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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 97 Documents
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Sistem Informasi Pengolahan Data Anggota Sanggar Senam Cantik Kerinci Menggunakan Bahasa Pemrograman Java Netbeans Hamsiah Hamsiah
Explorer Vol 2 No 2 (2022): July 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v2i2.275

Abstract

Sanggar Senam Cantik merupakan suatu organisasi yang bergerak dalam bidang olahraga. Sistem yang sedang berjalan di Sanggar Senam Cantik saat ini masih terkendala pada proses pengentrian data, pengolahan data, pencarian data dan pembuatan kartu tanda anggota. Pencatatan masih dilakukan secara manual, sehingga laporan yang dihasilkan kurang efektif dan efisien. Sistem Informasi Pengolahan Data Sanggar Senam Cantik Kerinci bertujuan untuk memudahkan admin/petugas dalam pengentiran data, pengolahan data, pencarian dan pembuatan kartu tanda anggota, sehingga tidak membutuhkan waktu yang lama dalam proses pengolahan data. Dengan dirancangnya sistem informasi ini, diharapkan segala kendala yang ada dapat teratasi dengan baik dan bisa lebih meningkatkan pelayanan terhadap anggota sanggar senam cantik sehingga terwujud sistem yang efektif dan efisien
Analisis Klasifikasi Mobil Pada Gardu Tol Otomatis (GTO) Menggunakan Convolutional Neural Network (CNN) Sayuti Rahman; Adinda Titania; Arnes Sembiring; Mufida Khairani; Yessi Fitri Annisah Lubis
Explorer Vol 2 No 2 (2022): July 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v2i2.286

Abstract

The concept of a smart city is the most important issue in the development aspect of big cities in the world. Where the city must promise a more comfortable, organized, healthy and efficient life. Smart transportation is part of a smart city that is useful for improving better urban planning. Smart transportation also applies to toll roads, such as automating toll road retribution payments. Automatic Toll Gate (GTO) in Indonesia still uses sensors. However, sensors often misclassify trailers. In addition, the use of sensors also requires additional costs in installation and maintenance. Currently, every toll gate is equipped with cameras for various purposes. By utilizing the camera for vehicle type classification, the cost of the GTO will be reduced. For this reason, utilizing a digital camera with computer vision for vehicle type classification is the solution. Convolutional Neural Networks (CNN) is the most popular technique today in solving computer vision problems. Exploit the existing CNN by replacing the last fully connected output according to the number of vehicle classes. The test results show that mobilenet V2 is better in the classification of vehicle types, the best accuracy is Alexnet 93.81% and Mobilenet 96.19%. Computer vision by utilizing CNN is expected to replace the use of sensors so that implementation costs are cheaper.
Pengukuran Luas Daun Aktual Berbasis Pengolahan Citra Digital Menggunakan Operasi Morfologi pada OpenCV Nirina Nurhazelin; Ardi Wijaya
Explorer Vol 6 No 2 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v6i2.2655

Abstract

Leaf area measurement is an important parameter in plant growth analysis because it is directly related to the photosynthesis process and biomass productivity. However, manual measurement methods are still destructive, time-consuming, and prone to errors due to variations in leaf morphology. This study aims to develop an actual leaf area measurement system based on Digital Image Processing using morphological operations that can work automatically, non-destructively, and in real-time. The research stages include image acquisition, preprocessing (grayscale and Gaussian blur), segmentation using the Otsu method, image enhancement with morphological operations, contour detection, and pixel-based area calculation converted to cm² units through a calibration process. Testing was carried out on 50 leaf samples consisting of perfect leaves, perforated leaves, and damaged leaves. The evaluation results showed an overall MAPE value of 22.20% with a system accuracy of 77.80%. The best performance was obtained in the perfect leaf category with an accuracy of 94.59%, followed by perforated leaves at 81.70%, while damaged leaves showed the lowest accuracy of 51.50%. These results indicate that the proposed method is effective on leaves with relatively intact shapes, but the level of accuracy is affected by morphological complexity and leaf contour irregularities
Optimasi Kinerja Model LeNet Berbasis Deep Learning untuk Klasifikasi Citra Menggunakan Pendekatan Hyperparameter Tuning Nita Syahputri; Ommi Alfina
Explorer Vol 6 No 2 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v6i2.2670

Abstract

Abstract−The development of Deep Learning has made significant contributions to the field of image classification, particularly through the use of Convolutional Neural Networks (CNN). However, one of the main problems in implementing CNN models is the suboptimal performance of the model due to inappropriate hyperparameter selection. Simple models such as LeNet are often considered to have limited performance compared to modern architectures, even though with the right approach, this model still has the potential to produce competitive performance. Therefore, this study aims to improve the performance of the LeNet model in image classification through a hyperparameter tuning approach. The methods used in this study include data preprocessing, dataset division, implementation of the LeNet model as a baseline, and hyperparameter optimization including learning rate, batch size, optimizer, and number of epochs. The dataset used is a handwritten number image that has been normalized and transformed to match the CNN model input. Model performance evaluation was carried out using accuracy, precision, recall, and F1-score metrics. The results showed that hyperparameter optimization provided a significant performance improvement over the LeNet model. The baseline model produced an accuracy of 97.52%, while the best optimized model achieved an accuracy of 98.93%. Furthermore, precision, recall, and F1-score values ​​also improved, indicating the model's improved and more balanced classification capabilities. Thus, this study demonstrates that hyperparameter optimization is an effective approach to improving the performance of simple CNN models without increasing architectural complexity.
Analisis Komparatif Kerentanan Website SMK At-Tamimi dengan Hosting InfinityFree Menggunakan Metode Penetration Testing Nabila Nabila; Firman Jaya; Nur Azizah
Explorer Vol 6 No 2 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v6i2.2673

Abstract

This research is motivated by the increasing security needs of school digital services, while the management and configuration of systems on several school websites still has the potential to cause security gaps. The purpose of this study is to analyze and compare the security level of SMK At-Tamimi's website with InfinityFree hosting and recommend the most relevant improvements. The method used is non-destructive penetration testing with a comparative descriptive approach, through the stages of footprinting, scanning-fingerprinting, vulnerability verification, and reporting. The test was carried out on two school websites, namely SMK At-Tamimi and MAN 2 Situbondo, using the ZAP by Checkmarx application and supporting checks on the website security configuration. The test results show the difference in risk levels between websites. Both websites generate 9 alerts. The SMK At-Tamimi website contains 1 finding of High risk (11.1%), 2 Medium (22.2%), 2 Low (22.2%), and 4 Informational (44.4%), while MAN 2 Situbondo does not contain High (0%) findings, with 2 Medium (22.2%), 4 Low (44.4%), and 3 Informational (33.3%). The MAN 2 Situbondo website has the highest level of security because there are no indications of high-risk vulnerabilities as well as better configuration. The SMK At-Tamimi website is in a lower security position compared to its comparative website, because there is an indication of Cloud Metadata Potentially Exposed, but it is not accompanied by proof of access that can be validated within the test limit. The findings of the study indicate the need to strengthen the security configuration of school websites through the implementation of HTTPS/SSL, the addition of security headers, and regular system updates. Additionally, the use of paid hosting can be an alternative to gaining broader control over security configurations and reducing the risk of security misconfiguration.
Monitoring Suhu dan Kelembapan Ruang Server Berbasis IoT Menggunakan ESP32, DHT22, Blynk, dan ThingSpeak Amirul Mukminin; Nur Azizah; Firman Jaya
Explorer Vol 6 No 2 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v6i2.2699

Abstract

Penelitian ini bertujuan merancang dan mengimplementasikan sistem pemantauan suhu dan kelembaban ruang server berbasis Internet of Things (IoT) menggunakan mikrokontroler ESP32 dan sensor DHT22. Sistem ini terintegrasi dengan Blynk untuk pemantauan real-time, ThingSpeak untuk penyimpanan data historis, serta notifikasi otomatis melalui Telegram dan indikator LED lokal berdasarkan ambang batas 30°C. Penelitian mengikuti model pengembangan prototipe, meliputi perancangan perangkat keras dan perangkat lunak, simulasi, integrasi, dan pengujian langsung di ruang server STKIP PGRI Situbondo. Hasil pengujian menunjukkan sistem mampu membaca data lingkungan secara akurat, mentransmisikannya ke platform cloud, dan menampilkan informasi secara lokal melalui layar OLED. Fluktuasi suhu selama operasional server tercatat dengan baik, dengan puncak awal mencapai 34,2°C dan stabil pada rentang 20–26°C. Sistem berhasil mengaktifkan notifikasi dan indikator LED hanya ketika ambang batas terlampaui, sehingga tidak terjadi peringatan palsu pada kondisi normal. Kesimpulannya, sistem pemantauan berbasis IoT ini menyediakan pemantauan real-time, pencatatan data historis, dan mekanisme peringatan dini yang efektif. Sistem ini mendukung pemeliharaan ruang server secara proaktif. Pengembangan selanjutnya dapat mencakup penambahan sensor redundan, saluran komunikasi cadangan, atau mekanisme kontrol pendinginan otomatis untuk meningkatkan keandalan dan fungsionalitas sistem.
Penerapan Algoritma K-Means pada Pengelompokkan Dampak Bermain Game Online Terhadap Minat Belajar Anda Tri Hidayat; Efan Efan; Yadi Yadi
Explorer Vol 6 No 2 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v6i2.2738

Abstract

This study aims to group students' level of learning interest based on the intensity of playing online games using the K-Means Clustering algorithm. The issue being addressed is the increasing activity of gaming, which can potentially affect students' learning behavior, but there hasn't been a structured mapping of student characteristics yet. The data used comes from questionnaires filled out by 65 respondents with 6 main variables, including playing frequency, playing duration, playing time, and learning interest indicators. The method used is K-Means with steps of preprocessing, normalization using StandardScaler, and testing the number of clusters using the Elbow method. The study results show that the optimal number of clusters is 3, with a silhouette score of 0.323 and a Davies-Bouldin Index of 1.465. It produced three groups, namely: (1) low learning interest, (2) medium learning interest, and (3) high learning interest. The clustering results showed that the majority of students were in the Medium Learning Interest category with 37 students (56.92%), followed by Low Learning Interest with 16 students (24.62%), and High Learning Interest with 12 students (18.46%). The silhouette score yielded a value of 0.323303, indicating a fairly good cluster structure. This study shows that most students are in a medium condition, meaning they still play games without significantly affecting their learning interest. The contribution of this research is providing a data-based approach to categorize students' learning interest levels related to online gaming activities, and it also serves as a basis for schools to design more effective monitoring and educational strategies. The research provides a mapping of student characteristics based on data that can be used as a basis for making decisions in academic guidance.
Implementasi Naive Bayes untuk Mengklasifikasi Pembelajaran Inovatif Terhadap Motivasi Belajar Damar Firdaus; Efan Efan; Yadi Yadi
Explorer Vol 6 No 2 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v6i2.2741

Abstract

Learning motivation is a key factor in determining students' success in the educational process. Innovative learning methods are believed to increase motivation through interactive and engaging approaches. However, identifying the level of student motivation based on learning activities still becomes a challenge. This study aims to classify the influence of innovative learning on student motivation using the Naïve Bayes algorithm. The research method applies the CRISP-DM framework, which consists of business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Data were collected through observation, interviews, questionnaires, and literature studies conducted at MTS Guppi Pagar Alam. The dataset consists of 70 students with attributes including teaching methods, learning media, classroom interaction, task creativity, and learning independence. The classification process divides data into training (80%) and testing (20%) sets. The results show that the Naïve Bayes model achieves an accuracy of 87%, indicating that the algorithm performs effectively in classifying student motivation levels. The findings reveal that classroom interaction and task creativity significantly influence learning motivation. This study contributes to providing insights for educators in designing more effective and adaptive learning strategies.
Integrasi Strategi Pre-processing Data untuk Optimalisasi Akurasi Algoritma Backpropagation Widodo Saputra; Saifullah Saifullah; Eka Irawan; Anjar Wanto
Explorer Vol 6 No 2 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v6i2.2743

Abstract

Backpropagation is one of the artificial neural network algorithms widely used in classification and prediction processes due to its ability to recognize data patterns accurately. However, the performance of this algorithm is highly influenced by the quality of the input data. Unstructured data, differences in data scales, missing values, and irrelevant features can reduce the model’s accuracy. This study aims to analyze the effect of integrating data pre-processing strategies to optimize the accuracy of the Backpropagation algorithm. The dataset used in this research was obtained from the Badan Pusat Statistik (BPS) in the form of Open Unemployment Rate data for the population aged 15 years and above in North Sumatra Province from 2019 to 2024. The applied pre-processing stages included data cleaning, normalization, missing value handling, and feature reduction. The research method was conducted by comparing the model testing results using standard pre-processing and partial pre-processing on several network architectures. The results showed that the implementation of pre-processing strategies was able to improve the performance of the Backpropagation model. The highest accuracy value was obtained in the 3-56-1 architecture with an increase from 80.00% to 85.88%. In addition to improving accuracy, the model training process became more stable and the error convergence was achieved faster. Therefore, the integration of data pre-processing strategies has proven to be effective in optimizing the accuracy of the Backpropagation algorithm for numerical data-based prediction problems
Optimasi Support Vector Machine Menggunakan Particle Swarm Optimization pada Analisis Sentimen Ulasan Shopee COD Eka Irawan; Wendi Robiansyah; Widodo saputra; Anjar Wanto
Explorer Vol 6 No 2 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v6i2.2744

Abstract

The Cash on Delivery (COD) service provided by the Shopee e-commerce platform often elicits a large volume of user reviews that exhibit unconventional language structures, prompting the need for a precise and automated sentiment analysis mechanism. This research endeavor seeks to categorize sentiments expressed in Shopee reviews as either positive or negative by leveraging the Support Vector Machine (SVM) algorithm, which has been fine-tuned using Particle Swarm Optimization (PSO). A key obstacle in text analysis lies in the vast feature space, which can impair model efficacy. Thus, PSO is utilized as a feature selection technique to identify the most pertinent set of terms from the TF-IDF feature extraction. The findings reveal that the integration of PSO successfully decreased feature dimensionality by 45% from the initial set of 1,000 features. Despite the substantial reduction in features, the SVM-PSO model achieved an enhanced accuracy of 81.21%, surpassing the baseline model's 78.79%. With an AUC value of 0.845, it is evident that the model retains stability and effectiveness in discerning sentiment even with a considerably reduced feature set. This investigation illustrates the efficacy of PSO optimization in eliminating extraneous features and refining the model's focus on sentiment-carrying vocabulary.

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