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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Indonesian Journal of Artificial Intelligence and Data Mining Journal of Economic, Bussines and Accounting (COSTING) SEINASI-KESI JUSIM (Jurnal Sistem Informasi Musirawas) JSAI (Journal Scientific and Applied Informatics) Jurnal Ilmu Komputer Dan Informatika Jurnal Pengabdian Kepada Masyarakat Ceria Jurnal Pengabdian Kepada Masyarakat
Purba, Mariana
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Nursing Social Sciences Other

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Peningkatan Kompetensi Pembuatan Website Menggunakan Framework Code Igniter Bagi Mahasiswa Purba, Mariana; Darmo, Cahyo Pambudi; Paisal, Paisal; Putra, Azhar Andika; Subarkah, Pungkas; Riyanto, Riyanto; Marcos, Hendra
Jurnal Pengabdian Kepada Masyarakat Ceria (JPKMC) Vol. 2 No. 1 (2024): Jurnal Pengabdian Kepada Masyarakat Ceria (JPKMC)
Publisher : PT. Gelora Cipta Nusantara

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

Abstract

Permasalahan utama pada mahasiswa ialah mengenai penguasaan teknologi informasi khususnya dalam penguasaan kompetensi dalam membuat website menggunakan framework code igniter. Metode pelaksanaan yang digunakan ialah persiapan, pelaksanaan dan evaluasi. Pelaksanaan kegiatan ini berjalan dengan lancar dimulai dari pembukaan sampai dengan penutupan. Materi yang disampaikan meliputi konsep dasar dari framework code igniter, konsep dasar framework code igniter, konsep MVC dan penerapannya pada website. Hasil yang didapatkan setelah pelatihan ini ialah para mahasiswa selaku peserta  menyampaikan bahwa adanya peningkatan kompetensi mengenai penggunaan Framework Code Igniter sebagai salah satu metode dalam pembuatan website yang dinamis.
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%.
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
Co-Authors Abd Rahman Andi Nugroho, Andi Anggriani, Dina Anita Ratnasari Asminah Asminah, Asminah Ayumi, Vina Azhar Andika Putra Bakhtiar Darmo, Cahyo Pambudi Deniswara, Ashyka Bilbina Dianing Asri, Sri Dwi Fitri Brianna Ghufron, Akhmad Hadiguna Setiawan Hendra Marcos, Hendra Herlambang Ida Farida Inge Handriani, Inge Iryani, Lemi K, Bakhtiar Kemas Welly Angga Permana Lemi Iryani M. Syidik Rizky Mailana, Siska Marissa Utami Naibaho, Apriyanti Nia Umilizah Noprisson, Handrie Nur Ani Octavia, Pipin Padya, Inka Rizki Pipin Octavia Pungkas Subarkah Purba, Tina Putra, Erwin Dwika Putri Maharani Riyanto Riyanto Rudiansyah Salamah, Umniy Sarwati Rahayu Simorangkir, Noviyanti Sulis Sandiwarno Syamsiar, Syamsiar Umilizah , Nia Uus Rusmawan, Uus Wachyu Hari Haji Wati, Ade Sukma Wibisono, M Bayu Yadi Yadi, Yadi