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All Journal Foristek Jurnal Ilmu Komputer dan Informasi Komunikasi Fisika Indonesia Seminar Nasional Teknologi Informasi Komunikasi dan Industri Jurnal ULTIMA InfoSys Jurnal ULTIMATICS CARADDE: Jurnal Pengabdian Kepada Masyarakat Jurnal Ilmiah Media Sisfo Jurnal Nasional Komputasi dan Teknologi Informasi ScientiCO : Computer Science and Informatics Journal Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Brilliance: Research of Artificial Intelligence Journal of Business Studies and Management Review Jurnal Pengabdian Masyarakat Pinang Masak Jurnal Penelitian Teknologi Informasi dan Sains SEWAGATI: Jurnal Pengabdian Kepada Masyarakat Bridge: Jurnal Publikasi Sistem Informasi dan Telekomunikasi International Journal of Economics, Business and Innovation Research
Rizqa Raaiqa Bintana
Universitas Jambi
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Environmental Awareness of Students at SDN Pauh, Tungkal Jaya District and SDN Mendis Jaya, Bayung Lencir District, Musi Banyuasin Regency Hizazi, Achmad; Mansur, Fitrini; Bintana, Rizqa Raaiqa; Yosep Tri Krisnanto; Nanang Yuliyapranata
International Journal of Economics, Business and Innovation Research Vol. 4 No. 05 (2025): August - September, International Journal of Economics, Business and Innovatio
Publisher : Cita konsultindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63922/ijebir.v4i05.2189

Abstract

This paper describes the school's efforts to increase environmental awareness of students at SDN Pauh, Tungkal Jaya District, and SDN Mendis Jaya, Bayung Lencir District, Musi Banyuasin Regency. The focus of awareness is seen in daily behavior, such as attitudes towards waste in the surrounding area, concern for plants and animals in the area, and an understanding of the importance of maintaining cleanliness and environmental sustainability. To achieve this goal, both schools carry out various programs such as providing trash cans, environmental simulations, awareness campaigns through t-shirts worn by teachers and school staff, distributing environmental awareness posters, and planting trees around the school
Analisis Kesuksesan Sistem Informasi SKPI Universitas Jambi Menggunakan Model DeLone and McLean Widya, Reidila Ariska; Aryani, Reni; Bintana, Rizqa Raaiqa
Jurnal Ilmiah Media Sisfo Vol 19 No 2 (2025): Jurnal Ilmiah Media Sisfo
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/mediasisfo.2025.19.2.2456

Abstract

The University of Jambi as a higher education institution also prioritizes technological advances in its operations. In 2021, the Diploma Companion Certificate (SKPI) information system was developed, which was then officially released in 2022. This system functions to support data collection of student achievements and activities. However, until now, the SKPI information system is still in the refinement stage for the latest versions and features. This study aims to determine the success rate of the SKPI information system using the DeLone and McLean model. The approach used is quantitative, with response-based data collection and analysis techniques from students of the Faculty of Science and Technology, University of Jambi. The sampling method in this study uses the Isaac and Michael formula, with a sample of 100 respondents and an error rate of 10%. The distribution of the questionnaire was carried out online. The data obtained were analyzed using the Partial Least Square - Structural Equation Modeling (PLS-SEM) method, which included outer model testing, inner model, and hypothesis testing. The results showed that of the 9 hypotheses tested, 5 hypotheses had a positive and significant influence on the success of the SKPI information system, while the other 4 hypotheses did not show a significant influence.
Business Intelligence Roadmap for Tableau Dashboard Development in Higher Education Fatoni, Yuda; Utomo, Pradita Eko Prasetyo; Bintana, Rizqa Raaiqa
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.7094

Abstract

Universities are increasingly required to make data-driven decisions, yet many are still hindered by static and non-interactive reports. This study addresses these challenges at the Jambi University with the aim of designing and developing a series of interactive dashboards using Tableau, applying an adaptive framework based on the Business Intelligence Roadmap. The research methodology includes three main stages: pre-development, development, and post-development. The technical process involves Extract, Transform, Load (ETL) of data from different datasets into a MySQL database that serves as a centralized data source before visualization. The main results of this study are seven functional dashboard prototypes that were successfully developed, covering data analysis of lecturers, graduates, and other strategic areas. This dashboard is capable of presenting key insights, such as the lecturer-to-student ratio and lecturer qualification profiles (29.8% holding a PhD), in a visual and interactive manner. Furthermore, the prototype was successfully integrated into a web interface, demonstrating the technical feasibility of its implementation. This study concludes that the application of an adapted BI Roadmap is an effective approach for dashboard development in an academic environment. The results not only provide a decision-support tool for the Jambi University but also offer a methodological framework that can be replicated.
Implementasi Model Support Vector Machine Dalam Analisa Sentimen Masyarakat Mengenai Kebijakan Penerapan Aplikasi Mypertamina Salsabila Dwi Fitri; Dewi Lestari; Rizqa Raaiqa Bintana; Reni Aryani; Mohamad Ilhami; Yolla Noverina
Bridge : Jurnal Publikasi Sistem Informasi dan Telekomunikasi Vol. 2 No. 2 (2024): Bridge: Jurnal Publikasi Sistem Informasi dan Telekomunikasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/bridge.v2i3.180

Abstract

The policy for using the MyPertamina application issued does not rule out the possibility of differences of opinion due to changes in the policy. There are many positive, neutral, and negative responses to the MyPertamina application implementation policy. To see the public's reaction to the MyPertamina application implementation policy, it can be seen through various media, including social media. Twitter is a social network that is widely used by people in Indonesia. The number of Twitter users in Indonesia reached 18.45 million in 2022, making Indonesia the fifth largest Twitter user country in the world. Researchers conducted a sentiment analysis of the search results for tweets containing the keyword "MyPertamina" using the support vector machine algorithm. 382 tweet data were obtained and classified using the support vector machine algorithm. Support vector machine is a supervised learning algorithm for data classification. SVM is very fast and effective in solving text data problems. Text data is suitable for classification with the SVM algorithm because the basic nature of text tends to be high-dimensional. Of the 382 data analyzed, the support vector machine classification using the RBF kernel with parameter C=2 gave the highest accuracy value of 80.51%, precision value of 81%, recall value of 81%, and F1 score value of 80%.
Penerapan Support Vector Machine dan Latent Dirichlet Allocation dalam Analisis Sentimen Terhadap Pengalaman Pengguna Aplikasi Alfagift Desi Hartati; Ulfa Khaira; Rizqa Raaiqa Bintana
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 8, No 5 (2025): Oktober 2025
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v8i5.9866

Abstract

Abstrak − Aplikasi alfagift merupakan salah satu aplikasi belanja online yang banyak diunduh di Google Play Store, menerima ribuan ulasan dari pengguna setiap harinya. Proses meninjau ulasan ini secara manual menjadi tidak efisien karena keterbatasan tenaga dan waktu, sehingga berpotensi memperlambat respons tim dalam mengambil keputusan strategis. Untuk mengoptimalkan proses ini, penerapan model machine learning sangat diperlukan untuk mengotomatisasi analisis sentimen dan identifikasi tema utama dalam ulasan. Penelitian ini bertujuan untuk menganalisis sentimen ulasan pengguna aplikasi Alfagift menggunakan metode Support Vector Machine (SVM) dan mengidentifikasi topik utama dalam ulasan dengan pendekatan Latent Dirichlet Allocation (LDA) . Dataset yang digunakan telah melalui proses preprocessing berupa case folding , tokenizing, filtering , dan stemming . Hasil evaluasi menunjukkan bahwa model SVM dengan kernel polynomial memberikan akurasi tertinggi sebesar 95,9%, menandakan kemampuan yang baik dalam membedakan ulasan positif dan negatif. Pendekatan Latent Dirichlet Allocation (LDA) berhasil mengidentifikasi topik dominan, dengan nilai koherensi optimal sebesar 0,3164 (ulasan positif, 8 topik) dan 0,3177 (ulasan negatif, 7 topik). Berdasarkan analisis, mayoritas pengguna menyampaikan ulasan positif terkait kemudahan belanja dan promo menarik, meskipun beberapa masalah teknis masih menjadi keluhan. Penelitian ini menunjukkan bahwa kombinasi SVM dan LDA efektif untuk mengevaluasi kepuasan pengguna serta mengidentifikasi area perbaikan layanan Alfagift.Kata Kunci: Alfagift; Analisis Sentimen; LDA; SVM; Ulasan Pengguna; Abstract − The Alfagift app is one of the most downloaded online shopping apps on the Google Play Store, receiving thousands of user reviews daily. Manually reviewing these reviews is inefficient due to limited resources and time, potentially slowing down the team's ability to make strategic decisions. To optimize this process, implementing machine learning models is essential to automate sentiment analysis and identify key themes within reviews. This research aims to analyze the sentiment of user reviews of the Alfagift application using the Support Vector Machine (SVM) method and to identify the main topics in the reviews using the Latent Dirichlet Allocation (LDA) approach. The dataset used has undergone preprocessing including case folding, tokenization, filtering, and stemming. The evaluation results show that the SVM model with a polynomial kernel provides the highest accuracy of 95.9%, indicating a good ability to differentiate between positive and negative reviews. The Latent Dirichlet Allocation (LDA) approach successfully identified dominant topics, with an optimal coherence value of 0.3164 (positive reviews, 8 topics) and 0.3177 (negative reviews, 7 topics). Based on the analysis, the majority of users provided positive reviews regarding the ease of shopping and attractive promotions, although some technical issues remain a complaint. This study demonstrates that the combination of SVM and LDA is effective for evaluating user satisfaction and identifying areas for service improvement at Alfagift.Keywords: Alfagift; Sentiment Analysis; LDA; SVM; User Reviews;
Co-Authors Achmad Hizazi Agus Zainal Arifin Arsa, Daniel Aryani, Reni Asyhadi Mufsi Sadzali Benedika Ferdian Hutabarat Budiman, Naufal Budiman, Wahyu Chastine Fatichah Darlis Herumurti Desi Hartati Dewi Lestari Dewi Lestari Dewi Lestari Diana Purwitasari Dwi Kurniawan Dwi Shinta Angreni Fairuz Quzwain Fatoni, Yuda Fitrini Mansur Hadaya, M. Syahan Afdhal Hasbi Adzzikri Iful Amri Jefri Marzal Jesi Pebralia Khaira, Ulfa Makmun Wahid, Makmun Mardhotillah, Bunga Mauladi Mauladi, Mauladi Mochammad Farisi Mohamad Ilhami Multahah, Cut mutia fadhila putri, mutia fadhila Nanang Yuliyapranata Noverina, Yolla Novita Sari Novita Sari Nurhadi, Miranda Sukma Ratri Enggar Pawening Rayandra Asyhar Rustan Salsabila Dwi Fitri Surya Agustian Syamsyida Rozi Teguh Sumarsono Tio Darmawan Ulfa Khaira, Ulfa Utomo, Pradita Eko Prasetyo Wahyu Akbar Sya’bani Wicaksono, Sangaji Widya, Reidila Ariska Wirdayanti Yenny Yuniarti Yolla Noverina Yosep Tri Krisnanto Yuhana, Umi Laili Yundi Fitrah Yurinanda, Sherli Yusuf Anshori Z, Gusmanely Zainil Abidin