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All Journal Jurnal Ilmiah Universitas Batanghari Jambi JITK (Jurnal Ilmu Pengetahuan dan Komputer) JSiI (Jurnal Sistem Informasi) Jurnal Tekno Insentif Progresif: Jurnal Ilmiah Komputer Jurnal Teknologi Informasi dan Multimedia bit-Tech Jurnal Sisfotek Global Tematik : Jurnal Teknologi Informasi Komunikasi Jurnal Sisfotek Global Academic Journal of Computer Science Research (AJCSR) Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Tren Bisnis Global JPEG Jurnal Topik Global
M. Bucci Ryando
Institut Teknologi dan Bisnis Bina Sarana Global
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences

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Figma Go to School: Inovasi Pembelajaran UI/UX Design untuk Generasi Digital Ryando, M. Bucci; Saputra, Agung Rizki; Alifyani, Davina; Fadillah, Fandi; Arliyansyah, Wahyu
JURNAL PENGABDIAN GLOBAL Vol 5, No 1 (2026): Jurnal Pengabdian Global (JPEG)
Publisher : JURNAL PENGABDIAN GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38101/jpeg.v5i1.16359

Abstract

Program Pengabdian Masyarakat (PKM) ini dilaksanakan di SMK Mitra Permata, Kabupaten Tangerang, untuk menjembatani kesenjangan kompetensi antara siswa Teknik Komputer dan Jaringan (TKJ)—yang berfokus pada infrastruktur jaringan—dan tuntutan industri digital, yang membutuhkan keterampilan desain antarmuka pengguna (UI) dan pengalaman pengguna (UX). Tujuan program ini adalah untuk meningkatkan pemahaman dan keterampilan dasar siswa dalam desain UI/UX menggunakan platform Figma. Implementasinya menggabungkan pelatihan teori, praktik desain, dan bimbingan Pembelajaran Berbasis Proyek (PjBL) selama 14 sesi yang melibatkan 60 siswa kelas 10 TKJ. Hasil menunjukkan peningkatan kompetensi yang signifikan, seperti yang ditunjukkan oleh hasil post-test di mana 73,81% peserta melaporkan peningkatan pemahaman UI/UX, 83,33% merasa didukung dalam proses desain digital melalui Figma, dan 80,95% menunjukkan peningkatan keterampilan kolaborasi. Selain itu, 30 prototipe aplikasi e-commerce dihasilkan sebagai output digital. Program ini berkontribusi untuk meningkatkan kesiapan dan daya saing siswa SMK dalam memenuhi kebutuhan industri digital yang terus berkembang.
Perbandingan Algoritma K-Nearest Neighbor dan Naive Bayes untuk Klasifikasi FoMO Pengguna Media Sosial Muhammad Haromaen; Marthin Piskana; M. Bucci Ryando; Wira Hadinata
Progresif: Jurnal Ilmiah Komputer Vol 21, No 2 (2025): Agustus
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v21i2.2784

Abstract

The intensive use of social media among students poses a risk of triggering Fear of Missing Out (FoMO), which negatively affects mental health and learning focus. This study aims to develop a classification model to detect FoMO tendencies among students at SMAN 11 Kabupaten Tangerang. A quantitative approach was used, employing the K-Nearest Neighbor (KNN) and Naïve Bayes algorithms. The analyzed variables include gender, duration of social media use, access frequency, desire to stay updated, and its impact on productivity. Data were collected from 244 respondents and processed through pre-processing, modeling, and evaluation stages. Validation results show that KNN achieved the highest accuracy at 94.69%, while Naïve Bayes reached 93.06%. These findings indicate that KNN is more effective in detecting FoMO tendencies based on numerical data and has the potential to support early intervention efforts in educational settings.Keywords: Fear of Missing Out; K-Nearest Neighbor; Social Media; Classification; Naive Bayes AbstrakPenggunaan media sosial secara intensif di kalangan pelajar berisiko memunculkan gejala Fear ofaMissing Out (FoMO), yang berdampak negatif pada kesehatan mental dan fokus belajar. Penelitian ini bertujuan untuk mengembangkan model klasifikasi kecenderungan FoMO pada pelajar SMAN 11 Kabupaten Tangerang. Metode yang digunakan adalah pendekatan kuantitatif dengan algoritma K-NearestiNeighbor (KNN) dan NaïveiBayes. Variabel yang dianalisis meliputi jenis kelamin, durasi penggunaan media sosial, frekuensi akses, keinginan untuk tetap update, dan pengaruh terhadap produktivitas. Data dikumpulkan dari 244 responden dan diproses melalui pre-processing, modeling, dan evaluasi. Hasil validasi menunjukkan bahwa KNN menghasilkan akurasi tertinggi sebesar 94,69%, sementara Naïve Bayes mencapai 93,06%. Temuan ini menunjukkan bahwa KNN lebih efektif untuk mendeteksi kecenderungan FoMO berbasis data numerik dan berpotensi mendukung pengembangan intervensi dini dalam konteks pendidikan.Kata kunci: Fear of Missing Out; K-Nearest Neighbor; Media Sosial; Klasifikasi; Naive Bayes
Enhancing Leave Management Systems with Design Thinking-Based UI/UX Development Nur Fairus Ramadhanti; Achmad Sidik; M. Bucci Ryando
bit-Tech Vol. 7 No. 3 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v7i3.2291

Abstract

This study addresses the inefficiencies in the employee leave management process of a company operating in both the service and manufacturing sectors, which currently relies on a manual, document-based system devoid of centralized data integration. Such a system has led to administrative bottlenecks, documentation inaccuracies, and reduced operational transparency, thereby hampering employee satisfaction and organizational productivity. To overcome these limitations, the Design Thinking methodology was adopted as a user-centered approach for the development of an intuitive and functional web-based leave management application. The research employed the five phases of Design Thinking—empathize, define, ideate, prototype, and test—to ensure that the system's design aligns with user expectations and organizational goals. Primary data were gathered through interviews and questionnaires administered to employees and human resource personnel, enabling the identification of key pain points in the existing workflow. A prototype was developed and subsequently evaluated using the System Usability Scale (SUS), a widely accepted instrument for measuring perceived usability. The system achieved a usability score of 87.45% based on responses from 10 users, indicating a high level of user satisfaction and system acceptance. These findings demonstrate the effectiveness of the Design Thinking approach in producing a leave management system that not only enhances administrative efficiency but also fosters a positive user experience. The study contributes to the growing body of literature on user-centered system design and provides a replicable framework for organizations seeking to digitally transform HR administrative functions through iterative, human-centered design methodologies.
DEEP LEARNING APPROACH FOR RECOGNIZING SUBSIDIZED GAS RECIPIENTS USING CONVOLUTIONAL NEURAL NETWORKS Achmad Sidik; M. Bucci Ryando; M. Ramaddan Julianti; Agus Rifaldi
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 4 (2026): JITK Issue May 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i4.7454

Abstract

Inaccurate targeting in subsidized LPG distribution remains a persistent policy challenge in Indonesia, where manual verification processes are vulnerable to misuse and administrative error. Addressing this gap, the present study develops and evaluates a biometric identity verification system based on Convolutional Neural Networks (CNNs) to improve the accuracy and accountability of subsidy allocation at the point of distribution. Following the CRISP-DM framework, two CNN architectures with fundamentally different design philosophies were compared: ResNet-IR, optimized for representational depth and recognition accuracy, and MobileFaceNet, designed for computational efficiency on resource-constrained hardware. Both models were sourced from the InsightFace framework as pre-trained models and evaluated on a locally acquired dataset of 111 registered subsidy recipients from Pajang Village, Tangerang City. Evaluation across face identification (1:N) and face verification (1:1) tasks reveals that ResNet-IR consistently outperforms MobileFaceNet, achieving an accuracy of 94.7% with a precision, recall, and F1-score of 0.9043, compared to MobileFaceNet’s accuracy of 93.7% and F1-score of 0.8862. The primary contribution of this work is to demonstrate, for the first time in the Indonesian subsidy distribution context, that deep learning-based facial recognition can serve as a viable, deployable mechanism for biometric identity verification in public service programs offering a technically grounded pathway toward more transparent and equitable subsidy targeting.
Co-Authors Achmad Sidik Achmad Sidik Agus Rifaldi Akbar, Muhammad Ikmal Alifyani, Davina Arliyansyah, Wahyu Arni Retno Mariana Asep Rio Saputra Batu Bara, Muhamad Aldi Mudin Bayu Irfan Aditya Bijaksana, Rhafael Dedi Dedi Dedi Dedi Eka Sumiati Fadillah, Fandi Ferawati Ferawati Ferry Maulana Ibrahim Fredy Susanto Haerunnisa, Siti Halim Agung Hidayatullah, Ilham Iqbal, Muchamad Julianti, M. Ramaddan Khoirul Anwar M. Ramaddan Julianti Makmur Raya Marthin Piskana Maruf, Rohim Muhamad Naufal Robih Muhammad Haromaen Nabila Ana Mantika Nova Teguh Sunggono Novara Aulist Zakia Nur Asiyah Nur Fairus Ramadhanti Nurholipah Nurholipah Nurmaesah, Nunung Pudoli, Ahmad Rahmat Tullah Raya, Makmur Ridwan Abdul Hakim Saepul Nazar Saputra, Agung Rizki Septihadi, Ali Nugroho Setiawan, Prayoga Sidik, Achmad Siti Maesaroh Sri Rahayu Sutarman Sutarman Syahidah, Kurnia Syifa Aulia Tasya Amelia Tina Amalia Triono Triono Wahyu Nurifai Widiawati Widiawati Wira Hadinata Zainul Hakim