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JSAI (Journal Scientific and Applied Informatics)
Published by Universitas Muhammadiyah Bengkulu
ISSN : 26143062     EISSN : 26143054     DOI : -
Core Subject : Science,
Computer Science & IT
Jurnal terbitan dibawah fakultas teknik universitas muhammadiyah bengkulu. Pada jurnal ini akan membahas tema tentag Mobile, Animasi, Computer Vision, dan Networking yang merupakan jurnal berbasis science pada informatika, beserta penelitian yang berkaitan dengan implementasi metode dan atau algoritma.
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Articles 471 Documents
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PENGEMBANGAN PLATFORM WEB LAYANAN OUTSOURCING DAN MANAJEMEN KARYAWAN: Pengembangan Platform Website Layanan Outsourcing Manajemen Karyawan PT.Buntariva Mitra Solusi Cofely, Chinoble; Ramayanti, Desi
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.v8i2.8741

Abstract

PT. Buntariva Mitra Solusi, which is engaged in providing services, faces significant problems due to the use of manual systems in managing service requests, employee data, and evaluating service provider performance, which causes delays in the process and decreases the level of client trust. This study aims to design and implement an integrated web platform that is able to automate and unify all of the company's main business processes, including service request management, employee data management, contract arrangements, and real-time monitoring and evaluation of service provider performance. The development method applied is the Software Development Life Cycle (SDLC) with a waterfall model, which allows for systematic and structured project management. The platform implementation is carried out using the PHP programming language, MySQL database, and Apache as a web server. System testing is carried out using the Black Box method to ensure that all functions run according to specifications without any significant errors. The test results show that the system can increase transparency, speed up operational processes, and provide easy access to information for management and clients.
Analisis Sentimen Terhadap Kompetensi Pedagogi Dosen Menggunakan Word Embedding dan Random Forest pada Data Umpan Balik Mahasiswa Ayumi, Vina; Purba, Mariana; Rahman, Abd
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.v8i2.8761

Abstract

Perkembangan teknologi dan media digital telah mendorong cara evaluasi kinerja dosen yang tidak hanya berbasis kuantitatif, tetapi juga didukung oleh analisis data kualitatif. Salah satu pendekatan yang efektif adalah analisis sentimen terhadap umpan balik mahasiswa terkait informasi berharga mengenai persepsi dan pengalaman terkait kompetensi pedagogi dosen. Penelitian ini mengolah dataset sentimen umpan balik mahasiswa di Universitas Sjakhyakirti dengan menggunakan metode word embedding (WE) dan random forest (RF) untuk mengklasifikasikan sentimen positif dan negatif. Dataset yang digunakan berjumlah 6.164 data kuesioner, terdiri dari 3.800 data sentimen positif dan 2.364 data sentimen negatif. Data kemudian dibagi menjadi data pelatihan (70%), validasi (10%), dan pengujian (20%). Evaluasi kinerja model pada data pengujian menunjukkan bahwa model WE-RF mampu mengklasifikasikan sentimen dengan tingkat presisi, recall, dan F1-score masing-masing sebesar 0,805; 0,724; dan 0,762 untuk kelas positif, serta 0,618; 0,719; dan 0,664 untuk kelas negatif. Akurasi pada tahap pengujian yang diperoleh mencapai 72,2% yang menujukkan bahwa model ini cukup efektif untuk menganalisis sentimen dalam konteks kompetensi pedagogi dosen.
Model Deep Learning Berbasis Word2Vec dan LSTM untuk Klasifikasi Umpan Balik Kompetensi Profesional Dosen Ayumi, Vina; Purba, Mariana; Rahman, Abd
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.v8i2.8762

Abstract

This study aimed to develop a deep learning model based on Word2Vec and Long Short-Term Memory (LSTM) to classify sentiment in student feedback on lecturers' professional competence. Manual analysis of large volumes of evaluation text data required significant time and resources, thus an automated method was needed to assist the sentiment classification process. Word2Vec was used to represent words as fixed-dimension numerical vectors, which then served as input to the LSTM model. The LSTM model was selected for its ability to process sequential data and retain relevant long-term contextual information in the text. The dataset consisted of 6,124 evaluation texts, divided into 3,800 positive and 2,324 negative samples. The dataset was split into training (70%), validation (10%), and testing (20%) subsets. The model was trained for 50 epochs, achieving a training accuracy of 81.20% and a validation accuracy of 77.10%. Evaluation using a confusion matrix on the testing data showed that the model correctly classified 587 positive and 359 negative samples, while producing 106 false positives and 173 false negatives. These results indicated that the combination of Word2Vec and LSTM was effective in classifying sentiment in lecturer competence evaluation texts, with a testing accuracy of 77.2%.
Klasifikasi Teks Umpan Balik Kompetensi Sosial Dosen di Perguruan Tinggi Menggunakan Word2Vec dan CNN-1D 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.v8i2.8763

Abstract

The advancement of artificial intelligence technology supported the development of automatic sentiment classification. This study aimed to develop a deep learning model based on Word2Vec and one-dimensional Convolutional Neural Networks (CNN-1D) to classify the sentiment of textual feedback regarding lecturers’ social competence in higher education. The dataset consisted of 6,124 feedback texts collected from student questionnaires at Universitas Sjakhyakirti. The data were proportionally divided into 70% for training, 10% for validation, and 20% for testing. The developed Word2Vec-CNN-1D model demonstrated performance with a training accuracy of 85.10% and a validation accuracy of 79.10%. During the testing phase, the model achieved an accuracy of 76.2% in classifying the feedback texts into positive and negative classes. Evaluation metric analysis showed that for the positive class, the model attained a precision of 0.827, recall of 0.760, and F1-score of 0.792, while for the negative class, it obtained a precision of 0.679, recall of 0.761, and F1-score of 0.717. The results indicated that the Word2Vec and CNN-1D model was more effective at identifying positive sentiments, whereas the performance for the negative class could still be improved in the classification of textual feedback on lecturers’ social competence.
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%.
Studi Pendahuluan: Evaluasi Kualitas Layanan Komunikasi di Lingkungan Akademik Menggunakan Analisis Sentimen Ramayanti, Desi; Mulya, Imam; Ratnasari, Anita; Dianing Asri, Sri; Maulana, Ivan; Rizal, Muhammad
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.v8i2.8765

Abstract

Google Chat was used in the Faculty of Engineering and Informatics to support academic and non-academic collaboration, with advantages such as real-time, structured communication that is easily accessible by lecturers, students, and administrative staff. This study aimed to analyze the sentiment of Google Chat conversations using the support vector machine (SVM) model to categorize sentiment into positive, negative, and neutral. The data preprocessing process included tokenization, stemming, stop words removal, and text cleaning, using Python with NLTK, Scikit-learn, and Pandas libraries. The study adopted a quantitative approach to analyze sentiment patterns from Google Chat conversations about the quality of services at the Faculty of Engineering and Informatics, as well as experiments to develop and test machine learning models. The preliminary study showed a dataset of 3,735 cleaned data, with invalid or empty elements removed. The data was then categorized into three sentiments: positive, negative, and neutral, based on sentiment analysis of communication text related to services in the academic environment. The next research will focus on selecting the appropriate classification model to provide accurate assessments of the faculty’s service quality.
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.

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