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Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Published by Universitas Teknologi Akba Makassar
ISSN : 20886705     EISSN : 26215608     DOI : https://doi.org/10.35585/inspir.v12i2.21
Core Subject : Science,
Computer Science & IT
Inspiration: Jurnal Teknologi Informasi dan Komunikasi is a scientific journal that publishes research results in the field of Information and Communication Technology (ICT). The ICT research area that is the focus of this journal can be seen on the Focus and Scope page. Journals are published twice a year, in June and December. Papers submitted for Inspiration: Jurnal Teknologi Informasi dan Komunikasi in volume 12 Number 2 in 2022, using a new template and be written in English for an initial review stage by the editor and a further review process by a minimum of two reviewers. This is a peer-review journal in information technology and communication research fields. Focus and scope of this journal deal with some research topics, including: - Artifical Intelligence - Database - Data Communication - Computer Networks - Software Engineering - Human Computer Interaction - Microcontrollers - Robotics - Data Mining - Image Processing - Information Retrieval - Natural Language Processing - Content Based Image Retrieval - Green Computing - Internet of Things
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 68 Documents
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Understanding Consumer Behavior in Live Streaming E-Commerce: A Technology Acceptance Model Perspective Herlina, Herlina; Andry, Johanes Fernandes; Mulyana, Teady Matius Surya; Wilujeng, Fuji Rahayu; Sasongko, Yohanes Probo Dwi; Rachmad, Teguh Hidayatul
Inspiration: Jurnal Teknologi Informasi dan Komunikasi Vol. 15 No. 1 (2025): Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Pusat Penelitian dan Pengabdian Pada Masyarakat Sekolah Tinggi Manajemen Informatika dan Komputer AKBA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35585/inspir.v15i1.105

Abstract

Live streaming has rapidly become a transformative feature within the landscape of online shopping, not only serving as an interactive marketing tool but also reshaping consumer decision-making processes in the digital marketplace. Far beyond a mere add-on, live streaming represents a disruptive innovation that significantly alters consumer–vendor engagement and accelerates the evolution of e-commerce ecosystems. To gain a deeper understanding of this phenomenon, the present study employs the Technology Acceptance Model (TAM) as a theoretical lens to investigate the drivers of consumer adoption and sustained usage of live streaming in online shopping contexts. Data were collected from 150 active users of live streaming features through purposive accidental sampling and analyzed using path analysis with AMOS software. The results provide strong empirical evidence that the TAM constructs Perceived Usefulness (PU), Perceived Ease of Use (PEU), Attitude Toward Using (ATU), Behavioral Intention to Use (BIU), and Actual System Use (AU) are all significantly and positively interrelated. These findings underscore the strategic importance of consumer perceptions of utility and ease of use in fostering favorable attitudes, strengthening behavioral intentions, and translating into actual system usage. This research not only validates TAM in the context of live streaming but also offers critical implications for global e-commerce stakeholders and technology developers by highlighting how interactive features can be optimized to enhance consumer engagement, trust, and long-term digital marketplace sustainability.
Naïve Bayes-Based Intelligent Model For Identification and Analysis of Learners' Intelligence Potential Jumarlis, Mila; Mirfan, Mirfan; Suardi M; Sharma, Vivek; R Mudalim, Nurhasana
Inspiration: Jurnal Teknologi Informasi dan Komunikasi Vol. 15 No. 1 (2025): Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Pusat Penelitian dan Pengabdian Pada Masyarakat Sekolah Tinggi Manajemen Informatika dan Komputer AKBA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35585/inspir.v15i1.109

Abstract

Academic achievement, especially report card scores, is frequently the only factor used to evaluate a student's intelligence in education, ignoring other aspects, including kinesthetic, musical, interpersonal, and intrapersonal intelligence. This traditional method limits opportunities for students whose abilities are outside of typical academic disciplines, marginalizing them.  In order to close this gap, the current work is to: (1) develop a decision support system (DSS) that uses the Naïve Bayes approach to determine students' intelligence potential; and (2) integrate the method into the DSS to accomplish rigorous and precise identification. Using PHP and a MySQL database, the system was created utilizing the Unified Modeling Language (UML), which included use case, activity, sequence, and class diagrams. Because of its track record of handling labeled data and producing highly accurate probabilistic predictions, Naïve Bayes was chosen. To capture all of the learner characteristics, information was gathered through interviews and observation. Based on the results, the suggested method successfully recognizes various forms of intelligence and offers tailored learning suggestions that are in line with each student's abilities. The objective, effective, and data-driven approach to intelligence identification provided by this study helps educators create more inclusive and talent-oriented teaching methods. Ultimately, the model fortifies the paradigm of holistic education by guaranteeing that teaching practices are enriched by the recognition, cultivation, and integration of a variety of intelligences.
Gamification Enhanced Micro Learning in Blended Learning: Effects on Knowledge and Skills Disa, Syaharullah; Sitti Arni
Inspiration: Jurnal Teknologi Informasi dan Komunikasi Vol. 15 No. 1 (2025): Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Pusat Penelitian dan Pengabdian Pada Masyarakat Sekolah Tinggi Manajemen Informatika dan Komputer AKBA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35585/inspir.v15i1.112

Abstract

Innovative learning approaches are essential to address 21st-century educational challenges and enhance learner engagement. This study examines the impact of integrating gamification into micro-learning to improve student knowledge and skills within blended learning environments. A quasi-experimental design assigned 56 students to either a treatment group, which used an Android application featuring gamification and micro-learning principles, or a control group, which received conventional instruction. T-test results indicated a significant post-intervention improvement in knowledge and skills for the treatment group (t = -17.352, p < 0.001) compared to the control group (t = -7.402, p < 0.001). An independent t-test also showed a significant difference between the groups' final results (p < 0.001), with the treatment group averaging 91.96 and the control group 78.87 on the post-test. These findings demonstrate that gamification-enhanced micro-learning can increase student motivation, engagement, and critical thinking and problem-solving skills. The study underscores the important role of educators as facilitators who connect play-based experiences with meaningful learning outcomes.
Lightweight Deep Learning Approach Using 1D-CNN and Attention for Sequential Credit Card Fraud Detection Nugroho H, Yabes Dwi; Rahmawati, Aulia; Araz, Rezty Amalia; Nuryono, Aninditya Anggari
Inspiration: Jurnal Teknologi Informasi dan Komunikasi Vol. 15 No. 1 (2025): Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Pusat Penelitian dan Pengabdian Pada Masyarakat Sekolah Tinggi Manajemen Informatika dan Komputer AKBA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35585/inspir.v15i1.115

Abstract

Fraudulent activity in credit card transactions continues to be a pressing concern in the financial industry, primarily because transaction data is highly complex and heavily skewed toward legitimate cases. To address this issue, the present study proposes a hybrid deep learning framework that merges the strengths of a one-dimensional convolutional neural network (1D-CNN) with the selective capabilities of an attention mechanism. The performance of this enhanced model was rigorously compared with a conventional 1D-CNN, employing widely recognized evaluation metrics such as accuracy, precision, recall, and the F1-score. The experimental outcomes demonstrate that introducing the attention layer substantially improves the network’s ability to recognize critical temporal dependencies in transaction sequences. As a result, the model achieved exceptional performance levels, with an accuracy of 98%, precision of 97%, recall of 98%, and an F1-score of 98%. These findings provide strong evidence of the superiority of the attention-based approach, highlighting its effectiveness in producing more reliable and resilient fraud detection systems. Beyond the algorithmic gains, the research contributes a practical foundation for real-time applications in financial security, enabling institutions to curtail potential losses, reinforce public confidence in digital payment services, and enhance the efficiency of day-to-day operations.
Implementation of A Combination Of WP and Moora Methods in Recruitment Of Casual Daily Laborers to Become Permanent Employees Sanjaya, Bima; Irawan, Muhammad Dedi
Inspiration: Jurnal Teknologi Informasi dan Komunikasi Vol. 15 No. 1 (2025): Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Pusat Penelitian dan Pengabdian Pada Masyarakat Sekolah Tinggi Manajemen Informatika dan Komputer AKBA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35585/inspir.v15i1.117

Abstract

The Industrial Revolution 4.0 has emphasized the strategic role of information technology in supporting organizational decision-making, particularly in workforce recruitment. Within this context, PTPN IV, a leading company in the plantation sector, continues to encounter challenges in promoting Casual Daily Laborers (BHL) to permanent employees through an objective and transparent selection process. To address this issue, this study introduces a decision support system (DSS) that integrates the Weighted Product (WP) method with Multi-Objective Optimization by Ratio Analysis (MOORA). The WP method is applied to calculate preference values based on weighted criteria, while MOORA facilitates multi-criteria optimization, thereby strengthening the accuracy of the ranking process. Empirical findings indicate that the combined approach significantly enhances decision-making effectiveness, producing more consistent and fair outcomes compared to conventional methods. The practical implication of this research lies in providing PTPN IV with a reliable tool to improve efficiency, ensure fairness, and strengthen accountability in employee promotion decisions, which ultimately contributes to organizational performance and competitiveness.
Evaluating ERP Information Security Using ISO/IEC 27001:2013 Standard in Agricultural Technology Enterprises Hastuti, Puji; Dewi Cantika, Nourma Islam; Pratama, Aditya; Saputra, Dhanar Intan Surya
Inspiration: Jurnal Teknologi Informasi dan Komunikasi Vol. 15 No. 1 (2025): Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Pusat Penelitian dan Pengabdian Pada Masyarakat Sekolah Tinggi Manajemen Informatika dan Komputer AKBA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35585/inspir.v15i1.120

Abstract

Data security is an essential component of ERP system management, particularly given the increasing cyber dangers in the digital age.  This study assesses the maturity of ERP-based information security at PT BroilerX Yogyakarta using the ISO/IEC 27001:2013 standard.  The growing dependence on digital platforms in agriculture has expedited the implementation of ERP solutions, like Odoo, to optimize business processes.  This research employs a qualitative descriptive case study methodology to examine the implementation of ten ISO domains, utilizing data gathered from interviews with the IT team and analyzed through the Capability Maturity Model Integration.  The findings indicate that the majority of domains attained a maturity level of 4 (Managed), signifying consistent and thoroughly documented processes, although Human Resource Security and Incident Management remained at level 3 (Defined), highlighting the necessity for enhanced automation and proactive incident management.  These findings underscore both the strengths and flaws in ERP security implementation and offer actionable advice to enhance the Information Security Management System (ISMS).  This research contextualizes ISO/IEC 27001:2013 within a poultry agritech enterprise, thereby enhancing understanding of the integration of international security standards in technology-driven agribusiness and providing practical insights for similar organizations aiming to bolster their resilience against cyber threats.
Implementation of Random Forest for Sentiment Analysis of YouTube Comments Related To The TNI Law Revision virna, virna afrilianty; Nur Insani, Chairi; Arifin, Nurhikma; Furqan Rasyid, Muhammad
Inspiration: Jurnal Teknologi Informasi dan Komunikasi Vol. 15 No. 2 (2025): Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Pusat Penelitian dan Pengabdian Pada Masyarakat Sekolah Tinggi Manajemen Informatika dan Komputer AKBA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Social media platforms such as YouTube have become important spaces for the public to express opinions on various policy issues, including the Draft Bill on the Indonesian National Armed Forces (RUU TNI). However, research on sentiment analysis of YouTube comments remains limited, particularly in the application of multi-class classification using the Random Forest algorithm. This study aims to implement Random Forest for classifying sentiments in YouTube comments related to the RUU TNI into three categories: positive, negative, and neutral. The dataset consists of 7118 comments, divided into 5694 training data and 1423 testing data. Sentiment labeling was conducted using a lexicon-based approach, while text representation was carried out using TF-IDF. To address data imbalance, class weighting was applied, and model parameter optimization was performed using the GridSearchCV technique. The optimal parameter combination obtained was n_estimators=300, max_depth=None, max_features='log2', and min_samples_split=20. The evaluation results show that the model with class weighting achieved an accuracy of 80.27%, while the model without weighting achieved 79.42%. These findings indicate that applying class weighting and parameter optimization effectively improves sentiment classification performance of public opinions on the RUU TNI policy on YouTube.
COMPARISON OF RANDOM FOREST AND K-NEAREST NEIGHBOR ALGORITHMS FOR PREDICTION OF CUSTOMER CREDIT RISK BASED ON TRANSACTION HISTORY AND INCOME Harefa, Kecitaan Harefa
Inspiration: Jurnal Teknologi Informasi dan Komunikasi Vol. 15 No. 2 (2025): Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Pusat Penelitian dan Pengabdian Pada Masyarakat Sekolah Tinggi Manajemen Informatika dan Komputer AKBA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The increase in the number of credit applications requires financial institutions to have an accurate risk assessment system in order to minimize the potential for bad loans. The main problem that often arises is the difficulty in objectively assessing the level of customer risk based on varied transaction history and income data. To overcome this, this study compared two classification algorithms in machine learning, namely Random Forest and K-Nearest Neighbor (KNN), to predict customer credit risk. The goal is to determine the algorithm that provides the best level of prediction accuracy and stability. The research method includes data collection from the credit risk dataset, the pre-processing stage (data cleaning, encoding, and normalization), the separation of the data into training and testing sets, then model training using both algorithms. Evaluations were conducted based on accuracy, precision, recall, F1-score, and ROC-AUC metrics. The test results showed that the Random Forest algorithm provided superior performance to KNN, with an accuracy rate of around 90%, while KNN only reached around 84%. This shows that Random Forest is more effective at handling data with complex and non-linear variables. In conclusion, the use of the Random Forest method can be the optimal solution for financial institutions in identifying customers' credit risks more accurately and efficiently.

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