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Jurnal Kridatama Sains dan Teknologi
Published by Universitas Ma'arif Nahdlatul Ulama
ISSN : 26566966     EISSN : 26856921     DOI : -
Core Subject : Science, Education, Agriculture, Social, Engineering,
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Social Sciences
Jurnal KRIDATAMA SAINS DAN TEKNOLOGI diterbitkan oleh Universitas Ma’arif Nahdlatul Ulama (UMNU) Kebumen Pendidikan (Education). Teknologi (technology), Penelitian (research). Bahasa Inggris (Language English), Bahasa Indonesia (Language Indonesian), Olahraga (Sport), Anak Usia Dini (early childhood education), Teknik Informatika (Technical Information), Teknik Sipil (civil Engineering). Pertanian (agriculture), Peternakan (animal husbandry).
Arjuna Subject : Umum - Umum
Articles 280 Documents
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Development of a Web-Based 360-Degree Virtual Tour for AEWO Mulyaharja Tourism Village Using the MDLC Fami, Amata; Nasir, Muhammad; Renanti, Medhanita Dewi; Wicaksono, Aditya; Aziezah, Nur; Barus, Irma R.G.; Indriasari, Sofiyanti
Jurnal Kridatama Sains dan Teknologi Vol 7 No 02 (2025): Jurnal Kridatama Sains dan Teknologi
Publisher : Universitas Ma'arif Nahdlatul Ulama Kebumen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53863/kst.v7i02.1919

Abstract

Digital tourism increasingly requires interactive and immersive media, yet virtual tour development in rural and community-based settings often lacks methodological rigor, structured usability evaluation, and user-centered design. To address this gap, this study develops a web-based 360-degree Virtual Tour for AEWO Mulyaharja using the Multimedia Development Life Cycle (MDLC) framework. Introductory Augmented Reality (AR) elements were included to enrich visual presentation, although these features were not the primary focus of evaluation. The system was developed through the six MDLC stages and assessed using an adapted usability instrument derived from the principles of the System Usability Scale (SUS), simplified to accommodate respondents with varying levels of digital literacy. Thirty participants tested the system and rated five aspects: attractiveness, ease of use, information completeness, intention to visit, and cross-device accessibility. The Virtual Tour achieved an average usability score of 81.68 percent, categorized as “very good,” indicating that the platform is engaging, clear, and accessible for potential users. Scientifically, the study contributes to digital tourism literature by integrating MDLC with Human–Computer Interaction considerations and demonstrating the contextual application of an adapted usability measure for community-based environments. Practically, the Virtual Tour enhances AEWO Mulyaharja’s digital visibility and offers a replicable model for rural tourism digitalization. Future research may explore deeper evaluation of AR functionality, broader respondent groups, and additional usability metrics
Implementasi Sistem Absensi Guru Dan Tenaga Kependidikan Berbasis Geolokasi Dan Validasi Jam Kerja Sukron, Moh.; Rendy, Moh.; Hamdani, Moh Haris
Jurnal Kridatama Sains dan Teknologi Vol 8 No 01 (2026): Jurnal Kridatama Sains dan Teknologi (In Progress)
Publisher : Universitas Ma'arif Nahdlatul Ulama Kebumen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53863/kst.v8i01.1924

Abstract

Improving the accountability and work discipline of teachers and educational staff requires an accurate, transparent, and tamper-resistant attendance system. This study implements a geolocation-based attendance system with work-hour validation at MI Misbahul Munir Sogaan Pakuniran Probolinggo to ensure that attendance is recorded within a predetermined location and time window. The system was developed using a Research and Development (R&D) approach comprising needs analysis, design, implementation, and testing. A web-based application was built using HTML5, PHP (CodeIgniter), and MySQL, leveraging the Geolocation API to obtain user coordinates and validate them against the school reference point and a predefined radius. The implementation results indicate geolocation accuracy of up to 3.2 meters in open areas; however, when used indoors, the deviation increased to 8 meters, suggesting a radius adjustment for certain zones. Functional testing using black-box testing across 15 main scenarios (authentication, check-in/check-out, and reporting) achieved a 100% success rate under normal conditions, and the system fulfilled approximately 95% of the requirements identified during the analysis stage. In addition, the system contributed to improved punctuality, reflected by a reduction in average lateness from 22.5 minutes to 6.8 minutes based on attendance recapitulation before and after implementation. With geolocation and work-hour validation, the system reduces opportunities for fraud, accelerates reporting, and enables real-time monitoring for the school principal. This study contributes a practical reference for adopting web-based geolocation attendance systems in primary Islamic schools and similar educational institutions
Potensi Ekstrak Daun Kelor dan Air Buah Lontar sebagai pengencer alami dalam Preservasi Semen Babi: Literatur Review Banamtuan, Adyanto; Mafefa, Nitty Cendrabagusti
Jurnal Kridatama Sains dan Teknologi Vol 7 No 02 (2025): Jurnal Kridatama Sains dan Teknologi
Publisher : Universitas Ma'arif Nahdlatul Ulama Kebumen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53863/kst.v7i02.1925

Abstract

Boar semen preservation is a method of preserving semen that serves to extend its shelf life and increase its volume, thereby impacting the number of females that are inseminated. Preserved semen is directly related to the quality of the diluent used. Patented diluents are the preferred choice for semen dilution, but they are expensive and not always available, so many studies have examined the use of natural diluents for preservation. The purpose of this literature study is to examine the effectiveness of moringa leaf extract and palmyra juice as natural diluents in semen preservation. The method used in this literature study was to collect various updated sources obtained from journals found through Google Scholar, Scopus, and Sinta. The selection of sources was based on their relevance to topics such as moringa leaf extract, palmyra juice, boar semen, preservation, antioxidants, and artificial insemination. Based on the research that has been conducted, moringa leaf extract and palmyra juice provide good results in improving semen quality, particularly in increasing sperm motility and viability, as well as providing an environmentally friendly and affordable alternative diluent for farmers. The use of palmyra juice has been added as a natural diluent that can protect bovine spermatozoa during preservation, while the addition of moringa leaf extract in tris-egg yolk diluent can suppress abnormal sperm abnormalities in sheep during cold storage. Overall, the use of both moringa leaf extract and palmyra juice offers an effective and efficient alternative solution in boar semen preservation for artificial insemination programs
Comparative Analysis of Random Forest and XGBoost for Detecting Phishing Websites: A Machine Learning Approach Perdana, Yogi
Jurnal Kridatama Sains dan Teknologi Vol 7 No 02 (2025): Jurnal Kridatama Sains dan Teknologi
Publisher : Universitas Ma'arif Nahdlatul Ulama Kebumen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53863/kst.v7i02.1933

Abstract

Phishing attacks represent one of the most significant cybersecurity threats in the digital era, with over 300,000 complaints reported globally in 2023. In Indonesia, the National Cyber and Crypto Agency reported anomalous traffic related to phishing reaching 47,231,390 incidents in 2023, making it one of the greatest threats to the national digital ecosystem. The complexity of increasingly sophisticated modern phishing attacks requires machine learning-based automatic detection approaches to overcome the limitations of ineffective manual detection methods. This study presents a comparative analysis of Random Forest and XGBoost algorithms for automatic phishing website detection using machine learning techniques. Although both algorithms have proven effective in the cybersecurity domain, comprehensive comparisons considering aspects of performance, interpretability, and computational efficiency in the context of phishing detection remain limited, creating a research gap that needs to be filled to optimize national phishing detection systems. The research methodology includes data collection, preprocessing, model implementation, hyperparameter optimization using randomized search with 5-fold stratified cross-validation, and comparative analysis. Experimental results demonstrate that optimized XGBoost delivers the best performance with 97.78% accuracy and 73% faster training time, while Random Forest offers interpretability advantages with 97.65% accuracy. Feature importance analysis reveals SSL certificate status and anchor URL characteristics as the most critical discriminative features. This study concludes that optimized XGBoost is the more optimal choice for production deployment of real-time phishing detection systems, while Random Forest is more suitable for scenarios requiring model transparency. These findings contribute to the development of national phishing detection systems that support the Indonesian government's digitalization program and protect the public from increasing cybersecurity threats.
Evaluasi Model Machine learning untuk Prediksi Keparahan Kanker Berdasarkan Data Real-world Global Sudriyanto, Sudriyanto; Fatah, Abdul; Putra, Moh Dafa Wahna
Jurnal Kridatama Sains dan Teknologi Vol 7 No 02 (2025): Jurnal Kridatama Sains dan Teknologi
Publisher : Universitas Ma'arif Nahdlatul Ulama Kebumen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53863/kst.v7i02.1940

Abstract

Cancer is one of the leading causes of death worldwide and places a significant burden on healthcare systems. Information on cancer severity is crucial for prioritizing treatment and resource planning. This study aims to develop and compare machine learning-based cancer severity classification models using global cancer patient data from 2015–2024. The dataset comprises 50,000 patients with various demographic, lifestyle, environmental, and clinical attributes, as well as severity scores (Target Severity Score). The dataset used in this study was obtained from the open data platform Kaggle (www.kaggle.com), which contains global cancer patient data from 2015 to 2024. The severity score is converted into a binary variable with two classes: low and high severity. The research steps include data preprocessing (cleaning, categorical transformation of variables with one-hot encoding, standardization), data division into training and testing data with a stratified 80:20 ratio, and the development of three classification models: Logistic Regression, K-Nearest Neighbors (K-NN), and Support Vector Machine (SVM) with RBF kernel. Model performance was evaluated using accuracy, precision, recall, F1-score, and confusion matrix, and validated with 5-fold cross-validation. Experimental results showed that Logistic regression achieved 99.82% accuracy, 99.86% precision, 99.78% recall, and 99.82% F1-score, with very small classification errors. SVM achieved 98.22% accuracy with also high performance, while K-NN only achieved an accuracy of around 79.42%. Cross-validation results confirmed that Logistic regression had the highest average accuracy and the most stability. Thus, Logistic regression is recommended as the primary model for predicting cancer severity in this dataset and has the potential for further development as a component of a clinical decision support system
Pengembangan Modul Pembelajaran Berbasis Sensor Inertial Measurement Unit (IMU) untuk Meningkatkan Keterampilan Teknik Tendangan Sepak Bola Mahasiswa Pendidikan Jasmani Irawan, Yogi Ferdy
Jurnal Kridatama Sains dan Teknologi Vol 7 No 02 (2025): Jurnal Kridatama Sains dan Teknologi
Publisher : Universitas Ma'arif Nahdlatul Ulama Kebumen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53863/kst.v7i02.1953

Abstract

The development of sensor technology in sports education opens new opportunities to improve the quality of learning motor skills techniques. This study aims to develop a learning module based on Inertial Measurement Unit (IMU) sensors to improve football kicking technique skills among Physical Education students. This research uses the Research and Development (R&D) method with the ADDIE model conducted from January to March 2025 at the Physical Education, Health and Recreation Study Program, Ma'arif Nahdlatul Ulama University Kebumen. The research subjects consisted of 40 fourth-semester students divided into an experimental group (n=20) and a control group (n=20). Research instruments included MPU-6050 IMU sensors, an Android-based mobile application, kicking technique assessment rubrics, and user response questionnaires. The results showed that the IMU sensor-based learning module effectively improved students' football kicking technique skills. The experimental group experienced an increase in average scores from 65.4 to 82.7 (p<0.001), while the control group increased from 64.8 to 71.3 (p<0.05). There was a significant difference between groups in the post-test (p<0.001) with Cohen's d effect size of 2.18 (very large category). Expert validation showed that the learning module obtained a score of 4.6 out of 5.0 (very feasible category). Student responses showed a satisfaction level of 89.2%.
Analisis Pemahaman Peserta Latsar CPNS terhadap Instrumen Analisis Isu: Pohon Masalah, Fishbone, dan SWOT Diniyati, Nurul; Kurniadewi, Yogtavia Indah; Lestari, Restu; Rosmawati, Arvita
Jurnal Kridatama Sains dan Teknologi Vol 7 No 02 (2025): Jurnal Kridatama Sains dan Teknologi
Publisher : Universitas Ma'arif Nahdlatul Ulama Kebumen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53863/kst.v7i02.1958

Abstract

This study analyzes the level of understanding among participants in the Basic Training for BRIN Civil Servant Candidates (CPNS) regarding the use of issue-analysis instruments, namely Problem Tree, Fishbone, and SWOT. The research employed a mixed-methods approach with an Explanatory Sequential design, involving 19 participants from diverse scientific backgrounds and employment statuses. The findings indicate that participants demonstrated the highest initial understanding of the SWOT analysis (73.3%), whereas 62% showed limited understanding of the Problem Tree, and 57.1% had limited comprehension of the Fishbone method. No significant relationship was found between participants’ academic background or employment status and their level of understanding. SWOT was most frequently selected due to its simple structure, cross-context flexibility, and familiarity among participants. In contrast, comprehension of the Problem Tree and Fishbone was mainly achieved through experiential learning activities. The combination of SWOT and Problem Tree emerged as the most widely used because both methods complement one another in analyzing organizational issues at macro and micro levels. This combination enables participants to produce more comprehensive, focused, and relevant issue analyses to support policy alternative formulation. These findings underscore the importance of experiential learning approaches in civil service training to strengthen integrative and reflective policy-analysis competencies
Perancangan UI/UX Website Point of Sale (POS) Menggunakan Metode Design Thinking di PT Hayaa Investama Group Azzahra, Naesya; Utomo, Pradita Eko Prasetyo; A., Muhammad Razi
Jurnal Kridatama Sains dan Teknologi Vol 8 No 01 (2026): Jurnal Kridatama Sains dan Teknologi (In Progress)
Publisher : Universitas Ma'arif Nahdlatul Ulama Kebumen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53863/kst.v8i01.1981

Abstract

The process of managing sales transactions, inventory, and financial records at PT Hayaa Investama Group is still done manually. This causes the work process to be less efficient and risks causing recording errors. This condition indicates the need for the implementation of a web-based Point of Sale (POS) system that can support the company's operational activities more effectively, especially in terms of ease of use. This study aims to design the interface and user experience of the web-based Point of Sale system at PT Hayaa Investama Group using the Design Thinking method. This method is implemented through five stages, namely Empathy, Definition, Ideation, Prototype, and Testing. Data collection was carried out through observation and interviews with employees and company owners to obtain an overview of user needs. The result of this study is a High-Fidelity prototype equipped with features for managing transactions, inventory, suppliers, purchases, sales, and reports. Prototype testing was conducted using Maze Usability Testing and resulted in a score of 80% from 9 respondents with 7 block missions. The test results indicate that the developed system design is quite easy to use and can be understood by users
Analisis Determinan Karakter Siswa Menggunakan Explainable Machine Learning (SHAP) dan Klasterisasi Profil Sekolah Studi Kasus Rapor Pendidikan Provinsi Bali Dananjaya, Md. Wira Putra; Krisnawijaya, Ngakan Nyoman Kutha; Prathama, Gede Humaswara; Paramartha, I Gusti Ngurah Darma; Gama, Adie Wahyudi Oktavia
Jurnal Kridatama Sains dan Teknologi Vol 7 No 02 (2025): Jurnal Kridatama Sains dan Teknologi
Publisher : Universitas Ma'arif Nahdlatul Ulama Kebumen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53863/kst.v7i02.1988

Abstract

Strengthening student character is a key performance indicator in the Merdeka Belajar curriculum, but the identification of the school environment's most influential determinants of character achievement is often assumed. This study aims to quantitatively deconstruct the relationship between school climate and student character quality in Bali Province. Using the Indonesian Education Report dataset released by the Ministry of Primary and Secondary Education (Kemendikdasmen) for the 2023-2025 period with a total of 727 data entries, this study applies the Educational Data Mining methodology with the Random Forest algorithm enhanced by the Synthetic Minority Over-sampling Technique (SMOTE) to address data inequality. The novelty of this study lies in the use of SHapley Additive exPlanations (SHAP) for model transparency and K-Means Clustering for zoning mapping. Experimental results show the model is able to predict character achievement with 77.03% accuracy. The SHAP analysis revealed the interesting finding that Climate for Diversity (influence score of 0.45) and Climate for Gender Equality (0.22) were the strongest predictors, far exceeding the influence of Climate for Security (0.13). This finding challenges the common assumption that physical security is the single most important factor. Furthermore, the clustering analysis identified three school typologies in Bali, including one "Vulnerable" cluster that scored critically on gender equality and diversity despite having adequate security scores. This study recommends shifting the focus of education policy in Bali from a physical security approach to strengthening tolerance and gender equality programs, which have been shown to have a more statistically significant impact
Pendekatan Transformer Deep Learning dalam Meramalkan Harga Minyak Sumatran Light Crude Candrawengi, Ni Luh Putu Ika; Amritha, Yadhurani Dewi; Dananjaya, Md. Wira Putra
Jurnal Kridatama Sains dan Teknologi Vol 7 No 02 (2025): Jurnal Kridatama Sains dan Teknologi
Publisher : Universitas Ma'arif Nahdlatul Ulama Kebumen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53863/kst.v7i02.1993

Abstract

Time series forecasting plays an important role in understanding the dynamics of volatile data that depends on long-term historical patterns, such as crude oil prices. Parametric statistical approaches often face limitations due to strict assumptions, making nonparametric deep learning methods a more flexible alternative. This study proposes the application of a Transformer-based deep learning model to predict the price of Sumatran Light Crude Oil (SLC), utilizing a self-attention mechanism to capture long-term dependencies in time series data. Experiments were conducted by evaluating various configurations of multi-head attention and number of layers, while keeping the model dimensions and input-output windows consistent. The results show that the Transformer configuration with 16 heads and 4 layers provides the best performance with a Root Mean Square Error (RMSE) value of 8.19818. These findings indicate that Transformer is capable of effectively modeling long-term trends in SLC prices, although its sensitivity to short-term fluctuations is still limited. The main contribution of this research lies in the use of Transformer as an alternative approach to forecasting crude oil prices in Indonesia, which was previously dominated by statistical methods and recurrent models. In practical terms, the results of this study provide a basis for the development of a more adaptive oil price forecasting system to support energy analysis and data-driven decision making

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