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All Journal Jurnal Teknik Jurnal Informatika dan Teknik Elektro Terapan JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) Among : Jurnal Pengabdian Masyarakat Journal of Information System and Technology (JOINT) Jutech: Jurnal Teknologi Informasi Smart Techno (Smart Technology, Informatic and Technopreneurship) JUSTIFY : Jurnal Sistem Informasi Ibrahimy Jurnal Aplikasi Sistem Informasi dan Elektronika Abdimas Mandalika Kohesi: Jurnal Sains dan Teknologi International Applied Science Journal Of Humanities Community Empowerment Jurnal Penelitian Teknologi Informasi dan Sains ABDIMASTEK
Fitriyah, Nur Qodariyah
Universitas Muhammadiyah Jember
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Perbandingan Algoritma K-Nearest Neighbors dan Naïve Bayes dalam Penentuan Penerima Bantuan di Desa Banyuputih Kidul Thoriq Wahyu Hidayatullah; Ulya Anisatur Rosyidah; Nur Qodariyah Fitriyah
Jurnal Penelitian Teknologi Informasi dan Sains Vol. 4 No. 1 (2026): : JURNAL PENELITIAN TEKNOLOGI INFORMASI DAN SAINS (JPTIS)
Publisher : Institut Teknologi dan Bisnis (ITB) Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jptis.v4i1.3733

Abstract

The distribution of social assistance represents a key government strategy to enhance the welfare of low-income communities. Nevertheless, its implementation frequently faces challenges related to inaccurate targeting, often caused by uneven data collection and subjective decision-making processes in identifying eligible beneficiaries. This study aims to compare the performance of the K-Nearest Neighbors (KNN) and Naïve Bayes algorithms in determining eligibility for social assistance recipients in Banyuputih Kidul Village. Both models were evaluated using a confusion matrix with performance indicators including accuracy, precision, recall, and F1-score. The findings reveal that the KNN algorithm outperformed Naïve Bayes in identifying recipients of the PKH social assistance program, achieving an evaluation score of 99%, compared to 86% for Naïve Bayes. These results indicate that KNN provides higher predictive reliability for eligibility classification. This research is expected to support the development of an objective, data-driven decision support system that can assist village governments in distributing social assistance more accurately and transparently.
Pengenalan Teknologi Kecerdasan Artifisial kepada Siswa SMP Muhammadiyah melalui Praktik Pembuatan Model AI Non-Coding Triawan Adi Cahyanto; Nur Qodariyah Fitriyah
Abdimas Mandalika Vol 5, No 4 (2026): Juni
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/am.v5i4.39144

Abstract

Abstract:  This community service activity aims to improve artificial intelligence (AI) literacy among students of SMP Muhammadiyah through a non-coding, practice-based learning approach. The activity was conducted on March 30, 2026, involving 20 junior high school students. Google’s Teachable Machine platform was used to enable students to build AI image classification models without requiring prior programming knowledge. The program consisted of four sessions: introduction to AI concepts, live demonstration, hands-on model creation, and discussion on AI ethics and risks. Results showed that all student groups successfully created AI models capable of recognizing hand gestures (rock-paper-scissors) with a minimum accuracy of 80%, with some groups achieving above 90%. Students demonstrated high enthusiasm and active participation. A learning module was developed for sustainable use by teachers and students. This activity successfully strengthened the partnership between Universitas Muhammadiyah Jember and SMP Muhammadiyah in technology-based education.Abstrak: Kegiatan pengabdian kepada masyarakat ini bertujuan meningkatkan literasi kecerdasan artifisial (AI) siswa SMP Muhammadiyah melalui pendekatan pembelajaran berbasis praktik non-coding. Kegiatan dilaksanakan pada 30 Maret 2026 dengan melibatkan 20 siswa. Platform Teachable Machine dari Google digunakan agar siswa dapat membuat model klasifikasi gambar berbasis AI tanpa memerlukan pengetahuan pemrograman sebelumnya. Program terdiri atas empat sesi: pengenalan konsep AI, demonstrasi langsung, praktik pembuatan model, dan diskusi etika serta risiko AI. Hasil menunjukkan seluruh kelompok siswa berhasil membuat model AI yang mampu mengenali gestur tangan (batu-kertas-gunting) dengan akurasi minimal 80%, dan beberapa kelompok mencapai akurasi di atas 90%. Modul pembelajaran telah dikembangkan untuk digunakan secara berkelanjutan oleh guru dan siswa. Kegiatan ini berhasil memperkuat kemitraan antara Universitas Muhammadiyah Jember dengan SMP Muhammadiyah dalam bidang pendidikan berbasis teknologi.
Design and Implementation of a Web-Based Mobile Phone Sales Forecasting Application Using the Single Exponential Smoothing Method Putra, Aldho Chahya Vinada; Dasuki, Moh.; Qodariyah Fitriyah, Nur
Smart Techno (Smart Technology, Informatics and Technopreneurship) Article in Press
Publisher : Primakara University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study was conducted to address the inventory management issues at iCare Apple Store, where mobile phone stock planning has traditionally relied on estimation, leading to the risk of both stock shortages and overstocking. The objective of this study was to design and implement a web-based mobile phone sales forecasting application using the Single Exponential Smoothing (SES) method. The dataset consisted of monthly sales data for the iPhone XR and iPhone XS from January 2021 to December 2024. The forecasting process was performed by testing alpha values ranging from 0.1 to 0.9, while forecasting accuracy was evaluated using the Mean Absolute Percentage Error (MAPE). The results indicated that the optimal alpha value for the iPhone XR was 0.1, yielding a MAPE of 27.04% and a forecast of 11.45 units, which was rounded to 11 units. For the iPhone XS, the forecasting model produced a MAPE of 52.33% with a forecast of 2.45 units, rounded to 2 units. These findings demonstrate that the Single Exponential Smoothing method can be effectively implemented in a web-based application and can support decision-making in determining appropriate inventory levels for subsequent periods.
Co-Authors Abdul Azis agus milu susetyo Ari Eko Wardoyo Daryanto Dasuki, Moh. Dinata, Noeril Agian Septa Erwanto , Tedi Fitriyah, Aurelia Agustin Fitriyah, Bait Syaiful Rijal Fitriyah, Cetta Ugama Putra Ginanjar Abdurrahman Hasbullah Hasbullah Lintang Setyo Kurniawati Mochammad Zainur Rahman A. Mohammad Hasan Oktavianto, Hardian Putra, Aldho Chahya Vinada Putra, Dua Rizki Dharma Rahman, Miftahur Rintyarna , Bagus Setya Rosyidah, Ulya Anisatur Sulistyo, Henny Wahyu Sutikno Sutikno Sutikno Thoriq Wahyu Hidayatullah Triawan Adi Cahyanto Ulya Anisatur R. Widiyanto, Alfian Wijaya, Guruh Yusril Izzi Arlisa Amiri Zaenab Kurnia Zainul Arifin Zakiyyah, Amalina Maryam