerapkan algoritma pemelajaran mesin yaitu logistic regression (LR). Tahap dari penelitian ini antara lain crawling dan labeling dataset, pre-processing, pemodelan menggunakan classifier logistic regression, serta evaluasi kinerja classifier. Tahapan dalam penelitian ini seperti preprocessing, klasifikasi dan evaluasi dilakukan menggunakan bahasa pemrograman Python dengan bantuan scikit-learn library. Berdasarkan hasil eksperimen, model penelitian dengan menggunakan fitur ekstraksi CountVectorizer mencapai kinerja yang lebih baik daripada TfidfVectorizer. Eksperimen dengan menggunakan ekstraksi fitur TfidfVectorizer mencapai akurasi 92% (F1 score: 0.9181, precision: 0.9191 recall: 0.9181, kappa: 0.8363) sedangkan menggunakan akurasi CountVectorizer mencapai 94% (F1 score: 0.9355, precision: 0.9406, recall: 0.9356, kappa: 0.8715).
Rancang Bangun Aplikasi Manajemen Data Stok Barang Untuk Industri Kuliner Multi-Branch Yogga Herlambang, Helwy; Ayumi, Vina
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.v7i3.7414

Abstract

Technological advancements have also led to the creation of web applications for the promotion of culinary and online services, increasing the visibility and accessibility of culinary businesses. Stock applications are essential for maintaining operations, managing resources, and adapting to market demands in the culinary business. The purpose of this study is to develop and implement a stock management system for PT. ABC specifically aims to improve the accuracy of stock data, facilitate inventory tracking, increase transparency in stock management and implement a system that can be accessed online and in real-time. This research was conducted in the period from April to July 2024. Data collection was carried out by conducting literature studies, observations and interviews conducted with the general manager and administrator of the logistics division). Based on the results of the research, the stock data management application for the multi-branch culinary industry consists of a login menu, a master menu, a transaction & invoice menu, a report menu and a settings menu. The login page is used to log in to the dashboard, using a username and password. The master menu page consists of several sub-menus, namely: Products & Stock, Product Categories, Suppliers, and Customers & Divisions. The Transactions & Invoices menu page consists of several sub-menus, namely: all orders and invoices.
Aplikasi Promosi dan Penjualan Business-to-Business Berbasis Web (Studi Kasus PT. Revass Utama Medika) Nengsiana, Nengsiana; Ayumi, Vina
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.v7i3.7415

Abstract

The existence of a sales information system in the current digital era is increasingly important for companies in managing their business processes. The purpose of this research is to analyze the design of a Business-to-Business Promotion and Sales information system so that it makes it easier for companies to integrate information needs that can support overall services and adjust sales and product marketing strategies that are more targeted. The research methods used include field studies and observations by conducting interviews using the waterfall method, modeling design using the UML method. The results of the analysis carried out show that the manual sales information used in this company is inefficient and it is proposed to create a web-based sales and promotion application. The aim of making this application is to improve time efficiency in processing and transactions as well as more targeted product marketing. This application has several quite complete features such as adding and deleting products, a sales graph dashboard, transaction features, shipping options features and sales report retrieval features.
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.
Klasifikasi Penyakit Tanaman Berdasarkan Analisis Citra Daun Padi Menggunakan Metode SVM Dan CLAHE Ayumi, Vina
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.v7i3.7559

Abstract

Rice disease has been found in Indonesia and needs to be identified early for the prevention process. Blast and brown spot disease in rice is considered the most prominent and dangerous disease. This study will focus on identifying four rice leaf disease detections, including Bacterial Blight, Blast, Brown Spot and Tungro. This study aims to determine the effect of contrast enhancement method on SVM method for plant disease classification based on rice leaf image analysis using SVM and CLAHE methods. The dataset consists of four classes namely: Bacterial Blight, Blast, Brown Spot and Tungro with .jpg format. The dataset consists of 480 data for each class. Based on the experimental results, the accuracy of the SVM model reached 100% for the training stage and 94.81% for the testing stage. The accuracy of the CLAHE-SVM model reaches 100% for the training stage and 95.95% for the testing stage. Based on the accuracy value, the CLAHE-SVM model has better performance than the SVM model
E-Arusun: Pengembangan Aplikasi Untuk Manajemen Data Administrasi Rumah Susun Berbasis Web Menggunakan Metodologi Prototype Wina Firly, Tiara; Ayumi, Vina
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.v7i3.7573

Abstract

Managing apartment buildings is a critical aspect of property management, encompassing leasing, resident administration, facility maintenance, and complaint handling. Manual management often faces various challenges, such as inefficient data organization, slow responses to complaints, and a lack of operational transparency. This study aims to develop E-Arusun with a focus on Rumah Susun Sewa (Rusunawa) KS. Tubun Jakarta as the research object. The application utilizes the prototype methodology, starting from the requirements gathering phase, prototype development, evaluation, coding, and system testing. E-Arusun was developed using PHP as the server-side programming language and HTML and CSS for the user interface. The system is designed to address various challenges in managing apartment buildings by providing key features such as register, login, user management, apartment management, kiosk management, rental transaction management, and complaint management. Additionally, tenants can utilize features such as viewing bills, submitting complaints, and monitoring payment status
Perancangan Aplikasi Website Pembelajaran Daring untuk Peningkatan Kompetensi Karyawan Nusa Computer Menggunakan Python Flask dan MongoDB Desmon, Johanes; Ayumi, Vina
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.v7i3.7593

Abstract

This study aims to develop and implement a web-based e-learning application specifically designed to meet the internal training needs of Nusa Computer. The application was designed using the waterfall methodology, which consists of requirement analysis, system design, implementation, integration, and testing stages. The requirements analysis process was conducted through surveys using Google Forms distributed to Nusa Computer employees to identify training needs and challenges. The application's backend was built using Python Flask, while the frontend was designed with HTML5, CSS3, and JavaScript. MongoDB was used as the database to store user information and learning materials in a structured document format. The interface prototype was designed using Figma, while UML diagrams, such as use case diagrams and class diagrams, were created using draw.io to map user interaction flows and data structures. Based on the analysis results, the developed application includes key features such as learning material management, discussion forums, a vocabulary dictionary, and a dedicated page for discussions with mentors.
Forest Fire Detection Using Transfer Learning Model with Contrast Enhancement and Data Augmentation Ayumi, Vina; Noprisson, Handrie; Ani, Nur
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 1 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i1.75692

Abstract

Forest damage due to fire is unique of the catastrophes that can disrupt and damage the existing ecosystem. There needs to be a quick response to fires because disaster management takes longer, and the impact of the damage will be more severe. To process images to detect fire in the forest, we need to build a suitable deep-learning model. This study proposed research on forest fire detection using an Xception and MobileNet model. Moreover, this research optimizes the accuracy of the model by applying Contrast-Limited-Adaptive-Histogram-Equalization (CLAHE) and data augmentation to tackle the problem of the forest fire image dataset. Based on the experiment, MobileNet with CLAHE obtained 99,66% accuracy in the test phase. In the same phase, MobileNet with CLAHE obtained a value F1-score of 1.00, a value of precision of 0.99, and a value of recall of 1.00. If compared to other model performances, MobileNet with CLAHE obtained the best result.
Model GHT-SVM for Traffic Sign Detection Using Support Vector Machine Algorithm Based On Gabor Filter and Top-Black Hat Transform Noprisson, Handrie; Ayumi, Vina; Dwika Putra, Erwin; Utami, Marissa; Ani, Nur
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 3 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i3.75778

Abstract

A factor that can hinder the detection and recognition of traffic signs is the variation in lighting in the image of traffic signs. This study aims to detect traffic symbols using Gabor Filter (GFT), Top Hat Transform (THT), and Black Hat Transform (BHT) methods on the Support Vector Machine (SVM) algorithm for traffic sign dataset images with data problems that tend to have dark backgrounds at night and bright backgrounds during the day. From the experimental results, GHT-SVM gets the highest accuracy compared to HSV-SVM, HSV-RF, HSV-KNN, and H2T-SVM models. Based on experimental results, H2T-SVM from HOG ⊕ ENT ⊕ BHT ⊕ SVM results get the best accuracy of 86.42%. The Gabor Filter (GFT) parameters used are the number of filters with a value of 16, ksize with a value of 30, sigma with a standard deviation value of 3.0, lambd with a sinusoidal factor value of 10.0, gamma with a spatial aspect ratio value of 0.5 and psi with a phase offset value of 0 while the Top Hat Transform (THT) and Black Hat Transform (BHT) methods use filterSize sizes with values (3, 3).
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 Yakub Hadiguna Setiawan Hong Wei Ida Farida Ida Nurhaida Ida Nurhaida Ida Nurhaida Inge Handriani, Inge Iryani, Lemi Jumaryadi, Yuwan Laksono Trisnantoro Mailana, Siska Mariana Purba Mariana Purba Naufal, Muhamad Fahri Nengsiana, Nengsiana Noprisson, Handrie Nur Ani PAISAL PAISAL Purba, Mariana Putra, Erwin Dwika Remmy A. M. Zen Salamah, Umniy Sarwati Rahayu Sri Dianing Asri Tjahjono, Benny Uus Rusmawan, Uus Wachyu Hari Haji Wachyu Hari Haji Wina Firly, Tiara Yogga Herlambang, Helwy