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INDONESIA
SKANIKA: Sistem Komputer dan Teknik Informatika
Published by Universitas Budi Luhur Jakarta
ISSN : -     EISSN : 27214788     DOI : 10.36080
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Engineering
SKANIKA: Sistem Komputer dan Teknik Informatika adalah media publikasi online hasil penelitian yang diterbitkan oleh Program Studi Sistem komputer dan Teknik Informatika, Fakultas Teknologi Informasi, Universitas Budi Luhur. Scope atau Topik Jurnal: Kriptografi, Steganografi, Sistem Pakar / Artificial Intelligence , Sistem Penunjang Keputusan, Bioinformatika, Kecerdasan Komputasional, Semantics Web dan Ontologies, Data Mining,Text Mining,Natural Language Processing, Pengelolaan Citra Digital, Otomasi Berbasis Sensor, Wireless Sensor Network, Network Management dan Maintenance, Sistem Operasi, Sosial Network Analysis, Security, Augmented Reality, Game Development, Virtual Reality, Webservice / API, Internet of Things (IoT)
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 356 Documents
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PENERAPAN YOLOV13 UNTUK DETEKSI ALFABET BAHASA ISYARAT INDONESIA SECARA REAL-TIME Muhammad Noval Rais; Sawali Wahyu
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 9 No 2 (2026): Jurnal SKANIKA Juli 2026
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v9i2.3872

Abstract

Indonesian Sign Language (BISINDO) is the primary communication medium for the deaf community. However, public understanding remains limited, and recognizing similar gestures and adapting to dynamic lighting pose significant challenges. This study applies the YOLOv13-Nano architecture with HyperACE and FullPAD modules for static BISINDO alphabet detection (A–Z). The model was trained on 5,835 images from the Mendeley Dataset (70:15:15 split) using MediaPipe Hands auto-labeling, and implemented on a FastAPI dashboard with debouncing and temporal auto-spacing logics. Offline testing shows YOLOv13-Nano achieved 89.22% classification accuracy, 83.02% mAP50, 90.63% Precision, 89.62% Recall, 89.74% F1-Score, and 23.62 ms inference time. This model outperforms K-Nearest Neighbors (KNN) baselines, where KNN (Full Image) achieved 9.90% accuracy and KNN (Hand Cropped) achieved 11.23% accuracy. Limited real-time testing on the test subject yielded a Word Accuracy of 87.4% under normal conditions, 88.3% under extreme low-light, and 90.4% alphabet accuracy against complex backgrounds. These results demonstrate the potential of YOLOv13-Nano to support a responsive real-time BISINDO translation system under various real-world environmental conditions.
DETEKSI EKSPRESI AFEKTIF MAHASISWA PADA SISTEM PEMBELAJARAN PEMROGRAMAN MENGGUNAKAN YOLOV13N Verell Hermawan; Sawali Wahyu
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 9 No 2 (2026): Jurnal SKANIKA Juli 2026
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v9i2.3895

Abstract

Programming learning requires repeated problem-solving processes that may influence students’ affective states. However, these changes are difficult to continuously observe through manual observation or self-reporting. This study develops a YOLOv13n-based Facial Emotion Recognition system to detect four affective states—engagement, confusion, frustration, and boredom—in real time and integrate the detected results with learning activities in a web-based application. Detection is performed while students read learning modules and complete assessments through multiple-choice quizzes or interactive coding exercises. The model was fine-tuned using a combined dataset of 1,660 images, consisting of 953 images from Roboflow Universe and 707 hard samples, with stratified training, validation, and testing splits of 80:10:10. Evaluation on 173 test images achieved a precision of 0.994, recall of 0.982, F1-score of 0.988, mAP@0.5 of 0.994, and mAP@0.5:0.95 of 0.982, with an inference time of 6.2 ms per image. The application of confidence filtering, sliding window, and majority voting reduced label changes by 77.20%, improving temporal stability. Black-box testing across 27 scenarios confirmed that all application functions operated as designed. The system provides descriptive indicators rather than psychological diagnosis, demonstrating potential for monitoring affective expression patterns during programming learning.
IMPLEMENTASI METODE NER STATISTIK BERBASIS WEB UNTUK EKSTRAKSI ENTITAS PADA BERITA BENCANA ALAM TVRI Muhammad Iqbal Shiddiq; Indra Indra
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 9 No 2 (2026): Jurnal SKANIKA Juli 2026
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v9i2.3897

Abstract

Disaster news published on TVRI's portal is typically presented in unstructured text containing important details such as disaster type, location, time, and related organizations, making manual information extraction inefficient. This study implements a web-based Statistical Named Entity Recognition (NER) using the Naive Bayes algorithm to extract disaster, location, time, date, and organization entities from 200 TVRI disaster news articles. Training data was created through expert-validated manual labeling using the BIO (Begin-Inside-Outside) scheme, producing 40,667 labeled tokens converted into CurrentWord, Token Type, CurrentTag, Bef1Tag, and Class features, with Laplace Smoothing applied to address zero probability. Testing was conducted before and after applying Random Undersampling to assess its effect on class balance. Before Random Undersampling, the model achieved 87.91% accuracy, 55.73% precision, 58.66% recall, and 56.63% F1-score, after application, accuracy dropped to 83.17% and recall to 55.50%, while precision rose to 62.74% and F1-score to 57.45%. These results show that Random Undersampling improved the precision-recall balance despite lower accuracy, proving that Naive Bayes-based Statistical NER can automatically extract entities, though the relatively small F1-score improvement (about 0.82 percentage points) indicates a need for further testing.
KLASIFIKASI KEPUASAN PELANGGAN APLIKASI TVRI KLIK BERDASARKAN ANALISIS SENTIMENT ULASAN MENGGUNAKAN ALGORITMA SVM DAN NAIVE BAYES Tabri Eko Miyardi; Yohanes Setiawan
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 9 No 2 (2026): Jurnal SKANIKA Juli 2026
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v9i2.3930

Abstract

User reviews on the Google Play Store are unstructured data that is valuable for evaluating the quality of public broadcasting streaming application services. This study compares the performance of Support Vector Machine (SVM) and Multinomial Naive Bayes in classifying sentiment from 2,006 user reviews of the TVRI Klik application collected through web scraping. The sentiment labels are determined based on star ratings, namely four and five as positive, three as neutral, and one and two as negative. Labeling results in an unbalanced distribution of classes with negative classes dominating the data. Pre-processing includes case folding, tokenization, stopword removal, and stemming using Sastrawi, followed by TF-IDF feature extraction. SMOTE is applied to the training data to balance all three sentiment classes. With an 80:20 data sharing scheme, SVM obtained the highest test data accuracy of 95.13%, while Naive Bayes reached 86.59%. Wordcloud shows that negative reviews are dominated by technical complaints, while positive reviews highlight the ease of access to national broadcasts. The findings show that SVM with SMOTE is more stable and accurate for monitoring public sentiment towards streaming applications of government broadcasting institutions.
SEGMENTASI KATEGORI PRODUK BERDASARKAN RESPONSIVITAS PROMOSI PADA BISNIS FASHION RETAIL MENGGUNAKAN AGGLOMERATIVE HIERARCHICAL CLUSTERING Wahiddin Ishak; Rusdah Rusdah
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 9 No 2 (2026): Jurnal SKANIKA Juli 2026
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v9i2.3926

Abstract

Fashion retail companies often repeat identical promotion types without proportional sales gains because no empirical mapping shows which product categories respond to which promotional mechanisms. This study segments product categories by promotional responsiveness using Agglomerative Hierarchical Clustering (AHC) with Ward's Linkage. The data comprise 103.9 million sales transaction rows from 2023 at PT Matahari Department Store, Tbk., which, after cleaning, department-level aggregation, label encoding, and Min-Max normalization, yielded 6,089 records covering 790 departments, 14 promotion names, and six promotion periods specific to the Indonesian market. We determined the optimal number of clusters using the Silhouette Coefficient, Davies-Bouldin Index, and SSE reduction, and cross-validated it against the merge-distance jump in the dendrogram. Ward's Linkage consistently outperformed Average Linkage across all tested values of k, with the best configuration at four clusters (Silhouette 0.474; DBI 0.049). The four clusters exhibit sharply different responsiveness: one cluster of 317 departments contributes 99.11% of promotion-related units sold with a PromoHitRate of 0.986, while another cluster of 316 departments records no promotional sales at all. The near-uniform distribution of promotion types and periods across clusters indicates that the performance gap stems not from exposure bias but from each department's internal effectiveness. These segmentation results provide a basis for more targeted promotional budget allocation and more efficient inventory management.
PREDIKSI PERMINTAAN OBAT MENGGUNAKAN TIME SERIES DAN MACHINE LEARNING UNTUK SISTEM PERINGATAN DINI KETERSEDIAAN OBAT Muhamad Satriadi; Rusdah Rusdah
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 9 No 2 (2026): Jurnal SKANIKA Juli 2026
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v9i2.3928

Abstract

Effective pharmaceutical inventory management is crucial for sustaining healthcare services in hospitals. Inaccurate demand planning can lead to stockouts or overstock situations, resulting in budget inefficiencies of 25–40%. This study aims to develop and compare pharmaceutical demand forecasting models and implement the results into the SmartMed application, which integrates Safety Stock, Reorder Point (ROP), and an early warning system at RSUD Kota Bogor. The study employs the Knowledge Discovery in Databases methodology using data from January 2020 to December 2025. The study compared five forecasting methods—ARIMA, Exponential Smoothing, Prophet, Random Forest, and XGBoost—using MAE, RMSE, and MAPE metrics. Evaluation results indicate that Random Forest was the best model for four medication types, XGBoost for three, ARIMA for two, and Exponential Smoothing for one, while Prophet did not emerge as the best model for any. For Amlodipine 10 mg, Random Forest yielded an MAE of 715.94, an RMSE of 845.07, and a MAPE of 6.72% (rated "Very Good"). The best-performing models were subsequently incorporated into SmartMed to support inventory monitoring via Safety Stock, ROP, and the early warning system. This system is expected to help management make proactive decisions about pharmaceutical inventory.

Page 36 of 36 | Total Record : 356

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