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Hidra Amnur
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Kampus Politeknik Negeri Padang, Jurusan Teknologi Informasi. Gedung E. Limau Manis, Pauh. Padang - Sumatera Barat. Indonesia
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INDONESIA
JITSI : Jurnal Ilmiah Teknologi Sistem Informasi
Published by Society of Visual Informatics
ISSN : 27224619     EISSN : 27224600     DOI : 10.30630/jitsi
Core Subject : Science,
Computer Science & IT
The journal scopes include (but not limited to) the followings: Computer Science : Artificial Intelligence, Data Mining, Database, Data Warehouse, Big Data, Machine Learning, Operating System, Algorithm Computer Engineering : Computer Architecture, Computer Network, Computer Security, Embedded system, Coud Computing, Internet of Thing, Robotics, Computer Hardware Information Technology : Information System, Internet & Mobile Computing, Geographical Information System Visualization : Virtual Reality, Augmented Reality, Multimedia, Computer Vision, Computer Graphics, Pattern & Speech Recognition, image processing Social Informatics: ICT interaction with society, ICT application in social science, ICT as a social research tool, ICT education
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 174 Documents
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Digitalisasi Penjualan dan Jasa Perbaikan Elektronik Menggunakan Nuxt.Js dan Laravel Rest API Wahyu Bulkhoir; Taufik Gusman; Fanni Sukma
JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Vol 7 No 2 (2026)
Publisher : SOTVI - Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/jitsi.7.2.610

Abstract

Digital transformation has become an urgent necessity for Micro, Small, and Medium Enterprises (MSMEs) to enhance competitiveness in the global era. Berkah Laris, a business engaged in the sales and repair services of electronic goods, still relies on manual recording for transactions and inventory management, which potentially leads to inefficiencies and data errors. This study aims to design and develop a web-based information system using the Nuxt.js framework as the frontend and Laravel REST API as the backend, with MySQL as the database. The Waterfall method was applied in the stages of requirements analysis, system design, coding, testing, and implementation. The system development results show that the features for sales management, service handling, and inventory control can operate automatically, in real-time, and are well-documented. The implementation of this system is proven to improve operational efficiency, data transparency, and customer satisfaction. This research provides a tangible contribution to the application of information technology in supporting the digitalization of MSMEs, particularly in the electronic sales and service sector
Feature Extraction of Multichannel EMG Signals for Shoulder Joint Movement Patterns Paulus Susetyo Wardana; Lince Markis; Rika Rokhana
JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Vol 7 No 2 (2026)
Publisher : SOTVI - Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/jitsi.7.2.614

Abstract

Electromyography (EMG) signals provide information about muscle activity and can support rehabilitation and prosthetic control systems. This study aims to extract and analyze features of multichannel EMG signals recorded from seven shoulder joint movement patterns. EMG data were acquired using surface electrodes placed on eight dominant muscles associated with shoulder joint motion, namely Deltoid1, Deltoid2, Infraspinatus, Supraspinatus, Teres Major, Latissimus Dorsi, Pectoralis1, and Pectoralis2. The recorded movements included resting, shoulder flexion, shoulder extension, shoulder abduction, shoulder adduction, external rotation, and internal rotation. The proposed processing procedure consisted of signal acquisition, rectification, transformation into the frequency domain using Discrete Fourier Transform, and feature extraction using Linear Envelope, Modified Mean Frequency (MMNF), and Modified Median Frequency (MMDF). The results show that Linear Envelope can describe temporal energy changes in each movement pattern, while MMNF and MMDF can identify groups of similar signal patterns and distinguish several movements through specific muscle channels. Resting movement had very small amplitude changes, while active shoulder movements produced different dominant energy patterns across subjects. MMNF and MMDF produced two main similarity groups, although the distinguishing muscles differed among subjects. These findings indicate that multichannel EMG feature extraction is useful as an initial basis for shoulder movement pattern analysis; however, further development is required to improve online acquisition, automatic gain adjustment, and classification robustness.
Model Arsitektur Sistem Informasi Terintegrasi AI untuk Pemantauan dan Intervensi Anak dengan Autism Spectrum Disorder Harkat Christian Zamasi; Arden Sagiterry Setiawan
JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Vol 7 No 2 (2026)
Publisher : SOTVI - Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/jitsi.7.2.616

Abstract

The increasing number of children with Autism Spectrum Disorder (ASD) in Indonesia shows a continuing upward trend; however, it is still confronted with various fundamental challenges, such as the low level of parental understanding regarding early detection, the limited availability of medical professionals and therapists, and the absence of an integrated information system and data center. These conditions result in diagnosis, therapy, and developmental data of children with ASD being scattered and not continuously documented. On the other hand, the advancement of information technology, particularly Artificial Intelligence (AI), offers significant potential in supporting early detection, behavioral analysis, and the provision of personalized and data-driven intervention recommendations. This study aims to design an abstract information system architecture model integrated with AI to support monitoring and intervention processes for children with ASD. The method used is the Design Science Research (DSR) approach, which includes stages of problem identification, model design, conceptual artifact development, and validation through use case scenarios. The study results in an integrated information system architecture model comprising user applications, a data integration layer, AI analytics modules, and a human-in-the-loop mechanism. The contribution of this study is an AI-based integrated conceptual information system framework designed to address the limitations of existing systems, which are generally fragmented and isolated and serves as a foundation for the development of ASD monitoring and intervention systems in Indonesia.
Model Klasifikasi Diabetes Menggunakan XGBoost Dengan Optimasi Seleksi Fitur Dan Hyperparameter Berbasis PSO Sheila putri aprilianti; Andrian Sah; Siti Nurhayati; Rasna; Jusmawati
JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Vol 7 No 2 (2026)
Publisher : SOTVI - Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/jitsi.7.2.620

Abstract

The rising global burden of diabetes mellitus has increased the need for accurate, technology-based early detection systems. This study develops a diabetes classification model using Extreme Gradient Boosting (XGBoost) optimized through a two-stage Particle Swarm Optimization (PSO) scheme: Binary PSO (BPSO) for feature selection and Global Best PSO (GBPSO) for hyperparameter tuning. Data were obtained from the Kaggle Diabetes Prediction Dataset (100,000 records; eight clinical attributes: gender, age, hypertension, heart disease, smoking history, BMI, HbA1c level, and blood glucose level). The extreme class imbalance (91.5% normal vs 8.5% diabetes) was addressed using the SMOTETomek hybrid technique. BPSO retained all eight features as the optimal combination (best cost 0.0329; F1-weighted 96.71%), while GBPSO produced the best hyperparameter configuration (n_estimators=416, learning_rate=0.237, max_depth=3, min_child_weight=3; best cost 0.0308, converging at the 11th iteration). The final model achieved 97.15% test-set accuracy, a ROC-AUC of 0.9779, and a diabetes-class precision of 0.93. The model was deployed as a Streamlit-based web system classifying patients into three risk categories: Not Indicated, Early Risk Indicated, and Diabetes Indicated. Preliminary validation on five real patient records from an anonymized partner hospital in Jayapura City showed classification results fully consistent with patients' clinical status (5 of 5 correct), indicating potential clinical applicability, although larger-scale testing is still required. These findings demonstrate that integrating XGBoost with a two-stage PSO optimization scheme produces an accurate and clinically applicable diabetes classification model.

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