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International Journal of Computer and Information System (IJCIS)
Published by Institut Teknologi Bisnis AAS Indonesia
ISSN : -     EISSN : 27459659     DOI : https://doi.org/10.29040/ijcis
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering
The aim of this journal is to publish quality articles dedicated to all aspects of the latest outstanding developments in the field of informatics engineering. Its scope encompasses the applications of (but are not limited to) : 1. Artificial Intelligence 2. Software Engineering 3. System Design Methodology 4. Data mining and Big Data 5. Human and Computer Interaction 6. Mobile Computing 7. Soft Computing 8. Animation 9. Multimedia and Image Processing 10. Parallel/Distributed Computing 11. Machine Learning 12. Computational Lingustics 13. Data Comunication 14. Networking
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 206 Documents
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Analysis of the Effect of Social Media Compression on the Accuracy of Deepfake Detection Using MobileNetV3 and Compressed Data Augmentation Erika Ramadhani; M. Fahrul Ramadhan
International Journal of Computer and Information System (IJCIS) Vol 7, No 3 (2026): IJCIS : Vol 7 - Issue 3 - 2026
Publisher : Institut Teknologi Bisnis AAS Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/ijcis.v7i3.279

Abstract

Deepfakes are synthetic audio-visual content that is artificially engineered that is increasingly difficult to distinguish from real content and is mostly circulated through social media platforms. The problem studied in this study is the decline in the accuracy of the deepfake detection model when the test content has gone through a series of social media compression processes, such as H.264 re-encoding and resolution degradation, which removes some of the forensic traces of the pixels that are the basis for detection. The objectives of this study were to measure the magnitude of the decline in detection accuracy in the lightweight architecture of MobileNetV3, to find the compression threshold where the decline began to be significant, and to test the augmentation of compressed data as a mitigation strategy. The method used was a quantitative experiment on the Celeb-DF v2 dataset with three levels of compression (raw, c23, and c40) simulated using FFmpeg; The MobileNetV3-Small model was trained on raw data and then tested at all three levels of compression, then compared to training scenarios using combined data of all three levels of compression. Test results on 5,180 face images of standard test data showed a decrease in accuracy from 70.23% in raw data to 69.54% in c23 and 58.11% in c40, with the sharpest decrease occurring between medium compression and high compression and mainly due to a drop in recall from 89.79% to 61.24%. Training scenarios with compressed data augmentation were shown to increase AUC at all levels of compression, from 59.51% to 64.29% at c40, although accompanied by a slight decrease in accuracy on raw data. The contribution of this study is empirical evidence of the compression resistance of lightweight architectures that are rarely studied as well as practical mitigation recommendations for the development of deepfake detection systems on mobile devices.
Investigating the Impact of Augmented Reality (AR) as an Interactive Tool in Teaching English Vocabulary: A Literature Review Tira Nur Fitria
International Journal of Computer and Information System (IJCIS) Vol 7, No 2 (2026): IJCIS : Vol 7 - Issue 2 - 2026
Publisher : Institut Teknologi Bisnis AAS Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/ijcis.v7i2.277

Abstract

This research investigates the impact of Augmented Reality (AR) as an interactive tool in teaching English vocabulary. This study employed a literature review design to investigate the impact of AR as an interactive tool in teaching English vocabulary. Research on AR in vocabulary learning has consistently demonstrated its positive impact on students' motivation, retention, and learning outcomes. Key findings from 28 studies highlight several advantages of AR in vocabulary instruction. Studies on AR in vocabulary learning have consistently shown its positive influence on students’ motivation, retention, and learning outcomes. AR has been proven to significantly improve vocabulary retention, enabling students to remember words for longer periods compared to traditional methods such as flashcards or lecture-based approaches. It also creates a more engaging and interactive environment, which increases students’ enthusiasm and motivation to learn vocabulary. Research further indicates that AR is effective across different age groups, from preschool to high school, supporting both low-achieving and advanced learners. Many studies highlight that students using AR perform better in vocabulary tests than those using conventional learning strategies. Comparisons between AR and tools like flashcards or multimedia suggest that AR is more engaging, interactive, and effective in enhancing vocabulary mastery. However, several challenges exist in implementing AR, including limited access to technology, a lack of teacher training, and the relatively high cost of AR devices and applications. The success of AR-based learning also depends on the support of parents and teachers, as parental involvement and teacher readiness play a vital role in ensuring its effective integration into the classroom. Overall, AR holds great potential as an innovative tool to enhance vocabulary learning, provided that the technological, financial, and pedagogical challenges are addressed.
Design and Functional Validation of an AI-Enabled Social Accounting and Performance Excellence Decision-Support System for Dental Clinic Networks Mahameru Rosy Rochmatullah; Muqorobin Muqorobin; Didik Prasetyanto; Dewi Setyoningsih
International Journal of Computer and Information System (IJCIS) Vol 6, No 4 (2025): IJCIS : Vol 6 - Issue 4 - 2025
Publisher : Institut Teknologi Bisnis AAS Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/ijcis.v6i4.306

Abstract

Multi-unit dental service organizations require integrated monitoring of organizational performance, service quality, patient experience, and social impact. Conventional dashboards usually report historical indicators but provide limited support for linking performance deviations to traceable managerial action. Objective: This study aimed to design, develop, and functionally validate an artificial-intelligence (AI)-enabled decision-support system integrating Social Accounting and Performance Excellence principles for dental clinic networks. Methods: A Design Science Research approach guided problem identification, requirements analysis, artifact design, prototype development, demonstration, and functional evaluation. The prototype integrates organizational KPIs, social-impact indicators, branch comparison, SOP/audit functionality, role-based processes, SQLite persistence, and AI-supported managerial insights. In addition to the documented local functional test, a reproducible synthetic engineering dataset comprising 30 branch-month records (five fictional branches over six months) was generated to verify KPI calculations, social-impact aggregation, prioritization, and anomaly-oriented decision logic when real operational data were unavailable. The synthetic observations contain no real patient or clinic records and are not treated as UAT evidence. Results: The study produced an executable Alpha v0.1 prototype that runs on localhost and supports indicator input, data persistence, KPI summarization, analytical insight generation, branch-level comparison, and audit-oriented workflow. The available technical evidence reports 10 of 10 predefined local functional checks as PASS (100%). In the supplementary synthetic verification, the 30 records yielded a network mean performance index of 85.2, mean social-impact index of 80.5, and mean combined score of 83.3. The deliberately stressed fictional branch B04 was classified as high priority in all six simulated months, whereas B02 produced the highest mean combined score (90.4), demonstrating the expected discrimination of the analytical rules. Conclusion: The artifact demonstrates the technical feasibility of integrating Social Accounting, Performance Excellence, and AI-supported organizational analytics in a unified dental-network decision-support system. The synthetic exercise strengthens engineering verification of the analytical logic but does not substitute for relevant-environment user validation, real-data validation, or clinical evaluation.
Regression-PID: Bare-Metal Predictive Temperature Control via Multiple Linear Regression on an ESP32-Based IoT Egg Incubator Zainal Arifin; Siti Rokhmah; Tino Feri Efendi
International Journal of Computer and Information System (IJCIS) Vol 7, No 2 (2026): IJCIS : Vol 7 - Issue 2 - 2026
Publisher : Institut Teknologi Bisnis AAS Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/ijcis.v7i2.281

Abstract

Abstract - The success of egg hatching depends on the stability of the incubation temperature within a highly strict tolerance (±0.3°C). Conventional PID controllers in egg incubators are reactive, correcting temperature only after an error is detected, which makes them prone to overshoot during warm-up. This study proposes Regression-PID — a PID controller augmented with a Multiple Linear Regression (MLR) predictive model deployed as bare-metal arithmetic in ESP32 firmware, without any machine learning framework. Trained offline using Ordinary Least Squares on historical temperature and duty cycle data, the optimal window W = 5 yields 11 coefficients (R² = 0.7634, RMSE = 0.1512°C) stored in 44 bytes of flash. The 30-second-ahead temperature prediction drives the proportional and derivative terms; the integral term uses actual temperature to guarantee steady-state error elimination. A comparative experiment was performed on an ESP32-based IoT egg incubator with real-time MQTT telemetry to a cloud backend. Regression-PID reduces overshoot by 68.1% (0.44 vs. 1.38°C), ISE by 89.7% (0.71 vs. 6.92 °C²·s), IAE by 74.6%, and steady-state standard deviation by 73.9% (0.014 vs. 0.053°C); both modes maintained the ±0.3°C tolerance band 100% of the time. Computational overhead is only +12.17 µs per cycle (0.0012% of the 1-second period) with deterministic latency. Performance differences are confirmed by Mann-Whitney U (p = 0.0009, r = 0.777) and Wilcoxon Signed-Rank (p < 0.0001). These results demonstrate that linear regression is sufficient for predictive thermal control in quasi-linear systems, with minimal complexity and no compromise to real-time feasibility.
Development and Usability Evaluation of an Android-Based Marker-Based Augmented Reality Application for Residential Property Promotion Devi Afriyantari Puspa Putri; Endah Sudarmilah; Fatah Yasin Al Irsyadi; Zildan Alfatih Agustian; Alif Pandu Raharjo
International Journal of Computer and Information System (IJCIS) Vol 7, No 3 (2026): IJCIS : Vol 7 - Issue 3 - 2026
Publisher : Institut Teknologi Bisnis AAS Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/ijcis.v7i3.280

Abstract

The growing demand for interactive digital marketing has encouraged the adoption of Augmented Reality (AR) to overcome the limitations of conventional property promotion media. However, existing studies have primarily emphasized system implementation or marketing outcomes, with limited attention to evaluating application quality from the users' perspective. This study aims to develop an Android-based marker-based AR application for residential property promotion, verify its functionality through black-box testing, and evaluate its usability using a validated and reliable evaluation instrument. The application was developed following the Four-D (4D) development model and implemented using Unity and the Vuforia SDK to integrate printed property brochures with interactive three-dimensional visualization. Functional verification was conducted using black-box testing, while usability evaluation involved 51 respondents, comprising 45 public users and six property developers. The evaluation instrument was first validated and tested for reliability before descriptive statistical analysis was performed. The results showed that all application features operated according to the specified functional requirements. The questionnaire demonstrated acceptable validity and reliability, with a Cronbach's Alpha coefficient of 0.678. User evaluation indicated positive acceptance, achieving overall scores of 85.11% from public users and 85.33% from developers. These findings demonstrate that the proposed application provides an effective and user-friendly approach to residential property promotion by combining interactive three-dimensional visualization with conventional printed brochures. The proposed development and evaluation framework may serve as a reference for future AR-based promotional applications
Public Sentiment Analysis of Indonesia’s Free Nutritious Meals (MBG) Program Using Lexicon-Based Labeling, SMOTE, and Machine Learning Kharisma Wiati Gusti; Anni Alvionita Simanjuntak
International Journal of Computer and Information System (IJCIS) Vol 7, No 3 (2026): IJCIS : Vol 7 - Issue 3 - 2026
Publisher : Institut Teknologi Bisnis AAS Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/ijcis.v7i3.302

Abstract

The Free Nutritious Meals (MBG) Program is a strategic policy of the Indonesian government aimed at improving national nutrition and has elicited diverse public reactions on social media. This study analyses public sentiment toward the MBG program using 11,003 cleaned public comments. Because the dataset lacked sentiment labels, a lexicon-based labelling approach using the Indonesian InSet Lexicon was applied to automatically classify comments as positive, negative, or neutral. The labelled data were then divided into 80% training data and 20% testing data using stratified sampling, and TF-IDF was used for feature extraction. Because the resulting class distribution was imbalanced (61.9% negative, 20.3% positive, and 17.8% neutral), the Synthetic Minority Over-sampling Technique (SMOTE) was applied only to the training data. Four classification algorithms (Naive Bayes, Support Vector Machine, Logistic Regression, and Random Forest) were trained and compared with and without SMOTE. The results show that SMOTE improved the macro-average F1-score across all models, particularly Naive Bayes (from 0.42 to 0.67). At the same time, SVM remained the best-performing model, achieving 84.83% accuracy and a macro F1-score of 0.81 after SMOTE, with a slight accuracy trade-off for substantially better recall in the neutral and positive minority classes. These findings indicate that combining lexicon-based labelling with SMOTE and classical machine learning provides a more balanced and reliable tool for monitoring public sentiment toward government policy programs such as MBG. Keywords: sentiment analysis; Free Nutritious Meals; lexicon-based labeling; SMOTE; machine learning

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