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All Journal Proceedings of KNASTIK Semantik Jurnal Simetris Jurnal Sains dan Teknologi Prosiding SNATIF Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal Informatika dan Teknik Elektro Terapan E-Dimas: Jurnal Pengabdian kepada Masyarakat Sistemasi: Jurnal Sistem Informasi RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal SOLMA JURNAL ILMIAH INFORMATIKA Jurnal Pengabdian Masyarakat AbdiMas Jurnal DISPROTEK EDUMATIC: Jurnal Pendidikan Informatika Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Jurnal SITECH : Sistem Informasi dan Teknologi Journal of Information Systems and Informatics JATI (Jurnal Mahasiswa Teknik Informatika) Jatilima : Jurnal Multimedia Dan Teknologi Informasi Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Teknik Informatika (JUTIF) Indonesian Journal of Technology, Informatics and Science (IJTIS) Jurnal UNITEK Jurnal Pendidikan dan Teknologi Indonesia Bima Abdi: Jurnal Pengabdian Masyarakat Muria Jurnal Layanan Masyarakat Jurnal Locus Penelitian dan Pengabdian Abdi Cendekia: Jurnal Pengabdian Masyarakat Al-DYAS : Jurnal Inovasi dan Pengabdian kepada Masyarakat APPA : Jurnal Pengabdian Kepada Masyarakat Jurnal Sistem Informasi dan Manajemen International Journal of Artificial Intelligence and Science
Pratomo Setiaji
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.
KLASTERING LAGU BERBASIS AKTIVITAS PENDENGAR MENGGUNAKAN ALGORITMA SELF ORGANIZING MAP BERDASARKAN FITUR AUDIO SPOTIFY: ACTIVITY BASED SONG CLUSTERING USING THE SELF ORGANIZING MAP ALGORITHM BASED ON SPOTIFY AUDIO FEATURES Ridho Agiel Syahputra Siallagan; Muhammad Arifin; 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.v11i2.8017

Abstract

This study aims to cluster songs based on listener activities using the Self Organizing Map (SOM) algorithm with Spotify audio features. The dataset used in this study was obtained from Kaggle with a total of 20,718 songs. The variables used include Danceability, Energy, Valence, and Acousticness. The research stages consist of data pre-processing, searching for the best SOM parameters using Grid Search, SOM clustering, clustering evaluation using Silhouette Score and Davies-Bouldin Index (DBI), activity labeling, and implementation of a Streamlit-based web dashboard. The results show that the best SOM parameters were obtained using a 2x3 grid, 3000 iterations, sigma 1.0, and learning rate 0.5 with a Silhouette Score of 0.2348 and a DBI value of 1.2894. The Silhouette Score indicates that the separation between clusters is moderate but not perfect, which is reasonable because music data often have continuous and overlapping audio characteristics. Meanwhile, the DBI value indicates that the similarity between clusters is still relatively high, although the clusters can still be interpreted based on their dominant audio characteristics. The clustering process produced six activity clusters, namely Workout, Dancing, Gaming, Sleeping, Studying, and Hanging Out. The Gaming cluster became the largest cluster with 31.35% of the data, while the Workout cluster became the smallest with 9.53%. The web dashboard implementation successfully visualized clustering results, music activity distributions, cluster characteristics, and music recommendations based on listener activities. The study concludes that the SOM algorithm is capable of clustering songs based on Spotify audio feature similarities at a moderate level and can be implemented in activity-based music recommendation systems.
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.
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 - Supriyono Adi Nugroho Adisaputra, Rizky Aditiya, Natasya Aditya, Ahsanu 'Amala Adiyono, Soni Amaliyah, Fitriyah Amelia, Dwi Andy Prasetyo Utomo Anis Fakhriyyah Anteng Widodo Aprilia Damayanti Arif Setiawan Artiarno, Andrean Maulana Aynullutfihana, Nanda Azzahro, Erina Bagus Aris Setiawan Diana Laily Fithri Eko Darmanto Faby Melia Shanni Fajar Nugraha Fakhriyyah, Anis faridah ayu shefia Fatimah Az Zahra Ferianti, Lydya Ayu Feriyan Agusta Ilham Dwi Pranoto Irfan Santiko Kurniasari, Nita Laily Fithri, Diana Lisa Rachmawati Maula, Ahmad Inzul Moh Sendi Febriyanto Muhammad Arifin Muhammad Ulinnuha Ainun Noor Rachman Nanda Aulia Salsa Bila Nia Zuliyana, Nia Noor Latifah Nugroho, Mochamad Bagus Iqbal Adi Nurhaliza, Maulin Pambudi, Satrio Prayogo, Adhie Putri Kurnia Handayani Putri, Rizka Ferbriliana R Rhoedy Setiawan Rahmawati, Yulinda Ramadlan As'ari, Wahyu Rani Setiawaty Ridho Agiel Syahputra Siallagan Rohmah, Putri Anjilis Romadhon, Zainur Rosalva Denisia Yulia Yahya Semit, Danial Setiawan, Faris Apri Setyanto, Rangga Setyawan, Moh Dodi Suciningtyas - Suciningtyas - Suciningtyas Suciningtyas Suciningtyas Suciningtyas, Suciningtyas Suku Rahayu, Sri Intan Supriyono Supriyono Supriyono Supriyono Syafiul Muzid Tamaulina Br Sembiring Tengku Riza Zarzani N Wahyu Wibowo, Angga Widodo, Wahyu Kurniawan Ade Nur Wiwit Agus Triyanto Yoanas, Sahenda Yudie Irawan Yutia Nia Nesicha Zahra, Fatimah Az