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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 357 Documents
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Analysis of Factors Influencing User Acceptance of the TEKAD Application Using Technology Acceptance Model (TAM) Among Village Apparatus in Manokwari Regency Surya Putri Kusumawati; Josua Josen A. Limbong; Lilis Indrayani
J-INTECH ( Journal of Information and Technology) Vol 14 No 03 (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.v14i03.2406

Abstract

Using the Technology Acceptance Model (TAM), this study examined how village apparatus in Manokwari Regency accepted the TEKAD (Integrated Village Economic Transformation) application. The TEKAD app was used to administer a questionnaire to 33 village apparatus in Prafi, Masni, and North Manokwari. Above 0.60 Cronbach's Alpha indicates all research indicators were valid and reliable. So the instruments were suitable for analysis. Perceived Ease of Use and Actual Use positively and significantly affected TEKAD application acceptance, according to the t-test. Technology acceptance was unaffected by perceived usefulness, attitude, and behavior. In addition, the F-test showed that all TAM model variables had a 0.000 significance level. The model's R² of 0.705 suggests strong predictability of technology acceptance among village apparatus. These findings suggest that Perceived Ease of Use is a key factor in TEKAD application acceptance and should be prioritized in future development. In addition, Actual Use should be maintained, as it contributes significantly to technology acceptance. Therefore, efforts to improve the application should focus on enhancing its ease of use, usability, and users’ perceptions, while strengthening digital literacy training for village apparatus to support effective and sustainable digital governance.
Capability Level Evaluation of Information Security Using COBIT 2019 APO13 and DSS05: A Case Study of the E-Mansinam Safety Patrol System Asrawati; Lorna Yertas Baisa; Alex De Kweldju Kweldju
J-INTECH ( Journal of Information and Technology) Vol 14 No 03 (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.v14i03.2435

Abstract

The increasing reliance on digital information systems has made information security governance a critical requirement for maintaining secure and reliable organizational operations. PT Pertamina Patra Niaga Fuel Terminal Manokwari utilizes the Safety Patrol feature within the E-Mansinam Website to support Health, Safety, Security, and Environment (HSSE) activities, including hazard reporting, safety monitoring, and corrective action management. This study evaluates the information security governance capability of the Safety Patrol feature using the COBIT 2019 framework, focusing on the APO13 (Managed Security) and DSS05 (Managed Security Services) domains. A descriptive quantitative approach was employed through observations, interviews, questionnaires, and documentation. The collected data were analyzed using Capability Assessment, Gap Analysis, and Root Cause Analysis (RCA). The results show that the APO13 domain achieved an average capability level of 4.04, exceeding the organization's target of Level 4, whereas the DSS05 domain achieved an average capability level of 3.93, indicating that several operational security service processes require further improvement. The largest capability gap was identified in DSS05.07 with a gap value of 0.44. Root Cause Analysis revealed that inconsistent security monitoring, incomplete documentation, limited periodic evaluations, and varying levels of personnel awareness were the primary factors contributing to the identified capability gaps. Based on these findings, this study proposes practical recommendations to strengthen information security governance and support the continuous improvement of the Safety Patrol feature, particularly in operational security service management.
Application of Support Vector Regression for Rice Production Prediction in Manokwari Regency Sri Fitrawati; Marlinda Sanglise; Christian Dwi Suhendra
J-INTECH ( Journal of Information and Technology) Vol 14 No 03 (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.v14i03.2439

Abstract

Annual fluctuations in rice production present significant challenges for agricultural planning and regional food security, particularly in areas where agricultural productivity is strongly influenced by environmental and production-related factors. Developing an accurate forecasting model is therefore essential to support evidence-based decision-making and improve agricultural resource management. Although Support Vector Regression (SVR) has demonstrated promising performance in agricultural prediction, previous studies have primarily focused on large-scale datasets or relied on default model parameters, limiting their applicability to local agricultural conditions. Therefore, this study aims to develop and evaluate a GridSearchCV-optimized Support Vector Regression model for predicting annual rice production in Manokwari Regency. Secondary data were obtained from the Agricultural Statistics Database maintained by the Indonesian Ministry of Agriculture, rainfall records from Rendani Meteorological Station, and statistical publications issued by the Central Statistics Agency of Manokwari Regency. The proposed framework integrates data preprocessing, systematic hyperparameter optimization using GridSearchCV, and Support Vector Regression modelling. The predictive capability of the model was assessed using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R²). The results demonstrate that the selected model employed the Radial Basis Function (RBF) kernel with C = 1000, gamma = 0.01, and epsilon = 0.001, achieving an MAE of 460.15, RMSE of 718.86, MAPE of 5.36%, and an R² value of 0.9578. These findings demonstrate satisfactory predictive performance and suggest that the proposed model has the potential to serve as a decision-support tool for agricultural production planning and regional food security management in Manokwari Regency.
Clustering of Myopia Patients Based on Severity Level, Geographic Area, Age, and Gender Using the K-Means Algorithm: A Case Study of Indra Optik Blitar Ayuna Rahim Imannur; Abdi Pandu Kusuma; Wahyu Dwi Puspitasari
J-INTECH ( Journal of Information and Technology) Vol 14 No 03 (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.v14i03.2455

Abstract

Indra Optik Blitar manages a large volume of patient medical record data that has not yet been utilized to support strategic decision-making. This study aims to cluster myopia refractive error patients based on their severity level, region, age, and gender characteristics using the K-Means algorithm, and to evaluate the quality of the resulting clusters using the Silhouette Coefficient. The research process follows the Knowledge Discovery in Database (KDD) framework, encompassing data selection, preprocessing, feature engineering, Min-Max Scaling normalization, and clustering execution. From 14,863 raw data entries, 6,821 valid records were obtained after three filtering layers: demographic, geographic, and clinical. Feature engineering was performed by converting four raw refraction variables into a single Worst_SE (Worst Spherical Equivalent) variable and transforming district address data into latitude and longitude coordinates. Feature weighting was automatically optimized using the Optuna framework with the Tree-structured Parzen Estimator (TPE) algorithm through 100 optimization trials. The optimal number of clusters was determined using a combination of the Elbow Method and Silhouette Coefficient, resulting in K = 5. The K-Means algorithm with K-Means++ initialization achieved convergence at iteration 20, producing five clusters with distinctive characteristics. Evaluation using the Silhouette Coefficient yielded an average score of 0.4144, which, given the high dimensionality and heterogeneity of the combined clinical, demographic, and geographic features, represents a moderately well-separated cluster structure suitable for practical application. The five clusters formed represent patient segments that differ clinically, demographically, and geographically, providing a data-driven foundation for Indra Optik Blitar to design more targeted marketing strategies, eye health socialization programs, and social assistance initiatives.  
Sentiment Analysis of the Free Nutritious Meal (MBG) Program on TikTok Using Naïve Bayes Classifier Sukma Ajeng Griyandari; Fahrur Rozi; Yayak Kartika Sari
J-INTECH ( Journal of Information and Technology) Vol 14 No 03 (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.v14i03.2458

Abstract

The Free Nutritious Meal (MBG) Program is an Indonesian government initiative to improve nutritional quality and support human resource development. As a public policy, it has generated diverse public responses on social media, particularly TikTok. The novelty of this study lies in utilizing TikTok comments, which remain underexplored in previous MBG sentiment studies, and employing expert-validated manual sentiment labeling to improve annotation reliability. This study applies the Naïve Bayes Classifier to analyze public sentiment toward the MBG Program using 3,190 TikTok comments, of which 1,294 were retained after relevance filtering, data cleaning, and manual labeling validated by two Indonesian language experts. Text preprocessing included cleaning, case folding, tokenization, normalization, stopword removal, and stemming, followed by TF–IDF feature weighting. The model was evaluated using stratified 10-fold cross-validation. It achieved 72.80% accuracy, 69.90% weighted precision, 73.20% weighted recall, and 71.40% F1-score. Negative sentiment dominated public discussion of the MBG Program. These findings demonstrate that combining expert-validated manual labeling, TF–IDF, and the Naïve Bayes Classifier provides a reliable approach for sentiment analysis of Indonesian social media text and offers useful insights for evaluating the implementation of the MBG Program
GrabFood Technology Acceptance in Manokwari using Expanded TAM Findy Indah Puspita; Andreas Leonardo Sumendap; Ratna Juita
J-INTECH ( Journal of Information and Technology) Vol 14 No 03 (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.v14i03.2519

Abstract

The development of application-based Food delivery services has increased the use of digital technology in daily life. However, research related to the variables that affect technology adoption by GrabFood users in Eastern Indonesia, especially Manokwari City, is still relatively limited. This study examines factors affecting public acceptance of GrabFood technology in Manokwari City using an expanded TAM Model. The model includes Perceived Ease of Use (PEOU), Perceived Usefulness (PU), Attitude Toward Use (AT), Behavioral Intention (BI), Trust (TR), Service Quality (SQ), and Subjective Norm (SN). This quantitative study used questionnaires to collect data from GrabFood users in Manokwari City. Data were analyzed using PLS-SEM with SmartPLS 4. The results showed that all indicators met the required validity and reliability criteria. PEOU positively and significantly affected PU (β = 0.335, p < 0.001) and AT (β = 0.283, p < 0.001). PU positively affected AT (β = 0.345, p < 0.001) and BI (β = 0.150, p = 0.034), while AT positively affected BI (β = 0.272, p = 0.002). Trust and Service Quality positively affected PU but did not directly affect BI. Subjective Norm positively affected AT and BI. The model explained 39.1% of the variance in PU, 44.5% in AT, and 45.5% in BI, indicating moderate explanatory power. These findings support the expanded TAM model in explaining GrabFood technology acceptance in Manokwari and suggest that service providers improve ease of use, user benefits, service quality, and user trust.
Integrating User Perception and Technical Assessment for Comparative Software Quality Evaluation of Shopee and Tokopedia Based on ISO/IEC 25010 Rizky Ramadhan; Achmad Zakki Falani; Eman Setiawan
J-INTECH ( Journal of Information and Technology) Vol 14 No 03 (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.v14i03.2521

Abstract

This study evaluates and compares the software quality of Shopee and Tokopedia based on four ISO/IEC 25010 characteristics, namely usability, performance efficiency, reliability, and portability. A quantitative evaluative-comparative approach was employed using data from 56 respondents and the USE Questionnaire for usability assessment. Technical evaluation was conducted using GTMetrix for performance efficiency, Apache JMeter for reliability, and cross-platform testing on Google Chrome, Mozilla Firefox, Android, and iOS for portability. The results show that Shopee achieved a usability score of 79.74%, while Tokopedia obtained 79.07%, with both classified as Good. For performance efficiency, Tokopedia achieved a Performance Score of 63%, whereas Shopee obtained 26%, which was classified as Bad. Reliability testing showed comparable results, with both applications achieving a 100% success rate and 0% error rate. The average response time was 1,522 ms for Shopee and 1,550 ms for Tokopedia. In terms of portability, Shopee achieved an overall success rate of 92.5%, while Tokopedia achieved 85%, both classified as Very Good. Overall, Shopee demonstrated better usability and portability, while Tokopedia demonstrated better performance efficiency. The reliability results were considered comparable under the applied 100-user baseline testing scenario. These findings demonstrate that software quality comparison should consider multiple ISO/IEC 25010 characteristics rather than relying on a single quality dimension.

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