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All Journal Telematika : Jurnal Informatika dan Teknologi Informasi Techno LPPM Register: Jurnal Ilmiah Teknologi Sistem Informasi SEINASI-KESI
Bambang Yuwono
Jurusan Teknik Informatika UPN “Veteran” Yogyakarta
Computer Science & IT Electrical & Electronics Engineering

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Analisis User Experience dan Redesign Website LBH Jakarta Dengan Metode Post Study System Usability Questionnaire Rahmawan, Fauzan Ahmat; Krisnanik, Erly; Yuwono, Bambang; Yulistiawan, Bambang Saras
PROSIDING SEINASI-KESI Vol 1, No 1 (2022): SEMINAR NASIONAL INFORMATIKA, SISTEM INFORMASI, DAN KEAMANAN SIBER
Publisher : Fakultas Ilmu Komputer UPN Veteran Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Lembaga Bantuan Hukum (LBH) Jakarta adalah lembaga yang pada awalnya dibentuk untuk memberikan bantuan hukum bagi orang-orang yang tidak mampu agar dapat memperjuangkan hak-hak mereka. Website menjadi salah satu media yang digunakan oleh LBH Jakarta untuk menyebarkan informasi terkait permasalahan yang sedang dikawal maupun berbagai informasi lainnya yang dapat membantu perjuangan LBH Jakarta. Pada penelitian ini peneliti mengambil populasi yaitu orang yang menggunakan atau mengakses website tersebut yang berada di Jakarta dan Depok. Setelah itu peneliti menggunakan Teknik stratified sampling untuk mengetahui atau menggelompokkan sample yang akan digunakan. Berdasarkan hasil penelitian yang telah dilakukan maka dapat disimpulkan bahwa nilai usability pada website LBH Jakarta berdasarkan perhitungan PSSUQ masih belum maksimal sehingga diperlukan perancangan ulang desain web tersebut.
PENGEMBANGAN SISTEM OFFICE AUTOMATION (SOA) MENGGUNAKAN E-MAILING SYSTEM ONLINE Florestiyanto, Mangaras Yanu; Himawan, Hidayatulah; Kaswidjanti, Wilis; Yuwono, Bambang
Journal TECHNO Vol. 1 No. 1 (2015)
Publisher : Universitas Pembangunan Nasional Veteran Yogayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/journal techno.v1i1.1508

Abstract

Sistem Office Automation (OA) dikembangkan oleh banyak institusi untuk menunjangpeningkatan kinerja sebuah institusi atau organisasi. Sistem ini bertujuan untuk mengalihkanfungsi sumber daya manual perkantoran yang banyak menggunakan tenaga manusia menujufungsi otomasi menggunakan peralatan mekanis e-mailing system secara online. Penelitian inibertujuan untuk mengembangkan e-mailing system/sistem informasi persuratan elektronisdengan melakukan pemantauan persuratan terutama pada permasalahan alur perjalanan suratdan disposisi serta tindak lanjutnya. Penelitian ini menggunakan metode Difine SystemSpecification yang dapat mengatasi kelemahan pada proses alur perjalanan surat dan disposisiserta tindak lanjutnya. Hasil penelitian ini mampu memproses alur perjalanan surat maupuntindak lanjut disposisi surat dengan pemantauan yang lebih baik.System Office Automation (OA) developed by many institutions in support of improving theperformance of an institution or organization. This system aims to divert resources function thatmany manual office using manpower towards automation functions using mechanical equipmente-mailing system online. This research aims to develop e-mailing system / electroniccorrespondence with the information system monitoring the flow of correspondence, especially onissues trips letters and dispositions as well as follow-ups. This study uses difine SystemSpecification which can address the weaknesses in the process flow and disposition journey letterand follow-up. Results of this study were able to process the flow of letters and follow-up tripdisposition letter with better monitoring.Keywords: e-Mailing System, Sistem Office Automation, disposition, Electronic letters
Effect of information gain on document classification using k-nearest neighbor Perwira, Rifki Indra; Yuwono, Bambang; Siswoyo, Risya Ines Putri; Liantoni, Febri; Himawan, Hidayatulah
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 8 No 1 (2022): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v8i1.2397

Abstract

State universities have a library as a facility to support students’ education and science, which contains various books, journals, and final assignments. An intelligent system for classifying documents is needed to ease library visitors in higher education as a form of service to students. The documents that are in the library are generally the result of research. Various complaints related to the imbalance of data texts and categories based on irrelevant document titles and words that have the ambiguity of meaning when searching for documents are the main reasons for the need for a classification system. This research uses k-Nearest Neighbor (k-NN) to categorize documents based on study interests with information gain features selection to handle unbalanced data and cosine similarity to measure the distance between test and training data. Based on the results of tests conducted with 276 training data, the highest results using the information gain selection feature using 80% training data and 20% test data produce an accuracy of 87.5% with a parameter value of k=5. The highest accuracy results of 92.9% are achieved without information gain feature selection, with the proportion of training data of 90% and 10% test data and parameters k=5, 7, and 9. This paper concludes that without information gain feature selection, the system has better accuracy than using the feature selection because every word in the document title is considered to have an essential role in forming the classification.
Robust Classification of Beef and Pork Images Using EfficientNet B0 Feature Extraction and Ensemble Learning with Visual Interpretation Taufiq Akbar, Ahmad; Saifullah, Shoffan; Prapcoyo, Hari; Yuwono, Bambang; Rustamaji, Heru Cahya
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 11 No 1 (2025): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v11i1.4045

Abstract

Distinguishing between beef and pork based on image appearance is a critical task in food authentication, but it remains challenging due to visual similarities in color and texture, especially under varying lighting and capture conditions. To address these challenges, we propose a robust classification framework that utilizes EfficientNet B0 as a deep feature extractor, combined with an ensemble of Regularized Linear Discriminant Analysis (RLDA), Support Vector Machine (SVM), and Random Forest (RF) classifiers using soft voting to enhance generalization performance. To improve interpretability, we incorporate Gradient-weighted Class Activation Mapping (Grad-CAM) to visualize classification decisions and validate that the model focuses on relevant regions of the meat, such as red-channel intensity and muscle structure. The proposed method was evaluated on a public dataset containing 400 images evenly split between beef and pork. It achieved a hold-out accuracy of 99.0% and a ROC-AUC of 0.995, outperforming individual learners and demonstrating strong resilience to limited data and variation in imaging conditions. By integrating efficient transfer learning, ensemble decision-making, and visual interpretability, this framework provides a powerful and transparent solution for binary meat classification. Future work will focus on fine-tuning the CNN backbone, applying GAN-based augmentation, and extending the approach to multiclass meat authentication tasks.
Human Skin Disease Detection using Convolutional Neural Network Method with Hyperparameter Tuning to Determine the Best Parameter Combination Aritonang, Riki Martua; Florestiyanto, Mangaras Yanu; Yuwono, Bambang
Telematika Vol 20 No 2 (2023): Edisi Juni 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i2.9161

Abstract

Purpose: Obtaining the best hyperparameter combination for optimization of the Convolutional Neural Network method, for classifying skin diseases.Design/methodology/approach: Using the CNN method with hyperparameter tuning in determining the best hyperparameter combination. System development is performed with the Python programming language.Findings/result: The best combination of hyperparameter tuning results is RMSprop optimizer, APL dropout value is 0.05, dropout is 0.5 , dense layer is 64, and produces an accuracy of 97,81%.Originality/value/state of the art: This study has differences in terms of the types of skin diseases classified, the architecture of the CNN model, the hyperparameters tested and the combination results obtained compared to previous studies.
Co-Authors Aritonang, Riki Martua Bambang Saras Yulistiawan Erly Krisnanik Febri Liantoni, Febri Heru Cahya Rustamaji Hidayatulah Himawan Mangaras Yanu F Prapcoyo, Hari Rahmawan, Fauzan Ahmat Rifki Indra Perwira Saifullah, Shoffan Siswoyo, Risya Ines Putri Taufiq Akbar, Ahmad Wilis Kaswidjanti