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bit-Tech
Published by Komunitas Dosen Indonesia
ISSN : 2622271X     EISSN : 26222728     DOI : https://doi.org/10.32877/bt
Core Subject : Science,
Computer Science & IT
The bit-Tech journal was developed with the aim of accommodating the scientific work of Lecturers and Students, both the results of scientific papers and research in the form of literature study results. It is hoped that this journal will increase the knowledge and exchange of scientific information, especially scientific papers and research that will be useful as a reference for the progress of the State together.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 642 Documents
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Smart Campus: Desain dan Implementasi Sistem Monitoring dan Kontrol Lampu dan AC Pratama, Afis Asryullah; Pradana, Reza Putra; Kurniasari, Arvita Agus; Rosyady, Ahmad Fahriyannur; Setyohadi, Dwi Putro Sarwo
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3406

Abstract

The rapid growth of information and communication technology (ICT) has improved many aspects of community life, including access to information, productivity, and innovation. However, the widespread use of digital devices also increases energy consumption due to technological infrastructure and inefficient user behavior, such as leaving equipment powered on when not in use. While technological development can support energy efficiency, developing new energy systems requires complex research. Automation through the Internet of Things (IoT) offers a more practical solution for energy management. In the educational sector, the smart campus concept represents the digital transformation of campus infrastructure to improve operational efficiency and user comfort. This study aims to design and implement a practical, localized, secure, highly interconnected, and scalable monitoring and control system for lights and air conditioners within a campus environment. The system was developed by reviewing previous studies, evaluating available hardware, selecting appropriate network architectures and communication protocols, implementing IoT devices, and integrating them with a server platform. The system utilizes Zigbee communication and a local MQTT broker with authentication to ensure secure and reliable connectivity. Using devices from multiple manufacturers enables interoperability and vendor independence, while scalability is achieved through simple device installation and pairing. Experimental results show reliable performance with response times of 1–3 seconds without errors. Automation features allow lights and air conditioners to activate before working hours and turn off automatically at night if left on, improving energy efficiency and convenience in a smart campus environment.
Analysis of User Acceptance of Police Service Application Based on the UMEGA Model Alamsyah, Arya Hafidz; Faroqi, Asif; Rinjeni, Tri Puspa
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3419

Abstract

Digitization of police services has become an essential component in improving the quality and responsiveness of public services. However, the adoption rate of the Polri Super App remains suboptimal, indicating challenges in user acceptance that cannot be fully explained by the core constructs of the UMEGA model. This condition highlights the need for an analytical approach that incorporates institutional factors, service quality, and public trust. This study explicitly contributes by expanding the UMEGA model through the integration of institutional capacity, information quality, service quality, and multidimensional trust, offering a more comprehensive framework for understanding digital policing adoption. A quantitative survey was conducted involving 432 respondents who had used the National Police Super App within the last six months. Data were analyzed using Partial Least Squares–Structural Equation Modeling (PLS-SEM) to assess construct validity, reliability, and structural relationships among variables. The results show that performance expectancy, information quality, service quality, computer self-efficacy, attitude, and trust significantly influence users’ intentions and actual usage behavior, while perceived risk and facilitating conditions show no significant effect. These findings indicate that institutional quality, information accuracy, and public trust play dominant roles in shaping user acceptance. Practically, the results provide guidance for policymakers to strengthen digital policing services through improved service reliability, enhanced data transparency, and targeted user capability support. The study concludes that expanding the UMEGA model offers a more complete understanding of digital policing service adoption and provides an empirical basis for improving the quality and sustainability of technology-based public services.
A Branching Narrative 2D Action RPG Game to Enhance Learning About the Ambarawa Battle Nandaru, Laudy Nurdibya; Putra, Chrystia Aji; Sihananto, Andreas Nugroho
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3420

Abstract

This study develops a historical educational game titled Pertempuran Ambarawa to address the persistent challenge of low student engagement and limited contextual understanding in history classrooms, where learning is often dominated by memorization-based instruction. To provide a more interactive and reflective learning experience, the game integrates a branching narrative structure, 2D action RPG mechanics, and stealth–strategy minigames within the Interactive Digital Narrative (IDN) framework. This approach is intended to enhance learners’ historical reasoning by situating them in decision-based scenarios that mirror the complexities of the Ambarawa Battle. The game was implemented in Unity with 2D pixel-art aesthetics and evaluated through a pre-test–post-test design involving 25 junior high school students. Results show a significant improvement in historical comprehension, with mean scores increasing from 59.2 to 79.2 and the Wilcoxon Signed-Rank test yielding p = 0.00077 (p < 0.05). User experience was assessed using the GUESS-18 instrument, achieving an overall rating of 4.29 (Very Good), with the Education and Branching Narrative dimensions receiving the highest scores. These findings indicate that narrative interactivity and contextualized gameplay meaningfully contribute to learning effectiveness. Overall, the study demonstrates that combining branching narratives with RPG-based exploration provides a compelling alternative learning medium, offering both pedagogical value and strong user acceptance in history education.
Aplikasi OMR untuk Pemeriksaan Lembar Jawaban menggunakan DexiNed Prastyo, Kus Dwi; Junaidi, Achmad; Aditiawan, Firza Prima
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3425

Abstract

Digital image processing is a field of computer science that focuses on analyzing and interpreting digital images to extract meaningful information. One of its applications is Optical Mark Recognition (OMR), a technology used to detect marks on documents. OMR is commonly utilized for evaluating answer sheets. However, conventional OMR systems typically rely on specialized scanners that are expensive and lack flexibility. Although Computer-Based Testing (CBT) offers the convenience of automated scoring, its implementation heavily depends on the availability of technological infrastructure such as computers, internet connectivity, and a stable power supply. This study develops a real-time Optical Mark Recognition (OMR) application capable of performing answer sheet assessment directly on the client side. The system utilizes the DexiNed method for edge detection of the answer areas. The application is web-based and built using JavaScript and OpenCV.js to process images directly from the user's device camera. Testing was carried out under various scenarios, including different lighting intensities, scanner positions, pencil types, and shading quality. The results show that the application can detect marked answers with an accuracy up to 100%, although some limitations were observed under certain technical conditions. Weaknesses were found in low lighting conditions using a 5 watt lamp at a distance of 3 meters, light reflections, and the camera angle was not aligned with the answer sheet. Overall, the application provides an efficient and flexible alternative for answer sheet assessment without requiring dedicated scanning devices, making it suitable for educational institutions with limited infrastructure.
Implementasi CNN Untuk Klasfikasi Emosi Dalam Lagu Berdasarkan Fitur Audio Pakpahan, Fredrik Sahalatua; Haromainy, M. Muharrom Al; Mulyo, Budi Mukhamad
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3438

Abstract

Music is a powerful art form for conveying and evoking emotions; however, the vast volume of digital music data makes manual emotion categorization difficult. This study aims to implement a Convolutional Neural Network (CNN) to classify emotions in instrumental songs based on audio features. The dataset used is the Database for Emotional Analysis of Music (DEAM), containing 1,802 songs with valence and arousal annotations, which is divided with a 70:15:15 ratio for training, validation, and testing. The feature extraction methods applied include Mel-Frequency Cepstral Coefficients (MFCC) with variations of 13, 24, and 30 coefficients, and Mel-spectrograms with variations of 128, 256, and 512 bins. Data is processed through pre-emphasis and framing stages before being input into a CNN architecture with four convolutional blocks. Evaluation was conducted using 4-quadrant classification scenarios and a simplification into 2 quadrants. The results showed that in the 4-quadrant classification, the best model was achieved using MFCC with 30 coefficients with an accuracy of 66%, but model performance was hindered by extreme minority class imbalance. Conversely, simplifying the emotion space into 2 quadrants (valence or arousal) significantly improved accuracy to 77%. This study concludes that while increasing feature resolution has a minor impact, simplifying emotion dimensions proves more effective in addressing complexity and data imbalance in music emotion classification.
Perbandingan Kinerja Model CNN untuk Klasifikasi Kematangan Pisang pada Android Low-End Orlando, Owen; Tanuwijaya, Evan
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3442

Abstract

The ripeness level of bananas is an important indicator that determines the quality, selling value, and suitability of distribution in the agricultural supply chain. However, manual maturity assessments are still subjective and difficult to apply consistently on a large scale. The use of Convolutional Neural Networks (CNN) offers a more accurate and objective solution, but most previous studies have only evaluated high-powered devices so they do not reflect the real performance of low-spec smartphones. This study aims to compare the efficiency of three lightweight CNN architectures: MobileNetV1, EfficientNetB0, and NASNetMobile for the classification of banana ripeness and evaluate its feasibility of being implemented on low-end Android devices. The research method included model training using the Banana Ripeness Classification dataset containing 13,478 images with three maturity classes. Augmentation-based oversampling was applied to address data imbalances, while all three models were trained on transfer learning strategies before being converted to the TensorFlow Lite format. Direct testing was conducted on the Samsung Galaxy A3 (2016) device to measure accuracy, inference time, model size, and RAM usage. The experimental results showed that MobileNetV1 provided the best performance with an accuracy of 98.14%, an inference time of 287.57 ms, and a model size of 3.23 MB, much more efficient than EfficientNetB0 and NASNetMobile. In conclusion, MobileNetV1 is the most optimal architecture for Android-based banana ripeness classification applications on low-spec devices, while making an empirical contribution to the selection of efficient CNN models for mobile implementation in the context of digital agriculture.
Sebuah Kerangka Kerja Nielsen Honeycomb ISO Terintegrasi untuk Evaluasi Pengalaman Pengguna Komparatif Basis Data Akademik Saleh, Rayhan Zahwan; Lubis, Muharman
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3462

Abstract

Scholarly databases operate as everyday workspaces for discovery, screening, and citation management, so small usability frictions can compound into substantial research overhead. This study compares the user experience (UX) of five widely used platforms: Web of Science, Scopus, Google Scholar, DBLP, and SciProfiles. UX is treated as task success in realistic research workflows and confidence in record quality rather than surface level visual design. An integrated evaluation framework combines ISO 9241 usability principles, a ten-heuristic checklist, and a UX honeycomb model, and operationalizes them into eight dimensions and 30 criteria (maximum score 120). Unlike unweighted checklists, the framework uses Analytic Hierarchy Process (AHP) weighting to make trade offs among UX dimensions explicit and to support consistent cross platform benchmarking. Criteria cover search and relevance cues, metadata and export reliability, interface consistency, mobile responsiveness, accessibility, and credibility signals. Platforms were assessed through task based testing and a structured review of platform guidance and user feedback. Weighted scores were normalized for comparison. Results show a likely advantage for subscription based systems in precision search and metadata handling, with Web of Science ranking highest, followed by Scopus and Google Scholar, while DBLP and SciProfiles score lower yet remain useful for niche needs such as open metadata access and profile oriented discovery. The framework can be reused as a rubric for training, platform selection, and periodic UX audits.
Comparative Study of BiLSTM, RoBERTa, and DistilGPT-2 Models in Sentiment Analysis on YouTube Comments Tobing, ​​Willas Daniel Rorrong Lumban; Saputri, Theresia Ratih Dewi
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3467

Abstract

The proliferation of social media has transformed the communication landscape and positioned platforms like YouTube as vital repositories of public sentiment. Manual analysis is no longer feasible because of the exponential volume of user-generated content making automated solutions critical. This study addresses this challenge by evaluating the efficacy of three deep learning architectures including BiLSTM, RoBERTa, and DistilGPT-2 for multi-class sentiment classification, contributing a novel empirical comparison between recurrent, encoder, and generative models on noisy text. The research utilizes the "YouTube Comments Sentiment Dataset" sourced from Kaggle containing over one million entries distributed across Positive, Negative, and Neutral classes with a relatively balanced composition. Methodologically the models were trained to convergence using early stopping and assessed based on weighted F1-scores alongside training duration. The results demonstrate that transformer-based models numerically outperformed the recurrent architecture as RoBERTa achieved the highest F1-score of 0.77 surpassing BiLSTM (0.71) by a margin of 6 percentage points. Transformers also exhibited superior efficiency by converging within 5 epochs compared to 16 for BiLSTM. Despite these numerical gaps statistical analysis via ANOVA revealed that the performance differences were not significant (P > 0.05). Conclusively RoBERTa offers the highest raw accuracy, but DistilGPT-2 emerges as the most practical choice for resource-constrained applications involving limited memory or computational power. It provides a strategic balance of comparable performance and rapid training capabilities even though challenges remain in distinguishing ambiguous neutral comments.
NLP Based Tourism Service Optimization on Multilingual Voice Chatbot Jundullah, Muhammad; Ermin, Ermin; Surahmanto, Muhammad; Fakhri, La Jupriadi; Muslim, Hatari
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3473

Abstract

The rapid advancement of artificial intelligence has created significant opportunities to enhance tourism services, a vital sector of Indonesia’s economy, particularly in Raja Ampat as a leading ecotourism destination. (R1-1 Background) A persistent challenge in this region is effective communication for homestay management, where limited human resources and linguistic diversity constrain service quality. (R1-1 Methodology) This study evaluates a multilingual voice chatbot integrating Natural Language Processing, Large Language Models, and a Retrieval-Augmented Generation architecture, supporting Indonesian, English, and a virtual local language. System performance is quantitatively assessed using Speech-to-Text accuracy measured by Word Error Rate, intent classification metrics, semantic retrieval effectiveness, and end-to-end evaluation. The proposed pipeline includes speech data collection, text normalization, multilingual embedding, vector storage, semantic retrieval, and response generation. (R1-2 Results) Results show that STT quality strongly determines downstream performance. Indonesian achieves the lowest WER (0.14) and the highest intent F1-score (0.89), while the virtual language records the highest WER (0.25) and the lowest intent F1-score (0.65). The semantic retriever attains a Mean Average Precision of 0.55, indicating moderate document ranking quality. The integrated end-to-end system achieves an F1-score of 0.857 with a user satisfaction score of 4.4. (A-1 Contribution) Compared with existing tourism chatbots, the proposed system uniquely combines multilingual voice interaction with RAG-based grounding to improve response reliability in low-resource settings. (A-2 Conclusion and applicability) These findings demonstrate practical effectiveness for homestay services and highlight scalability to other multilingual tourism regions in Indonesia and beyond.
Sistem Absensi Desktop Menggunakan Face Recognition dan Pendekatan Adaptive Attendance Monitoring Afandy, Imam; Akbar, Fawwaz Ali; Mumpuni, Retno
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3487

Abstract

Manual attendance processes in higher education often face severe constraints regarding time inefficiency and vulnerability to data manipulation, specifically the prevalent issue of proxy attendance. Although Face Recognition technology has been widely adopted, most existing systems utilize a "once recognition" method, which fails to validate the student's presence throughout the entire lecture duration. This study aims to bridge this gap by developing an automatic desktop-based attendance system that integrates Face Recognition with a novel Adaptive Attendance Monitoring (AAM) approach. The proposed system utilizes a robust deep learning pipeline employing the Multi-Task Cascaded Convolutional Neural Network (MTCNN) for face detection and alignment, followed by FaceNet for generating 128-dimensional feature embeddings. To ensure real-time performance, the processing is accelerated by CUDA GPU technology on an NVIDIA RTX 4060 Ti. The system architecture follows a decoupled Client-Server model based on REST API, ensuring scalability and low-latency data transmission. The primary novelty of this research is the AAM algorithm, which continuously calculates the cumulative duration of a student's presence. A student is validated as "Present" only if they maintain visibility for at least 80% of the total session duration, effectively eliminating the "check-in and leave" loophole. Experimental results demonstrate that the system achieves a 100% recognition accuracy at an optimal distance of 1.0 meter under normal lighting conditions, with a processing latency consistently maintained under 100ms. These findings confirm that the proposed desktop-edge architecture significantly outperforms traditional mobile-based solutions in terms of stability, security, and continuous monitoring capabilities.

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