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INDONESIA
JURNAL TEKNOLOGI DAN OPEN SOURCE
Published by Universitas Islam Kuantan Singingi
ISSN : 26557592     EISSN : 26221659     DOI : 10.36378/jtos
Core Subject : Science,
Computer Science & IT
Jurnal Teknologi dan Open Source menerbitkan naskah ilmiah. yang berkaitan dengan sistem informasi, teknologi informasi dan aplikasi open source secara berkala (2 kali setahun). Jurnal ini dikelola dan diterbitkan oleh Program Studi Teknik Informatika Fakultas Teknik, Universitas Islam Kuantan Singingi. Tujuan penerbitan jurnal ini adalah sebagai wadah komunikasi ilmiah antar akademisi, peneliti dan praktisi dalam menyebarluaskan hasil penelitian.
Arjuna Subject : -
Articles 486 Documents
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Dental and Oral Disease Image Classification Using MobileViT Architecture on Imbalanced Datasets Dwi Pambudi Utomo; Septian Eko Prasetyo
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 9 No. 1 (2026): Jurnal Teknologi dan Open Source, June 2026
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v9i1.5723

Abstract

Dental and oral diseases represent a high-prevalence global health issue, yet their management still relies heavily on the subjective visual inspections of medical professionals. While automated diagnostic systems exist, previous studies predominantly employ conventional Convolutional Neural Networks (CNNs) that struggle to capture global anatomical dependencies, or standard Vision Transformers (ViTs) whose massive parameter counts hinder deployment on clinical edge devices. Furthermore, existing research is frequently constrained by limited disease classes and fails to explicitly resolve severe clinical data imbalance. To bridge these gaps, this study proposes a comprehensive multi-class oral disease image classification system using MobileViT, a lightweight hybrid architecture that efficiently combines local CNN convolutions with global transformer attention mechanisms. Evaluated on a large-scale dataset encompassing six disease classes calculus, dental caries, gingivitis, aphthous ulcers, tooth discoloration, and hypodontia, the inherent class imbalance is algorithmically addressed through a WeightedRandomSampler integrated with multi-level data augmentation utilizing RandAugment and RandomErasing. The dataset is partitioned into a 70:15:15 ratio for training, validation, and testing. Experimental results demonstrate that the proposed model achieves an accuracy of 93.61%, precision of 94.76%, recall of 93.61%, and an F1-score of 93.75% on the test set. An ablation study reveals that the combination of augmentation and sample weighting improves the F1-score by 4.2 points compared to the baseline without specific treatments. Furthermore, MobileViT explicitly outperforms conventional architectures including ResNet50, EfficientNetB0, and MobileNetV3. This research demonstrates that lightweight hybrid vision transformers can effectively resolve prior representational and imbalanced data limitations for clinical oral disease classification.
Development of a Machine Learning Model for Predicting River Pollution Levels Caused by Illegal Gold Mining Activities in Kuantan Singingi Regency Jasri; Aprizal; Erlinda; Febri Haswan; Amirel Hafief Agiel
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 9 No. 1 (2026): Jurnal Teknologi dan Open Source, June 2026
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v9i1.5724

Abstract

Illegal Gold Mining (PETI) activities in Kuantan Singingi Regency have caused river water pollution and posed serious threats to environmental sustainability and public health. Conventional water quality monitoring methods still have limitations because they rely on periodic laboratory testing and are unable to provide rapid predictive results. Therefore, this study developed a Machine Learning-based prediction system to analyze river pollution levels caused by illegal gold mining activities. The study utilized water quality parameters consisting of pH, temperature, Total Suspended Solid, Dissolved Oxygen, Biological Oxygen Demand, Chemical Oxygen Demand, and mercury concentration. The dataset was processed through several preprocessing stages, including data cleaning, normalization, feature selection, and data splitting. Several Machine Learning algorithms, namely Random Forest, Support Vector Machine, and Artificial Neural Network, were implemented and compared to determine the best prediction model. The results showed that the Random Forest algorithm achieved the best performance with high accuracy and stable classification results. Furthermore, the developed model was integrated into a web-based system equipped with pollution visualization features, river information, and a Geographic Information System. The system is expected to support environmental monitoring and assist decision-making in river pollution management in Kuantan Singingi Regency.
Optimizing Human Resource Selection Through TOPSIS-Based Multi-Criteria Decision Making Astrid Paradhita; Myrtana Pusparisti; Agustin Amborowati
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 9 No. 1 (2026): Jurnal Teknologi dan Open Source, June 2026
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v9i1.5729

Abstract

Human resource quality is a pivotal determinant of organizational success, yet recruitment often suffers from subjectivity and inefficiency. This study addresses challenges in applicant mapping and assessment bias by implementing a Decision Support System (DSS) using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The research utilizes four selection criteria based on standard Indonesian recruitment frameworks : General Intelligence Test (30%), National Insight Test (10%), fild ability test (20%), and Interview Performance (40%). Methodologically, the TOPSIS method was employed to rank candidates based on their geometric distance from the positive and negative ideal solutions. Results demonstrate that the TOPSIS-based DSS achieved 90% alignment with historical corporate hiring decisions. Furthermore, the system improved decision-making effectiveness by 98.78%, accelerating the overall recruitment timeline by 30%. This study contributes to the field of HR technology by providing a scalable, objective framework for multi-criteria candidate evaluation. By integrating mathematical rigor into personnel selection, the proposed system minimizes human error and optimizes organizational efficiency in the Indonesian corporate context.
Design and Development of a Web-Based Customer Queue Information System on a Local Area Network Using PHP (Case Study: PT BPD Jambi Sharia Branch) Andreo Yudertha; Zulhadi Abdillah; Khalilah Silky; Zainul Muttakin4; AZN padila
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 9 No. 1 (2026): Jurnal Teknologi dan Open Source, June 2026
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v9i1.5730

Abstract

Optimal customer service is a key factor in improving banking service quality. At PT BPD Jambi Sharia Branch, queue management is still conducted manually using paper-based queue numbers, which is inefficient and prone to errors. This study aims to design and develop a web-based customer queue information system operating on a local area network (LAN). The system was developed using the Waterfall method, including requirement analysis, system design, implementation, and testing. The technologies used are PHP and MySQL as the database. The system is designed without a login mechanism because it is intended for use in a controlled internal environment. The results show that the system improves service efficiency, simplifies queue management, and reduces errors in queue calling. Therefore, this system supports the digitalization of customer service in the banking environment. Furthermore, the system is expected to enhance operational accuracy, accelerate service time, and provide a more organized queue experience for customers at the branch and improve overall system performance.
Hybrid Model of Isolation Forest and Long Short-Term Memory Autoencoder for Digital Forensic Anomaly Detection in Manufacturing IoT Networks Muammar; Sandhy Fernandez; Arif Riyandi; Sena Wijayanto
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 9 No. 1 (2026): Jurnal Teknologi dan Open Source, June 2026
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v9i1.5732

Abstract

The development of Internet of Things (IoT) technology in the manufacturing sector creates opportunities for efficiency while also increasing vulnerability to sabotage threats that are difficult to detect manually. This study aims to design and evaluate an artificial intelligence-based hybrid model that combines Isolation Forest and Long Short-Term Memory Autoencoder to detect anomalies in the context of digital forensics in manufacturing industrial IoT networks. The research design uses an experimental approach with a simulated dataset representing 35 working days of smart factory operations, covering 127 sabotage scenarios distributed across six types of logs. The methodology applied is a two-layer cascade architecture, where Isolation Forest serves as a statistical anomaly detector in the first layer, followed by Long Short-Term Memory Autoencoder as a time-series pattern validator in the second layer. The evaluation results show that Isolation Forest independently achieved an F1-score of 0.84, Long Short-Term Memory Autoencoder achieved 0.87, while the hybrid model produced an F1-score of 0.93 with a precision of 0.91 and a recall of 0.95. These findings confirm that the hybrid cascade approach significantly outperforms each individual method. This study concludes that the integration of both methods provides a more accurate and efficient digital forensic solution for detecting sabotage incidents in industrial IoT environments.
UI/UX Design of a Web-Based Community Discussion Forum Application for Rumah Kreatif Wadas Kelir using User-Centered Design and User Acceptance Testing Akmal Rafly Dzunurain; Sarah Astiti
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 9 No. 1 (2026): Jurnal Teknologi dan Open Source, June 2026
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v9i1.5739

Abstract

Rumah Kreatif Wadas Kelir (RKWK) is a community literacy organization established in Purwokerto, Central Java. As its members and volunteers expanded across different regions, communication through social media and instant messaging became unstructured, leading to knowledge loss, ineffective coordination, and limited access to information. This study aims to design a User Interface (UI) and User Experience (UX) for a web-based discussion forum using the User-Centered Design (UCD) approach. The research followed four stages: identifying user needs through interviews and questionnaires involving 30 members, analyzing the context of use with user personas, user journey maps, and use case diagrams, developing a high-fidelity prototype in Figma, and evaluating the design through User Acceptance Testing (UAT). The prototype incorporates a mobile-first interface, community-based topic categorization, and accessibility-oriented typography. UAT evaluated interface navigation, aesthetics, topic creation, search functionality, responsiveness, and overall satisfaction. The prototype achieved an average acceptance score of 84.6%, categorized as “Very Good.” These findings indicate that the proposed UI/UX design effectively supports communication, collaboration, and knowledge sharing within the RKWK community.
Designing the UI/UX of a Mobile-Based Library Catalog Application for the XYZ Community Using the Design Thinking Method Faza Bilwildi Emyu; Sarah Astiti
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 9 No. 1 (2026): Jurnal Teknologi dan Open Source, June 2026
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v9i1.5743

Abstract

The XYZ Community library maintained a collection of over 800 book titles yet relied entirely on manual catalog management, which substantially impeded members from efficiently discovering and accessing available resources. This study designed a mobile-based library catalog application UI/UX for XYZ Community members by applying the Design Thinking method across five iterative stages: Empathize, Define, Ideate, Prototype, and Test. In the Empathize stage, data were collected through interviews and observations involving 20 community members. The Define stage produced problem statements derived from Point of View and How Might We frameworks. The Ideate stage produced 12 priority features derived from identified user pain points through participatory brainstorming. A high-fidelity prototype comprising six primary screens Splash Screen, Onboarding, Home, Catalog, Book Detail, and About Us was developed using Figma. Usability evaluation using the System Usability Scale (SUS) administered to 30 respondents yielded a mean score of 82,7, classified as Excellent (Grade B). All respondents confirmed that the interface was easy to learn and operate, and the prototype is deemed ready for the implementation stage.
Business Intelligence Dashboard Using Power BI for Sentiment Analysis and Tokopedia Product Performance Abdurrahman Dzaki Alhadi; Melda Agarina
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 9 No. 1 (2026): Jurnal Teknologi dan Open Source, June 2026
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v9i1.5745

Abstract

Customer reviews on e-commerce platforms provide valuable insights into product performance and consumer satisfaction. However, most existing studies focus primarily on sentiment classification and model evaluation, with limited integration of sentiment data and business indicators such as product price, sales volume, and category. This study developed a Business Intelligence (BI) dashboard using Microsoft Power BI to analyze Tokopedia product performance by integrating customer sentiment, ratings, product prices, categories, review counts, and sold counts. A descriptive quantitative approach was employed using a secondary dataset consisting of 65,543 reviews, 5,521 products, 856 anonymous shops, and 13 attributes. Sentiment labels were transformed into numerical sentiment scores (positive = 1, neutral = 0, negative = −1) through a rule-based mapping method to support quantitative analysis. The research process included data inspection, preprocessing, sentiment score transformation, data modeling, dashboard development, correlation analysis, and functional evaluation. Results showed that positive sentiment dominated the dataset, accounting for 97.56% of all reviews. The Food and Beverage category recorded the highest review volume and average sold count, while Electronics had the highest average product price. Spearman correlation analysis revealed a moderate negative relationship between product price and sold count (−0.443) and a strong positive relationship between review count and sold count (0.722). The dashboard supports data-driven product evaluation, pricing, and marketing decisions.
An IoT-Based Model for Monitoring Soil pH, Temperature, and Moisture to Support Precision Agriculture M. Abdul Qodir Jaelani; Muhammad Adie Syaputra; Budi Sutomo
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 9 No. 1 (2026): Jurnal Teknologi dan Open Source, June 2026
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v9i1.5760

Abstract

Manual measurement of soil pH, temperature, and moisture often produces fragmented field data that are difficult to use for periodic land monitoring. This study developed an Internet of Things-based prototype for monitoring soil conditions to support precision agriculture. The system integrated a soil pH sensor, a waterproof DS18B20 soil temperature sensor, a soil moisture sensor, an ESP32 NodeMCU, Wi-Fi communication, data storage, and a web-based dashboard. The research followed a prototype development model covering requirement analysis, system design, hardware and software implementation, testing, evaluation, and refinement. Field testing was conducted from February to April 2026 at 08:00, 12:00, and 16:00. The results showed that soil pH ranged from 6.47 to 6.70 with an average of 6.58, soil temperature ranged from 21.30°C to 28.61°C with an average of 24.57°C, and soil moisture ranged from 73.67% to 90.00% with an average of 87.71%. Functional testing indicated that the prototype could read, transmit, store, and visualize soil data through the dashboard during operation. The proposed model is feasible as an early-stage monitoring system for data-driven soil management, although future accuracy validation with calibrated instruments is still required.
Focusguard Student Learning Activity Monitoring System Based on Camera and IoT Andika Pratama; Apriansyah; Kemas Muhammad Wahyu Hidayat
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 9 No. 1 (2026): Jurnal Teknologi dan Open Source, June 2026
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v9i1.5764

Abstract

The development of information and communication technology has brought real changes in the world of education, especially in the way the learning process is designed, implemented, and evaluated. Universities no longer only focus on delivering material, but are also required to ensure that students remain actively involved during the learning process. Student involvement is one of the important indicators of learning success because it is directly related to the attention, participation, and quality of students' understanding of the material presented. With that, the researcher utilizes technology which is a solution to help monitor and improve the quality of learning. Information technology, especially the Internet of Things (IoT), allows physical devices to connect with digital systems so that the monitoring process can be carried out in real-time and continuously a monitoring system for student learning activities that is able to work in real-time, is non-invasive, and is easy to implement. Therefore, this study proposes FocusGuard, which is a camera-based student learning activity monitoring system and the Internet of Things (IoT). This system is designed to monitor the general state of classroom activities through visual observation, detect passive conditions in a certain time span, and provide alerts as indicators of declining learning activities. It is hoped that this system can help lecturers and educational institutions in supporting the implementation of smart learning and improving the quality of the learning process in higher education.

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