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All Journal Information Technology and Telematics Dinamik Jurnal Ilmiah Dinamika Teknik Elkom: Jurnal Elektronika dan Komputer Scientific Journal of Informatics Jurnal Ilmiah Giga Proceeding SENDI_U Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA CogITo Smart Journal JURNAL INSTEK (Informatika Sains dan Teknologi) Jurnal Teknik Informatika UNIKA Santo Thomas INTECOMS: Journal of Information Technology and Computer Science Jurnal Teknologi Sistem Informasi dan Aplikasi IKRA-ITH ABDIMAS JURIKOM (Jurnal Riset Komputer) Jurnal Informasi dan Komputer JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Building of Informatics, Technology and Science Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi JURNAL MAHAJANA INFORMASI Jurnal Abdimas Mandiri Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) JATI (Jurnal Mahasiswa Teknik Informatika) Journal of Computer System and Informatics (JoSYC) TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Pengabdian kepada Masyarakat J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Jurnal Pengabdian Masyarakat Intimas (Jurnal INTIMAS): Inovasi Teknologi Informasi Dan Komputer Untuk Masyarakat Jurnal Pengabdian Pada Masyarakat Jurnal Kabar Masyarakat SmartComp Fundamentum: Jurnal Pengabdian Multidisiplin Servis : Jurnal Pengabdian dan Layanan kepada Masyarakat Andan Jejama: Indonesian Journal Of Community Engagement Jurnal Teknik Informatika dan Teknologi Informasi
Veronica Lusiana
Universitas Stikubank (UNISBANK) Semarang
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Perancangan Desain Jaringan Menggunakan UISP Design Center bagi Tenaga Pendidik dan Kependidikan Bidang TIK Kabupaten Semarang Jefri Alfa Razak; Veronica Lusiana; Sariyun Naja Anwar; Mohammad Riza Radyanto; Endang Lestariningsih
FUNDAMENTUM : Jurnal Pengabdian Multidisiplin Vol. 3 No. 4 (2025): November : FUNDAMENTUM : Jurnal Pengabdian Multidisiplin
Publisher : Asosiasi Peneliti Dan Pengajar Ilmu Sosial Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62383/fundamentum.v3i4.1235

Abstract

This community service project was motivated by the limited technical competency among TIK (Information and Communication Technology) personnel in Semarang Regency schools in designing optimal computer network infrastructure. This situation resulted in slow, unstable, and poorly managed digital learning services within these schools. The main objective of this activity was to enhance the competency of 24 TIK personnel to enable them to design efficient, secure, and professional networks tailored to their schools' needs. The solution provided was hands-on training using the UISP Design Center, a web-based network design platform. The training was conducted face-to-face over two days and covered hierarchical topology, VLAN and QoS management, and practical design simulation. Measurement results demonstrated a significant increase in the participants' cognitive competency, evidenced by the average score rising from 45.2% on the pre-test to 82.5% on the post-test. Participants successfully applied these concepts to produce accurate designs for network topology, logical segmentation, and Bills of Materials (BOM). The high level of participant satisfaction confirms the effectiveness of this training in closing the skill gap. This activity successfully enhanced the network planning capability of the partner schools.
Klasifikasi Tingkat Kematangan Buah Pisang Raja Menggunakan Metode CNN Berbasis Android Febriana, Trissa Noor Aulia; Lusiana, Veronica
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 7 No. 1 (2024): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jtsi.v7i1.37790

Abstract

Raja banana (Musa paradisiaca L.) is a banana cultivar commonly enjoyed in Indonesia. In addition to being consumed as a fresh fruit, Pisang Raja is often processed into various banana-based foods, such as banana chips, fried bananas, banana fritters, and other banana products. For farmers, post-harvest sorting of Pisang Raja requires a significant amount of time and effort. Therefore, a system is needed to assist farmers and the community in general to determine the ripeness level of Pisang Raja fruit more efficiently and clearly. The classification process of Pisang Raja fruit ripeness levels is carried out through precision calculations in a system, using a dataset consisting of 300 images covering 3 types of Pisang Raja ripeness levels. The classification process for the ripeness levels of Pisang Raja fruit utilizes the Convolutional Neural Network (CNN) method with the TensorFlow module for training and testing data. Based on experimental results, the accuracy in classifying the ripeness levels of Pisang Raja fruit reaches a value of 95%."
Perbandingan Proses Klasterisasi Data Menggunakan K-Means Clustering dan Agglomerative Hierarchical Clustering Hartono, Budi; Lusiana, Veronica; Al Amin, Imam Husni
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8766

Abstract

Large amounts of data require good processing and analysis. One of the data analysis techniques is data clustering, which is grouping data into several groups or data clusters based on the similarity of data characteristics. This study observed the clustering process and cluster results using the K-Means and Agglomerative Hierarchical Clustering (AHC) algorithms or methods. Clustering was carried out using three different amounts of data, namely 10 (A10 data), 30 (B30 data), and 60 (C60 data), with choices of two, three, and four clusters. The experimental results obtained were that the A10 data cluster was the same, but the C60 data was different. Both methods provide the same cluster results, namely in the number of cluster members and their data numbers; conversely, different cluster results are obtained if there are differences in the number of cluster members. The B30 data cluster results for three clusters are the same, while for two and four clusters they are different. The results of this study are expected to provide a better understanding of the data clustering process and can be a basis for selecting a more appropriate clustering method.
Klasifikasi Dokumen Publik Berbasis NLP: Otomatisasi Proses Informasi Menuju Keterbukaan Data yang Adaptif dan Transparan Retnowati Retnowati; Veronica Lusiana; Eko Nur Wahyudi
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 2 (2025): Agustus: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i2.5693

Abstract

In the era of public information disclosure, digital documents have become strategic assets in supporting transparent, accountable, and participatory governance. Effective management of these documents is essential to ensure that public information services are responsive and accessible. However, document classification tasks carried out by Public Information and Documentation Officers (PPID) still rely heavily on manual processes, which are time-consuming, inefficient, and prone to human error. To address this challenge, this study aims to develop an intelligent classification model for public documents using Artificial Intelligence (AI) and Natural Language Processing (NLP), integrated within the Data Lifecycle Management (DLM) framework. The proposed solution was designed using the Design Science Research (DSR) methodology and implemented through Agile development practices. Evaluation was conducted in a simulated laboratory environment that mirrors real-world PPID operations.The developed model leverages transformer-based architectures, particularly BERT (Bidirectional Encoder Representations from Transformers), and is compared against traditional algorithms such as Naive Bayes and K-Nearest Neighbors (KNN). Experimental results show that the BERT model achieves superior performance, with an accuracy of 89%, precision of 0.88, recall of 0.89, and F1-score of 0.88. These metrics confirm that Transformer-based models are highly effective for classifying public documents into categories of information accessibility: available at all times, periodic, immediate, and exempted from disclosure.This research highlights the potential of AI-powered classification to streamline public information services, reduce workload, and enhance compliance with information disclosure laws. The findings support national development priorities such as RPJMN 2025 by contributing to digital transformation in the public sector. The study also provides a replicable framework for other government agencies aiming to implement adaptive and transparent document classification systems.
Klasifikasi Tingkat Kematangan Buah Kersen Menggunakan Citra HSI Dengan Metode K-Nearest Neighbor (KNN) Pratama, Krisna Aditya; Atmaja, Wahyu Priyo; Lusiana, Veronica
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 11, No 1 (2022): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v11i1.3171

Abstract

Buah kersen sering dianggap buah yang tidak bermanfaat, ternyata buah kecil ini memiliki banyak manfaat tersembunyi. Dalam artikel ini penulis ingin mengidentifikasi dan mengklasifikasi kematangan buah kersen menggunakan citra HSI dengan metode KNN. Tujuan penelitian ini adalah memberikan hasil berupa tingkat kematangan buah kersen menggunakan aplikasi matlab. Dengan menggunakan metode K-Nearest Neighbor (KNN) untuk menentukan tingkat kematangan buah kersen. Data training yang digunakan berjumlah 18 data, terdiri dari 6 data matang, 6 data setengah matang dan 6 data mentah. Kata kunci : Matlab, KNN, HSI
SISTEM MONITORING DAN ESTIMASI LAMA WAKTU KUNJUNGAN PELANGGAN MENGGUNAKAN ALGORITMA YOLO BERBASIS DEEP LEARNING (STUDI KASUS: JARAK COFFEE & EATERY) Melly Anggraini; Veronica Lusiana
INTECOMS: Journal of Information Technology and Computer Science Vol. 9 No. 2 (2026): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/1jt60018

Abstract

Perkembangan teknologi Artificial Intelligence (AI) dan Computer Vision telah memberikan dampak signifikan terhadap sistem monitoring. Penelitian ini mengembangkan sistem monitoring dan estimasi lama waktu kunjungan pelanggan menggunakan algoritma YOLO berbasis deep learning di Jarak Coffee & Eatery. Permasalahan utama adalah kurangnya efektivitas metode monitoring manual dalam mengelola data kunjungan pelanggan yang menyebabkan kesulitan mendapatkan informasi akurat mengenai durasi kunjungan. Tujuan penelitian ini adalah mengimplementasikan algoritma YOLO untuk mendeteksi dan melacak pelanggan secara real-time serta mengembangkan sistem yang mampu menghitung durasi kunjungan secara otomatis. Menggunakan metode Waterfall dengan tahapan analisis kebutuhan, perancangan sistem, implementasi menggunakan YOLOv11 dan BoT-SORT, serta pengujian. Hasil model YOLOv11 mencapai performa sangat baik dengan mAP@0.5 sebesar 96,5%, precision 93,4%, dan recall 93,4%, serta mampu menghitung durasi kunjungan dengan threshold minimal 30 detik untuk memfilter false positive. Sistem menghasilkan output visualisasi real-time dan log CSV yang dapat digunakan untuk analisis durasi kunjungan dan optimalisasi operasional. Kata Kunci: Algoritma YOLO, Deep Learning, Computer Vision, Object Detection, Durasi Kunjungan Pelanggan
SISTEM DETEKSI KETERSEDIAAN KURSI BERBASIS DEEP LEARNING MENGGUNAKAN ALGORITMA YOLO UNTUK MONITORING KETERSEDIAAN TEMPAT DUDUK (STUDI KASUS: JARAK COFFEE & EATERY) Afian Ibnu Yuwono; Veronica Lusiana
INTECOMS: Journal of Information Technology and Computer Science Vol. 9 No. 2 (2026): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/vd3jrx22

Abstract

Perkembangan teknologi Artificial Intelligence (AI) khususnya dalam bidang computer vision memungkinkan pengembangan sistem untuk memantau ketersediaan tempat duduk secara otomatis. Penelitian ini bertujuan mengembangkan sistem deteksi ketersediaan kursi berbasis deep learning menggunakan algoritma YOLOv11 untuk memonitor ketersediaan tempat duduk di JARAK COFFEE & EATERY. Permasalahan yang diangkat adalah belum adanya sistem pemantauan real-time yang dapat memberikan informasi akurat mengenai kursi kosong, yang dapat berdampak pada penurunan efisiensi pelayanan dan kepuasan pelanggan. Tujuan penelitian adalah membuat model deteksi yang dapat mengenali objek "kursi" dan "orang", mengimplementasikan logika analisis hubungan spasial berbasis Intersection over Union (IoU) untuk menentukan status keterisian kursi, serta membangun prototipe sistem yang memproses dataset video rekaman cctv. Metode pengembangan sistem menggunakan pendekatan Waterfall, dimulai dari pengumpulan data video, pelabelan dataset, pelatihan model YOLOv11, hingga implementasi dan pengujian. Hasil pengujian menunjukkan model yang dikembangkan memiliki kinerja yang baik dengan nilai mean Average Precision (mAP@0.5) sebesar 95,7%. Sistem berhasil mendeteksi objek dan menghitung kursi tersedia secara real-time dengan memanfaatkan logika IoU, meskipun performa dapat dipengaruhi oleh faktor seperti pencahayaan dan oklusi antar objek.  Kata Kunci: algoritma YOLO, Ketersediaan Kursi, Object Detection, Intersection Over Union
KLASIFIKASI JENIS BATIK SEMARANGAN MENGGUNAKAN METODE CONVOLUTION NEURAL NETWORK (CNN) Afifah, Wulan Nur; Lusiana, Veronica
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 1 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i1.5873

Abstract

Batik merupakan jenis kain yang memiliki motif khas dan dibuat dengan teknik khusus. Batik Semarangan memiliki motif yang unik, terinspirasi oleh aktivitas budaya, tempat-tempat bersejaran di kota Semarang, serta tokoh legendaris Semarang. Beberapa motif batik yang terkenal dari Semarang antara lain adalah Motif Warak Ngendok, Motif Tugu Muda, serta motif yang menampilkan ikon-ikon kota Semarang seperti Lawang Sewu, Blekok Srondol, Jembatan Mberok, dan Asem. Dalam penelitian ini, digunakan 78 citra batik Semarangan yang terbagi menjadi tiga motif yaitu Motif Landmark, Motif Naturalis Flora, dan Motif Naturalis Fauna. Data citra batik ini dibagi menjadi dua set, yaitu 66 citra batik untuk data latih dan 12 citra batik untuk data uji. Metode yang digunakan untuk mengklasifikasi batik Semarang adalah metode Convolutional Neural Network (CNN), sebuah jenis metode jaringan saraf tiruan yang sangat efektif dalam pengenalan pola visual. CNN dipilih karena kemampuannya yang tinggi dalam mengidentifikasi dan mengklasifikasikan berbagai fitur visual dari gambar batik. Hasil dari model CNN ini menunjukkan tingkat akurasi sebesar 75% dalam mengklasifikasikan jenis-jenis batik Semarangan. Penelitian ini menunjukkan potensi besar dalam menggunakan teknologi modern untuk melestarikan dan mempromosikan warisan budaya melalui identifikasi motif digital batik yang kompleks, memungkinkan batik Semarang dikenal dengan lebih mudah di pasar global serta mendukung upaya pelestarian dan pengembangan produk budaya lokal.
Pipeline Klasifikasi Kelayakan Bantuan Siswa berbasis Data Administrasi Sekolah dengan Interpretasi Fitur Saefurrohman Saefurrohman; Novita Mariana; Budi Hartono; Rina Candra Noor Santi; Veronica Lusiana
Dinamik Vol 31 No 2 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i2.10653

Abstract

This study develops a classification pipeline for assessing student aid eligibility using administrative school data with feature interpretation to support accountable preliminary review. The dataset consists of 1,242 anonymized student records from one senior high school in Ambarawa, Central Java. Prior to modeling, direct identity attributes and label-leakage-prone features, such as eligibility reasons and aid card numbers, were removed. Five classification algorithms were evaluated using an 80:20 stratified train-test split and five-fold cross-validation. Random Forest was selected as the main model because it provided a balance between predictive performance and feature interpretability, achieving an accuracy of 0.6988, an F1-score of 0.6939, and a ROC-AUC of 0.7736. Gradient Boosting achieved a higher ROC-AUC of 0.7867; however, its performance gain was relatively small, while the feature importance results from Random Forest were easier to communicate to non-technical school staff in an implementation context. Feature interpretation indicates that father’s income was the most influential predictor, with an importance score of 0.1894, whereas distance and transportation mode provided secondary accessibility signals. The proposed pipeline is not intended as an automated decision-making mechanism, but as a tool for generating a priority list of candidates requiring human verification and local retraining before being adapted by other schools.
Analisis Metode Elbow SSE, Silhouette Score, dan Jaccard Stability dalam Pemilihan Jumlah Klaster Data yang Optimal Budi Hartono; Veronica Lusiana
TIN: Terapan Informatika Nusantara Vol 6 No 8 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i8.9271

Abstract

This study discusses the selection of the optimal number of clusters (K) in the K-Means algorithm by utilizing a combination of the Elbow method with the SSE (Sum of Squared Errors) and Silhouette Score metrics. The main problem is that the optimal K value is unknown. Choosing K that is too small can combine different patterns (under-clustering), and choosing K that is too large can break the same pattern into several clusters (over-clustering). The experiment used two-dimensional test data with variations in the number of data 20, 30, 40, 50, and 60. K-Means was run in the range of K = 2 to K = 8, then the SSE value was calculated to form the Elbow curve and the average Silhouette value to evaluate the quality of the cluster. This study added a cluster stability test using the Jaccard Stability value. The highest Silhouette value of 0.4619 was obtained from the data 20 for K = 2. The highest Jaccard stability value of 0.9507 was obtained from 60 data sets for K = 2. The experimental results show that the Elbow method, Silhouette value, and Jaccard stability can be used complementarily in determining the optimal K. In some test data, both metrics produce consistent K recommendations, while in certain test data, Elbow can provide several candidates, so that validation using the Silhouette value is needed to select the optimal K.
Co-Authors Adi Nugroho, Hermawan Afian Ibnu Yuwono Aisyah Nur Ramadhayani Alfian, Riyan Antono Adhi ARIFUDIN, WAHYU Aryasatya, Rakryan Atmaja, Wahyu Priyo budi hartono Budi Hartono Budi Hartono Budi Hartono Deva Ega Marinda Dewi Handayani Untari Ningsih Dian Kristiawan Nugroho Dimas Wahyu Edy Supriyanto Edy Winarno Eka Ardhianto Eko Nur Wahyudi Endang Lestariningsih Febriana, Trissa Noor Aulia Felix Andreas Sutanto Hari Murti Hartono, Budi Hartono, Budi Hidayat, Agung Rizqi Ilham Ramadhan, Hafidh Imam Husni Al Amin Iskandar Zulkarnein Jefri Alfa Razak Krisna Aditya Pratama Kristianto, Teguh Ma’sum, Muhammad Ali Melly Anggraini Miftah Khul Janah Muhammad Muslimin Ningsih, Dewi Handayani .U Novita Mariana Oka Rosalin, Agnes Pratama, Krisna Aditya R. Soelistijadi Radyanto, Mohammad Riza Ramadhani, Insan Rara Sri Artati Redjeki Ratnaduhita, Aurellia Razak, Jeffri Alfa Retnowati Retnowati Rina Candra Noor Santi Saefurrohman Saefurrohman Sariyun Naja Anwar Setiaji, Isnan Sri Eniyati Sri Wahyuni Sumarno, Davis Danendra Tahta Febri P, Gilar Taufan Maynard Prananda Sancaka1 Teguh Khristianto Tistantia Pusdita Umi Kholifah WAHYU PRIYO ATMAJA Wahyu Priyo Atmaja Widiyanto Tri Handoko Widyawati, Lanjar Wismarini, Th Dwiati Wiwien Hadikurniawati Wulan Nur Afifah, Wulan Nur Zulkarnein, Iskandar