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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) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab 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 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 Abdi Cendekia: Jurnal Pengabdian Masyarakat Jurnal Hasil Pengabdian Masyarakat (JURIBMAS) 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
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IMPLEMENTASI SEGMENTASI PELANGGAN MENGGUNAKAN ALGORITMA K-MEANS DENGAN MODEL RFM (STUDI KASUS PANDHAWA SEJAHTERA DROPSHIP) Yutia Nia Nesicha; Wiwit Agus Triyanto; Pratomo Setiaji
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.7924

Abstract

Pandhawa Sejahtera Dropship is a business in the dropship service sector. This business faces challenges in developing targeted marketing strategies due to the absence of transaction data-based customer segmentation. This study aims to implement customer segmentation using the Recency, Frequency, Monetary (RFM) method and compare the K-Means and Fuzzy C-Means algorithms in grouping customers based on transaction data. The transaction data used amounted to 2,376 records over a one-year period. The methods applied include data preprocessing, RFM value calculation, Min-Max normalization, determination of the optimal number of clusters using the Elbow Method and Silhouette Score, and cluster quality evaluation using the Davies-Bouldin Index (DBI). The results showed that the optimal number of clusters is 3 (k=3) with a Silhouette Score of 0.6242. The three clusters formed are: Cluster 0 (Champions) with 362 customers (25%) characterized by low recency (45.2 days), high frequency (3.8 times), and high monetary (Rp 1,256,780); Cluster 1 (Regular) with 724 customers (50%) characterized by moderate recency (215.3 days), low frequency (1.2 times), and moderate monetary (Rp 345,670); and Cluster 2 (At Risk) with 362 customers (25%) characterized by high recency (345.6 days), very low frequency (1.0 times), and low monetary (Rp 124,890). Based on the method comparison, the K-Means algorithm produced a DBI value of 0.77 and a Silhouette Score of 0.54, better than Fuzzy C-Means with a DBI value of 1.05 and a Silhouette Score of 0.39. Thus, the K-Means algorithm is declared as the best method for customer segmentation on Pandhawa Sejahtera Dropship transaction data. These segmentation results can serve as a basis for developing more targeted and efficient marketing strategies.
Implementation of Image Processing in Scanning KTP Data using Optical Character Recognition (OCR) Bachtiar Hanafi; Pratomo Stiaji; Wiwit Agus Triyanto
SISTEMASI Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

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.
Sentiment Analysis of Money Lover App Reviews using Random Forest and Naïve Bayes Nanda Aulia Salsa Bila; Wiwit Agus Triyanto; Pratomo Setiaji
SISTEMASI Vol 15, No 2 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

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 faridah ayu shefia; Pratomo Setiaji; Wiwit Agus Triyanto
SISTEMASI Vol 15, No 2 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

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.
Image Forensics Analysis of the Authenticity of Digital Payment Evidence using the K-Nearest Neighbor Algorithm Feriyan Agusta; Pratomo Setiaji; Wiwit Agus Triyanto
SISTEMASI Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

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.
Aplikasi SI-APIK Sebagai Solusi Penyusunan Laporan Keuangan bagi UMKM Makanan dan Minuman Zuliyati Zuliyati; Wiwit Agus Triyanto; Hutomo Rusdianto
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 Muhammad Muzakkiy; Muhammad Eldo Ridwan; Muhamad Dwi Ilyas; Wiwit Agus Triyanto
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.
Komparasi Metode You Only Look Once Versi 8 (Yolov8) Untuk Sistem Deteksi Gender Berdasarkan Citra Wajah Anis Fakhriyyah; Wiwit Agus Triyanto; Pratomo Setiaji
Jurnal SITECH : Sistem Informasi dan Teknologi Vol. 8 No. 1 (2025): JURNAL SITECH VOLUME 8 NO 1 TAHUN 2025
Publisher : Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/sitech.v8i1.15340

Abstract

Perkembangan teknologi dan kemudahan akses data mendorong peningkatan kebutuhan sistem berbasis kecerdasan buatan, terutama dalam bidang pengolahan citra wajah. Data wajah menjadi salah satu jenis data personal yang mudah diperoleh dan banyak digunakan dalam penelitian, terutama dalam identifikasi gender secara otomatis. Identifikasi ini bersifat efisien, non-invasif, cocok diterapkan pada sistem digital untuk meningkatkan keamanan dan pengalaman pengguna. Salah satu metode yang efektif untuk deteksi wajah dan klasifikasi gender secara real-time adalah YOLO (You Only Look Once). Pada penelitian ini nantinya menggunakan metode You Only Look Once versi 8 (YOLOv8) untuk pendeteksian objek dengan mengimplementasikan tiga sub versi didalamnya yaitu nano (YOLOv8n), small (YOLOv8s), medium (YOLOv8m) untuk mendeteksi dan mengklasifikasikan gender berdasarkan citra wajah. Setiap subversi memiliki karakteristik tersendiri dalam hal kecepatan, akurasi, dan kebutuhan komputasi. Penelitian ini bertujuan untuk membandingkan performa ketiganya untuk memperoleh model deteksi gender yang paling optimal. Dengan pendekatan ini diharapkan dapat mendukung pengembangan sistem cerdas yang mampu mengidentifikasi jenis kelamin secara otomatis dan akurat.
Klasifikasi Ekspresi Emosi Wajah Bahagia dan Tidak Bahagia Menggunakan Arsitektur Mobilenetv2 Berbasis Deep Learning Fatimah Az Zahra; Pratomo Setiaji; Wiwit Agus Triyanto
Jurnal SITECH : Sistem Informasi dan Teknologi Vol. 8 No. 1 (2025): JURNAL SITECH VOLUME 8 NO 1 TAHUN 2025
Publisher : Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/sitech.v8i1.15546

Abstract

Penelitian ini bertujuan membangun sistem klasifikasi ekspresi wajah dua kelas (happy dan not happy) menggunakan arsitektur Convolutional Neuran Network (CNN) berbasis MobileNetV2 yang ringan dan efisien. Dataset yang digunakan merupakan gabungan dari FER2013, Pinterest, dan Roboflow, yang telah melalui proses augmentasi dan preprocessing. Model dilatih menggunakan metode 5-Fold Cross Validation untuk memperoleh evaluasi yang lebih stabil dan menyeluruh. Hasil penelitian menunjukkan bahwa model mencapai rata-rata akurasi validasi sebesar 81,49%, dengan nilai precision, recall, dan F1-score yang seimbang. Model kemudian diimplementasikan dalam sistem web berbasis Flask, memungkinkan pengguna mengunggah gambar dan memperoleh hasil klasifikasi dalam bentuk label teks. Pengujian menggunakan gambar wajah pribadi menunjukkan bahwa sistem memiliki kemampuan generalisasi yang baik pada data nyata di luar data latih. Penelitian ini menunjukkan bahwa arsitektur MobileNetV2 dapat diandalkan untuk tugas klasifikasi ekspresi wajah dua kelas berbasis gambar statis dan berpotensi dikembangkan lebih lanjut untuk aplikasi praktik di bidang pendidikan, interaksi manusia-komputer, dan layanan publik.
Co-Authors - Universitas Muria Kudus, Muhammad Arifin - Universitas Muria Kudus, Nanik Susanti A.A. Ketut Agung Cahyawan W Ahmad Adam Farokhi Ahmad Alif Candra Selamet Alif Catur Murti, Alif Catur Anastasya Latubessy Anis Fakhriyyah Arif Setiawan Bachtiar Hanafi Diana Laily Fithri Dimyati Utoyo Erlina Nofianti Faby Melia Shanni Fajar Nugraha Fakhriyyah, Anis Farid Noor Romadlon faridah ayu shefia Fatimah Az Zahra Ferianti, Lydya Ayu Feriyan Agusta Fernando Candra Yulianto Fernando Candra Yulianto Fikri Hamdhan Fithri, Diana Layli H. Himawan Hartiningsih Hartiningsih Hasan Basri Hidayatullah, Muhamad Arzak Hutomo Rusdianto 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 Muhamad Dwi Ilyas Muhammad Arifin Muhammad Eldo Ridwan Muhammad Fahrino Haykal Febrian Muhammad Khasan Luthfi Muhammad Muzakkiy Muzakkiy, Muhammad Nanda Aulia Salsa Bila Nandalisa Lisa Fa’ati Rahmawati Nia Zuliyana, Nia Nisa, Nila Akhidatul Noor Latifah Nurhaliza, Aulia Nurhaliza, Maulin Pambudi, Satrio Pramita, Alvina Gusti Pratomo Setiaji Pratomo Setiaji Pratomo Setiaji Pratomo Stiaji Putri, Rizka Ferbriliana R Rhoedy Setiawan Ratna Wijayani, Dianing Rendy Afandy Retno Tri Handayani Riawan Yudi Purwoko Ridwan, Muhammad Eldo Rizal Naufal Farras Arkanda Rosalva Denisia Yulia Yahya Semit, Danial Setiawan, Faris Apri Slamet Kusmanto, Agung Sonia Shekha Anggriani Sri Septiana, Deyana Fitri Suku Rahayu, Sri Intan Supriyono Supriyono Syafiul Muzid Sya’diah, Ary Kania Syifa Amalia Tamami, Ghufron Teguh Prasetyo Vincent Suhartono Wahyu Wibowo, Angga Wardhani, Indah Kusuma Widiya Amelia Putri Widodo, Wahyu Kurniawan Ade Nur Yudie Irawan Yuniarsi Rahayu Yutia Nia Nesicha Zahra, Fatimah Az Zuliyati Zuliyati Zuliyati Zuliyati