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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 Idealis : Indonesia Journal Information System 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 Abdi Cendekia: Jurnal Pengabdian Masyarakat Jurnal Hasil Pengabdian Masyarakat (JURIBMAS) Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer IC Tech: Majalah Ilmiah Ahsana: Jurnal Penelitian dan Pengabdian kepada Masyarakat Jurnal Sistem Informasi dan Manajemen Jurnal Pengabdian Masyarakat Tekno 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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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.
Sistem Informasi Manajemen Stok dan Produksi Pakaian Anak Berbasis Web pada UMKM Linda Collection Menggunakan Metode Safety Stock Iskandar Iskandar; Wiwit Agus Triyanto; Diana Layli Fithri; Muhammad Arifin
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.v8i1.15556

Abstract

UMKM Linda Collection, yang bergerak di bidang produksi pakaian anak, menghadapi kendala dalam pengelolaan stok dan proses produksi akibat pencatatan manual yang mengakibatkan ketidaksesuaian data dan risiko kekurangan maupun kelebihan stok. Penelitian ini bertujuan untuk mengembangkan sistem informasi manajemen stok dan produksi berbasis web yang dilengkapi dengan metode Safety Stock guna mengoptimalkan pengendalian persediaan. Metode pengembangan sistem menggunakan pendekatan System Development Life Cycle (SDLC) model waterfall, sedangkan perancangan sistem menggunakan Unified Modeling Language (UML). Sistem yang dibangun memungkinkan pengguna untuk memantau stok secara real-time, menghitung persediaan pengaman, mengelola transaksi produksi, dan menyusun laporan berbasis data. Hasil implementasi menunjukkan bahwa sistem ini mampu meningkatkan efisiensi proses bisnis, meminimalkan risiko stock-out maupun overstock, serta mendukung pengambilan keputusan yang lebih akurat di lingkungan UMKM.
Optimasi Hiperparameter Extreme Learning Machine Menggunakan Particle Swarm Optimization dan Indikator Teknikal untuk Peramalan Bitcoin Bagus Putra Sulung; Wiwit Agus Triyanto; Pratomo Setiaji
IDEALIS : InDonEsiA journaL Information System Vol. 9 No. 2 (2026): Jurnal IDEALIS Juli 2026
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/idealis.v9i2.3812

Abstract

Highly volatile price changes in crypto assets like Bitcoin complicate precise forecasting. The Extreme Learning Machine (ELM) artificial neural network approach offers high computational speed but is prone to performance instability due to random weight initialization and manual hyperparameter determination. To overcome this problem, this study proposes a hybrid PSO-ELM algorithm to automate the search for optimal parameters, namely the number of hidden neurons and the regression regularization penalty. Evaluated using a five-fold Walk-Forward Validation to prevent data leakage, the model comparatively tested five input feature scenarios based on technical indicators, which are mathematical calculations from historical prices to identify market patterns. Results demonstrate the hybrid PSO-ELM outperforms conventional static models, reducing average error (MAPE) by 18.67 percent. The cross-scenario comparison reveals that applying the Simple Moving Average technical indicator yields the best forecasting model, achieving a 26.65 percent error reduction and a final MAPE accuracy of 2.15 percent. The contribution of this research is providing empirical evidence that automatic parameter optimization combined with a random fluctuation filtering feature is proven to be more robust and accurate in responding to extreme volatility compared to the stacking of various complex derivative momentum indicators.
Analisis Sentimen Ulasan Mobile Legends: Bang Bang dalam Bahasa Indonesia Menggunakan Random Forest, KNN, TF-IDF, dan SMOTE Ahmad Alif Candra Selamet; Pratomo Setiaji; Wiwit Agus Triyanto
IDEALIS : InDonEsiA journaL Information System Vol. 9 No. 2 (2026): Jurnal IDEALIS Juli 2026
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/idealis.v9i2.3819

Abstract

Mobile Legends: Bang Bang (MLBB) is one of the most popular mobile games, generating a large number of user reviews on the Google Play Store. The large volume of reviews makes manual sentiment analysis impractical, requiring an automated machine learning approach. This study compares the performance of Random Forest and K-Nearest Neighbors (KNN) for classifying sentiment in Indonesian-language MLBB reviews. A total of 6,994 reviews were obtained through web scraping and preprocessing. The proposed framework includes text preprocessing, rating-based sentiment labeling, TF-IDF feature extraction, SMOTE-based class balancing, model training, and evaluation using Accuracy, Precision, Recall, F1-score, Macro-F1, Weighted-F1, and 5-fold cross-validation. Random Forest with SMOTE achieved the best performance, with an Accuracy of 84.0% and a Macro-F1 score of 80.8%, outperforming KNN with SMOTE, which achieved 73.8% Accuracy and 71.9% Macro-F1. The ablation study demonstrates that the effectiveness of SMOTE is model-dependent, improving Random Forest but degrading KNN performance. This study provides empirical evidence of SMOTE impact on different classifiers and employs cross-validation-based K selection to prevent test data leakage.
Classification of Aquatic Species in Cultivation Ponds via Image Processing and Machine Learning Arif Setiawan; Angga Wahyu Wibowo; Pratomo Setiaji; Wiwit Agus Triyanto; Muhammad Arifin
International Journal of Artificial Intelligence and Science Vol. 2 No. 1 (2025): March
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/IJAIS.v2.i1.9

Abstract

Fish cultivation is a vital economic activity for coastal communities, yet traditional farming methods often face challenges such as environmental instability, feeding inefficiencies, and water pollution. Effective monitoring of underwater environments is essential to improve fish quality and farming efficiency. A crucial part of this process is the accurate classification of fish and non-fish objects. This study proposes a method for underwater classification using morphometric feature extraction and machine learning techniques. The research process involves six main steps: (1) preparation of Region of Interest (ROI) detection data, (2) extraction of morphometric features—length (L) and width (W), (3) feature computation, (4) data partitioning for training and testing, (5) classification using Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbor (KNN), and (6) evaluation using a confusion matrix. Among all models tested, the Random Forest algorithm yielded the highest accuracy at 93%, with classification results showing True Positive = 349, False Positive = 28, True Negative = 223, and False Negative = 0. The findings highlight RF’s potential for enhancing automated fish monitoring in smart aquaculture systems.
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 Ahmad Alif Candra Selamet Alif Catur Murti Anastasya Latubessy Angga Wahyu Wibowo Anis Fakhriyyah Arif Setiawan Bachtiar Hanafi Bagus Putra Sulung Diana Laily Fithri Diana Layli Fithri Dimyati Utoyo Dzamhal Fairuchan 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 Fitri Ramandani H. Himawan Hartiningsih Hartiningsih Hasan Basri Hidayatullah, Muhamad Arzak Hutomo Rusdianto Ilyas, Muhamad Dwi Iskandar Iskandar Iskandar Iskandar Jamhari Jamhari Jayanti Putri Purwaningrum Kevin Putra Adama Khoiruz Zahro Khusnul Himam, Muhammad Latifah Nur Ahyani Lutfiyatul Malikhah 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 Nada Kusumawardani 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 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 Dhani Prayoga 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