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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Akuntansi Indonesia Jurnal Simetris Prosiding SNATIF Jurnal Pseudocode E-Dimas: Jurnal Pengabdian kepada Masyarakat Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal SOLMA Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal DISPROTEK International Journal of Elementary Education EDUMATIC: Jurnal Pendidikan Informatika Jurnal SITECH : Sistem Informasi dan Teknologi Abdimas Toddopuli: Jurnal Pengabdian Pada Masyarakat Jurnal Pengabdian kepada Masyarakat Nusantara Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) Journal of Information Technology Ampera Indonesian Journal of Technology, Informatics and Science (IJTIS) Journal of Software Engineering Ampera Jurnal UNITEK Devotion: Journal of Research and Community Service Jurnal Pengabdian Masyarakat (ABDIRA) Indonesian Journal of Networking and Security - IJNS SPEED - Sentra Penelitian Engineering dan Edukasi Muria Jurnal Layanan Masyarakat Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Locus Penelitian dan Pengabdian Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer IC Tech: Majalah Ilmiah Jurnal Sistem Informasi dan Manajemen Sasambo: Jurnal Abdimas (Journal of Community Service) International Journal of Artificial Intelligence and Science IC Tech: Majalah Ilmiah
Wiwit Agus Triyanto
Universitas Muria Kudus
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Nursing Public Health Social Sciences Other

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Sistem Presensi Karyawan Rokok Pada Pr-Oemega Menggunakan Qr Code Berbasis Website Kevin Putra Adama; Wiwit Agus Triyanto
Abdimas Toddopuli: Jurnal Pengabdian Pada Masyarakat Vol. 7 No. 2 (2026): Volume 7, No 2, Juni 2026
Publisher : Universitas Cokroaminoto Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30605/atjpm.v7i2.7692

Abstract

PR-OEMEGA merupakan perusahaan yang bergerak di bidang industri rokok yang memiliki banyak karyawan. Saat ini, sistem presensi yang berjalan masih menggunakan metode konvensional (manual), yang memiliki berbagai kelemahan seperti antrean saat jam masuk dan pulang, risiko manipulasi data (titip absen), serta proses rekapitulasi laporan bulanan yang memakan waktu lama dan rentan human error. Penelitian ini bertujuan untuk merancang dan membangun sistem informasi presensi karyawan menggunakan teknologi Quick Response (QR) Code berbasis website. Metode pengembangan sistem yang digunakan adalah System Development Life Cycle (SDLC) model Waterfall, yang meliputi tahapan analisis kebutuhan, desain sistem, implementasi, dan pengujian. Sistem ini dirancang menggunakan bahasa pemrograman PHP dan database MySQL. Hasil dari penelitian ini adalah sebuah aplikasi presensi berbasis web yang memungkinkan karyawan melakukan absensi dengan memindai QR Code melalui smartphone, serta memudahkan bagian administrasi dalam memantau kehadiran dan mengelola laporan penggajian secara real-time, akurat, dan efisien
Image Forensics Analysis of the Authenticity of Digital Payment Evidence using the K-Nearest Neighbor Algorithm Agusta, Feriyan; Setiaji, Pratomo; Triyanto, Wiwit Agus
SISTEMASI Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i1.5728

Abstract

The rapid growth of digital transactions has also increased the risk of digital payment evidence forgery, such as screenshot manipulation or digital image editing. This study aims to develop an automated authenticity validation system for digital payment evidence by integrating Image Processing, Image Forensics, and Optical Character Recognition (OCR) technologies. The processing pipeline begins with image preprocessing, followed by forensic feature extraction and OCR-based text analysis, which are then classified using the K-Nearest Neighbor (KNN) algorithm. This study evaluates 15 experimental scenarios based on combinations of training and testing data ratios (90:10, 80:20, 70:30, 60:40, and 50:50) and random state values (42, 32, and 22). Model performance is assessed using accuracy, precision, recall, and F1-score metrics across a range of k values from 1 to 15. The results indicate that the optimal performance is achieved at k = 7, with an accuracy of 97.1%. The proposed system is able to efficiently distinguish between authentic and manipulated digital payment evidence. The system is implemented as an Android application that allows users to upload payment evidence via the device camera or gallery, after which the system automatically analyzes its authenticity. The findings demonstrate that the integration of image forensic techniques and the K-Nearest Neighbor (KNN) algorithm effectively detects indications of manipulation in digital payment evidence and enhances the efficiency of the verification process within the digital financial services ecosystem.
Implementation of Image Processing in Scanning KTP Data using Optical Character Recognition (OCR) Hanafi, Bachtiar; Stiaji, Pratomo; Triyanto, Wiwit Agus
SISTEMASI Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i1.5856

Abstract

The Indonesian National Identity Card (Kartu Tanda Penduduk or KTP) serves as the primary identification document for Indonesian citizens in various administrative processes across both the public and private sectors. However, manual data entry of KTP information is still commonly practiced, leading to potential input errors, delays, and inefficiencies. This study aims to develop an Android-based application capable of automatically scanning and extracting KTP data using Optical Character Recognition (OCR) enhanced with a Convolutional Neural Network (CNN). The CNN is applied during the image preprocessing stage to improve text area segmentation and detection accuracy prior to the OCR process. The application is developed using Python, Dart, and PHP, and is designed with a user-friendly interface. Extracted data—including name, national identification number (NIK), place and date of birth, and address—are stored in a MySQL database through web API integration. The research adopts a software engineering approach comprising requirement analysis, system design, implementation, and testing. Experimental results indicate that the integration of CNN into the OCR system improves character recognition accuracy up to 86.7%, particularly for low-quality or noisy images. Therefore, the proposed application is expected to provide an effective solution for faster, more accurate, and more efficient population data digitization.
Implementasi Sistem Informasi Berbasis Web Untuk Pemesanan Produk Pada Coffee Shop Warunge Pakdhe Khusnul Himam, Muhammad; Agus Triyanto, Wiwit
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 7 No. 1 (2026): Edisi Januari - April
Publisher : Lembaga Dongan Dosen

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

Abstract

Teknologi informasi mendorong UMKM untuk beradaptasi secara digital guna meningkatkan efisiensi operasional. Coffee Shop Warunge Pakdhe menghadapi masalah krusial berupa sistem pemesanan manual yang rawan kesalahan pencatatan, keterlambatan layanan 15-20 menit pada jam sibuk, serta keterbatasan pemantauan pesanan. Kegiatan pengabdian ini bertujuan mengimplementasikan sistem informasi pemesanan berbasis web sebagai solusi digitalisasi pelayanan. Metode pelaksanaan menggunakan pendekatan difusi iptek yang meliputi tahap analisis kebutuhan, perancangan sistem, pelatihan intensif penggunaan dasbor admin, serta pendampingan operasional langsung. Sistem ini memungkinkan pelanggan melakukan pemesanan secara mandiri dan memverifikasi riwayat melalui nomor meja. Hasil kegiatan menunjukkan bahwa implementasi sistem berhasil meningkatkan efisiensi waktu layanan sebesar 40-50% dan mereduksi kesalahan pencatatan pesanan hingga nol persen. Mitra kini mampu mengoperasikan sistem secara mandiri, yang berdampak pada ketertiban administrasi transaksi dan peningkatan kualitas layanan bagi pelanggan
Sentiment Analysis of Money Lover App Reviews using Random Forest and Naïve Bayes Bila, Nanda Aulia Salsa; Triyanto, Wiwit Agus; Setiaji, Pratomo
Sistemasi: Jurnal Sistem Informasi Vol 15, No 2 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i2.5859

Abstract

This study aims to analyze user sentiment toward the Money Lover application and to compare the performance of two different machine learning algorithms, Random Forest and Naïve Bayes, in binary classification of review data. A total of 3,000 comments were collected using web scraping techniques and then classified into positive and negative sentiment categories. The preprocessing stage included text cleaning, normalization, tokenization, stopword removal, and stemming. In the next stage, term weighting was performed using TF-IDF to convert the text into numerical vector representations. The results provide insights into the overall sentiment tendencies of users toward the Money Lover application and demonstrate the effectiveness of both algorithms in processing textual reviews within the financial domain. Based on model evaluation, the Random Forest algorithm achieved superior average performance, with an accuracy of 94%. Meanwhile, the Naïve Bayes algorithm showed slightly lower performance, achieving an accuracy of 92%. These findings were supported by cross-validation results and ROC curve analysis, which indicated that Random Forest consistently outperformed Naïve Bayes. The performance difference suggests that an ensemble-based approach such as Random Forest is better able to handle textual variation in review data, resulting in more stable and accurate sentiment classification.
Sentiment Analysis of CapCut Application Reviews using Support Vector Machine with the SMOTE Technique shefia, faridah ayu; Setiaji, Pratomo; Triyanto, Wiwit Agus
Sistemasi: Jurnal Sistem Informasi Vol 15, No 2 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i2.5948

Abstract

The growing popularity of short-form video content across various social media platforms has increased the use of cross-device video editing applications, accessible through smartphones, desktops, and web-based services. CapCut is one of the most widely used applications for creating creative content, and user reviews on the Google Play Store serve as an important indicator for evaluating user experience quality. However, review datasets are often imbalanced, with positive sentiment dominating and neutral sentiment appearing in much smaller proportions, which poses challenges for sentiment classification. This study aims to analyze user sentiment toward CapCut reviews using Support Vector Machine (SVM) and applying the Synthetic Minority Over-sampling Technique (SMOTE) to address data imbalance. The data were collected by scraping reviews from the Google Play Store, resulting in 4,381 cleaned review entries after the data cleaning stage. The reviews then underwent text preprocessing, TF-IDF feature weighting, and model training. The experimental results show that the SVM model achieved an accuracy of 73.54% with a weighted F1-score of 0.736. These findings indicate that SMOTE contributes to improving model performance on minority classes. Overall, this study provides insights into user perceptions of CapCut and highlights the potential of SVM as an effective sentiment classification method for text-based application reviews.
Transformasi Digital Pembelajaran melalui Koding dan AI di SMP N 2 Karanganyar Triyanto, Wiwit Agus; Irawan, Yudie; Susanti, Nanik
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 7 No. 1 (2026): Edisi Januari - April
Publisher : Lembaga Dongan Dosen

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

Abstract

Transformasi digital pendidikan menuntut kesiapan guru dalam mengintegrasikan koding dan kecerdasan artifisial (AI) ke dalam pembelajaran. Permasalahan utama di SMP Negeri 2 Karanganyar Demak adalah rendahnya literasi koding dan pemahaman AI guru, serta belum optimalnya pemanfaatan fasilitas TIK dalam mendukung Kurikulum Merdeka. Kegiatan ini menawarkan solusi melalui pelatihan dan pendampingan partisipatif berbasis workshop, blended learning, serta implementasi pembelajaran berbasis proyek. Program dilaksanakan dalam tiga tahap: asesmen awal (pretest), peningkatan kapasitas melalui praktik terstruktur, dan pendampingan implementasi di kelas disertai evaluasi (posttest). Sebanyak 29 guru terlibat sebagai peserta utama dan 504 siswa sebagai penerima manfaat tidak langsung. Hasil evaluasi menunjukkan peningkatan rata-rata kompetensi guru sebesar 38% berdasarkan skor pretest–posttest. Seluruh peserta berhasil menghasilkan proyek koding sederhana, dan 82% guru menyusun modul ajar terintegrasi AI. Implementasi di kelas berdampak pada meningkatnya keterlibatan, kreativitas, dan kolaborasi siswa. Luaran kegiatan berupa modul digital, video tutorial pembelajaran, serta terbentuknya komunitas guru digital sebagai strategi keberlanjutan. Program ini menegaskan bahwa penguatan kapasitas guru merupakan kunci akselerasi transformasi digital sekolah yang adaptif dan berkelanjutan.
Aplikasi SI-APIK Sebagai Solusi Penyusunan Laporan Keuangan bagi UMKM Makanan dan Minuman Zuliyati, Zuliyati; Triyanto, Wiwit Agus; Rusdianto, Hutomo
Muria Jurnal Layanan Masyarakat Vol. 5 No. 1 (2023): Maret 2023
Publisher : Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/mjlm.v5i1.7712

Abstract

UMKM need to increase their productivity through technological improvements, so that the production process becomes more effective and efficient. Service partners are UMKM engaged in the food and beverage sector in Kudus Regency, namely Catering Bu Yati. The problem faced is the low management of the business and the unpreparedness of resources in the face of technological change so that the business is managed conventionally and traditionally. This service method uses technological guidance, application training and business management assistance. The output of this activity is the use of neat applications, increased income, as well as national seminars and publications through scientific articles in the form of proceedings or service journals.
Pembuatan Perpustakaan Digital Untuk Membangun Desa Cerdas di Era Modern Muzakkiy, Muhammad; Ridwan, Muhammad Eldo; Ilyas, Muhamad Dwi; Triyanto, Wiwit Agus
Muria Jurnal Layanan Masyarakat Vol. 7 No. 2 (2025): September 2025
Publisher : Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/mjlm.v7i2.15975

Abstract

This community service program aims to support digital transformation in rural areas through the establishment of a digital library as an initial step toward realizing a smart village. The program targets village officials, youth organizations, students, and the general public who have limited access to digital literacy resources. The implementation method adopts a collaborative and participatory approach, consisting of community needs assessment, joint program planning, installation of a web-based library system, content management training, and follow-up mentoring. The results show an increase in the community’s ability to access digital information, the formation of the Nganguk Digital Literacy Community as library managers, and a growing culture of reading among residents. This program demonstrates that the adoption of information technology at the village level can serve as a catalyst for inclusive and sustainable social transformation, driven by collaboration and a shared spirit of learning. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk mendukung transformasi digital desa melalui pembangunan perpustakaan digital sebagai langkah awal menuju desa cerdas (smart village). Sasaran kegiatan mencakup perangkat desa, pemuda Karang Taruna, pelajar, dan masyarakat umum yang masih memiliki keterbatasan dalam literasi digital. Metode pelaksanaan dilakukan dengan pendekatan kolaboratif partisipatif melalui tahapan identifikasi kebutuhan masyarakat, perencanaan program bersama, instalasi sistem perpustakaan berbasis web, pelatihan pengelolaan konten, serta evaluasi dan pendampingan lanjutan. Hasil kegiatan menunjukkan adanya peningkatan kemampuan masyarakat dalam mengakses informasi digital, terbentuknya Komunitas Literasi Digital Nganguk sebagai pengelola perpustakaan digital, serta meningkatnya minat baca dan kolaborasi warga dalam menjaga keberlanjutan layanan literasi desa. Program ini membuktikan bahwa penerapan teknologi informasi di tingkat desa dapat menjadi katalisator perubahan sosial yang inklusif dan berkelanjutan melalui semangat gotong royong dan pemberdayaan masyarakat lokal.
Computer Vision-Based Information System for Early Detection of Elderly Patient Falls using YOLOv12 Wiwit Agus Triyanto; Fernando Candra Yulianto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 5 (2025): October 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i5.6858

Abstract

Falls in elderly patients are a significant public health problem due to their high frequency and potential to cause serious injury or even death. Traditional fall detection systems often rely on wearable sensors, which can be intrusive and uncomfortable for long-term monitoring. This study proposes a non-intrusive computer vision-based information system for early fall detection using the YOLOv12 (You Only Look Once version 12) object detection model. The system integrates real-time video processing with a lightweight convolutional neural network architecture to detect falls in indoor care settings. A dataset of 10,793 annotated images, including simulated fall scenarios and daily activities, was used to train and validate the proposed model. The proposed system achieved a Mean Average Precision (mAP) of 90.60%, demonstrating robust performance under various lighting conditions and camera angles when compared with the YOLOv8, YOLOv11, and YOLO-NAS models. This study contributes to the development of intelligent healthcare systems that improve the safety and quality of life of elderly patients through proactive monitoring and rapid response capabilities.
Co-Authors - Universitas Muria Kudus, Muhammad Arifin - Universitas Muria Kudus, Nanik Susanti A.A. Ketut Agung Cahyawan W Agusta, Feriyan Alif Catur Murti, Alif Catur Amalia, Syifa Anastasya Latubessy Arif Setiawan Bila, Nanda Aulia Salsa Diana Laily Fithri Dimyati Utoyo Erlina Nofianti Fajar Nugraha Fakhriyyah, Anis Farid Noor Romadlon Ferianti, Lydya Ayu Fernando Candra Yulianto Fernando Candra Yulianto Fikri Hamdhan Fithri, Diana Layli H. Himawan Hanafi, Bachtiar Hartiningsih Hartiningsih Hasan Basri Hidayatullah, Muhamad Arzak Hutomo Rusdianto Hutomo Rusdianto, Hutomo Ilyas, Muhamad Dwi Iskandar Iskandar Jamhari Jamhari Jayanti Putri Purwaningrum Kevin Putra Adama Khoiruz Zahro Khusnul Himam, Muhammad Latifah Nur Ahyani Maula, Ahmad Inzul Mochammad Imron Awalludin Muhammad Arifin Muzakkiy, Muhammad Nandalisa Lisa Fa’ati Rahmawati Nanik Susanti Nia Zuliyana, Nia Nisa, Nila Akhidatul Noor Latifah Nurhaliza, Aulia Nurhaliza, Maulin Pambudi, Satrio Pramita, Alvina Gusti Pratomo Setiaji Pratomo Setiaji Pratomo Setiaji Putri, Rizka Ferbriliana R Rhoedy Setiawan Ramandani, Fitri Ratna Wijayani, Dianing Retno Tri Handayani Riawan Yudi Purwoko Ridwan, Muhammad Eldo Rizal Naufal Farras Arkanda Selamet , Ahmad Alif Candra Semit, Danial Setiawan, Faris Apri shefia, faridah ayu Slamet Kusmanto, Agung Sonia Shekha Anggriani Sri Septiana, Deyana Fitri Stiaji, Pratomo Suku Rahayu, Sri Intan Supriyono Supriyono Syafiul Muzid Sya’diah, Ary Kania Tamami, Ghufron Teguh Prasetyo Vincent Suhartono Wahyu Wibowo, Angga Wardhani, Indah Kusuma Widodo, Wahyu Kurniawan Ade Nur Yudie Irawan Yuniarsi Rahayu Zahra, Fatimah Az Zuliyati Zuliyati Zuliyati Zuliyati