Budi Susanto
Fakultas Teknologi Informasi, Universitas Kristen Duta Wacana

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Pengembangan Sistem Informasi Website KPU Daerah Istimewa Yogyakarta Argo Wibowo; Budi Susanto
Jurnal Teknik Informatika dan Sistem Informasi Vol 2 No 2 (2016): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v2i2.626

Abstract

Nowadays information is a very important data. Disclosure of information is necessary so the information that  provided could be easily accessed by the public. We need a system where people can get access to information easily, and the system administrator can also process the data with valid information. The provided data must be quickly available to the public. Komisi Pemilihan Umum Daerah Istimewa Yogyakarta (KPU DIY) aware of the importance of the availability of information for the public. KPU DIY already have a website to provide information for public, but they still have access constraints in processing the information independently. So it is necessary to make a change of website management. This research aims to assist KPU DIY in building their website so it can be better used by the public and KPU DIY can independently manage their website content. Results of the questionnaire show that users are more interested, have more same viewpoint, as well as more motivated using the new website. The new website is also considered to be more efficient and new, thus users can use the system properly. Keywords— Content, Independent, Information, System.
Perancangan Data Warehouse Perguruan Tinggi untuk Kinerja Penelitian dan Pengabdian kepada Masyarakat Agata Filiana; Andhika Galuh Prabawati; Maria Nila Anggia Rini; Gloria Virginia; Budi Susanto
Jurnal Teknik Informatika dan Sistem Informasi Vol 6 No 2 (2020): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v6i2.2557

Abstract

Accreditation is an evaluation to ensure the quality of a study program at higher education by completing two main documents for qualitative data and self-evaluation, namely Study Program Performance Report (LKPS) and Self Evaluation Report (LED) respectively. Data used for this process must be consistent and accurate, therefore a central repository is proposed. A data warehouse for the domain of Research and Community Service is built using the snowflake schema and a five-step approach methodology proposed for the educational data warehouse. The schema consisted of one fact table, ten dimension tables, and four bridge tables. Using a business intelligence tool, data from the data warehouse is queried and provided in a dashboard. The data warehouse successfully returned the qualitative data needed for LKPS. As for the evaluation data for LED, the data warehouse is able to fulfill seven out of ten requirements. The data for the remaining three are provided with a slightly different orientation to the requirement, this was due to the data source.
Semantic Web Seni Pertunjukan Indonesia Virginia, Gloria; Susanto, Budi; Proboyekti, Umi; Nugraha, Silvanus Satno
Jurnal Transformatika Vol. 19 No. 2 (2022): January 2022
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v19i2.4327

Abstract

Documentation of performance arts is an effort of cultural heritage to maintain the dignity and nobility of a nation.   Semantic web is a promising alternative that support the objective.   From the user side, the website will be more informative, while from the technical side, the website has ontology (a well-defined formal knowledge which is highly potential to be related to other websites). This research is an effort to developed a performance arts ontology using Methontology method.   To enrich the ontology, two ontologies were merged .   Linked-data principal was implemented by the use of dbpedia.   The ontology was evaluated based on 4 parameters (consistency, completeness, verification, and validation) and proved to be effective while implemented in a website.
Implementasi Transfer Learning Pada AlexNet Untuk Klasifikasi Motif Batik Yogyakarta Cahyaningtyas, Angela Gracia; Suwarno, Sri; Susanto, Budi
Jurnal Terapan Teknologi Informasi Vol 9 No 2 (2025): Jurnal Terapan Teknologi Informasi
Publisher : Fakultas Teknologi Informasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21460/jutei.2025.92.432

Abstract

This study discusses the implementation of Transfer Learning using the AlexNet architecture for classifying Yogyakarta Batik motifs, specifically Kawung, Parang, and Truntum. The dataset consists of 1,110 batik images that underwent preprocessing, augmentation, and data splitting. The research was conducted in two main experimental scenarios: applying Average Pooling and Max Pooling with a more complex classifier, and training the model without additional pooling layers using a simpler classifier. Furthermore, the experiments compared the model’s performance across different frozen layers and two optimizers (Adam and SGD). The results show that the best configuration was obtained using the SGD optimizer with Average Pooling and three frozen layers, achieving a test accuracy of 99.10%. In contrast, the Adam optimizer tended to produce lower and less stable performance. Experiments without pooling also reached high accuracy, but were less optimal than those with pooling. Therefore, this study demonstrates that the choice of pooling technique, classifier complexity, frozen layers, and optimizer plays a crucial role in achieving optimal performance of AlexNet for Batik classification.
Pemodelan Objek Budaya Keris Berbasis Semantic Web Susanto, Budi; Antarani, Mariaty Octavia; Virginia, Gloria; Proboyekti, Umi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 11 No 1: Februari 2024
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.20241116727

Abstract

Keris merupakan objek budaya Indonesia yang tergolong belati, yaitu senjata ‘bermata dua’ yang bilahnya ada yang lurus dan yang luk. Keris memiliki data deskripsi, meliputi bilah, warangka, jejeran, mendhak, pendhok, ganja, pesi, dhapur, tangguh, pamor, fungsi dan kegunaan, tradisi perlakuan dan penilaian keris. Begitu luasnya deskripsi informasi dari objek keris, menuntut tersedianya suatu infrastruktur yang mendukung representasi pengetahuan objek keris. Salah satu pendekatan yang dapat dilakukan adalah dengan kerangka semantic web dan direpresentasikan dalam bentuk ontologi. Metode yang digunakan pada penelitian ini adalah on-to-knowledge. Tahapan dari metode ini adalah feasibility, kick-off, refinement, evaluasi, serta aplikasi dan evolusi. Tetapi dalam membangun ontologi keris hanya digunakan empat tahapan, sedangkan tahapan ke lima tidak digunakan. Feasibility adalah tahap uji kelayakan penelitian. Kick-off adalah tahapan permulaan penelitian. Refinement adalah tahapan membangun graf yang sempurna dengan aplikasi Protégé. Evaluasi adalah tahapan evaluasi logika ontologi, dengan menggunakan reasoner hermit dan DL query pada Protégé. Penelitian ini melaporkan hasil dari tahapan yang dilakukan dalam implementasi metode on-to-knowledge. Hasil akhir penelitian ini adalah representasi pengetahuan objek budaya keris berbasis OWL. 
Klasifikasi motif Batik Keraton menggunakan arsitektur fine-tuning ResNet-50. Santosa, Stefaron Budhi; Chrismanto , Antonius Rachmat; Susanto, Budi
AITI Vol 23 No 1 (2026)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v23i1.46-60

Abstract

Batik is an Indonesian cultural heritage known for its diverse motifs; however, manual classification of these motifs remains a significant challenge. This study aims to develop a batik motif classification model using the ResNet50 architecture enhanced with data augmentation to improve model accuracy. The dataset consists of four batik motif classes: Kawung, Mega Mendung, Parang, and Truntum. In this research, the model was trained using fine-tuning on ResNet50, with additional CNN layers for feature extraction. The results demonstrated that the proposed model achieved a highest accuracy of 97.80% on test data and 96.80% on validation data, significantly outperforming methods without data augmentation. Researchers will also compare accuracy with other deep learning models for classifying Keraton Batik images. This study concludes that applying a fine-tuned ResNet50 model with additional CNN layers and data augmentation effectively classifies batik motifs, offering substantial potential to automate batik motif recognition and supports digital preservation and development of batik in the cultural and creative industries.