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All Journal Foristek JUSIFO : Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Technomedia Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) CCIT (Creative Communication and Innovative Technology) Journal ScientiCO : Computer Science and Informatics Journal BERNAS: Jurnal Pengabdian Kepada Masyarakat Jurnal Promotif Preventif IAIC Transactions on Sustainable Digital Innovation (ITSDI) Jurnal Algoritma Applied Engineering and Technology ITIJ The Indonesian Journal of Computer Science Tadulako Science and Technology Journal Journal Social Engagement: Jurnal Pengabdian Masyarakat
Wirdayanti
Universitas Tadulako
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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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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.
Implementation of ResNet-50-Based Convolutional Neural Network For Mobile Skin Cancer Classification Asriani, Asriani; Lapatta, Nouval Trezandy; Nugraha, Deny Wiria; Amriana, Amriana; Wirdayanti, Wirdayanti
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9696

Abstract

The skin is one of the most important parts of the human body, serving vital functions such as protecting internal organs from injury, shielding against direct bacterial exposure, regulating body temperature, and more. However, the skin is also susceptible to diseases, one of which is skin cancer. Skin cancer can be extremely dangerous if not treated promptly, as it can lead to death. Therefore, early detection is crucial. This study proposes a technology-based solution by classifying skin cancer using a convolutional neural network (CNN) with a ResNet50 architecture implemented into a mobile application via a REST API using Flask. The HAM10000 dataset, consisting of 10,015 skin lesion images across seven classes, was used for model training. Various testing scenarios were conducted to determine the optimal parameter combination. The best results were achieved with an accuracy of 83.84%, precision and recall of 83%, and an F1-score of 83%, using a training data configuration of 70%, dropout of 0.4, and a batch size of 64. The model implemented in this Android application can perform early detection of skin cancer quickly, practically, and easily accessible to the general public, though healthcare professionals must still supervise it. However, although this model can assist users in making early predictions, the prediction results from this model are only a tool for early detection and do not replace clinical diagnosis by professional medical personnel.2) Figure 8 shows the display for taking pictures through the gallery or camera. Users can choose the image they want to upload from the gallery or the camera to be analysed and predicted by the model.
Co-Authors ., Rezki A. Masse, Burhannudin Abul Azis, Muhammad Dzaky Aisyah Warsid Aldy, Moh.Fajrin Sigit Amriana Amriana Amriana Amriana Andi Hendra Anita Ahmad Kasim ARDIANSYAH ARDIANSYAH Ardiansyah, R Asriani Asriani, Asriani Bakri Budiman, Wahyu Chairunnisa Ar. Lamasitudju Deni Irawan, Deni Deni Luvi Jayanto Deny Wiria Nugraha Dwi Shinta Angreni Hajra Rasmita Ngemba Ihalauw, Sahron Angelina Indah Safitri Kade Dwi Arsana, I Gusti Ngurah Agung Laala, Jonathan Zebina Laila, Rahma Lamasitudju, Chairunnisa Masykur, M Miftah Miftah Miftah, Miftah Mohammad Yazdi Pusadan Mufthi, Mufthi Muhammad Akbar MUHAMMAD BAKRI Nadhar, Muhammad Ngurah, Gusti Nirwan Nirwan Nouval Trezandy Lapatta Noviantika, Noviantika Nursalim, Moh. Agung Qofifa, Sitti Nurlaili Rafika Sari Rahmah Laila Reinaldy Mansa, Jeremy Rinianty, Rinianty Rizka Ardiansyah Rizqa Raaiqa Bintana Ryfial Azhar, Ryfial Sabarudin Saputra Sahril Sahril Semmagga, Nuraeni Septiano Anggun Pratama Sri Khaerawati Nur Suryani Putri, Fadiah Syahrullah Syahrullah Syahrullah Syaiful Hendra Wahyu Setiawan Wiria Nugraha, Denny Wongkar, Noel Marcell Jonathan Yanti, Alda Yudhaswana, Yuri Yuri Yudhaswana Joefrie Yusuf Anshori