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INDONESIA
Jurnal Mandiri IT
Published by Institute of Computer Science
ISSN : 23018984     EISSN : 28091884     DOI : https://doi.org/10.35335/mandiri
Core Subject : Science, Education,
Computer Science & IT Library & Information Science Mathematics
The Jurnal Mandiri IT is intended as a publication media to publish articles reporting the results of Computer Science and related research.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 187 Documents
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Design and development of a web-based seminar and workshop management application at STMIK Jayakarta Pambudi, Grandis Dias; Budiman, Thomas; Sianipar, Anton Zulkarnain
Jurnal Mandiri IT Vol. 14 No. 1 (2025): July: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i1.429

Abstract

The rapid development of information technology offers opportunities to improve the efficiency and quality of academic event management, including seminars and workshops. This study aims to design and develop a web-based application that facilitates the digital management of seminars and workshops at STMIK Jayakarta. The application includes key features such as online attendance recording using a unique event code, real-time interactive Q&A submission, and automatic certificate generation for both participants and speakers. The system adopts a two-role model—admin and non-admin—where non-admin users may act as participants, moderators, or speakers depending on the event, allowing for flexible role assignments. Developed using HTML, PHP, JavaScript, and MySQL, the application improves data accuracy, reduces the risk of data loss, and simplifies administrative processes. This system is expected to serve as a foundation for future development of similar academic applications.
Web-based booking application for services and care products at Lia Salon Angali, Fransiska Warkop; Yasin, Verdi; Yulianto, Akmal Budi
Jurnal Mandiri IT Vol. 14 No. 1 (2025): July: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i1.432

Abstract

Digital transformation encourages businesses in various sectors, including the beauty salon industry, to adopt technology as a means of improving efficiency and service. Lia Salon, as one of the beauty service providers, still applies a manual booking system that is considered less efficient and makes it difficult for customers. This research aims to implement a web-based booking application to facilitate the process of booking services and products online. Through a qualitative approach with a prototyping method, the system was developed based on the results of observations and interviews with salon owners and employees. The development results show that the application is able to display service and product information in a structured manner, supports the ordering process directly by customers, and allows automatic management of order data by the admin. With this application, Lia Salon can increase customer convenience as well as operational efficiency. This research recommends the utilization of similar technology for small and medium-sized businesses that face challenges in managing services manually.
Analyzing public sentiment on youtube comments regarding the free lunch policy using the Support Vector Machine (SVM) algorithm Septiana, Linda; Yasin, Verdy; Sianipar, Anton Zulkarnain
Jurnal Mandiri IT Vol. 14 No. 1 (2025): July: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i1.434

Abstract

The advancement of information technology and social media has reshaped how individuals express their opinions on public policies. YouTube has emerged as a major platform where public sentiment is openly shared, including reactions to the government’s Free Lunch Program for elementary school students. This study aims to analyze public sentiment toward the policy using the Support Vector Machine (SVM) algorithm with both linear and Radial Basis Function (RBF) kernels. A total of 1,883 YouTube comments were collected and manually labeled into three sentiment categories: positive, negative, and neutral. The preprocessing steps included cleansing, case folding, normalization, tokenization, stopword removal, and stemming, followed by TF-IDF transformation. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics, and validated using 10-Fold Cross Validation to ensure result consistency. The findings indicate that the SVM model with RBF kernel and 10-fold cross-validation achieved the highest accuracy at 81.46%. However, the linear kernel model provided a more balanced performance with superior precision, recall, and F1-score. These results highlight the importance of choosing the right kernel and validation strategy in developing sentiment analysis models, especially when dealing with imbalanced social media data.
Implementation of vision transformer for offensive language detection on tiktok social media Rahmawaty, Zulekha; Fitriastuti, Fatsyarina; Setyawan, Ryan Ari
Jurnal Mandiri IT Vol. 14 No. 1 (2025): July: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i1.435

Abstract

The rise of social media platforms such as TikTok has introduced new challenges in content moderation, particularly concerning the spread of offensive language and hate speech. One promising approach to addressing this issue is through automatic detection using deep learning technology. This study implements the Vision Transformer (ViT) to detect offensive language on the TikTok platform based on visual data in the form of comment screenshots. The dataset used consists of 1,401 labeled images categorized into two classes: offensive and non-offensive. The training process was conducted over 50 epochs without a validation split, and the evaluation was carried out using accuracy, precision, recall, and F1-score metrics. Results showed high performance, with an accuracy of 99.93%, precision of 0.9979, recall of 1.000, and F1-score of 1.000 at the 40th epoch, maintaining stability through the end of training. These findings demonstrate that ViT is effective in extracting visual features from image-based comments, even without access to raw text. This approach is particularly relevant in the context of TikTok, where comments often appear in visual formats such as thumbnails, screenshots, or reaction videos. This research opens up opportunities for the implementation of image-based offensive language detection systems that can enhance content moderation by adapting to various visual formats. Further development is recommended using a larger dataset and more systematic data splitting to test the model’s generalization capability.
Implementation of role-based access control, multi tenancy and audit logging in a single sign-on system Aswintama, Putranta; Haryanto, Eri; Setyawan, Ryan Ari
Jurnal Mandiri IT Vol. 14 No. 1 (2025): July: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i1.441

Abstract

As enterprises increasingly require centralized, secure, and efficient authentication mechanisms, Single Sign-On (SSO) has emerged as a strategic approach to managing user access. This study discusses the implementation of an SSO system based on Laravel Livewire with support from JSON Web Token (JWT) and OAuth, developed for PT Radiator Springs Indonesia. The system integrates three main components: Role-Based Access Control (RBAC) for access rights management, a Multi-Tenancy architecture for separating users across organizational units, and Audit Logging to track user activities. The analysis shows significant improvements in security, with 87.5% fewer unauthorized access attempts and enhanced user management efficiency, evidenced by a 71.43% reduction in time to onboard new users. Additionally, the system generates over 300+ audit log entries per day, improving monitoring and compliance capabilities.
Comparative study of machine learning algorithms for predicting drug induced autoimmunity using molecular descriptors Delfiero, Yusuf Rio; Hidayati, Ajeng; Saputra, Bagus Hendra
Jurnal Mandiri IT Vol. 14 No. 1 (2025): July: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i1.436

Abstract

Drug induced autoimmunity (DIA) poses significant challenges in pharmaceutical development due to its complex immunological mechanisms and delayed clinical manifestations. This study proposes a comparative evaluation of three ensemble machine learning models CatBoost, XGBoost, and Gradient Boosting for predicting DIA using molecular descriptors. A curated dataset of drug compounds with known autoimmune outcomes was analyzed through a systematic workflow incorporating preprocessing, stratified sampling, and model evaluation using accuracy, F1 score, and ROC AUC. Results indicate that CatBoost achieved the highest ROC AUC, while XGBoost demonstrated superior balance between precision and recall, as reflected by its F1 score. Feature importance analysis using SHAP highlighted key molecular properties such as SlogP_VSA10 and fr_NH2 as major contributors to prediction outcomes. The study provides a reproducible and interpretable framework for early toxicity screening, offering valuable insights for data driven decision making in drug safety assessment.
Development of PIR sensor-based security system and IoT-based esp-32 wrover cam module for monitoring military headquarters and vital objects Jatiyoso, Hero Benta; Riadi, M. Farrel; Atturoybi, Abdurrosyid; Mardamsyah, Adam; Tjahjadi, Hendrana
Jurnal Mandiri IT Vol. 14 No. 1 (2025): July: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i1.437

Abstract

Military headquarters security requires advanced systems to detect intrusions and ensure real-time monitoring. This study proposes an IoT-integrated security system using PIR sensors and an ESP-32 WROVER Cam module, designed to enhance detection accuracy, automate alerts, and improve remote surveillance capabilities. The system integrates a PIR sensor for motion detection, an ESP-32 Cam for image capture, and IoT protocols for real-time data transmission via Telegram. A LoRa module extends communication range for large-scale military environments. Simulations tested response time, detection accuracy, and notification reliability under varying conditions. The system achieved a response time of 1.1–1.6 seconds, 100% buzzer activation for alarms, and consistent Telegram notification delivery. Compared to existing systems, its key innovation lies in combining low-cost hardware with IoT connectivity and LoRa-based long-range communication (up to 20–30 km), enabling efficient threat detection and remote interaction. This approach offers a scalable, energy-efficient solution for military security, outperforming traditional systems through real-time responsiveness, reliable alerts, and secure data transmission. The integration of IoT and LoRa addresses gaps in existing object detection systems, particularly in large-area surveillance.
Design and development of an IoT-based archive room security system integrating RFID and fingerprint authentication for military document protection Tidar, R Haryo; Madramsyah, Adam; Rimbawa, H.A Danang; Sembali, Tryas Putranto
Jurnal Mandiri IT Vol. 14 No. 1 (2025): July: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i1.440

Abstract

The objective of this research is to design and implement a secure, IoT-based dual-authentication system for protecting classified military archive rooms, in response to the growing urgency of safeguarding sensitive documents against real threats such as espionage, unauthorized access, and data tampering. Military archives store critical information essential for national defense operations, yet many facilities continue to rely on outdated physical security systems vulnerable to intrusion and lacking auditability. This research presents the design and implementation of a dual-authentication archive security system based on Internet of Things (IoT), integrating Radio Frequency Identification (RFID) and fingerprint biometrics. The system is developed using the Waterfall model, involving sequential stages of requirement analysis, system design, implementation, testing, and evaluation. The NodeMCU ESP32 microcontroller serves as the central controller, enabling real-time data transmission via Wi-Fi and notification delivery through the Telegram API. The RFID module performs initial identification, while the fingerprint sensor confirms biometric authentication. A solenoid lock mechanism provides physical access control, activated only upon successful dual verification. System testing under simulated military archive conditions yielded an average response time of 4.59 seconds and an authentication accuracy of 90.6%. Additionally, the real-time notification feature enhanced situational awareness by informing administrators of all access events—both valid and unauthorized. The results indicate that combining RFID and fingerprint authentication significantly improves system security, auditability, and operational efficiency compared to single-factor or conventional methods. This system demonstrates the potential for scalable, adaptable application in high-security institutional environments. Future development may include integration of backup power supplies, encrypted communication protocols, and expansion toward a more comprehensive digital security architecture. This research contributes to the advancement of smart security systems in military infrastructure, promoting proactive threat mitigation and enhanced document protection.
Sentiment analysis towards naturalization of Indonesian National Team Players on social media x using the Naive Bayes method Lubis, Fahrian Zibran; Kurniawan, Rakhmat
Jurnal Mandiri IT Vol. 14 No. 1 (2025): July: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i1.412

Abstract

This study analyzes public sentiment toward naturalized players in the Indonesian National Team on social media platform X (formerly Twitter) using the Naïve Bayes method. Data were collected via Python's snscrape library through web crawling, encompassing 700 tweets from January 2023 to May 2024. The research methodology included data preprocessing (cleaning, case folding, tokenizing, stopword removal, and stemming), feature extraction with TF-IDF (Term Frequency-Inverse Document Frequency), and sentiment classification. Results revealed a dominant negative sentiment (87.5%) compared to positive sentiment (12.5%), with a model accuracy of 88%. The most frequent keyword, "main" (play), reflected public focus on player performance.The study contributes to the field in three key aspects: (1) It addresses a gap in literature by specifically examining sentiment toward naturalization policies in Indonesian football using social media data; (2) It demonstrates the effectiveness of Naïve Bayes in handling informal Indonesian language, achieving high accuracy despite linguistic complexities; (3) It provides actionable insights for policymakers, highlighting the need for greater transparency in naturalization processes. Limitations include potential bias due to imbalanced data and challenges in interpreting sarcasm. Recommendations for future research include expanding datasets to multiple platforms and testing advanced models like BERT for improved contextual analysis.
Equity by design: a rule-based framework for scholarship selection using the simple additive weighting method Nurcahyo, Widyat; Auliamiliyani, Nur
Jurnal Mandiri IT Vol. 14 No. 1 (2025): July: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i1.433

Abstract

This study presents the development of a decision support system designed to enhance the transparency and efficiency of scholarship recipient selection at Universitas Tama Jagakarsa. Utilizing the Simple Additive Weighting method, the system integrates key eligibility criteria—grade point average, parental income, and number of dependents—to compute preference scores that inform decision-making. The system was implemented using VB.NET and MySQL, following a waterfall model for development. Functional testing through black-box methods confirmed the system’s operational reliability, while user validation indicated strong acceptance and usability. Results demonstrate that the SAW algorithm effectively ranks applicants based on predefined weights and normalizations, offering a more objective and scalable alternative to manual selection processes. The system’s ability to process multi-criteria inputs and generate transparent, reproducible outcomes is a substantial contribution to scholarship administration in higher education. Findings suggest that integrating decision models into administrative processes can mitigate biases and improve institutional accountability. However, the study is limited by its focus on quantitative criteria and the use of a single decision-making algorithm. Future work should incorporate qualitative factors and assess alternative methods. The study affirms the potential of data-driven tools to support equitable access to educational opportunities, particularly for students from economically disadvantaged backgrounds.

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