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PERANCANGAN E-REMINDER AKTIFITAS MAHASISWA PADA FAKULTAS TEKNIK UNIVERSITAS BHAYANGKARA JAKARTA RAYA Dwi Swasono Rachmad; Gabriel Firsta Adnyana
Jurnal Teknologi Informasi dan Komputer Vol 4, No 1 (2018): Jurnal Teknologi Informasi dan Komputer
Publisher : LPPM Universitas Dhyana Pura

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (467.958 KB)

Abstract

ABSTRACT One of strategic that has been carried out by several universities in Indonesia to achieve quality academic and governance services is through the use of Information and Communication Technology (ICT) in the form of the use of academic information systems that contribute to improving the reputation of universities, as well as increasing user satisfaction. Lecture activities such as teaching and learning activities (KBM), the implementation of the semester exam, lecturer meeting is a daily operational activities. Teaching and learning activities are things that have become routine, but still often happens that students forget the academic activities that become obligations either because of a schedule change or other reasons. Regular schedule changes are usually distributed by the Administration to students or lecturers manually through a notice board The use of media boards raises difficulties for students, especially for students who have off-campus activities so the use of bulletin boards to convey information about lectures and academic activities among students became less effective. Therefore, an application is needed to help the delivery of information in realtime to remind students to carry out academic activities on schedule. Target to be achieved is to provide an e-reminder application of academic activities. This application consists of 3 users, the admin to enter the information mading, user (lecturer) to determine the schedule of guidance and user (student) to display the schedule of lectures, and the payment of the lecture along with notification of the schedule reminder so it can help and provide convenience for the administration in preparing and reminding activities to be carried out so that students can see about academic information and tuition payment information in real time both to students who are inside and outside the campus. Keywords : e-reminder, academic activities, mobile appABSTRAKBeberapa perguruan tinggi di Indonesia untuk mencapai pelayanan akademik dan tata kelola yang berkualitas adalah melalui pemanfaatan Teknologi Informasi dan Komunikasi (TIK) berupa penggunaan sistem informasi akademik yang berperan untuk meningkatkan reputasi perguruan tinggi, serta meningkatkan kepuasan pengguna Kegiatan belajar mengajar merupakan hal yang sudah menjadi rutinitas, namun masih seringkali terjadi mahasiswa yang lupa dengan aktifitas akademik yang menjadi kewajibannya karena adanya perubahan jadwal, ataupun hal lainnya. Perubahan jadwal regular biasanya didistribusikan oleh pihak Tata Usaha ke mahasiswa atau dosen secara manual melalui papan pengumuman. Penggunaan media papan pengumuman menimbulkan kesulitan bagi mahasiswa, terutama bagi mahasiswa yang mempunyai aktifitas di luar kampus sehingga penggunaan papan pengumuman untuk menyampaikan seputar informasi perkuliahan dan kegiatan akademik di kalangan mahasiswa menjadi kurang efektif. Aplikasi ini terdiri dari 3 pengguna, yaitu admin untuk memasukan informasi mading, user (dosen) untuk menentukan jadwal bimbingan dan user (mahasiswa) untuk menampilkan jadwal kuliah, dan pembayaran kuliah beserta notifikasi pengingat jadwal tersebut sehingga dapat membantu dan memberikan kemudahan bagi pihak tata usaha dalam menyusun serta mengingatkan kegiatan yang akan dilakukan sehingga mahasiswa dapat melihat seputar informasi akademik dan informasi pembayaran uang kuliah secara real time baik kepada mahasiswa yang berada didalam maupun diluar kampus.Kata kunci : e-reminder, aktifitas akademik, aplikasi mobile
Sentiment Analysis of the Kabur Aja Dulu Trend on X as a Basis for Designing a Public Sentiment Monitoring System Using Naïve Bayes and SVM Sutisna Sutisna; Tri Wahyudi; Dwi Swasono Rachmad; Fachrur Rozi
International Journal of Information Engineering and Science Vol. 2 No. 3 (2025): August : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v2i3.79

Abstract

Social media X (Twitter) has become the main platform for the Indonesian public to express opinions, including on the trend of 'kabur aja dulu' (let's just run away for a bit). This research aims to classify the sentiments of the public using the Naïve Bayes and Support Vector Machine (SVM) methods, and to compare the accuracy of both in sentiment analysis. Data was collected via the Twitter API with the hashtag #kaburajadulu, resulting in 2,067 tweets, which, after the cleansing process and manual labeling, left 385 data points. The analysis process followed the CRISP-DM stages, which include business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Model evaluation was conducted using a confusion matrix with accuracy, precision, and recall metrics. The classification results show that 82% of tweets have a positive sentiment and 18% negative. The Naïve Bayes algorithm achieved an accuracy of 86.49%, slightly lower than SVM, which reached 88.05%. In conclusion, Support Vector Machine is more effective in sentiment classification on public opinion data. This research contributes to the digital mapping of public opinion and recommends the development of automatic labeling methods as well as the exploration of advanced algorithms in the future.
Stabilization of Distance Measurement Between Landmarks for Gesture Recognition Using Polynomial Regression Dadang Iskandar Mulyana; Tri Wahyudi; Dwi Swasono Rachmad; Muhammad Khalid
International Journal of Applied Mathematics and Computing Vol. 1 No. 3 (2024): July : International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v1i3.120

Abstract

Gesture recognition technology enables computers and digital devices to detect, understand, and interpret human body movements through image processing techniques. This technology has significant potential to facilitate communication between individuals with hearing impairments and those without, thereby improving interaction and mutual understanding. However, the accuracy of gesture recognition systems is often influenced by variations in the distances between hand landmark points, which can introduce instability and reduce recognition performance. To address this issue, this study proposes a polynomial regression-based approach to stabilize distance measurements between hand landmarks in gesture recognition tasks. The proposed method calculates and normalizes landmark distances using polynomial regression to minimize measurement fluctuations and improve recognition accuracy. The system is implemented using the MediaPipe framework for real-time hand detection and tracking, while OpenCV is utilized for video processing and management. Experimental results demonstrate that the proposed approach significantly enhances the stability and accuracy of gesture detection. The developed system successfully recognizes hand gestures representing the letters A through F with an average accuracy exceeding 98.3%. Furthermore, the application of polynomial regression effectively reduces noise in landmark data, contributing to more reliable and accurate gesture recognition performance.
Traffic Condition Classification Using IoT on Raden Inten II Road Untung Surapati; Yuma Akbar; Dwi Swasono Rachmad; Hadi Gunawan
International Journal of Applied Mathematics and Computing Vol. 2 No. 4 (2025): October : International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v2i4.121

Abstract

Unmonitored traffic conditions often hinder decision-making processes in traffic management, particularly on secondary roads. Jalan Raden Inten II in East Jakarta is one of the connecting routes with heavy traffic activity at certain times, yet no integrated data-based monitoring system is currently available. This study proposes an Internet of Things (IoT)-based traffic condition classification system to identify Clear, Normal, or Congested states based on vehicle counts and speed categorization. The system is designed using an ESP32 microcontroller, an HB100 sensor to detect vehicle speed, and two AJ-SR04M ultrasonic sensors to detect vehicle presence. Data on vehicle counts and the percentage of slow-moving vehicles are periodically transmitted to the ThingSpeak platform and processed using the Threshold-Based Classification method. The classification results are visualized on a dashboard-based website equipped with charts, traffic condition status, and notifications when consecutive congestion is detected. Testing was conducted using simulation data over a specific period. Qualitative validation was carried out by comparing the classification results with traffic indicators from Google Maps. The results show that the system can classify traffic conditions with a good degree of agreement with external references, although discrepancies occurred at certain times due to the limitations of simulated data. This research demonstrates that a simple IoT approach can provide an affordable and effective solution for monitoring and classifying traffic conditions, with potential for real-world implementation in future studies.
Analisis Sentimen Publik terhadap Hashtag #kaburajadulu Menggunakan Kombinasi Algoritma Support Vector Machine (SVM) dan Random Forest Yuma Akbar; Frencis Matheos Sarimolle; Dwi Swasono Rachmad; Muhammad Derry Oktaviandi
International Journal of Applied Mathematics and Computing Vol. 2 No. 3 (2025): July : International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v2i3.129

Abstract

This study aims to analyze public sentiment toward the hashtag #KaburAjaDulu, which has circulated widely on the social media platform X (formerly Twitter). The hashtag reflects the growing anxiety among the public, especially younger generations, regarding socio-political issues in Indonesia. The data were collected using web scraping techniques, focusing on user-generated tweets that contain the hashtag. A comprehensive text preprocessing phase was conducted to clean the raw data by removing irrelevant elements such as URLs, emojis, numbers, and punctuation. The research applies a hybrid classification approach using a combination of Support Vector Machine (SVM) and Random Forest algorithms to categorize sentiment into three classes: positive, negative, and neutral. The performance of the model was evaluated using metrics such as accuracy, precision, recall, and F1-score to determine the effectiveness of the classification. The study aims to demonstrate that combining algorithms can improve classification performance compared to using a single algorithm. This research contributes to the field of sentiment analysis and provides valuable insights for researchers, policymakers, and social observers in understanding public opinion trends in digital media.