Article Per Year (5 Year)
p-Index From 2021 - 2026
3.836
P-Index
This Author published in this journals
All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Indonesian Journal of Artificial Intelligence and Data Mining 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
Unknown Affiliation
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Nursing Social Sciences Other

Published : 28 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 17 Documents
Search
Journal : JSAI (Journal Scientific and Applied Informatics)

 1   2 
Evaluasi Aplikasi Pemesanan Tiket Menggunakan Metode System Usability Scale (SUS) dan Model D&M IS Success Marissa Utami; Purba, Mariana; Dianing Asri, Sri; Noprisson, Handrie; Utami, Marissa; 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.v7i1.6444

Abstract

Software product development not only focuses on features but also usability aspects. User experience is very important in the evaluation of reusability to understand the user's interaction with the product or system. Reusability factors include user satisfaction, efficiency, and effectiveness to achieve specific goals. The main purpose of this study is to evaluate the usability aspect of online ticket booking applications. This evaluation process is important to identify development and improvements to user views and application usage satisfaction. In this study, the object studied was an online travel booking application in Indonesia. The research instrument uses a quantitative and qualitative mixed-method approach. For the quantitative approach, the System Usability Scale (SUS) is used and as a basis for a qualitative approach, the D&M IS Success Model approach is used. Based on the evaluation results, there are several points that should be improved including the interface design should be simple, the reduction in the size of memory used by applications, features to communicate with customer service easily, data integration, and time notifications to complete payments.
Perancangan Aplikasi Manajemen Persediaan Barang di Perusahaan Pengelola Jaringan Akses Telekomunikasi Menggunakan Unified Modelling Language dan Prototyping Purba, Mariana; Dianing Asri, Sri; Ghufron, Akhmad; Umilizah , Nia; 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.v7i1.6445

Abstract

Managing inventory in a telecommunications access network management company is very important because applications with good data management effectively increase the chances of success and maximize profits for the company. In addition, proper inventory data management is essential for identifying new market opportunities, forecasting risks, and understanding market trends. This study aims to clarify the design of inventory applications in accordance with the problems that exist at PT. XYZ is owned by the government as a case study location based on minimum service standards (SPM). This design uses unified modelling language (UML) such as use cases, activity diagrams, class diagrams and prototyping models to support the development of inventory applications in telecommunications access network management companies. The inventory management application in the telecommunication access network management company provides features for admins / users in processing supplier data, incoming goods data and outgoing goods data, and printing monthly reports on inventory of goods
Analysis of Travel Ticket Booking Application Services Based on Supporting Factors for Purchase Intention Marissa Utami; Purba, Mariana; Dianing Asri, Sri; Noprisson, Handrie; Utami, Marissa; 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.v7i1.6446

Abstract

Aplikasi pemesanan tiket perjalnan ini harus memiliki kualitas dari segi perspektif produk agar dapat meningkatkan purchase intention oleh pengguna. Purchase intention dari layanan aplikasi dapat dilihat dari beberapa faktor antara lain usabilitas (usability), harga (price), kemudahan penggunaan (ease of use), complementarity dan hiburan (entertainment). Penelitian ini akan mengusulkan model penelitian untuk identifikasi kualitas layanan aplikasi online travel booking berdasarkan perspektif produk untuk meningkatkan purchase intention berdasarkan analisis dataset yang dikumpulkan dari sampel responden. Dari hasil pengumpulan data, dari total 1267 kuesioner yang dikumpulkan hanya memperoleh 1029 kuesioner yang valid. Model diuji menggunakan skor tingkat signifikan two-tails sebesar 0,05 untuk pengujian hipotesis. Menurut analisis data, faktor complementary memiliki pengaruh terbesar purchase intention dengan nilai uji-t sebsar 6,771. Selain itu, faktor entertainment memiliki pengaruh terbesar kedua dengan t-nilai 5.334. Faktor usability memiliki pengaruh terhadap purchase intention terbesar ketiga nilai uji-t 4.620. Faktor ease of use memiliki pengaruh terbesar keempat dengan nilai uji-t 3.641.
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).
Studi Literatur: Transfer Learning Untuk Analisis Penyakit COVID-19 Berdasarkan Dataset Chest X-ray Purba, Mariana
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.v7i2.6571

Abstract

The urgency of the impact of the COVID-19 disease that attacks people around the world encourages special research, especially in the field of artificial intelligence. This study aims to conduct a literature study related to the use of artificial intelligence, especially transfer learning in analyzing COVID-19 disease based on chest X-ray datasets. The research method of this research adapts the Preferred Reporting for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. The results of the analysis of this data to answer research questions regarding the transfer learning model for the analysis of COVID-19 disease based on the chest X-ray dataset, it is known that the models used are MobileNet, Inception, VGG and ResNet. MobileNetV2 can be optimized by adding a global average pooling layer, dropout layer and dense layer and get an accuracy of 98.65%. InceptionV3 can be combined with Xception and get 98.8% accuracy. VGG-16 can be combined with ResNet-50 Xception and get 98.93% accuracy. ResNet-50 can be optimized by adding a dropout layer and a dense layer and getting an accuracy of 97.65%.
Analisis Usabilitas Sistem Informasi Akademik Berdasarkan Usability Scale (Studi Kasus: Universitas Mercu Buana) Rahayu, Sarwati; Nugroho, Andi; Sandiwarno, Sulis; Salamah, Umniy; Dwika Putra, Erwin; Purba, Mariana; Setiawan, Hadiguna
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.v7i3.7478

Abstract

The usability analysis on the website of Mercu Buana University (UMB) is an important research carried out to ensure that the site effectively supports the university's goals, especially in terms of the user's experience in completing academic and administrative goals with ethical and professional standards. This research was carried out during the period January 2024 to May 2024. The main purpose of this study is to measure the usability of the UMB website using a questionnaire method. The questionnaire used for the research adapted the System Usability Scale (SUS) which consisted of a total of 10 questions. Based on the calculation of each statement item having a minimum score of 0 and a maximum score of 2.5, the final score of each respondent ranged from 0 to l00. The average score obtained was 63,125. Based on the results of the score of 63,125, the UMB website has a score in the range of 50 to 70. This shows that the UMB website is in the "quite good" category but there is still a need for a little improvement. Some icons or layouts on the UMB website are not familiar to respondents. In addition, there needs to be guidelines developed to provide information on how to use the website for users who are using the UMB website for the first time.
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%.
Co-Authors Abd Rahman Andi Nugroho, Andi Anita Ratnasari Asminah Asminah, Asminah Ayumi, Vina Azhar Andika Putra Bakhtiar Darmo, Cahyo Pambudi 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 Nia Umilizah Noprisson, Handrie Nur Ani Octavia, Pipin Padya, Inka Rizki Pipin Octavia Pungkas Subarkah Putra, Erwin Dwika Putri Maharani Riyanto Riyanto Rudiansyah Salamah, Umniy Sarwati Rahayu Sulis Sandiwarno Syamsiar, Syamsiar Umilizah , Nia Uus Rusmawan, Uus Wachyu Hari Haji Wati, Ade Sukma Wibisono, M Bayu Yadi Yadi, Yadi