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All Journal KARMAPATI (Kumpulan Artikel Mahasiswa Pendidikan Teknik Informatika) ISSN: 2252-9063 Jurnal Pseudocode Journal of ICT Research and Applications SINTECH (Science and Information Technology) Journal WIDYA LAKSANA Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Indonesian Journal of Electrical Engineering and Computer Science Teknomatika (Jurnal Teknologi dan Informatika) INSERT: Information System and Emerging Technology Journal Jurnal Nasional Teknik Elektro dan Teknologi Informasi TEKNOSIA
I Md. Dendi Maysanjaya
Universitas Pendidikan Ganesha
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Transportation Other

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Journal : Jurnal Pseudocode

 1 
Perancangan Desain Website Cetak.id Menggunakan Metode User Centered Design (UCD) Ridwan, Muhammad; Maysanjaya, I Made Dendi; Pratiwi, Putu Yudia
Jurnal Pseudocode Vol 11 No 2 (2024): Volume 11 Nomor 2 September 2024
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/pseudocode.11.2.69-78

Abstract

Printing is currently a necessity for society in printing documents, printing banners, brochures, posters and so on. However, in its implementation there are problems such as customers canceling orders unilaterally, long queues at printing places, miscommunication between customers and service providers, and poor queue management. Based on these problems, there is a need for a system to manage the printing process. Before system development there needs to be a design to ensure that the system being built meets user needs. The research created a prototype design for ordering a print service called Cetak.id using the User Centered Design (UCD) method. At the testing stage, 2 tests were carried out by testing 5 aspects of usability. The first test was carried out based on the results of the design made, while the second test was carried out based on the results of the improved design from the first test. Based on the first and second tests for each group of users from service providers and service seekers, the results obtained were that the Learnability aspect had increased by 12% and 8%, respectively, the Efficiency aspect had increased by 0.043 goals/second and 0.023 goals/second, respectively. in the Memorability aspect there was an increase of 16.14% and 19.85% respectively, in the Error aspect there was a decrease of 0.04 and 0.09 respectively, while in the Satisfaction aspect using the Questionnaire for User Interaction Satisfaction (QUIS) each experienced an increase in indicator 1 of 0.64 and 0.72, indicator 2 is 0.7 and 0.3, indicator 3 is 0.83 and 0.73, indicator 4 is 0.87 and 0.7, and indicator 5 is 0.84 and 0.68. Keywords: Cetak.id, UCD, Usability Testing, QUIS
Perbandingan Kinerja Algoritma Naive Bayes dan K-Nearest Neighbor dalam Menganalisis Sentimen Pengguna Game Free Fire Sudiasta Putri, Nyoman Dinda Indira; Maysanjaya, I Made Dendi; Sunarya, I Made Gede
Jurnal Pseudocode Vol 12 No 2 (2025): Volume 12 Nomor 2 September 2025
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/pseudocode.12.2.53-59

Abstract

Free Fire is one of the most popular online games in Indonesia, yet it continues to receive a wide range of user reviews regarding gameplay experiences. These reviews reflect diverse user perceptions, including both praise and criticism, making sentiment analysis essential to understanding user satisfaction. This study aims to classify user sentiments toward Free Fire using a combined dataset collected from the Google Play Store and App Store, and to compare the performance of two text classification algorithms: Naive Bayes and K-Nearest Neighbor (KNN). The data were collected using web scraping techniques and manually labeled by expert validators. Text preprocessing involved cleansing, tokenizing, stopword removal, and stemming, followed by term weighting using the Term Frequency-Inverse Document Frequency (TF-IDF) method. The experimental results show that the Naive Bayes algorithm achieved the highest accuracy of 72.78%, while the KNN algorithm recorded a maximum accuracy of 45.91%. Based on these findings, Naive Bayes is proven to be more effective in classifying user sentiments related to Free Fire. The results of this study are expected to provide constructive insights for developers to improve the quality and user experience of the game.
Klasifikasi Severity Level Diabetic Macular Edema Berbasis ResNet-50 Maysanjaya, I Made Dendi; Pratiwi, Putu Yudia; Indradewi, I Gusti Ayu Agung Diatri
Jurnal Pseudocode Vol 13 No 1 (2026): Volume 13 Nomor 1 Februari 2026
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/pseudocode.13.1.9-13

Abstract

Diabetes is one of the most common diseases people suffer from today, and it can lead to complications such as blindness, heart disease, and kidney failure. The condition of blindness caused by this disease is known as diabetic retinopathy (DR). An ophthalmologist will use a fundus camera to examine the retina, looking for several clinical features, such as microaneurysms (MA), hemorrhages (HM), cotton-wool spots (CWS), and exudates. Based on these clinical symptoms, clinicians then determined the patient's level of diabetic macular edema (DME) severity. Although several studies have applied CNN-based architectures for diabetic retinopathy detection, limited attention has been given to the impact of dataset imbalance handling on DME severity classification, particularly using ResNet-50. This study highlights the significant impact of extensive data augmentation on classification performance in imbalanced DME datasets. Evaluate performance using the accuracy, precision, and recall metrics. We used the IDRiD dataset, which consists of 516 images split into a training set of 413 and a test set of 103. IDRiD divides the dataset into three classes, namely normal, moderate DME, and severe DME. In the preprocessing stage, we enhanced contrast using CLAHE and resized the images to 224x224 pixels. To address the imbalance, we applied 11 data augmentation methods. We experimented by comparing the performance of two models: one with and one without dataset augmentation. Based on the test results, the best performance was obtained with the model that included dataset augmentation, achieving an accuracy of 0.5961, a precision of 0.63, and a recall of 0.61, while the baseline model (without dataset augmentation) gained 0.4553, 0.36, and 0.34 for the accuracy, precision, and recall, respectively.
Co-Authors Ali Djamhuri Anak Agung Istri, Callysta Athalia Cahyadi, Kadek Wawan Dessy Seri Wahyuni Dharma Putra, I Gede Wira Driya, Putu Dhanu E. Elsa Herdiana Murhandarwati Gede Aditra Pradnyana Gede Saindra Santyadiputra, Gede Saindra Gede Surya Mahendra Hanung Adi Nugroho I Gede Agus Krisna Perdana I Gede Mahendra Darmawiguna I Gede Riyan Ardi Darmawan I Gede Rusdy Mahayana Putra I Gusti Ayu Agung Diatri Indradewi I Made Angga Darma Putra I Made Ardwi Pradnyana I Made Edy Listartha I Made Gede Sunarya Irfanda, Achmad Kadek Suwis Satria Atmaja Komang Jepri Kusuma Jaya Komang Pramayasa Kusumadewi, Ni Made Ayu Mita Made Satria Wibawa Made Windu Antara Kesiman Muhammad Ridwan Nababan, Elma Regina Ni Putu, Ana Rainita Noor Akhmad Setiawan Putu Hendra Suputra Putu Setiari, Gusti Ayu Putu Yudia Pratiwi Saraya, Fashan Sudiasta Putri, Nyoman Dinda Indira Walhidayah, Irfan Widhia K.Z Oktoeberza Yasa, I Gede Agus Sukariana