cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Mesran
Contact Email
mesran.skom.mkom@gmail.com
Phone
+6282161108110
Journal Mail Official
jurnal.josyc@gmail.com
Editorial Address
Jalan Sisingamangaraja No. 338, Medan, Sumatera Utara
Location
Kota medan,
Sumatera utara
INDONESIA
Journal of Computer System and Informatics (JoSYC)
Published by Forum Kerjasama Pendidikan TInggi
ISSN : 27147150     EISSN : 27148912     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Industrial & Manufacturing Engineering
Journal of Computer System and Informatics (JoSYC) covers the whole spectrum of Artificial Inteligent, Computer System, Informatics Technique which includes, but is not limited to: Soft Computing, Distributed Intelligent Systems, Database Management and Information Retrieval, Evolutionary computation and DNA/cellular/molecular computing, Fault detection, Green and Renewable Energy Systems, Human Interface, Human-Computer Interaction, Human Information Processing Hybrid and Distributed Algorithms, High Performance Computing, Information storage, Security, integrity, privacy and trust, Image and Speech Signal Processing, Knowledge Based Systems, Knowledge Networks, Multimedia and Applications, Networked Control Systems, Natural Language Processing Pattern Classification, Speech recognition and synthesis, Robotic Intelligence, Robustness Analysis, Social Intelligence, Ubiquitous, Grid and high performance computing, Virtual Reality in Engineering Applications Web and mobile Intelligence, Big Data
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 457 Documents
 42   43   44   45   46 
Analisis Sentimen Timnas Indonesia pada Data Tidak Seimbang Menggunakan Perbandingan Naïve Bayes dan IndoBERT Maharani Navila Salsa Bela; Putry Wahyu Setyaningsih
Journal of Computer System and Informatics (JoSYC) Vol 7 No 3 (2026): May 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v7i3.9601

Abstract

Social media platform X is widely used by the public to express opinions on the performance of the Indonesian National Team, especially in the fourth round of the 2026 World Cup Qualifiers. In this phase, the Indonesian National Team suffered two consecutive defeats, namely 2–3 to Saudi Arabia and 0–1 to Iraq, which triggered an increase in emotional responses and public criticism on social media. This condition makes sentiment analysis important to understand public perception more objectively. This study aims to analyze the sentiment of social media users X and compare the performance of the Naïve Bayes and IndoBERT models in imbalanced data conditions. The research data amounted to 1,268 tweets that were processed through a pre-processing stage, then automatically labeled using a lexicon-based approach as an initial labeling into two classes, namely positive and negative. The dataset was divided into training data and test data with a ratio of 70:30. The data distribution shows the dominance of negative sentiment at 84.1% and positive at 15.9%. Classification was performed using TF-IDF-based Naïve Bayes and IndoBERT-base-p1, with data imbalance management using random oversampling and class weighting. The results show that Naïve Bayes without treatment achieved 84% accuracy but failed to recognize the positive class. After oversampling, the positive class recall increased to 45%. IndoBERT achieved 85% accuracy, with positive recall increasing from 35% to 43% and the positive class F1-score increasing by 47% after applying class weighting. Despite the relatively high accuracy, the evaluation shows the importance of considering performance on minority classes. Overall, IndoBERT with class weighting provided more balanced results. However, the use of lexicon-based automatic labeling is a limitation of this study.
IoT-Based Smart Dorm Key System Using Voice Password and Fingerprint Authentication for Dormitory Access Control DTM Faiq Zariaqwila; Rita Purnamasari; Efri Suhartono
Journal of Computer System and Informatics (JoSYC) Vol 7 No 3 (2026): May 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v7i3.9681

Abstract

The Internet of Things (IoT) has become an important technology in the development of modern access control systems. This study presents the design and implementation of an IoT-based Smart Dorm Key system employing a two-factor authentication mechanism using voice password verification and fingerprint authentication to improve dormitory access security. The proposed system is designed for Telkom University dormitories and enables real-time access control, monitoring, and access log synchronization through internet connectivity. The system utilizes an ESP32 microcontroller as the main controller integrated with an AS608 fingerprint sensor, solenoid door lock, no-touch sensor, LCD display, and buzzer to support authentication and door operation. Voice password verification is performed through a mobile application as the first authentication layer before fingerprint verification is conducted as the second layer. Experimental results show that the fingerprint sensor achieved 100% accuracy under normal conditions, while its performance decreased under wet and dirty finger conditions with accuracy values of 8% and 11%, respectively. The no-touch sensor operated reliably with a maximum detection distance of 10.5 cm. The results indicate that the proposed system is capable of implementing layered authentication and real-time monitoring, although further improvement is required to enhance performance under non-ideal conditions.
Klasifikasi Tingkat Kematangan Roasting Biji Kopi Berbasis Ekstraksi Fitur Warna HSV Menggunakan Metode Naïve Bayes Gusti Ayu Devani Zelvia; I Made Gede Sunarya; I Gde Made Hanura; Ida Bagus Gede Putra Kenaka
Journal of Computer System and Informatics (JoSYC) Vol 7 No 3 (2026): May 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v7i3.9777

Abstract

Manual determination of coffee bean roasting levels through visual inspection has limitations, particularly in terms of subjectivity and human error. To address this, the present study develops an automatic classification system based on digital images to identify the roasting maturity level of coffee beans. The system uses six-dimensional HSV (Hue, Saturation, Value) color features — specifically the mean and standard deviation of each channel classified using the Naive Bayes Classifier (NBC) algorithm. Primary data (145 images) were collected only for the medium and dark classes, as these are the most common roasting levels in the local industry and were underrepresented in the secondary dataset from Kaggle (1,600 images), covering four classes: green, light, medium, and dark. A pixel normalization step was applied prior to HSV conversion to mitigate sensor bias between the primary (smartphone) and secondary (Kaggle) data sources. The images underwent size normalization to 224×224 pixels, then split into training data (75%) and test data (25%). Performance evaluation was carried out using a confusion matrix and metrics such as accuracy, precision, recall, and F1-score. The classification results show that the model achieves an accuracy of 83.48% (compared to 79.82% using only mean features), with the best performance in the light class (F1-score: 0.97) and medium class (F1-score: 0.90). The dark class had the lowest performance (recall: 0.61) due to spectral similarity with adjacent classes. These findings establish a lightweight baseline (inference time: 2.3 ms/image, model size: <1 KB) suitable for embedded and IoT implementations in small-scale coffee processing industries.
Klasifikasi Jenis Kelamin Berbasis Citra Mata Menggunakan Vision Transformer ViT dengan Strategi Discriminative Fine-Tuning Gde Made Hanura; Putu Hendra Suputra; Ni Putu Novita Puspa Dewi
Journal of Computer System and Informatics (JoSYC) Vol 7 No 3 (2026): May 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v7i3.9778

Abstract

Face-based biometric identification systems have significant limitations when a subject’s face is covered, whether due to mask usage after the COVID-19 pandemic or face veils for cultural and religious reasons. This creates real security gaps, as evidenced by the gender-disguise infiltration incident at Masjid Jannatul Firdaus in Makassar. In such situations, the eyes remain the only consistently exposed biometric feature. This study proposes the application of Vision Transformer (ViT-B/16) pretrained on ImageNet-21K with a progressive fine-tuning strategy based on the discriminative learning rate principle to classify gender from eye images. The Female and Male Eyes dataset from Kaggle consists of 11,525 eye images divided into training (64%), validation (16%), and testing (20%) sets. Experiments were conducted in two series: Series B tested variations in the number of unfrozen transformer blocks (0–6), and Series C tested discriminative learning rate ratios between the classifier and encoder (5:1, 10:1, 3:1). The optimal configuration with 6 unfrozen blocks and a 3:1 ratio achieved 95.70% accuracy, 97.67% precision, 92.69% recall, and 0.9569 weighted F1-score, surpassing MobileNet (93.90%) and K-Nearest Neighbor (68.81%). These results indicate that ViT with discriminative fine-tuning is effective for gender classification from eye images and has potential for biometric security applications.
Perbandingan Naïve Bayes, SVM, dan XGBoost dengan SMOTE untuk Analisis Sentimen Ulasan DANA dan OVO Eko Wardianto; Putry Wahyu Setyaningsih
Journal of Computer System and Informatics (JoSYC) Vol 7 No 3 (2026): May 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v7i3.10002

Abstract

User reviews of the DANA and OVO digital wallet applications provide valuable insights into user satisfaction and service-related issues. However, the large volume of review data and the imbalanced distribution of sentiment classes pose significant challenges for automated sentiment analysis. This study aims to compare the performance of Naïve Bayes, Support Vector Machine (SVM), and XGBoost for sentiment classification of user reviews, while evaluating the effectiveness of the Synthetic Minority Over-sampling Technique (SMOTE) in handling class imbalance for each algorithm. The dataset consists of 4,987 user reviews classified into three sentiment categories: positive, negative, and neutral. The research process includes text preprocessing, TF-IDF feature extraction, model training, and performance evaluation using a confusion matrix and standard classification metrics. The experimental results indicate that SMOTE significantly improves the performance of the Naïve Bayes classifier, whereas it provides little to no performance improvement for SVM and XGBoost; therefore, the final SVM and XGBoost models are trained using the original dataset. Among the evaluated algorithms, SVM achieves the best overall performance with an accuracy of 85.14% and a macro F1-score of 77.60%, followed by XGBoost with an accuracy of 82.32%. Furthermore, application-specific analysis reveals that the model achieves higher accuracy on DANA reviews (87.55%) than on OVO reviews (82.13%), suggesting differences in linguistic characteristics and sentiment distributions across the two platforms. This study provides a systematic comparison of machine learning algorithms for Indonesian digital wallet sentiment analysis and demonstrates that selective application of data balancing techniques can improve classification performance without necessarily benefiting all algorithms.
Deteksi Cyberbullying pada Komentar Instagram Berbahasa Indonesia Menggunakan Fine-Tuning IndoBERT dengan Analisis Kesalahan Model Dewi Irma Afriayanti; Budi Tjahjono
Journal of Computer System and Informatics (JoSYC) Vol 7 No 4 (2026): August 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v7i4.9330

Abstract

Cyberbullying on social media is difficult to detect automatically because comments may contain informal language, sarcasm, implicit body shaming, and meanings that depend on context. This study evaluates the ability of IndoBERT to detect cyberbullying in Indonesian-language Instagram comments and analyzes the model’s classification errors. The dataset consists of 1,050 comments collected from public Instagram posts and manually labeled into two classes: cyberbullying and non-cyberbullying. The IndoBERT-base-uncased model was fine-tuned and evaluated using a confusion matrix, accuracy, precision, recall, and F1-score. The experimental results show an accuracy of 84.76% and an F1-score of 0.8462. For the cyberbullying class, precision reached 0.9294, while recall was 0.7524, indicating that a portion of bullying comments were still missed as false negatives. Error analysis shows that difficult cases were mainly associated with sarcasm, implicit body shaming, informal language, and ambiguous comments. The contributions of this study are an empirical evaluation of IndoBERT on Indonesian Instagram comments, a clear identification of the research gap between conventional machine-learning approaches and the need for contextual Indonesian-language modeling, and an error analysis that provides practical evidence for improving AI-based content moderation. The findings indicate that IndoBERT has promising performance, but larger datasets, conversational context, and pragmatic language modeling are still required.
Analisis Sentimen Ulasan Pengguna Halodoc Menggunakan TextCNN pada Dataset Tidak Seimbang Berbasis NLP Fitriyani Fitriyani; Budi Tjahjono
Journal of Computer System and Informatics (JoSYC) Vol 7 No 4 (2026): August 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v7i4.9331

Abstract

This study analyzes user sentiment in Halodoc application reviews using a TextCNN architecture on an Indonesian-language dataset with extreme class imbalance. The dataset consists of 2,000 reviews collected from the Google Play Store and manually labeled into three classes: positive, negative, and neutral. The class distribution comprises 1,800 positive reviews (90%), 175 negative reviews (8.75%), and 25 neutral reviews (1.25%). To reduce bias toward the majority class, the TextCNN model was trained using class weighting and evaluated using accuracy, precision, recall, F1-score, and a confusion matrix. The model achieved an accuracy of 93.75% and a weighted F1-score of 0.94, while the macro F1-score was only 0.57. The recall for the neutral class was 0%, indicating that the model was unable to recognize the minority class effectively. The contributions of this study are threefold: an empirical evaluation of TextCNN on Indonesian Halodoc reviews with extreme class imbalance; an analysis of the effect of class distribution using class-level metrics and error analysis; and an identification of the practical implications of the classification results for monitoring digital healthcare service quality. These findings demonstrate that high accuracy alone is insufficient to represent model performance on imbalanced datasets; therefore, strategies such as oversampling, data augmentation, or hybrid approaches should be considered in future research.

Page 46 of 46 | Total Record : 457

 42   43   44   45   46 

Filter by Year

2019 2026


Reset
Filter By Issues
All Issue Vol 7 No 4 (2026): August 2026 Vol 7 No 3 (2026): May 2026 Vol 7 No 2 (2026): February 2026 Vol 7 No 1 (2025): November 2025 Vol 6 No 4 (2025): August 2025 Vol 6 No 3 (2025): May 2025 Vol 6 No 2 (2025): February 2025 Vol 6 No 1 (2024): November 2024 Vol 5 No 4 (2024): August 2024 Vol 5 No 3 (2024): May 2024 Vol 5 No 2 (2024): February 2024 Vol 5 No 1 (2023): November 2023 Vol 4 No 4 (2023): August 2023 Vol 4 No 3 (2023): May 2023 Vol 4 No 2 (2023): Februari 2023 Vol 4 No 1 (2022): November 2022 Vol 3 No 4 (2022): August 2022 Vol 3 No 3 (2022): May 2022 Vol 3 No 2 (2022): Februari 2022 Vol 3 No 1 (2021): November 2021 Vol 2 No 4 (2021): Agustus 2021 Vol 2 No 3 (2021): Mei 2021 Vol 2 No 2 (2021): February 2021 Vol 2 No 1 (2020): November 2020 Vol 1 No 4 (2020): August 2020 Vol 1 No 3 (2020): Journal of Computer System and Informatics (JoSYC) - May 2020 Vol 1 No 2 (2020): Journal of Computer System and Informatics (JoSYC) - Februari 2020 Vol 1 No 1 (2019): Journal of Computer System and Informatics (JoSYC) - November 2019 More Issue