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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 372 Documents
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Redesigning the UI/UX of the Performance Monitoring Website at Pegadaian Regional Office XII Surabaya Using the Lean UX Method Mahardhika, Muhammad Ilyasa; Fitri, Anindo Saka; Farista Ananto, Prasasti Karunia
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 2 (2025): Jurnal Teknologi dan Open Source, December 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i2.4824

Abstract

The performance monitoring website at Pegadaian Regional Office XII Surabaya played an important role in supporting evaluation activities; however, its suboptimal interface caused issues in usability, limited information access, and required manual coordination among staff. This study was conducted to redesign the user interface and user experience of the website in order to improve usability and user satisfaction. The research applied a practical approach using the Lean User Experience method, which emphasized iterative development through assumption formulation, prototyping, experimentation, and feedback analysis. The subjects of this study included staff from Business Analysis and Performance Evaluation, Distribution and Service Networks, as well as Marketing and Sales, who served as the primary users of the system. The initial design of the website received a System Usability Scale score of 43.5, which was categorized as unacceptable. After the redesign, the score significantly increased to 80, categorized as A-, indicating a very good level of usability. Furthermore, the results of the User Experience Questionnaire showed positive improvements in all dimensions, including attractiveness, clarity, efficiency, accuracy, stimulation, and novelty. This study concluded that the redesign successfully enhanced usability and user experience, making the system more effective in supporting performance monitoring and decision-making within Pegadaian.
House Price Prediction in Surabaya Using Backpropagation Neural Network Pamungkas, Dimas Fajri; Najaf, Abdul Rezha Efrat; Permatasari, Reisa
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 2 (2025): Jurnal Teknologi dan Open Source, December 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i2.4829

Abstract

This research develops a house price prediction system in Surabaya using the Backpropagation Neural Network (BPNN) method. The dataset was obtained through web scraping of property listings, resulting in 3,435 records with 52 attributes. To improve stability, the target variable (house price) was transformed using natural logarithms. Several neural network architectures were tested, and the best configuration [32, 64, 32] achieved Mean Absolute Error (MAE) of 0.3125, Root Mean Squared Error (RMSE) of 0.4201, R² of 0.7138, and Mean Absolute Percentage Error (MAPE) of 1.46%. A multi-run evaluation of 20 iterations confirmed consistency of results. The model was implemented as a web-based application using Flask, allowing users to predict house prices in real-time. This research shows that BPNN is reliable for property price forecasting and can support decision-making in the housing market.
Decision Support System for Extending PT Nitro Pratama Indonesia Outlet Cooperation Using the Fuzzy AHP-TOPSIS Radhyana Gayatri Faradilla; Rizka Hadiwiyanti; Abdul Rezha Efrat Najaf
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 2 (2025): Jurnal Teknologi dan Open Source, December 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i2.4847

Abstract

The increasingly competitive business environment required companies to make objective decisions, particularly in determining the feasibility of outlet contract extensions. PT Nitro Pratama Indonesia previously conducted this process manually, which often led to inconsistent decisions. This study aimed to develop a web-based decision support system to evaluate outlet cooperation systematically. A combination of Fuzzy Analytical Hierarchy Process (Fuzzy AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was applied. Fuzzy AHP was used to determine the weight of criteria under uncertain conditions, while TOPSIS was used to rank alternatives based on their proximity to the ideal solution. The study involved 50 active outlets located in Surabaya and Solo branches. The final criteria weights obtained were Outlet Revenue (0.33), Rental Cost (0.26), Outlet Location (0.16), Accessibility (0.11), Competitiveness (0.08), and Operational Cost (0.06). The ranking process generated objective recommendations that were consistent with manual calculations. Functional testing using Black Box Testing indicated that all system features operated properly. The system proved effective and relevant in supporting accurate and efficient decision-making for outlet contract extensions.
Development of a Certainty Factor-Based Expert System for Nutrition Consultation and Stunting Prevention in Coastal Areas: A Case Study of Bengkalis Regency Ratnawati, Fajar; Hardinata, Niky; Supendi
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 2 (2025): Jurnal Teknologi dan Open Source, December 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i2.4858

Abstract

This study developed a Certainty Factor (CF)–based expert system to support nutrition consultation and stunting prevention in coastal settings using Bengkalis Regency as a case study. A design science approach was employed to analyze local service constraints, acquire expert knowledge, formalize a rule base, implement an offline-first prototype (mobile client and admin dashboard), and evaluate its performance. Knowledge was elicited from nutritionists, midwives, and community health workers and encoded as IF–THEN rules with expert confidence weights; Evidence (anthropometry, infection history, infant and young child feeding, sanitation, and socioeconomic factors) was mapped to CF values ​​and combined to yield risk scores and categories with explainable rule traces. Functional testing showed all user-story scenarios passed as expected, while initial expert validation and usability checks indicated the prototype provided rapid and standardized assessments suitable for first-line services. The results suggest the CF approach is feasible for coastal contexts with limited connectivity and can accelerate early screening and referrals. Future work will expand the local knowledge base, integrate electronic records, and conduct wider field trials to measure effectiveness at scale
COMPARISON OF DEEP LEARNING MODELS LSTM AND BILSTM IN DIABETES PREDICTION: COMPARISON OF DEEP LEARNING MODELS LSTM AND BILSTM IN DIABETES PREDICTION Wahyuni, Refni; Irawan, Yuda
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 2 (2025): Jurnal Teknologi dan Open Source, December 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i2.4904

Abstract

Diabetes mellitus remains a major global health concern, requiring early detection to prevent severe complications and reduce mortality. This study developed and evaluated two deep learning architectures, Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM), for diabetes prediction using the Pima Indians Diabetes Dataset. The research methodology involved systematic preprocessing, including outlier handling with median imputation, data normalization, and training–testing data splitting (80:20). Both models were trained using 614 samples for training and 154 samples for testing, with 50 epochs and a batch size of 32. The evaluation was performed using accuracy, precision, recall, F1-score, and AUC metrics. Results indicated that LSTM achieved an accuracy of 74.03%, while BiLSTM slightly outperformed it with 74.68%. Confusion matrix analysis further revealed that BiLSTM reduced false negatives and provided more consistent learning stability compared to LSTM. Accuracy and loss curves confirmed BiLSTM’s superior convergence and generalization capability. These findings demonstrate that BiLSTM is more effective and reliable for diabetes prediction tasks. The study concludes that BiLSTM offers better potential for integration into decision-support systems, and future research could enhance performance through larger datasets, advanced optimization, and real-world clinical validation.
Predicting Tablet Drug Expenditures Using Python-Based Facebook Prophet in Pharmaceutical Installations Maulana, Kaka Rizki; Widiyono, Widiyono; Darmawan, Arief Soma
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 2 (2025): Jurnal Teknologi dan Open Source, December 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i2.4907

Abstract

The increasing complexity of pharmaceutical logistics requires accurate forecasting to ensure drug availability and minimize the risk of stock shortages. This study aims to develop a forecasting model to predict monthly tablet drug expenditure in the Pharmacy Department. The research stages include problem identification, data collection from historical drug expenditure records, data pre-processing, and implementation of the forecasting model. The method used is Facebook Prophet, which was chosen for its ability to capture seasonal patterns, trends, and holidays in time series data. Model performance evaluation was conducted using Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE). The results showed that the model produced an MAE of 3,621.25 and a MAPE of 4.93%, indicating that the prediction accuracy level was in the good category. These findings prove that the Prophet method is capable of providing reliable results in drug expenditure forecasting. The results of this study are expected to support decision-making in drug requirement planning and improve the efficiency of pharmaceutical logistics management.
Optimizing Prompt Engineering for AI-Based Logo Generation Using Response Surface Methodology Shermay; Aklani, Syaeful Anas; Firmansyah, Muhamad Dody
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 2 (2025): Jurnal Teknologi dan Open Source, December 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i2.4959

Abstract

This research developed an optimized prompt engineering framework for AI-based logo generation using Response Surface Methodology (RSM) with Central Composite Design (CCD). Despite rapid AI adoption, users face challenges in communicating design intent effectively, leading to inconsistent outputs. This study systematically tested 47 prompt combinations across five variables: prompt clarity, detail level, thematic description, visual elements, and color specification. The optimization identified eight critical components forming a structured template: Main Design Focus, Detail Elements, Thematic Style, Primary Colors, Complementary Colors, Rewording, Layout Size, and Element Limit. Experimental validation with 30 graphic designers demonstrated substantial improvements over unstructured prompts: visual consistency increased from 65% to 87%, iteration efficiency improved by 48.5% (from 6.6 to 3.4 attempts), and user satisfaction rose from 58% to 82%. Both manual designers and AI-experienced users successfully applied the framework with comparable effectiveness. This research contributes a systematic, optimization-based approach to prompt engineering in creative AI applications and provides a practical framework enhancing accessibility for non-technical users while maintaining professional quality standards in logo desin.
Sentiment Analysis of Marketplace Application Reviews Using Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) Arief Ichwani; Munawar; Rilla Gantino
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 7 No. 2 (2024): Jurnal Teknologi dan Open Source, December 2024
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v7i2.4972

Abstract

Shopee is one of the most popular online marketplaces in Indonesia, with more than 103 million users in 2023. Most users consider factors such as customer reviews, ratings, prices, and free shipping promotions before making a purchase. Analyzing user reviews is essential to understand consumer perceptions of services, identify satisfaction or dissatisfaction, and detect potential issues that need to be addressed. However, sentiment analysis faces challenges in processing text with diverse language styles, structures, and informal expressions. To overcome these challenges, this study applies machine learning algorithms—Support Vector Machine (SVM) and K-Nearest Neighbors (KNN)—for classifying sentiment in Shopee user reviews. Data labeling using the Lexicon InSet method produced 9,509 positive reviews (47.55%), 7,686 negative reviews (38.43%), and 2,805 neutral reviews (14.03%). Based on the Confusion Matrix results, SVM outperformed KNN, particularly in classifying negative and neutral sentiments with higher accuracy. These findings indicate that SVM is a more effective and efficient model for sentiment analysis of user reviews on the Shopee platform.
Design and Construction of a Sales Information System Using the Reactjs and Expressjs Frameworks: case study of Fa_al.store Wuryanto, Kevin Yohanes Wuryanto; Brastama Putra, Agung; Wibowo, Nur Cahyo
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 2 (2025): Jurnal Teknologi dan Open Source, December 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i2.4985

Abstract

Fa_al.store is a micro, small, and medium enterprise (MSME) that sells socks both online and offline. In its operational activities, the process of recording transactions and managing stock is still done manually, so the business owner experiences difficulties in monitoring product availability, assessing sales performance, and preparing periodic reports. This research aims to design and build a web-based sales information system that can facilitate transaction recording, product stock management, customer data, promotions, and presenting sales reports systematically and structured. The development of this system applies the Waterfall method, which includes the stages of needs analysis, design, implementation, and testing. On the frontend side, the ReactJS framework is used to build an interactive user interface, while the backend is developed using ExpressJS to handle business logic and communication with the database. This system is also integrated with the Midtrans payment gateway to support a secure and efficient online payment process. The results of testing using the User Acceptance Testing (UAT) method show that the system has been able to meet the functional needs of users with an acceptance rate of 94.2%, which indicates that the developed system is feasible to use and can help business actors in carrying out business processes effectively.
Design of Automatic Fan Base On Arduino Uno Microcontroler And DHT11 Sensor Batubara, Muhammad Sakban; Hutabarat, Purnama Helena
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 2 (2025): Jurnal Teknologi dan Open Source, December 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i2.5012

Abstract

The temperature of the human body can fluctuate depending on environmental conditions, particularly the surrounding room temperature. To maintain comfort, a cooling device capable of providing adequate airflow is required, and one commonly used solution is an electric fan. This study focuses on designing and implementing an automatic fan system controlled by an Arduino Uno microcontroller and a DHT11 temperature sensor. The system is programmed to activate the fan automatically when the detected room temperature exceeds a predetermined threshold. In the design phase, the Arduino Uno functions as the core controller due to its ability to process sensor input and manage hardware operations efficiently. The DHT11 sensor measures both temperature and humidity, transmitting the data to the microcontroller, which then delivers a control signal to the relay module. Following the design stage, system implementation is carried out using the C++ programming language through the Arduino IDE. The program continuously reads temperature values from the DHT11 sensor, enabling real-time decision-making. When the temperature reaches the specified limit, the microcontroller triggers the relay, causing the fan to operate automatically. The results of this study show that the fan responds accurately to temperature changes, providing a practical automatic cooling solution.

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