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All Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Abdimas Maduma
Muhammad Nandi Buchari
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Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Education

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Penerapan Teknologi Digital Untuk Optimalisasi Biaya Produksi dan Manajemen Keuangan Pada UMKM Tambol Dapok Punggur Ardiyansyah; Nurfia Oktaviani Syamsiah; Windi Irmayani; Muhammad Nandi Buchari; Muhammad Alghifary; Aldiansyah; Mutia Rahayu; Tiara Maulida
JURNAL ABDIMAS MADUMA Vol. 4 No. 3 (2025): Oktober, 2025
Publisher : English Lecturers and Teachers Association (ELTA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52622/jam.v4i3.521

Abstract

Program Pengabdian kepada Masyarakat ini dilaksanakan untuk meningkatkan kapasitas dan daya saing UMKM Tambol Dapok di Desa Punggur Kecil, Kecamatan Sungai Kakap, Kabupaten Kubu Raya. Permasalahan yang dihadapi mitra meliputi aspek manajemen, aspek produksi, serta belum optimalnya pencatatan biaya produksi dan manajemen keuangan. Kegiatan dilakukan dengan pendekatan partisipatif melalui tahapan analisis situasi, persiapan teknologi tepat guna, pelatihan dan pendampingan, serta monitoring dan evaluasi. Hasil kegiatan menunjukkan adanya peningkatan kapasitas mitra, ditandai dengan tersedianya aplikasi berbasis website untuk menghitung biaya produksi dan manajemen keuangan, desain kemasan baru menggunakan aplikasi Canva, akun Instagram resmi sebagai media promosi digital, serta peningkatan pengetahuan mitra dalam manajemen usaha. Dengan capaian ini, UMKM Tambol Dapok mulai mampu membangun identitas merek, memperluas pasar, dan lebih adaptif terhadap perkembangan teknologi digital. Secara praktis, mitra dapat mengelola usaha secara lebih efisien melalui pencatatan biaya produksi yang terstruktur, meningkatkan daya tarik produk melalui kemasan yang lebih profesional, serta memperluas jangkauan pemasaran dengan memanfaatkan media sosial. Selain itu, penerapan teknologi digital juga membuka peluang bagi UMKM untuk meningkatkan daya saing di pasar lokal maupun regional, sekaligus menjadi model pemberdayaan yang dapat direplikasi pada UMKM lain dengan permasalahan serupa. Kata Kunci : UMKM; Pemberdayaan Masyarakat; Manajemen Usaha; Digital Marketing; Teknologi Tepat Guna
Streamlit Based Network Intrusion Detection System Prototype with Machine Learning Algorithm Tiara Maulida; Muhammad Nandi Buchari; Teofilus Tirta Jumata; Putra Pratama Syahrival; Ali Mustopa
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1950

Abstract

Computer network security has become a crucial elemen in the digital era, with the increasing risk of attacks that could potentially disrupt systems and access critical data. An Intrusion Detection System (IDS) powered by Machine Learning is one effective way to automatically detect suspicious network activity. This study aims to create a prototype of a network Intrusion Detection System using Streamlit that applies Machine Learning algorithms, including Naïve Bayes and Random Forest, to classify normal network activity as an attack. The method used in this study is a quantitative approach with an experimental design utilizing a public dataset of labeled network traffic. The research process includes the stages of initial data processing, feature selection, model creation, performance evaluation, and implementation of the Streamlit interface. Test results show that the Naïve Bayes algorithm has the best performance, with an accuracy level reaching 0.8000, an error rate of 0.2000, and an F1 Score of 0.7273. Random Forest recorded an accuracy level of 0.7333, an error rate of 0.2667, and a lower F1 Score of 0.3333. These findings demonstrate that Naïve Bayes is more effective at detecting intrusions and recognizing anomalous network traffic patterns. The Streamlit based system implementation successfully provides an interactive and userfriendly interface, allowing users to perform analysis and understand classification result without in-depth technical expertise. Given the foregoing, the network intrusion detection system prototype built with Streamlit and a Machine Learning algorithm is considered suitable as a simple, informative, interactive, and efficient network security support tool. This research paves the way for future developments, such as the implementation of Deep Learning models and the integration of live network monitoring.
Co-Authors Aldiansyah Ali Mustopa, Ali Ardiyansyah Muhammad Alghifary Mutia Rahayu Nurfia Oktaviani Syamsiah Putra Pratama Syahrival Teofilus Tirta Jumata Tiara Maulida Windi Irmayani