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EXPLORER
Published by Forum Kerjasama Pendidikan TInggi
ISSN : -     EISSN : 27744647     DOI : https://doi.org/10.47065/explorer.v2i1.148
Core Subject : Science,
Computer Science & IT
EXPLORER Journal of Computer Science and Information Technology is a scientific journal published by the FKPT (Forum Kerjasama Pendidikan Tinggi). This journal contains scientific papers from Academics, Researchers, and Practitioners about research on Computer Science and Information Technology. EXPLORER Journal of Computer Science and Information Technology is published twice a year in January and July. The paper is an original script and has a research base on Computer Science and Information Technology. The scope of the paper includes several studies but is not limited to the study Artificial Intelligence, Computer Graphics and Animation, Image Processing, Cryptography, Computer Network Security, Modelling and Simulation, Multimedia, Computer Architecture Design, Computer Vision and Robotics, Parallel and Distributed Computing, Operating System, Information System, Mobile Computing, Natural Language Processing, Data Mining, Machine Learning, Expert System and Geographical Information System. Thus, we invite Academics, Researchers, and Practitioners to participate in submitting their work to this journal.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 7 Documents
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Search results for , issue "Vol 6 No 1 (2026): January 2026" : 7 Documents clear
Game Edukasi Interaktif Pembelajaran Tafsir Al-Qur’an Berbasis Mobile Menggunakan Metode MDLC Hardiansyah, Nanda; Sarudin, Sarudin
Explorer Vol 6 No 1 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v6i1.2224

Abstract

The objective of this research is to develop an interactive mobile-based educational game that presents Qur’anic exegesis (tafsir) with an attractive and user-friendly interface. The game is designed to cover three surahs: Al-Ikhlash, Al-Falaq, and An-Nas. The content includes Arabic text, Latin transliteration, translation, and a concise tafsir summarized from Tafsir Ibn Kathir Volume I. The development method employed is MDLC (Multimedia Development Life Cycle), which offers clear and systematic stages, ranging from concept, design, material collection, production, testing, to distribution. This structured process facilitates project management, progress monitoring, interim evaluation, and effective integration of user or stakeholder feedback. A trial conducted with 30 elementary school students yielded positive results: the UI/UX aspect received 89.6% (YES) responses, while the game content aspect reached 89.7% (YES). These findings indicate that the design, usability, as well as tafsir and quiz content were perceived as engaging, easy to understand, and beneficial. The results demonstrate that the developed application can enhance students’ interest, understanding, and motivation in learning Qur’anic exegesis in an enjoyable, interactive way, while being relevant to current technological developments. Thus, this research provides a tangible contribution to the innovation of digital learning media beneficial for Islamic education.
Rekomendasi Game Edukasi dengan Mengunakan Metode ItemBased Collaborative Filtering Harahap, Andrian Rajab; Dafitri, Haida; Chiuloto, Kalvin
Explorer Vol 6 No 1 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v6i1.2227

Abstract

In the digital era, choosing the right educational game is a challenge for parents, so a recommendation system is needed that is able to provide relevant suggestions according to children's needs. This research aims to build an educational game recommendation system using the Item-Based Collaborative Filtering method. This method works by analyzing user rating patterns for games, then calculating the level of similarity between games using the Adjusted Cosine Similarity and Weighted Sum algorithms to produce personalized recommendations. Data is obtained explicitly through user interaction in the form of likes and comments on available games. System testing was carried out involving 22 respondents. To the question "Do the recommended educational games help increase your child's knowledge or skills?", 54.5% of respondents answered "Strongly Agree" and 45.5% "Agree", with no negative responses. This shows that all respondents considered the recommended educational games to be positively beneficial for children's development. Meanwhile, to the question "Do the game recommendations suit your child's wishes?", 50% of respondents answered "Strongly Agree", 40.9% "Agree", and 9.1% "Neutral". These results indicate that the majority of respondents considered the system to be quite appropriate in adapting recommendations to children's interests.
Augmented Reality Berbasis Android untuk Pengenalan Alat Pernapasan Menggunakan Metode Marker Based Tracking Pangestu, M. Ridho; Suriati, Suriati
Explorer Vol 6 No 1 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v6i1.2229

Abstract

This study aims to design and implement an Augmented Reality (AR)-based learning application on the Android platform to introduce respiratory organs of living beings, namely lungs, gills, and tracheae, as an effort to improve students’ interest and understanding of biology subjects. The application was developed using the Marker Based Tracking method, chosen for its ability to accurately place virtual objects on predefined markers, resulting in more realistic and interactive 3D visualizations. Development was carried out using Unity 3D and Vuforia SDK as the core AR technology, Blender for creating 3D models, and Canva for designing the interface and markers, with the C# programming language used to manage interaction logic. The application allows users to scan markers to display 3D objects along with descriptive information, providing a more engaging learning experience compared to conventional media. Testing involving 100 students aged 10–14 years showed positive responses, with 93% stating the application is easy to understand, 83% rating the objects as clear, 94% feeling assisted in understanding the material, and 100% reporting greater enthusiasm and ease of learning compared to using books or teachers alone. These results demonstrate that AR can enhance engagement and effectiveness in learning biology. In conclusion, implementing AR in learning media offers an innovative approach that combines interactive visualization with modern technology and has the potential to be further developed by adding more object variations, organ function animations, and multi-platform support.
Klasifikasi Penyebaran Jaringan Wifi Provider Internet Menggunakan Algoritma XGBoost Berdasarkan Titik Koneksi Kabel Fiber Optik Manihuruk, Repaldo; Diansyah, Tengku Mohd
Explorer Vol 6 No 1 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v6i1.2544

Abstract

The rapid development of fiber-optic–based internet technology has led to an increasing demand for stable and evenly distributed WiFi networks. Although internet service providers such as XYZ have established extensive fiber-optic infrastructure, challenges in WiFi access point distribution remain common, particularly regarding uneven network coverage and limited data-driven analysis. These issues raise the question of how to determine optimal WiFi deployment locations to ensure consistent service quality. Therefore, this study aims to analyze the spatial distribution patterns of XYZ’s WiFi network based on fiber-optic connection points, apply the Extreme Gradient Boosting (XGBoost) algorithm to classify the feasibility of WiFi distribution, and evaluate the performance of the proposed model in improving network distribution efficiency. This research employs XGBoost as a classification method to predict suitable and unsuitable WiFi deployment locations using customer data connected via fiber-optic cables. The study focuses on data preprocessing, model construction using XGBoost, performance evaluation in classifying feasible and non-feasible locations, and data balancing techniques to address class imbalance. The dataset consists of 193 XYZ customer records, divided into 80% training data and 20% testing data. The results demonstrate that the XGBoost algorithm achieves high classification accuracy in WiFi network distribution. Consequently, the proposed model can serve as a data-driven recommendation tool for optimizing WiFi deployment, enabling service providers to deliver more evenly distributed, stable, and efficient internet services
Decision Support System for WP and MOORA Methods in Determining Performance Assessment of Outstanding Education Personnel Mesran, Mesran; Jaafar, Mime Azrina; Rosnizam, Rosnizam
Explorer Vol 6 No 1 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v6i1.2587

Abstract

Employee work performance on this date the education staff (Tendik) as a determinant of the success of the performance of the Public Service Agency (BLU). Students who have a good leadership spirit, which are listed as outstanding and superior tendik competencies, are expected to be able to spur other students to achieve as well. The assessment of the achievers is based on the provisions of Permendikbud No. 30 of 2018. Form F5 in the attachment of the Minister of Education and Culture Number. 30 of 2018 becomes an assessment variable in the educational activity support system. Work Achievement of Education Personnel is a major factor in the success of higher education performance. Good work results will have a good impact on universities. Educational staff who excel are also competent in higher education also support both the education staff and universities. Rewards or awards given to Education Personnel can be done by assessing the performance results of each Education Personnel. Rewards are given to Education Personnel as a form of appreciation for the performance carried out at universities. rewards to Education Personnel must be based on appropriate and accurate assessments. The problems found do not have to be used to evaluate the Education Personnel in presenting prizes. The performance assessment carried out must be objective, of course, with an objective assessment the results obtained on the educational performance assessment are not a problem for the education staff. Decision Support System is a computer-based information system that is used to assist in decision making by utilizing certain data and models to support a solution in solving a semi-structured and non-structured problem. The MOORA (Multi Objective Optimization On The Basis Of Ratio Analysis) and WP (Weighted Product) methods are methods in the Decision Support System that provide relatively precise results to the determined assessment. Research with the MOORA and WP procedures used produces and provides an assessment so that it can provide decision support to award outstanding Education Personnel
Peningkatan Pengarahan Beam dan Estimasi Sudut Kedatangan Berbasis CNN untuk Sistem Antena MIMO Cerdas Karim, Abdul; Purnama, Iwan; Ernawati, Andi
Explorer Vol 6 No 1 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v6i1.2592

Abstract

This study proposes a Convolutional Neural Network (CNN)–based approach to enhance the intelligence of MIMO antenna systems in Internet of Things (IoT) environments, particularly for modeling the relationship between wireless channel characteristics and achievable communication capacity. Modern MIMO systems face complex challenges due to dynamic channel conditions such as noise, path loss, and multipath fading, which significantly affect data transmission quality. In this research, channel-related features are processed through a structured preprocessing stage before being fed into a CNN model to learn nonlinear relationships among channel parameters. The developed model is designed to predict achievable channel capacity accurately as part of an adaptive and intelligent wireless communication framework. Experimental results show that the proposed CNN model achieves a Test Loss of 0.0317 and a Mean Absolute Error (MAE) of 0.1267 on unseen test data. Visualization of actual versus predicted values indicates that the model demonstrates good generalization across most data ranges, although some deviations remain at extremely high capacity values. Compared to conventional approaches, the CNN-based method shows superior capability in capturing complex correlations among MIMO channel parameters. Therefore, this approach contributes to the development of adaptive and efficient intelligent antenna systems, supporting the growing demands of next-generation IoT communication networks.
Analisis Komparatif CNN Ringan untuk Klasifikasi Penyakit Daun Tomat Menggunakan Visualisasi Grad-CAM Rahman, Sayuti; Hartono, Hartono; Sembiring, Arnes; Khahfi Zuhanda, muhammad; Aditya Pratama, Bayu; Martini, Dewi
Explorer Vol 6 No 1 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v6i1.2601

Abstract

Tomato leaf disease classification based on digital imagery has become an important approach in supporting smart agriculture, particularly for early detection of plant disease attacks. This study aims to compare the performance of several lightweight Convolutional Neural Network (CNN) architectures, namely MobileNetV3-Small, MobileNetV2, and EfficientNet-B0, in classifying tomato leaf diseases using the PlantVillage dataset. The dataset consists of 3,628 images distributed across 10 classes (9 disease classes and 1 healthy class), with a data split scheme of 80% for training and 20% for validation. Performance evaluation was conducted using classification reports, confusion matrices, and interpretability analysis through Grad-CAM and feature map visualization. The experimental results show that all models achieved very high accuracy, exceeding 99%. EfficientNet-B0 obtained the best performance with a validation accuracy of 99.59%, followed by MobileNetV2 at 99.45% and MobileNetV3-Small at 99.04%. However, model complexity increased along with accuracy, where EfficientNet-B0 had the largest number of parameters and FLOPs. Grad-CAM analysis revealed that higher-accuracy models demonstrated more precise activation focus on leaf lesion regions. This study confirms that lightweight CNN architectures are capable of delivering excellent classification performance while offering strong potential for deployment in plant disease detection systems on resource-limited devices

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