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Abd Rahman
Fakultas Teknik dan Informatika, Universitas Dian Nusantara, Indonesia
Computer Science & IT

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Aplikasi Dashboard Untuk Monitoring Lokasi dan Pengguna Parkir Berbasis Konsep MVC Abd Rahman; Handrie Noprisson
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.v8i2.8670

Abstract

The research aimed to develop a web-based parking dashboard application capable of integrating transaction data, membership management, parking slot monitoring, and real-time income reporting. The study was conducted at two parking management locations of PT. Oxient Konsultan Indonesia, namely True-Blue Hotel Cikini (Jakarta) and Intermark BSD (Tangerang). The research lasted for six months, from September to February 2025, with stages including literature study, application requirements analysis, application design, code development, testing, and reporting. The application was developed using CodeIgniter 4 for application structure management, Bootstrap 5 for the user interface design, MySQL with phpMyAdmin for database management, and Chart.js for data visualization. Development was carried out in Visual Studio Code with XAMPP as the local server, using hardware with an Intel Core i5-1135G7 processor and 16 GB RAM. Based on the research findings, the parking management dashboard application featured login options for admins and operators, as well as membership and transaction management. Admins could add new members, while operators were only able to input vehicle entry and exit transactions. The application provided daily reports and a statistical dashboard to display vehicle and income summaries.
Analisis Sentimen Terhadap Kompetensi Pedagogi Dosen Menggunakan Word Embedding dan Random Forest pada Data Umpan Balik Mahasiswa Vina Ayumi; Mariana Purba; Abd Rahman
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 Vina Ayumi; Mariana Purba; Abd Rahman
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%.
Co-Authors Mariana Purba Noprisson, Handrie Vina Ayumi Vina Ayumi