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All Journal Foristek JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Sisfokom (Sistem Informasi dan Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) CCIT (Creative Communication and Innovative Technology) Journal EDUMATIC: Jurnal Pendidikan Informatika ScientiCO : Computer Science and Informatics Journal InPrime: Indonesian Journal Of Pure And Applied Mathematics IAIC Transactions on Sustainable Digital Innovation (ITSDI) Advance Sustainable Science, Engineering and Technology (ASSET) Innovative: Journal Of Social Science Research The Indonesian Journal of Computer Science
Dwi Shinta Angreni
Universitas Tadulako
Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Public Health Other

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

 1 
Donor Segmentation Analysis Using the RFM Model and K-Means Clustering to Optimize Fundraising Strategies ., Rezki; Lapatta, Nouval Trezandy; Ardiansyah, Rizka; ., Wirdayanti; Angreni, Dwi Shinta
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8464

Abstract

This study aims to segment donors using the Recency, Frequency, Monetary (RFM) model and the K-Means algorithm to optimize fundraising strategies. The RFM model is used to measure donor engagement through three dimensions: Recency (the last time a donation was made), Frequency (the frequency of donations), and Monetary (the amount of donations). By utilizing RFM scores, donors are then grouped using the K-means algorithm to generate more specific donor segments. This study was conducted using donation data from a non-profit organization, focusing on strategies to improve donor loyalty and donation frequency. The segmentation results identified several key segments, including Loyal Donors, New Donors, Potential Donors, and Low-Priority Donors. Each segment exhibits different donation behavior characteristics and requires a different strategic approach. The implementation of these segmentation results is expected to help the organization design more effective communication strategies and donation programs, as well as improve donor retention and lifetime value. Additionally, this study identifies the potential for enhancing the analytical model for broader applications in the future. This research contributes to non-profit organizations by offering a more efficient approach to managing donor relationships.
Optimization of Urban Waste Collection Routes Using the Held-Karp Algorithm in a Web and Mobile-Based System Arsita, Tiara Juli; Lapatta, Nouval Trezandy; Joefri, Yuri Yudhaswana; Angreni, Dwi Shinta; Pratama, Septiano Anggun
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8832

Abstract

In 2023, the Environmental Agency of Palu City recorded a total waste production of 97,492 tons, of which 10.4% was plastic waste. The Palu City Government operates a fleet of garbage trucks on a predetermined collection schedule. However, garbage bins frequently overflow before their scheduled pickup, resulting in extended waste accumulation and inefficiency. This study proposes a web and mobile-based system to enhance waste management by integrating bin condition reporting and shortest route calculation for collecting full bins. The Held-Karp algorithm is utilized to address the Travelling Salesman Problem (TSP) for determining optimal collection routes. The system was developed using Golang, Flutter, ReactJS, and a MySQL database. API functionality was validated using Postman, and overall system functionality was tested using the black-box method. A case study involving 8 test points (1 starting point, 10 waste collection points, and 1 endpoint) demonstrated that the proposed system reduces travel time by up to 21.74%, costs by 22.29%, fuel consumption by 21.16%, and distance traveled by 21.16% compared to conventional methods. These results highlight the potential of the system to significantly optimize waste collection operations and support sustainable urban waste management practices.
Sentiment Analysis for the 2024 DKI Jakarta Gubernatorial Election Using a Support Vector Machine Approach Mariani, Mariani; Angreni, Dwi Shinta; Nur, Sri Khaerawati; Rinianty, Rinianty; Jayanto, Deni Luvi
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.9260

Abstract

This study analyzes public sentiment regarding candidates in the 2024 DKI Jakarta Gubernatorial Election utilizing a Support Vector Machine (SVM) approach. Recognizing the pivotal role of social media, particularly Twitter, in shaping public opinion, the research addresses the challenges of processing large volumes of unstructured data. Through systematic data preprocessing and feature extraction, the SVM model was applied, achieving a sentiment classification accuracy of 70%. The analysis revealed a distribution of sentiments where 36.1% of comments were positive, 33.4% negative, and 30.5% neutral. These findings illustrate the complexities of public discourse surrounding key political events, highlighting the model's efficacy and the nuances of sentiment detection. Moreover, discussions on model limitations elucidate areas for enhancement, suggesting future avenues including the adoption of more sophisticated algorithms and improved data processing techniques. This research contributes to the understanding of voter sentiment dynamics in a significant electoral context, providing insights that may assist campaign strategies and political analyses in Indonesia.
Co-Authors ., Rezki A Djufri, Isdar Abdul Mahatir Najar Aldy, Moh.Fajrin Sigit Anita Ahmad Kasim Anita, Ayu Ardiyansyah, Rizka Arsita, Tiara Juli Budiman, Wahyu Chairunnisa Ar. Lamasitudju Darojah, Murtafiatun Deni Luvi Jayanto Deny Wiria Nugraha Dwimanhendra, Muhammad Rifaldi Dwiwijaya, Kadek Agus Hajra Rasmita Ngemba Harani, Makruf Hardianti Hardianti, Hardianti I Wayan Sudarsana Karnita Sumbaluwu, Harlin Feby Laila, Rahma Lamasitudju, Chairunnisa Lesnusa, Gideon Namlea Mariani Mariani Maroso, Akwan Miftah Miftah, Miftah Mohammad Yazdi Pusadan Nouval Trezandy Lapatta Parenrengi, Andi Fathur Alamsyah A. Putri, Niluh Putu Aprillia Puspitadewi Sudarsana Rahmah Laila Reinaldy Mansa, Jeremy Rinianty, Rinianty Rizka Ardiansyah Rizqa Raaiqa Bintana Ryfial Azhar, Ryfial Sahril Sahril Septiano Anggun Pratama Sondakh, Clivent Gerhard Sri Khaerawati Nur Susanti, Maulidia Syahrullah Syahrullah Syaiful Hendra Ulhak, Muhamad Zia Wawagalang, A. Nolly Sandra Widiani, Ni Nengah Winarta, Ardhiansyah Wirdayanti Yudhaswana, Yuri Yunita Prastyaningsih Yuri Yudhaswana Joefrie Yusuf Anshori Zulkifli Zulkifli