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Jurnal Teknologi Dan Sistem Informasi Bisnis
Published by Universitas Dharma Andalas
ISSN : 29642132     EISSN : 26558238     DOI : 10.47233/jteksis
Core Subject :
Journal Teknologi dan Sistem Informasi Bisnis or Journal of Technology and Business Information Systems (JTEKSIS) E-ISSN: 2655-8238 P-ISSN : 2964-2132 is a journal published by the Information Systems Study Program at Dharma Andalas University for various groups who have an interest in the development of computer technology, both in a broad sense and specifically in certain fields related to computer information technology. Manuscripts accepted for publication are the results of field research, library research, observations and scientific works related to topics relevant to the Computer Technology situation. Journal Teknologi dan Sistem Informasi Bisnis or Journal of Technology and Business Information Systems is published 4 issues in a calendar year (January, Appril, July, October)
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Articles 62 Documents
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Rancang Bangun Sistem Monitoring Karyawan Berbasis Web Menggunakan Metode Waterfall Aldy Firmansyah; Muh Galuh Ardiyono; Jupron .
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol. 8 No. 3 (2026): Juli 2026
Publisher :  Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v8i3.30

Abstract

The administration of employee data at PT Menara Adi Cipta still relies on conventional methods, especially concerning attendance tracking, penalty assessments, and report creation. This situation causes delays in performance reviews and increases the risk of data inaccuracies. This study intends to design and implement a web-based Employee Monitoring System that enables real-time observation and streamlined data processing. The Waterfall approach was chosen as the system development life cycle, encompassing requirement identification, system architecture design, coding, and functional testing. The outcome is a digital monitoring platform that successfully integrates presence logs with staff evaluations. Ultimately, the application effectively boosts operational productivity and ensures the reliability of managerial reports within the company.
Optimasi Stacking Ensemble Bayesian untuk Prediksi Risiko Keterlambatan Pengiriman Stevano Titondea Prayoga Putra; Wuguh Pitono
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol. 8 No. 3 (2026): Juli 2026
Publisher :  Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v8i3.33

Abstract

Delivery delay in supply chain logistics is a critical problem that negatively impacts customer satisfaction and generates significant financial penalty costs for logistics companies due to inefficient delivery performance and uncertainty in global distribution networks. This study aims to develop a predictive model for delivery delay risk using an optimized machine learning approach that not only improves classification performance but also provides measurable economic benefits through cost reduction analysis. The proposed method employs a Bayesian-Optimized Stacking Ensemble framework combining XGBoost, LightGBM, and Random Forest as base learners, with Lasso Regression as the meta-learner. Feature selection is performed using Recursive Feature Elimination with Cross-Validation (RFE-CV), while hyperparameter optimization is conducted using the Tree-structured Parzen Estimator (TPE) algorithm implemented through Optuna. The model is trained and evaluated using the DataCo Smart Supply Chain dataset consisting of 180,519 transaction records, with performance measured using accuracy, F1-score, ROC-AUC, and confusion matrix analysis. The experimental results show that the proposed model achieves an accuracy of 90.12%, F1-score of 0.9013, and ROC-AUC of 0.9602, indicating strong predictive capability. Furthermore, the business impact analysis demonstrates a penalty cost reduction of USD 523,062 or 52.85% compared to the baseline scenario without prediction, confirming that the proposed approach provides both high predictive performance and significant economic value in supply chain operations.
Homeowners’ Perspectives on the Adoption and Use of IoT-Based Smart Home Security Systems. Dhini Oktaviani; Alif Abdul Aziz; Reyhan Adhimas; Zidni Ma'ruf
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol. 8 No. 3 (2026): Juli 2026
Publisher :  Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v8i3.37

Abstract

The pervasive adoption of Internet of Things (IoT) ecosystems has recalibrated domestic security; however, this shift masks a profound structural instability where the allure of streamlined functionality often compromises security architecture robustness. Although scholarly discourse frequently addresses the psychological catalysts of technology adoption, there remains an intellectual neglect concerning the longitudinal reliability of these systems within real-world infrastructure. This study seeks to redress this oversight by interrogating the daily engagements of six homeowners with their interconnected security environments. By leveraging the Technology Acceptance Model (TAM), we explore the socio-technical friction inherent in managing software-mediated protection. Our analysis exposes a fundamental paradox: while these systems offer enhanced surveillance, they remain tethered to fragile dependencies on power grids and network uptime. Empirical findings suggest that modern smart home architecture relies on a precarious "all-or-nothing" structure; power or connectivity fluctuations catalyze system-wide collapse, rendering the "security" provided null during infrastructural distress. These findings provide a trenchant indictment of current design philosophies. We argue that the sector must pivot from a fixation on aesthetic automation toward an engineering ethos predicated on autonomous edge-computing and redundant power-management, ultimately prioritizing structural resilience over the superficial convenience of connectivity.
Analisis Sentimen Ulasan Aplikasi PLN Mobile Menggunakan Naive Bayes Ni Kadek Anggita Pradnya Dewi; Ni Nyoman Vika Andini; Ria Dwi Ratna; I Made Suwija Putra
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol. 8 No. 3 (2026): Juli 2026
Publisher :  Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v8i3.41

Abstract

The rapid growth of PLN Mobile users has generated massive volumes of review data that are impractical to analyze manually, creating an urgent need for automated customer satisfaction monitoring. This study aims to develop an accurate sentiment classification model for PLN Mobile user reviews from the Google Play Store using the Naive Bayes algorithm. A dataset of 171,000 reviews was collected via web scraping, labeled into positive (rating 5) and negative (rating 1–2) classes, and balanced using an undersampling technique. Indonesian text preprocessing included text cleaning, repeated character removal, slang word normalization, negation handling, intensifier handling, stopword removal, and stemming using the Sastrawi library. Feature representation employed the Bag of Words method with CountVectorizer considering unigrams and bigrams. The Multinomial Naive Bayes model achieved an accuracy of 95.31% on the test set, with precision of 93% for the negative class and 97% for the positive class, recall of 98% for negative and 93% for positive, and an F1-score of 95% for both classes. These findings confirm that Naive Bayes with comprehensive Indonesian text preprocessing is effective for automated customer satisfaction monitoring in digital electricity services.
Hybrid AHP-SAW untuk Rekomendasi Laptop Mahasiswa Sistem Informasi Berdasarkan Kebutuhan Komputasi Akademik Meutia Sandrina; Yaslinda Lizar
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol. 8 No. 3 (2026): Juli 2026
Publisher :  Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v8i3.42

Abstract

The rapid development of information technology has increased the need for laptops as academic support devices for Information Systems students. However, the wide variety of laptop specifications and prices often makes the selection process difficult and subjective. This study aims to develop a Decision Support System for laptop recommendations using a hybrid Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW) method. AHP was applied to determine the priority weights of selection criteria, while SAW was used to rank laptop alternatives based on preference values. The criteria included price, RAM, processor, GPU, storage, and battery. Data were collected from official laptop websites, online marketplaces, and PassMark benchmark data. The study evaluated 12 laptop alternatives within a price range of Rp8,000,000–Rp12,000,000. The results indicate that processor and RAM are the most influential criteria in laptop selection. HP Pavilion Aero 13 achieved the highest preference value of 0.912, making it the best alternative. The proposed AHP-SAW approach effectively provides objective and systematic laptop recommendations that match the academic computing needs of Information Systems students.
Integrasi IoT dan Machine Learning untuk Monitoring Emisi Metana Peternakan Ruminansia Marshanda Apriestanti
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol. 8 No. 3 (2026): Juli 2026
Publisher :  Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v8i3.44

Abstract

Methane (CH₄) emissions from ruminant livestock farming are one of the major sources of greenhouse gases and simultaneously cause feed energy losses of approximately 2–12%, thereby reducing livestock production efficiency. In addition, conventional emission monitoring systems are costly and difficult to implement widely in smallholder farming systems. This study aims to review the development of Internet of Things (IoT) and Machine Learning (ML) technologies for monitoring and predicting methane emissions in ruminant livestock production, while also identifying opportunities for the development of data-driven decision support systems. The study employed a Systematic Literature Review (SLR) approach following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Relevant articles were retrieved from the Scopus database using the keywords “methane emission” and “machine learning.” Of the 89 articles initially identified, five studies met the inclusion criteria and were selected for further analysis. Data were analyzed using a descriptive-comparative approach by examining the technologies, sensor types, system architectures, data communication methods, and machine learning algorithms employed in each study. The results indicate a technological shift from conventional measurement methods toward intelligent systems integrating IoT, wireless sensor networks, cloud computing, and artificial intelligence. Among the machine learning approaches, Long Short-Term Memory (LSTM) demonstrated the best performance for time-series methane emission prediction, while statistical methods remained widely used for sensor calibration and data normalization. The integration of IoT and ML has significant potential to support precision livestock farming through real-time monitoring, emission forecasting, and automated decision-making, thereby improving feed efficiency and reducing greenhouse gas emissions.
Peramalan Temperatur Udara Rata-Rata Bandara Juanda Menggunakan Algoritma Random Forest Regression Ahmad Baihaqi
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol. 8 No. 3 (2026): Juli 2026
Publisher :  Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v8i3.49

Abstract

Dynamic changes in air temperature in the airport environment significantly affect the safety and efficiency of flight operations, particularly in determining aircraft load restrictions and calculating runway length requirements during takeoff. Therefore, precise daily air temperature forecasting at Juanda International Airport is crucial to support flight safety. This study aims to model and forecast daily average air temperature using a Machine Learning approach with the Random Forest Regression algorithm. This algorithm was chosen because of its superiority in handling complex historical time series data through pre-processing and feature engineering stages. Temperature fluctuation patterns are extracted using the sliding window method to produce lag features and rolling mean as predictor variables that allow the model to capture data characteristics non-linearly. Model performance evaluation is carried out using the Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) metrics. The test results show that the Random Forest Regression algorithm has excellent performance with an initial prediction error rate (MAPE) of 2.27%. Through the implementation of hyperparameter optimization, model accuracy was successfully improved, reducing the MAPE value to 2.14%. This high level of accuracy demonstrates the model's reliability and consistency in predicting temperature variability at the study site. These forecasting results are expected to be used as a decision-support tool for airport authorities in operational management and mitigating aviation weather-related risks.
Electrical engineering students' in-depth understanding of the theory and application of electronic components Muhammad Fatahillah; Arkan Fakih; Adly Fairuz; Zidni Ma'ruf
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol. 8 No. 3 (2026): Juli 2026
Publisher :  Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v8i3.50

Abstract

The development of technology requires electrical engineering students to have a deep understanding of electronic components. However, students often face conceptual and technical difficulties when dealing with complex active components. This study aims to examine in depth students' understanding and perceptions regarding the theory and application of electronic components. Using a qualitative method with a descriptive approach, data were gathered through structured interviews with electrical engineering students at Politeknik Negeri Sriwijaya. The results showed a significant contrast in student comprehension; passive components like resistors are easily mastered due to their simple working principles. Conversely, active components such as Integrated Circuits (ICs) and transistors pose major challenges due to diverse pin structures, heat sensitivity, and practical troubleshooting issues in the laboratory. To address these difficulties, the integration of interactive and innovative learning media is highly required. Tools such as Tinkercad-based virtual simulations, digital modules, and project-based learning methods have proven effective in making abstract concepts more concrete, enhancing students' analytical skills, and preventing component damage during experiments
Evaluasi Usability pada Aplikasi Al Quran Indonesia Menggunakan System Usability Scale (SUS) Sayyid Ammar Murtadho; Tri Suratno; Muhammad Razi A
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol. 8 No. 3 (2026): Juli 2026
Publisher :  Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v8i3.51

Abstract

The rapid development of information technology has encouraged digital transformation in the religious domain, including the emergence of digital Qur’an applications. Al Quran Indonesia is a widely used application that helps users read and understand the Qur’an practically, however, several issues and user complaints related to system performance and usability constraints are still reported. This study aims to evaluate the usabilitu level of the Al Quran Indonesia application using the System Usability Scale (SUS) method. A quantitive approach was employed, with data collected through a SUS questionnaire consisting of 10 Liker-scale statement. The study involved 100 students from Faculty of Science and Technology at Universitas Jambi as respondents. The data analysis techniques used include individual SUS score calculation, average score determination, and data interpretation base on standard usability criteria ( Adjectival Rating, Grade Scale, and Acceptability Ranges). The result indicate that the application achieved an average SUS score 84,85, which falls into the Best Imaginable (Grade A) category and is considered acceptable. This demonstrates a very good level of usability in terms of effectiveness, efficiency, and user satisfaction, although some minor issues were still identified, such as feature inconsistency and suboptimal system performance. Therefore, the findings are expected to provide valuable insight for developers to continuously improve application quality and user experience.
Evaluasi Usability Aplikasi Muslim Pro: Al Quran Azan Doa Menggunakan System Usability Scale (SUS) Renolga Sandhika Candra
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol. 8 No. 3 (2026): Juli 2026
Publisher :  Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v8i3.52

Abstract

The main problem underlying this research is that it is not yet known certainly and measurably to what extent the Muslim Pro application is easily understood, learned, and used effectively by its users. This lack of knowledge regarding the level of interaction and ease of navigation becomes an obstacle to objectively assessing the quality of the application's interface based on user experience standards. Departing from this problem, this research specifically aims to evaluate and measure the usability level of the Muslim Pro application in depth, basing it directly on the perceptions and actual experiences of the users. To achieve this objective, the method used is the System Usability Scale (SUS) as a measurement instrument through a structured questionnaire consisting of 10 standard statements. This research applies a quantitative approach using a purposive sampling technique targeted at students of the Faculty of Science and Technology, Universitas Jambi. From the data collection process, 102 respondent data were collected, which were then filtered to produce 99 data declared valid and suitable for analysis. The research results show that the average SUS score obtained is 69.82. This evaluation score places the Muslim Pro application in the "Marginal" category (marginally acceptable) and obtains a Grade C. These findings directly indicate that the Muslim Pro application has a fairly good level of usability. Nevertheless, interface design improvements are still recommended to optimize feature consistency and enhance the overall user experience to its maximum potential.

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