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All Journal JOIV : International Journal on Informatics Visualization Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Pengabdian Masyarakat - Teknologi Digital Indonesia JITEEHA: Journal of Information Technology Applications in Education, Economy, Health and Agriculture
Suksmawati, Affi Nizar
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Engineering Environmental Science Industrial & Manufacturing Engineering Social Sciences Other

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PEMBERDAYAAN BUDIDAYA CACING ANC MELALUI WEBSITE SISTEM INFORMASI DAN DIGITAL MARKETING Nurdiyansyah, Firman; Suksmawati, Affi Nizar; Ursaputra, Lionardi
Jurnal Pengabdian Masyarakat - Teknologi Digital Indonesia. Vol 4, No 1 (2025): Maret 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jpm.v4i1.1782

Abstract

Mendigitalisasi manajemen operasional dan meningkatkan kapasitas pemasaran digital di CV. Messi Putra Jaya, sebuah UMKM di Kecamatan Wagir, Kabupaten Malang yang bergerak dalam budidaya cacing African Nightcrawler (ANC), menjadi fokus utama program ini. Mitra menghadapi tantangan berupa pengelolaan data secara manual yang rentan terhadap kesalahan serta strategi pemasaran konvensional yang kurang efektif. Untuk mengatasi tantangan tersebut, program ini dirancang untuk meningkatkan efisiensi manajemen, memperluas jangkauan pemasaran, dan mendukung pengambilan keputusan berbasis data. Metode pelaksanaan dilakukan dalam empat tahapan, yaitu analisis kebutuhan, pengembangan sistem informasi berbasis web, pelatihan pemasaran digital, serta monitoring dan evaluasi. Hasil kegiatan menunjukkan dampak positif yang signifikan. Sistem informasi berbasis web yang dikembangkan memungkinkan pencatatan produksi dan transaksi keuangan dengan lebih efisien dan akurat. Selain itu, pelatihan pemasaran digital mendorong mitra untuk memanfaatkan media sosial dan e-commerce sebagai sarana pemasaran yang efektif, meningkatkan jangkauan pasar secara signifikan. Dalam waktu tiga bulan, program ini berhasil meningkatkan penjualan hingga 25% dan menciptakan budaya kerja yang lebih modern dan berbasis teknologi. Kendala utama yang ditemukan berupa keterbatasan waktu pendampingan dan adaptasi terhadap teknologi menjadi pembelajaran penting untuk perbaikan di masa mendatang. Dengan keberhasilan ini, program pengabdian memiliki potensi besar untuk direplikasi di wilayah lain dengan dukungan dari platform teknologi, pemerintah daerah, serta pelatihan lanjutan dalam pemasaran berbasis data dan sistem pembayaran digital.
User Experience Analysis in Website-Based Digital Invitation Design Suksmawati, affi Nizar; Peldon, Tshering
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 1 No. 1 (2024): February
Publisher : Lumina Infinity Academy Foundation

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Abstract

Human-Computer Interaction (HCI) studies how humans interact with computer technology, focusing on interface design that allows users to communicate and operate computer systems effectively and intuitively. The goal is to enhance user experience, productivity, and satisfaction through approaches that consider users' needs, preferences, and abilities. A digital invitation is created digitally, usually in electronic formats such as email, text messages, social media posts, or websites. Digital invitations can be images or text sent to guests via digital platforms, often accompanied by RSVP links or additional information. Creating and using digital invitations addresses various limitations of traditional physical invitations, such as accessibility issues, delivery costs and time, and environmental impact. This research involves creating a prototype of a digital invitation using design platforms like Canva and collecting and analyzing online questionnaire data to assess user efficiency, flexibility, and satisfaction. The results show that digital invitations have many advantages, including cost efficiency, reduced environmental impact, ease of management, and higher interactivity than physical invitations. The conclusion of this study emphasizes the importance of interactive and user-friendly design, as well as high user satisfaction with digital invitations. Recommendations include further development of interactive features, enhanced data security, testing on various devices, and promotion and education on the benefits of digital invitations.
Comparison of Machine Learning as an Inference Engine to Improve Expert Systems in Dengue Disease Istiadi, -; Marisa, Fitri; Joegijantoro, Rudy; Suksmawati, Affi Nizar; Rahman, Aviv Yuniar
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.3192

Abstract

Dengue disease remains a significant public health challenge in tropical and subtropical regions, with rising incidence and mortality rates over the past few decades. While expert systems have been developed for early detection, traditional approaches often rely on rigid rule-based inference engines, which are limited by their dependence on expert-defined structures and lack adaptability to evolving knowledge sources. This study introduces a novel approach to enhance the flexibility and adaptability of expert systems by integrating machine learning (ML) techniques into the inference engine, leveraging the growing availability of medical record data as a dynamic knowledge source. Using a dataset of 90 medical records, balanced to 126 items via the Synthetic Minority Over-sampling Technique (SMOTE), we evaluated the performance of multiple ML algorithms, including Decision Trees (DT), Support Vector Machines (SVM), and Artificial Neural Networks (ANN), against traditional models like Naive Bayes (NB) and K-Nearest Neighbors (KNN). The DT, SVM, and ANN models demonstrated exceptional performance, achieving average accuracy, precision, recall, and F1 scores of 97.73%, 98.33%, 97.22%, and 97.41%, respectively. The key innovation of this research lies in developing an adaptive inference engine that can dynamically learn from medical data, reducing reliance on static rule bases and enabling the expert system to evolve with new knowledge. This approach improves diagnostic accuracy and provides a scalable and flexible framework for addressing other infectious diseases. By bridging the gap between expert systems and machine learning, this study paves the way for more intelligent, data-driven healthcare solutions with significant implications for public health and disease management.
Optimasi K-Means Menggunakan Algoritma Genetika pada Metode User-based Collaborative Filtering Adilla, Axl; Suksmawati, Affi Nizar; Pertiwi, Affifah Mutiara; Pratama, Kharis Suryandaru
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 2 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i2.9382

Abstract

Collaborative filtering merupakan teknik sistem rekomendasi yang menggunakan informasi rating dari beberapa pengguna untuk memprediksi rating suatu item bagi pengguna tertentu. Namun, tidak semua pengguna memberikan rating pada seluruh item. Hal ini menyebabkan ketidakmampuan sistem dalam menentukan nearest neighborhood, sehingga rekomendasi yang dihasilkan menjadi lemah. Penelitian ini mengusulkan penggunaan Algoritma K-Means untuk mengelompokkan neighborhood yang sesuai. Penentuan awal titik pusat klaster pada Algoritma K-Means dioptimalkan menggunakan Algoritma Genetika. Evaluasi dilakukan dengan memvariasikan jumlah klaster optimal pada beberapa metode pengukuran yang digunakan, yaitu Jaccard Similarity Coefficient, Sørensen–Dice Coefficient, dan Hamming Coefficient. Hasil pengujian menggunakan pengukuran Jaccard Similarity Coefficient, Sørensen–Dice Coefficient, dan Hamming Coefficient memperoleh nilai fitness masing-masing sebesar 4.490, 4.979, dan 4.964 untuk jumlah klaster optimal 4 dan 6. Sementara itu, nilai MAPE rata-rata untuk ketiga metode pengukuran kemiripan tersebut sebesar 60%.
Co-Authors Adilla, Axl Aviv Yuniar Rahman Firman Nurdiyansyah Istiadi MARIFANI FITRI ARISA Peldon, Tshering Pertiwi, Affifah Mutiara Pratama, Kharis Suryandaru Rudy Joegijantoro, Rudy Ursaputra, Lionardi