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Journal of Information Technology
Published by Universitas Bhinneka Nusantara
ISSN : 23031425     EISSN : 2580720X     DOI : https://doi.org/10.32664/j-intech
Core Subject : Science,
Computer Science & IT
Journal of Information and Technology is a journal published by Bhinneka Nusantara University, Malang. The scope of this journal includes IT Governance, IS Strategic Planning, IS Theory and Practices, Management Information System, IT Project Management, Distance Learning, E-Government, Information Security and IT Risk Management, E-Business / E-Commerce, Big Data Research, and other related topics.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 325 Documents
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Forecasting Inventory Demand Under Volatile Sales Patterns Using the Prophet Algorithm Rafika Sari; Ratna Salkiawati; Nur`aini Puji Lestari; Aida Fitriyani
J-INTECH ( Journal of Information and Technology) Vol 14 No 01 (2026): Journal of Information and Technology
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v14i01.2032

Abstract

Inventory availability is a critical factor for companies to maintain operational continuity and customer satisfaction. However, many organizations still face challenges in forecasting demand, particularly when sales patterns are highly volatile and irregular. Although the Prophet forecasting algorithm has been widely used for time-series prediction, its behavior and robustness under unstable sales patterns remain insufficiently examined in practical inventory contexts. This study aims to evaluate the ability of the Prophet algorithm to forecast inventory demand using historical sales data characterized by fluctuating patterns. A quantitative time-series forecasting approach was applied using one year of secondary sales data obtained from PT XYZ. The data were cleaned to address missing values and aggregated into weekly time intervals to reduce noise. Five products with the highest transaction frequency were selected as case studies. Forecasting performance was evaluated using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The results show that Prophet is capable of generating reasonably accurate forecasts even under volatile demand conditions. The evaluation results indicate RMSE values ranging from 5.41 to 52.78 and MAPE values ranging from 5% to 23.46% across the five analyzed products. These findings provide empirical evidence that the Prophet algorithm can maintain forecasting robustness despite irregular demand patterns. However, the absence of comparisons with alternative forecasting models limits the strength of conclusions regarding its relative performance. This study contributes by providing empirical insight into the application of Prophet for inventory forecasting under volatile sales conditions and offers practical implications for improving inventory planning in data-driven decision-making environments.
Implementasi Web Scraping dan Data Mining untuk Evaluasi Kinerja Layanan PT Ceria Multimedia Yanuartha, Ravel; Fakta Sari, Dini
J-INTECH ( Journal of Information and Technology) Vol 14 No 01 (2026): Journal of Information and Technology
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v14i01.2192

Abstract

This research aims to implement web scraping techniques to collect testimonial data from the CeriaMultimedia website and perform sentiment analysis to evaluate service quality. The collected data consists of a limited number of testimonials, which are then processed through text preprocessing stages including case folding, tokenizing, filtering, and stemming. The sentiment classification process is conducted using machine learning methods based on TF-IDF weighting and classification algorithms. Due to the limited dataset, the analysis results are used primarily to demonstrate the implementation process rather than to draw generalized conclusions. The results show that the sentiment categories obtained include positive, negative, and neutral sentiments, although not all categories consistently appear in the testing phase. This research highlights the effectiveness of web scraping and text processing techniques while also indicating the need for a larger dataset to improve evaluation accuracy in future studies.
Drowsiness Detection using YOLOv12 Wisesa, Bradika Almandin Almandin; Vivin Mahat Putri; Evvin Faristasari; Sirlus Andreanto Jasman Duli; Satria Agus Darma
J-INTECH ( Journal of Information and Technology) Vol 14 No 01 (2026): Journal of Information and Technology
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v14i01.2212

Abstract

Drowsiness poses significant risks in safety-critical activities such as driving, industrial operations, and online learning. While advanced deep learning models (e.g., CNN-LSTM hybrids) achieve high accuracy in driver drowsiness detection, they often require substantial computational resources, limiting deployment on embedded or resource-constrained devices. This study addresses the research gap in lightweight, real-time, non-invasive drowsiness detection by developing an embeddable library using YOLOv12, an attention-centric single-stage detector known for balancing speed and accuracy. The model was trained on a custom dataset of 2312 video frame sequences (1011 "awake" and 1301 "drowsy" states, captured from varied angles under consistent lighting), augmented with standard techniques (e.g., brightness/contrast adjustments, flips, and rotations) to enhance generalization. It was evaluated through 80 real-time trials across multiple subjects. Performance metrics include accuracy of 93%, precision of 0.94, recall of 0.91, and F1-score of 0.93. The system detects drowsiness via facial bounding boxes followed by state classification (integrating eye/mouth aspect ratios) in real time. The main contribution is a proof-of-concept YOLOv12-based approach for non-invasive drowsiness monitoring, offering faster inference suitable for embedded applications (e.g., vehicle systems, meeting tools, or industrial safety) compared to heavier hybrid models. Limitations include some remaining sensitivity to extreme lighting/angles and dataset scale; future work will expand datasets, incorporate multi-modal cues, and further test robustness in diverse real-world conditions.
Optimizing Medical Equipment Inventory Management through Web-Based System Implementation for Real-Time Monitoring and Alerts Mega Wahyu Rhamadani; Abdullah Ardi; Adina Apriyani
J-INTECH ( Journal of Information and Technology) Vol 14 No 01 (2026): Journal of Information and Technology
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v14i01.2221

Abstract

Manual inventory processes at PT Borneo Sejahtera Medika resulted in an 18.2% stock discrepancy rate, frequent expired items, and delayed procurement decisions. This study develops and empirically evaluates a web-based inventory management system integrating real-time monitoring, automated expiration alerts, and demand forecasting. A six-month before–after analysis was conducted to measure system impact using discrepancy rate, operational performance indicators, and forecasting accuracy metrics. The results show that the discrepancy rate decreased from 18.2% to 13.6%, representing a relative improvement of 25.27%. Operational performance improved significantly, with stock checking time reduced by 52%, expired items reduced by 57%, and emergency procurement reduced by 31%. The forecasting module achieved a Mean Absolute Percentage Error (MAPE) of 5.00%, indicating acceptable short-term prediction accuracy. These findings demonstrate that the implemented system provides measurable improvements in data accuracy, operational efficiency, and inventory control within a healthcare distribution context. 
Self-Supervised Customer Representation Learning for Segmentation and Next-Purchase Prediction on UCI Online Retail Xin, Qi
J-INTECH ( Journal of Information and Technology) Vol 14 No 01 (2026): Journal of Information and Technology
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v14i01.2229

Abstract

Customer analytics in financial retail, payments, and bank marketing frequently relies on segmentation and propensity prediction, but transactional logs are sparse, high-dimensional, and only weakly labeled. This paper presents a fast and reproducible self-supervised learning pipeline that converts raw e-commerce transactions into customer representations and evaluates them on two downstream tasks: customer segmentation and next-purchase prediction. We conduct full experimental evaluation on the UCI Online Retail dataset (541,909 invoice-line transactions from 2010-12-01 to 2011-12-09). After deterministic cleaning (removing cancellations and non-positive prices/quantities), 397,884 valid line items remain, spanning 4,338 customers, 18,532 invoices, 3,665 products, and 37 countries. For each customer we construct an ordered invoice sequence and define a canonical item per invoice (the item with the largest aggregated quantity). For each invoice transition we build a dual-view customer state vector that concatenates a lifetime purchase count view and a recent-window view (30 days), then learn embeddings via TF-IDF reweighting and truncated SVD. To increase robustness we introduce a denoising ridge projection (DRP) objective: a linear denoising model trained to map corrupted TF-IDF state vectors back to clean SVD embeddings without using labels, which yields denoised customer embeddings for downstream models. Our main contribution is an applied, computationally light integration of TF-IDF+SVD embeddings with a denoising linear projection for reuse across segmentation and next-purchase prediction, rather than a fundamentally new learning paradigm. In next-purchase prediction restricted to the 200 most frequent target items, a multinomial logistic model trained on DualDRP embeddings achieves Hit@20=0.587, outperforming MostPopular (Hit@20=0.327) and Markov (Hit@20=0.291). In segmentation we apply k-means clustering and analyze cluster-level RFM statistics and dominant products, showing that the learned embeddings recover actionable segments such as high-value frequent buyers and low-activity long-tail customers. All results, tables, and figures are generated with fixed random seeds and are reproducible in this environment
Evaluation of the Online Media Advertising Cost Calculation System Using the Cost Per Click and Cost Per Mille Methods Edison Siregar, Master
J-INTECH ( Journal of Information and Technology) Vol 14 No 01 (2026): Journal of Information and Technology
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v14i01.2230

Abstract

Online advertising platforms commonly employ Cost-Per-Click (CPC) and Cost-Per-Thousand-Impressions (CPM) models to determine advertiser fees based on user engagement and exposure metrics. While these pricing arrangements are mathematically simple, ensuring calculation accuracy and the scalability of the costing system is crucial for maintaining financial transparency and operational security. This study examines an online media advertising costing system using CPC and CPM methods through a structured system validation approach. The evaluation includes calculation verification, consistency testing against manual calculations, performance benchmarking, scalability assessment, and sensitivity analysis. A simulated dataset of 100 ad records was used to assess the system's accuracy and behavioral performance. The results demonstrate no calculation deviation between manual and system outputs, predictable linear scalability, and logistic revenue sensitivity under rate variations. These findings indicate that the evaluated system exhibits reliable calculation performance under controlled conditions. This study contributes to the evaluation of applied information systems by providing a structured methodology for validating ad collection systems.
Assessing Technology Acceptance for SIAKAD Usage among Scholarly Community Catur Utami, Meinarini; Sugiarti, Yuni; Sanul Akbar, Muhammad; Qomarul Huda, Muhammad; Aini, Qurrotul
J-INTECH ( Journal of Information and Technology) Vol 14 No 01 (2026): Journal of Information and Technology
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v14i01.2246

Abstract

The deployment of academic information systems in higher education institutions necessitates both technological preparedness and user acceptance to guarantee sustained utilization. In practice, users frequently encounter challenges, including confusion when retrieving academic material, doubts regarding the accuracy of presented data, and dependence on manual verification despite the system's availability. Such circumstances diminish trust and render the system seen as a mere formal obligation rather than a beneficial academic resource. Differences in user perceptions of system quality, information quality, and behavioral aspects consequently affect the efficacy of system adoption. By integrating UTAUT 2 with the Delone & Mclean Model, this research aims to analyse the variables that influence user acceptance and the usage behaviour of the current academic information system. A quantitative explanatory approach was employed, utilizing survey data collected from 450 active users. PLS-SEM was employed to examine the data for the assessment of both measurement and structural models. The findings demonstrate that performance expectancy, effort, facilitating conditions, habit, system quality, and knowledge all have a substantial impact on behavioral intention and usage behavior.  Conversely, social influence does not substantially impact behavioral intention, indicating that system utilization is primarily motivated by functional requirements rather than normative pressure. Practically, the results suggest that universities should prioritize system reliability, accurate and timely academic information, responsive user support, and routine integration of SIAKAD into core academic workflows to strengthen continued usage.
Analyzing Key Success Factors for Driving Repurchasing Conversion in E-CRM: Customer Insights on Gojek Application in Batam Limanda, Mellberg; Eryc; Deu, Indasari
J-INTECH ( Journal of Information and Technology) Vol 14 No 01 (2026): Journal of Information and Technology
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v14i01.2248

Abstract

This study examines the determinants of repurchase intention in Gojek’s digital service environment by integrating the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB), with Customer Relationship Management (CRM) serving as the contextual framework. In this study, CRM is not operationalized as an independent variable but provides the contextual setting for understanding user interactions with the platform. Using a sequential mixed-methods design, Qualitative findings reveal that price sensitivity is the most dominant factor influencing repurchase decisions for key drivers of repurchase intention, followed by usability, functional efficiency, and trust. Quantitative analysis via Partial Least Squares Structural Equation Modeling (PLS-SEM) confirmed that Perceived Ease of Use significantly influences Perceived Usefulness, which shapes Attitude, while Attitude and Perceived Behavioral Control strongly predict Repurchase Intention; Subjective Norms exert a weaker effect. By positioning CRM as a contextual environment rather than a measured construct, this study extends the application of the TAM–TPB framework in digital ride-hailing services and provides practical insights for strengthening customer retention strategies.
Evaluating Business Process Management Maturity Using the Business Process Orientation Maturity Model: Evidence from a Coffee MSME in Jember, Indonesia Amalia, Karina Nine; Nugrahani, Tri Agustina
J-INTECH ( Journal of Information and Technology) Vol 14 No 01 (2026): Journal of Information and Technology
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v14i01.2266

Abstract

This study investigates the maturity level of Business Process Management (BPM) in three coffee micro and small enterprises (MSMEs) supported by the Integrated Business Service Center (PLUT) of Jember Regency—BC, PT. FAI, and PT. RKS. These enterprises have demonstrated potential as Go-Digital MSMEs, as indicated by their adoption of digital platforms such as social media, e-commerce, and point-of-sales systems, as well as their participation in digital business facilitation programs. However, current evaluations of their business development remain predominantly focused on financial growth, limiting a comprehensive understanding of their process performance and organizational readiness. This study applies the Business Process Orientation Maturity Model (BPOMM) to assess and compare BPM maturity across nine dimensions. Using a qualitative case study approach based on interviews, observations, and member checking, the findings reveal that all three MSMEs remain at Level 1 (ad hoc), indicating that their business processes are largely unstructured and lack systematic management. The comparative analysis further uncovers differences in organizational structure formalization, the use of key performance indicators (KPIs), the extent of business process documentation, and the level of digital system integration supporting operational processes. This study contributes by contextualizing BPM maturity assessment within digital MSMEs and providing empirically grounded insights into process-related gaps that hinder their readiness for scaling and global market expansion. The findings offer practical implications for improving BPM practices while also suggesting the need for adapting maturity models to better capture the characteristics of digitally transforming MSMEs.
Academic Information System At MA.Fathus Salafi Using The Scrum Method Based On The Laravel Framework Fathor Rosit; Nur Azizah; Firman Jaya
J-INTECH ( Journal of Information and Technology) Vol 14 No 01 (2026): Journal of Information and Technology
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v14i01.2240

Abstract

In today's digital era, academic data management in schools requires a system that is efficient, secure, and easily accessible. The development of a web-based Academic Information System was carried out to simplify the administration process, recording grades, attendance, lesson schedules, and managing student and teacher data. The development methodology uses a scrum approach that allows an iterative development process and is responsive to user needs. The technologies used include the Laravel version 11 framework and MySQL database, supporting fast, secure, and structured development. System testing was carried out using the black box method which showed that all the main features functioned according to specifications, and could be accessed easily by users. The implementation results show that the system is able to improve the efficiency of academic data management while making it easier for users to input, process data, and report in a real-time and organized manner. The developed system is proven to be able to increase transparency and accountability of  administrative processes in schools. The conclusion of this research shows that the application of academic information system based on web technology and scrum methodology is able to produce a reliable, fast, and effective system. It is recommended that school managers continue to utilize, develop, and conduct regular training so that the system remains relevant and able to optimally support academic activities in the futurePrivacy Policy.

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