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All Journal Jiko (Jurnal Informatika dan komputer) SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Informatika: Jurnal Pengembangan IT
Sawali Wahyu
Esa Unggul University
Computer Science & IT Control & Systems Engineering Engineering

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IMPLEMENTATION BLOCKCHAIN IN MOBILE APPLICATIONS SEMINAR ON E-CERTIFICATE VERIFICATION USING SMART CONTRACTS Sahri Ramadan; Sawali Wahyu; Budi Tjahjono; Riya Widayanti
JIKO (Jurnal Informatika dan Komputer) Vol 9 No 1 (2026)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v9i1.11356

Abstract

The increasing adoption of electronic certificates in academic and professional environments raises critical challenges related to authenticity, data integrity, and verification reliability. Conventional certificate management systems commonly rely on centralized architectures and manual validation procedures, which are vulnerable to manipulation, duplication, and single points of failure (SPoF). This study proposes a blockchain-based electronic certificate verification system implemented on a private Hyperledger Fabric network using smart contracts. The system records certificate verification metadata on a distributed ledger to ensure integrity and traceability while maintaining storage efficiency. Smart contracts automate the issuance and validation lifecycle, enabling transparent and tamper-resistant certificate management. The verification process is conducted by comparing document authentication data with records stored on the blockchain. Experimental evaluation demonstrates that the proposed system can accurately identify document alterations and consistently distinguish between valid and invalid certificates. The results indicate that the integration of blockchain and smart contracts as an active validation mechanism enhances transparency, reduces dependence on centralized authorities, and improves trust in mobile-based digital credential systems. Therefore, the proposed approach provides a secure and reliable framework for electronic certificate verification in academic environments.
MOBILE APPLICATION FOR IDENTIFICATION OF EMPLOYEE STRESS PATTERN USING DEEP LEARNING APPROACH Sawali Wahyu; Silvia Ratna Juwita; Ryan Putra Laksana; Lista Meria
JIKO (Jurnal Informatika dan Komputer) Vol 9 No 1 (2026)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v9i1.11527

Abstract

Employee stress has become a critical issue affecting organizational productivity, well-being, and performance, especially in dynamic work environments. This study proposes an integrated mobile-based stress prediction and recommendation system that combines Long Short-Term Memory (LSTM) and Neural Collaborative Filtering (NCF) to identify employee stress levels and provide personalized improvement recommendations. Experimental evaluation using 1000 datasets was used to test the LSTM and NCF models. The LSTM model was used to predict stress levels due to its ability to capture complex patterns in multidimensional data, while NCF was used to generate personalized recommendations based on collaborative patterns. The results showed that the LSTM model achieved superior classification performance with 98% accuracy and the recommendation evaluation showed good convergence performance, with a Hit Ratio reaching 0.92 and a Normalized Discounted Cumulative Gain (NDCG) reaching 0.89, indicating high recommendation relevance. Furthermore, the system usability evaluation using the System Usability Scale (SUS) involving 30 respondents resulted in an average score of 80.81, which is categorized as excellent usability. The integration of deep learning and collaborative filtering into a mobile platform provides an effective and intelligent solution for employee stress prediction and intervention. This study contributes to the development of an adaptive occupational health monitoring system and demonstrates the potential of AI-based mobile applications in supporting mental health management in the workplace.
ANALISIS SENTIMEN OPINI PUBLIK TERHADAP KASUS KORUPSI BAHAN BAKAR MINYAK OPLOSAN PT PERTAMINA DENGAN HYBRID MODEL DEEP LEARNING Muhammad Ramdhan Awali; Sawali Wahyu; Anik Hanifatul Azizah
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 8 No 2 (2025): Jurnal SKANIKA Juli 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v8i2.3525

Abstract

The corruption case related to oplosan fuel oil involving PT Pertamina has become a national issue that has drawn diverse responses from the public. Sentiment analysis of public opinion on social media can provide important insights for the government and stakeholders in understanding public perceptions of the case. This study aims to analyze public opinion sentiment regarding the alleged fuel adulteration corruption case involving PT Pertamina, using a hybrid deep learning model approach. Data were collected from the social media platform Twitter (X) between February 24 and March 19, 2025, resulting in 12,365 tweets after preprocessing. The study implements four model architectures: IndoBERT, CNN, LSTM, and a hybrid IndoBERT-CNN-LSTM model. Evaluation results show that IndoBERT achieved the highest accuracy at 90%, followed by CNN (86%), hybrid (84%), and LSTM with the lowest accuracy (69%). In addition, the K-Fold cross-validation scheme produced more stable model evaluation results than the Hold-Out method. Based on sentiment distribution analysis, public opinion was dominated by negative sentiment at 72%, while positive and neutral sentiments each accounted for 16%. These findings indicate that the public tends to respond negatively to the Pertamina fuel corruption issue. This study contributes to the understanding of public opinion on social media through a deep learning-based sentiment analysis approach and highlights the importance of selecting appropriate model architectures and validation strategies in the task of classifying Indonesian-language text.
Implementation of Augmented Reality Technology for Human Anatomy Learning Media Using Mobile-Based Gamification Models Bagus Rosyid Hamdani; Sawali Wahyu; Arief Ichwani; Popong Setiawati
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 2 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i2.10264

Abstract

 SMP Negeri 38 Bekasi faces low student learning outcomes in Science, particularly in human body anatomy material, as reflected in students' failure to meet the Minimum Mastery Criteria (KKM). This issue stems from the abstract nature of anatomy topics, which demand strong visualization skills, while current instruction still relies on conventional, text-based, two-dimensional media that are less interactive. To address this gap, this study develops a mobile-based learning application that integrates Augmented Reality (AR) and a gamification approach to present three-dimensional models of human body organs, complemented by interactive quizzes, a scoring system, and a leaderboard. The application was built using the Multimedia Development Life Cycle (MDLC) method. Usability testing with the System Usability Scale (SUS) showed that the AR Human Body Anatomy application achieved a score of 84.05 (Excellent), while the Management Dashboard achieved 80.5 (Excellent), indicating that both applications are highly usable and well-received by users. These findings suggest that AR-based learning media with gamification can support students' understanding of human body anatomy through three-dimensional visualization, and has the potential to increase learning motivation and engagement. This study contributes a practical AR-gamification model for science education that can serve as an alternative to conventional visualization methods in anatomy instruction, particularly for the achievement of the KKM standard.
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.
Co-Authors Azizah, Anik Hanifatul Bagus Rosyid Hamdani Budi Tjahjono Ichwani, Arief Meria, Lista Muhammad Noval Rais Muhammad Ramdhan Awali Popong Setiawati Riya Widayanti Ryan Putra Laksana Sahri Ramadan Silvia Ratna Juwita Verell Hermawan