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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 114 Documents
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Search results for , issue "Vol. 8 No. 1 (2025): bit-Tech" : 114 Documents clear
Decision Support System for Selecting the Best Employee Using the Simple Additive Weighting Method Galih Adi Nugraha; Wiji Lestari; Agustina Srirahayu
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2788

Abstract

Employee performance appraisal is a crucial aspect of human resource management, as it influences strategic decisions such as promotions, rotations, and incentives. However, manual evaluations are often prone to subjectivity and inefficiencies in terms of time and effort. This study aims to design and implement a decision support system (DSS) using the Simple Additive Weighting (SAW) method to determine the best employee objectively and measurably. The research adopts a software engineering approach with the waterfall model through stages of requirement analysis, system design, implementation, testing, and maintenance. The developed system is web-based and incorporates five key criteria: productivity, loyalty, work attitude, team contribution, and innovation. The testing results indicate that the system can process employee data, compute preference values, and display final rankings accurately and consistently with manual calculations. The system is also equipped with result export features and a user-friendly interface that facilitates the evaluation process. This study contributes a digital tool that reduces subjectivity in performance assessments and improves HR operational efficiency. In conclusion, the implementation of the SAW method in a web-based system is proven effective for supporting multi-criteria decision-making in selecting the best employee and is suitable for dynamic work environments.
Decision Support System for Selecting Santri Organization Leaders Using AHP and TOPSIS Nesti, Latifa Yulfitra; Firmansyah, Rivaldy; Iqbal, Muchamad; Arifin, Ahmad
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2790

Abstract

The election of the head of the student organization plays a vital role in the leadership regeneration process within Islamic boarding schools (pesantren). However, this process is often conducted manually and subjectively, leading to potential bias, limited transparency, and a lack of standardized documentation. To address this issue, this study aims to develop a web-based Decision Support System (DSS) that integrates the Analytical Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to facilitate a fair, measurable, and value-aligned selection process. The selection criteria were identified through interviews and observations, covering five main aspects: morality, discipline, leadership, academics, and socialization. AHP was applied to calculate the priority weights of the criteria, while TOPSIS was used to rank five alternative candidates based on their proximity to the ideal leader profile. The results show that morality emerged as the highest-weighted criterion (0.4672), reflecting the pesantren’s emphasis on personal integrity. Candidate Ziyan Kamil achieved the top preference score of 0.754, indicating the closest alignment with the ideal candidate profile. System functionality was validated using black-box testing, confirming successful implementation of all core features. In conclusion, the developed DSS supports a more transparent, objective, and accountable selection process. It contributes not only to digital transformation in Islamic educational institutions but also serves as a replicable model for participatory and value-based governance in other pesantren environments.
Measuring e-Filing Adoption as an e-Government Service Using the Technology Acceptance Model Putri, Syifa Aliya; Ratnasari, Chanifah Indah
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2804

Abstract

The digital transformation of public services in Indonesia, as mandated by Presidential Regulation No. 95 of 2018 on SPBE (Electronic-Based Government System), has led to the development of e-Filing—an electronic tax return reporting system that allows taxpayers to submit their Annual Tax Return (SPT) online. However, adoption among individual taxpayers remains uneven. This study investigates the factors influencing the acceptance and use of e-Filing among individual taxpayers registered at KPP Pratama Banjarmasin using the Technology Acceptance Model (TAM). Employing a quantitative explanatory approach, data were collected from 100 purposively selected respondents through structured questionnaires and analyzed using PLS-SEM. The findings reveal that perceived ease of use significantly influences users’ attitudes, and positive attitudes, in turn, strongly predict the intention to continue using e-Filing. However, perceived usefulness shows no significant effect on either user attitudes or usage intentions, highlighting a key divergence from core TAM assumptions. Moreover, intentions to use the system significantly influence actual usage, while ease of use and usefulness do not directly drive usage intentions. This study contributes uniquely by identifying a gap between perceived system benefits and actual behavioral intent, especially in the context of infrequent or assisted use among taxpayers. It recommends broader research that includes varied demographic groups and adopts extended models like UTAUT to explore external influences such as digital literacy, policy enforcement, and user support.
Comparative Analysis of K-Means and Gaussian Mixture Model in Clustering Global CO2 Emissions Paramarta, Valentinus; Rahman, Alrafiful; Priska, Lely; Roken Gurning, Rizon; Ayu Purwati, Widya
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2805

Abstract

As global CO2 emissions continue to rise, identifying meaningful patterns across countries has become increasingly vital for shaping effective climate policies. However, many existing approaches rely on uniform benchmarks that overlook national emission heterogeneity. To address this gap, this study applies two unsupervised machine learning techniques K-Means and Gaussian Mixture Model (GMM) to cluster countries based on CO2 emissions from both energy and industrial sectors. The dataset consists of six key indicators, including total emissions, growth rate, and sectoral shares. After handling missing values and applying Min-Max normalization, Principal Component Analysis (PCA) was used to reduce dimensionality and aid visualization. The core objective is to compare the effectiveness of K-Means and GMM in identifying emission-based country groupings. K-Means produced three distinct clusters with strong separation, including a unique cluster dominated solely by China due to its exceptional emission profile. GMM, by contrast, generated more flexible probabilistic clusters, better capturing overlapping patterns and internal variabilities among countries. Evaluation metrics showed that K-Means outperformed GMM in silhouette score and inertia, indicating clearer boundaries, while GMM was more adept at modeling complex, non-spherical distributions. These findings reveal the trade-offs between clarity and adaptability in clustering approaches. The study demonstrates how unsupervised learning can offer actionable insights for emission-based segmentation, enabling more nuanced and differentiated mitigation strategies. By highlighting algorithm-specific strengths, this research contributes to the advancement of machine learning applications in climate informatics and supports the development of targeted international environmental responses.
Digitization of Warehouse Stock Management Through Web-Based Information Systems Marpaung, Andreas Adiputra; Wijiyanto; Utomo, Bangun Prijadi Cipto
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2806

Abstract

Digitalization in warehouse stock management has become increasingly important, particularly for small and medium enterprises that still rely on manual recording methods. These traditional systems often lead to delays, data inaccuracies, and operational inefficiencies. This study aims to design and implement a web-based warehouse stock management information system to improve the recording process, increase accuracy, and support decision-making at Digital Connection, a company still using Microsoft Excel for inventory tracking. The system was developed using the waterfall method, which includes five structured stages: needs analysis, system design, implementation, testing, and maintenance. Functional testing was conducted through black box testing to validate the performance of all system features from a user perspective. The results demonstrate that the developed system enables real-time recording of incoming and outgoing goods, provides interactive data visualization through a dashboard, and issues automatic alerts for minimum stock thresholds. Compared to the previous manual system, the digital solution significantly enhances data accuracy, reduces the risk of duplication or loss, and speeds up reporting processes. This transition not only streamlines warehouse operations but also improves user responsiveness in stock management activities. In conclusion, the proposed information system offers an effective and adaptive approach for small businesses to transition from manual to digital warehouse management, contributing to operational efficiency and supporting broader digital transformation initiatives in logistics and supply chain environments.
IoT-Based Alcohol Presence Detection in Soy Sauce Using MQ-3 and ESP32 Almirah, Narita Tria; Taqwa, Ahmad; Salamah, Irma
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2811

Abstract

Ethanol is a compound that naturally forms during the fermentation process of products such as soy sauce, raising concerns about their halal status for Muslim consumers. This research develops an alcohol detection system based on the Internet of Things (IoT) that integrates an MQ-3 sensor, ESP32 microcontroller, and the Blynk mobile application. The MQ-3 sensor detects ethanol vapor, and the ESP32 processes the sensor data and transmits it via Wi-Fi, enabling real-time monitoring through both a 16x2 LCD display and the Blynk app. The system’s calibration process involves standard ethanol solutions with concentrations ranging from 0% to 10%. The sensor output is converted from analog-to-digital (ADC) values to voltage, parts per million (ppm), and percentage estimates. A regression analysis of the sensor data yielded the equation y = 684.59x + 3198.9, with an R2 value of 0.7288, indicating a moderate correlation between ethanol concentration and sensor readings. Using solutions with ethanol concentrations of 1% and 3%, a detection threshold of 5300 ppm was established. Testing on commercial soy sauce samples (0% and 3.08% ethanol) confirmed the system's ability to distinguish between products with and without detectable ethanol, validating its effectiveness. While not designed for precise quantitative analysis, this system offers a practical, economical, and portable solution for initial screening of alcohol in fermented food products, making it a valuable tool for halal product monitoring.
An Analysis of the Effectiveness of KAN and CNN Algorithms for Human Facial Emotion Classification Riswanto, Beny; Cahyo Edy Sahputro, Slamet
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2819

Abstract

Facial expression-based emotion recognition has various applications, including security, healthcare, human-computer interaction, and behavioral analysis. This study analyzes the effectiveness of the Kolmogorov Arnold Networks and Convolutional Neural Networks algorithms in classifying human facial expressions of emotion using the OSEMN. Experimental data were obtained from the FER-2013 dataset, which consists of 35,887 facial expression images categorized into seven primary emotions: happy, sad, angry, fearful, disgusted, surprised, and neutral. The CNN model was designed with four convolutional layers, while the KAN model used three convolutional layers and a B-spline-based approach to handle non-linear transformations. Evaluation was based on accuracy, precision, recall, F1-score, and computational efficiency. The results showed that CNN achieved higher accuracy but tended to overfit, particularly on emotion classes with imbalanced data distribution. On the other hand, KAN demonstrated more stable performance with lower computational resource consumption, making it more efficient for systems with limited power and memory. CNN was selected for its superior pattern recognition capability, while KAN was chosen due to its efficiency in resource-constrained environments. From the comparison, CNN performed better in detecting complex expressions, while KAN was more optimal in processing efficiency and classification stability. The choice of the most suitable algorithm depends on the specific needs of the system—whether prioritizing high accuracy (CNN) or computational efficiency (KAN). This study is expected to provide insights into the development of more adaptive and efficient deep learning-based emotion recognition systems for practical applications such as mobile devices, healthcare monitoring, and smart surveillance.
Naïve Bayes Algorithm Analysis For Student Graduation Timeliness Prediction Anwar, A. Nurul; Dani, Dani; Napila, Ade
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2825

Abstract

This study developed a student graduation prediction system using the Naïve Bayes algorithm, using PS1-PS4 scores, PK, and SKS as indicators of academic progress. This model achieved 88.33% accuracy and an ROC value of 0.900, indicating superior predictive ability. These results outperform other common models such as logistic regression and the C4.5 decision tree, which have approximately 85% accuracy in predicting student graduation. These results also outperform previous research in the same field, which had ROC values of approximately 0.85.Graduation predictions were categorized as "ON TIME" and "LATE" with high precision. The Naïve Bayes algorithm has proven effective in predicting student graduation, particularly in identifying factors that influence graduation timeliness, such as poor academic performance, difficulty completing final assignments, poor personal conditions, and lack of motivation and interest.By designing a graduation prediction system using the Naïve Bayes algorithm, this research aims to help educational institutions predict student graduation timeliness and provide appropriate interventions. This system can improve educational quality and reduce dropout rates, making it an important tool for educational institutions to improve graduate quality and achieve their academic goals.This research demonstrates that the Naïve Bayes algorithm can be an effective and accurate graduation prediction method, thus helping educational institutions develop strategies to improve educational quality and reduce dropout rates. Therefore, this research has the potential to significantly impact higher education institutions and assist them in achieving their academic goals.
Prototype of Monitoring and Controlling Rice Field Irrigation Based on Internet of Things Sudibyo, Heri; Asril; Rahman Sidiq, Andrey Eka
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2826

Abstract

The advancement of Internet of Things (IoT) technology has significantly impacted various sectors, including agriculture, by enabling automation and real-time monitoring. In rice farming, one persistent challenge is the reliance on manual irrigation, which often results in water waste, increased labor demands, and reduced crop yields due to delayed or inconsistent water management. This research aims to design and implement a prototype IoT-based irrigation system to address these inefficiencies. The proposed system allows farmers to remotely monitor and control water flow using a smartphone via the Telegram messaging application. Telegram is chosen for its ubiquity, cross-platform compatibility, and ease of use, making it accessible to farmers without specialized technical skills. The system uses an ESP32 microcontroller as the core processor, an HC-SR04 ultrasonic sensor to detect water levels, and an SG90 servo motor to automate the sluice gate mechanism. A 16x2 LCD is integrated for real-time field display. Telegram enables interactive control through command inputs (e.g., opening and closing gates) and automated notifications, providing continuous status updates. The system operates autonomously based on preset water level thresholds but also supports manual override. Testing results show that the system is responsive and accurate (within ±1 cm), and it significantly reduces the need for frequent field visits. This solution enhances irrigation precision, reduces labor, and contributes to better water resource management. By integrating commonly available digital tools with low-cost hardware, the prototype offers a practical and scalable solution for improving agricultural productivity, especially in remote rural areas.
Implementation of Content-Based Filtering in a Novel Recommendation System to Enhance User Experience Sanjaya, Imam; Sujjada, Alun; Pratama, Yudistira
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2833

Abstract

This study addresses a critical challenge in digital novel platforms: the difficulty of delivering personalized and accurate recommendations due to limited user interaction data. This limitation often leads to irrelevant or generic suggestions, which can diminish user engagement and hinder content discovery. The significance of solving this issue lies in enhancing user experience by ensuring that readers are presented with novels that truly align with their interests, even in the absence of extensive behavioral data. To overcome this problem, the study proposes an innovative hybrid recommendation system that integrates Content-Based Filtering (CBF) with the Random Forest algorithm. The system generates personalized recommendations by analyzing novel attributes such as title, genre, score, and popularity. The methodology involves extracting features from textual data using Term Frequency-Inverse Document Frequency (TF-IDF), followed by the calculation of cosine similarity to assess title relevance. These similarity scores are then combined with popularity predictions derived from the Random Forest model to produce final recommendations that reflect both content similarity and statistical relevance. The proposed system demonstrates strong performance, achieving an accuracy of 94.0%, precision of 81.4%, recall of 80.3%, and an F1-score of 80.8%. These results underscore the system’s capability to deliver accurate and diverse suggestions. By enhancing personalization and addressing the limitations of conventional CBF systems, this hybrid approach offers practical value for digital novel platforms. It serves as an effective tool for improving content discovery, increasing reader satisfaction, and supporting user retention in content-rich environments.

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