Jurnal Ilmu Komputer dan Informatika
Jurnal Ilmu Komputer dan Informatika (JIKI) merupakan sebuah jurnal ilmiah nasional yang mempublikasikan artikel hasil penelitian di bidang Ilmu Komputer, Informatika, dan Sistem Informasi, terutama pada pengembangan software, pengembangan sistem informasi, sistem komputer, jaringan komputer, algoritma dan komputasi, serta penerapan teknologi informasi. Jurnal Ilmu Komputer dan Informatika (JIKI) terdaftar di LIPI dengan P-ISSN : 2807-6664 dan E-ISSN : 2807-6591. Selain itu, JIKI terdaftar di Crossref dengan DOI : https://doi.org/10.54082/jiki.IDPaper. Jurnal Ilmu Komputer dan Informatika (JIKI) dipublikasikan 2 kali dalam setahun, yaitu pada bulan Juni dan Desember. Semua penerimaan naskah akan diproses secara double blind review oleh mitra bestari. Jurnal Ilmu Komputer dan Informatika (JIKI) menerima artikel ilmiah hasil penelitian dari beberapa bidang sebagai berikut : - Organisasi sistem komputer: Arsitektur komputer, sistem tertanam, komputasi waktu nyata - Jaringan : Arsitektur jaringan, protokol jaringan, komponen jaringan, evaluasi kinerja jaringan, layanan jaringan - Keamanan: Kriptografi, layanan keamanan, sistem deteksi intrusi, keamanan perangkat keras, keamanan jaringan, keamanan informasi, keamanan aplikasi - Organisasi perangkat lunak : Penerjemah, Middleware, Mesin virtual, Sistem operasi, Kualitas perangkat lunak - Notasi dan alat perangkat lunak : Paradigma pemrograman, Bahasa pemrograman, Bahasa khusus domain, Bahasa pemodelan, Kerangka kerja perangkat lunak, Lingkungan pengembangan terintegrasi - Pengembangan perangkat lunak : Proses pengembangan perangkat lunak, Analisis kebutuhan, Desain perangkat lunak, Konstruksi perangkat lunak, Penyebaran perangkat lunak, Pemeliharaan perangkat lunak, Tim pemrograman, Model sumber terbuka - Teori Komputasi : Model Komputasi, Kompleksitas Komputasi - Algoritma : Desain algoritma, Analisis algoritma - Matematika komputasi : Matematika diskrit, Perangkat lunak matematika, Teori informasi - Sistem informasi : Sistem manajemen basis data, Sistem penyimpanan informasi, Sistem informasi perusahaan, Sistem informasi sosial, Sistem informasi geografis, Sistem pendukung keputusan, Sistem kontrol proses, Sistem informasi multimedia, Penambangan data, Perpustakaan digital, Platform komputasi, Pemasaran digital, World Wide Web , Pengambilan informasi - Interaksi manusia-komputer : Desain interaksi, Komputasi sosial, Komputasi di mana-mana, Visualisasi, Aksesibilitas - Konkurensi: Komputasi bersamaan, Komputasi paralel, Komputasi terdistribusi - Kecerdasan buatan : Pemrosesan bahasa alami, Representasi dan penalaran pengetahuan, Visi komputer, Perencanaan dan penjadwalan otomatis, Metodologi pencarian, Metode kontrol, Filosofi kecerdasan buatan, Kecerdasan buatan terdistribusi - Pembelajaran mesin: Pembelajaran dengan pengawasan, Pembelajaran tanpa pengawasan, Pembelajaran penguatan, Pembelajaran multi-tugas - Grafik : Animasi, Rendering, Manipulasi gambar, Unit pemrosesan grafik, Realitas campuran, Realitas virtual, Kompresi gambar, Pemodelan padat - Komputasi terapan: E-commerce, Perangkat lunak perusahaan, Penerbitan elektronik, Cyberwarfare, Pemungutan suara elektronik, Video game, Pengolah kata, Riset operasi, Teknologi pendidikan, Manajemen dokumen.
Articles
60 Documents
<![CDATA[Fire Detection Using Logistic Regression with GLCM, RGB Ratio, RGB Intersection, and Color Moments ]]>
<![CDATA[Dickens, Pieter]]>;
<![CDATA[Mulyana, Teady Matius Surya]]>
<![CDATA[ Jurnal Ilmu Komputer dan Informatika Vol 5 No 1 (2025): JIKI - Juni 2025 ]]>
Publisher : <![CDATA[CV Firmos ]]>
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DOI: 10.54082/jiki.250
<![CDATA[Fires pose a significant threat to human safety and property, particularly in densely populated urban environments where rapid and accurate early detection is critical. This study proposes an automated fire detection system based on computer vision and Logistic Regression classification, utilizing a combination of texture and color-based features to improve detection performance. The proposed approach integrates Gray-Level Co-occurrence Matrix (GLCM), RGB Ratio, RGB Intersection, and Color Moments to extract discriminative features from fire and non-fire images. The dataset, obtained from Kaggle, was preprocessed through HSV-based color segmentation to isolate candidate fire regions before manual annotation. The extracted features were then used to train a Logistic Regression model with hyperparameter tuning of the max_iter parameter to achieve optimal convergence. Experimental results show that the proposed model achieved an accuracy of 86% and a recall of 84% on the training dataset, and an accuracy of 87% with a recall of 82% on the test dataset. Despite these promising results, some false negatives were observed, indicating the need for further refinement to improve sensitivity. Comparative evaluation with a Convolutional Neural Network (CNN) demonstrated that the Logistic Regression approach achieved higher average processing speed, reaching up to 16.2 FPS for video input, compared to 11 FPS for CNN, making it more suitable for real-time applications. Overall, the integration of multi-feature extraction with Logistic Regression offers a balance between accuracy and computational efficiency for early fire detection in real-world scenarios.]]>
<![CDATA[Evaluation of Transfer Learning-Based Convolutional Neural Networks (InceptionV3 and MobileNetV2) for Facial Skin-Type Classification ]]>
<![CDATA[Muttaqin, Naufal Hafizh]]>;
<![CDATA[Widodo, Agung Mulyo]]>
<![CDATA[ Jurnal Ilmu Komputer dan Informatika Vol 5 No 1 (2025): JIKI - Juni 2025 ]]>
Publisher : <![CDATA[CV Firmos ]]>
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DOI: 10.54082/jiki.264
<![CDATA[Manual classification of facial skin types often suffers from subjectivity and inconsistency due to reliance on human expertise. Accurate identification of skin types is crucial for selecting appropriate skincare solutions. This study evaluates the performance of two transfer-learning-based Convolutional Neural Networks (CNNs), InceptionV3 and MobileNetV2, for classifying facial skin types into four categories: normal, oily, dry, and acne-prone. A total of 1,733 facial images were collected from Kaggle and Roboflow and split into training, validation, and testing sets with a 70:20:10 ratio. Preprocessing involved normalization, augmentation, and resizing based on each model’s input size. Both models were fine-tuned and evaluated using accuracy, precision, recall, and F1-score metrics. InceptionV3 achieved the highest accuracy of 90.12% and a macro F1-score of 89.47%, particularly excelling in identifying normal and acne-prone skin. MobileNetV2 reached 81.15% accuracy and performed well on dry skin types. Confusion matrices and evaluation on new, unseen data confirmed the models’ generalization capabilities, though misclassifications still occurred among visually similar classes. These findings suggest that CNNs with transfer learning provide a robust foundation for developing AI-assisted facial skin-type classification systems, offering potential integration into dermatological applications.]]>
<![CDATA[Enhancing Fashion Product Sales Segmentation Using Random Forest with SMOTE and Hyperparameter Optimization ]]>
<![CDATA[Atmaja, Guntur Tri]]>
<![CDATA[ Jurnal Ilmu Komputer dan Informatika Vol 5 No 1 (2025): JIKI - Juni 2025 ]]>
Publisher : <![CDATA[CV Firmos ]]>
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DOI: 10.54082/jiki.280
<![CDATA[The rapid expansion of the fashion e-commerce sector has intensified the need for accurate sales segmentation to support targeted marketing and efficient inventory management. This study proposes a robust methodology for classifying fashion product sales into three categories: high-selling, moderately-selling, and low-selling, using the Random Forest algorithm integrated with the Synthetic Minority Over-sampling Technique (SMOTE) and hyperparameter optimization. A real-world dataset comprising over 20,000 product records from an online marketplace was preprocessed through missing-value handling, categorical encoding, and numerical feature standardization. Class labels were generated using quantile-based segmentation of sales volume, followed by class balancing with SMOTE. The Random Forest model was tuned using RandomizedSearchCV and evaluated through accuracy, precision, recall, F1-score, and Receiver Operating Characteristic–Area Under Curve (ROC-AUC) metrics. Experimental results demonstrate strong predictive performance, achieving an accuracy of 90.43%, macro-precision of 90.60%, macro-recall of 90.45%, macro-F1 of 90.50%, and macro ROC-AUC of 0.9783. Feature importance analysis revealed that price, category, and customer ratings were the most influential predictors of sales segmentation. These findings validate the effectiveness of ensemble learning combined with class imbalance handling for multi-class classification in retail datasets. From a scientific perspective, this research contributes to the literature by presenting a reproducible, data-driven framework for product segmentation in heterogeneous and imbalanced datasets. Practically, the proposed approach can guide fashion retailers in refining pricing strategies, optimizing marketing campaigns, and improving inventory decisions in competitive online marketplaces. The methodology is adaptable to other e-commerce domains, offering broader implications for business intelligence and predictive analytics.]]>
<![CDATA[Teachers’ and Students’ Perspectives on the Ethical Use of Artificial Intelligence in Vocational Education using Technology Acceptance Model Approach ]]>
<![CDATA[Kekado, Nathan Christianto]]>;
<![CDATA[Krismiyati, Krismiyati]]>
<![CDATA[ Jurnal Ilmu Komputer dan Informatika Vol 5 No 1 (2025): JIKI - Juni 2025 ]]>
Publisher : <![CDATA[CV Firmos ]]>
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DOI: 10.54082/jiki.281
<![CDATA[Artificial Intelligence (AI) is increasingly integrated into educational settings, offering benefits such as improved efficiency, personalization, and student engagement. However, its adoption also raises ethical concerns that require careful consideration. This study investigates teachers’ and students’ perspectives on the ethical use of AI in vocational education, employing the Technology Acceptance Model (TAM) extended with Ethical Awareness, Trust, and Subjective Norms. A quantitative research design was applied, supported by interviews for triangulation. Data were collected from 60 students and 5 teachers in the Computer and Network Engineering program at SMK Negeri 2 Salatiga, Indonesia, all of whom had prior AI usage experience. The results indicate that Ethical Awareness significantly influences Attitude Toward Use (p = 0.002, t = 3.070), Behavioral Intention (p < 0.001, t = 6.175), and Perceived Usefulness (p < 0.001, t = 4.330). Perceived Ease of Use was found to have a positive effect on Behavioral Intention (p = 0.004, t = 2.913). Trust exhibited a strong relationship with both Actual Use (p < 0.001, t = 3.543) and Attitude Toward Use (p = 0.009, t = 2.621). Reliability testing showed Cronbach’s Alpha values above 0.70 for all key constructs, with Average Variance Extracted (AVE) values exceeding 0.50, indicating strong internal consistency and validity. These findings emphasize that ethical awareness and trust are critical determinants in fostering AI adoption in education. The study provides actionable insights for policymakers, educators, and technology developers to design training programs and guidelines that address ethical considerations, thereby ensuring responsible and sustainable AI integration in educational environments.]]>
<![CDATA[Comparative Analysis of Gaussian Naïve Bayes and Categorical Naïve Bayes Algorithms with Laplace Smoothing in COVID-19 Detection ]]>
<![CDATA[Saputra, Dila]]>;
<![CDATA['Alauddin, Abdul Aziz Fahmi]]>;
<![CDATA[Azizan, Mochamad]]>
<![CDATA[ Jurnal Ilmu Komputer dan Informatika Vol 5 No 1 (2025): JIKI - Juni 2025 ]]>
Publisher : <![CDATA[CV Firmos ]]>
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DOI: 10.54082/jiki.286
<![CDATA[In January 2020, it was confirmed that COVID-19 can be transmitted from human to human through the upper respiratory tract with a high infection rate. The number of COVID-19 cases worldwide continued to increase rapidly through close contact, droplets, and airborne transmission. In response, governments and the WHO implemented preventive measures, including COVID-19 treatment preparation, increased emergency healthcare capacity, and patient screening. Early detection of COVID-19 became crucial in taking action, providing treatment, and protecting others. In the Naïve Bayes algorithm, a potential issue arises with the possibility of zero probabilities for some features or attributes in the COVID-19 prediction training data. Therefore, Laplace Smoothing is used to address this problem. This study aims to compare the average accuracy rates of Gaussian Naïve Bayes and Categorical Naïve Bayes algorithms using different proportions of training data but the same testing data for COVID-19 detection. The methods used in this research are Gaussian Naïve Bayes and Categorical Naïve Bayes with Laplace Smoothing implemented using the Python library called scikit-learn. The research results show that the Gaussian Naïve Bayes algorithm without Laplace Smoothing has an average accuracy of 0.902165, while with Laplace Smoothing, it has an average accuracy of 0.973448. For the Categorical Naïve Bayes algorithm, without Laplace Smoothing, it has an average accuracy of 0.983864, while with Laplace Smoothing, it has an average accuracy of 0.984273. In conclusion, Laplace Smoothing plays a significant role in improving the average accuracy of Naïve Bayes algorithms. Categorical Naïve Bayes achieves the highest average accuracy of 0.9840685 (with and without Laplace Smoothing), while Gaussian Naïve Bayes achieves 0.947549 (with and without Laplace Smoothing). Categorical Naïve Bayes has a higher average accuracy compared to Gaussian Naïve Bayes.]]>
<![CDATA[Logistic Regression with Min-Max Scaling and TF-IDF for App Classification and Recommendation on Google Play Store ]]>
<![CDATA[Anindita, Calista]]>;
<![CDATA[Laelatunuji, Wike]]>;
<![CDATA[Rusmini, Rusmini]]>
<![CDATA[ Jurnal Ilmu Komputer dan Informatika Vol 5 No 1 (2025): JIKI - Juni 2025 ]]>
Publisher : <![CDATA[CV Firmos ]]>
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DOI: 10.54082/jiki.288
<![CDATA[In the rapidly evolving mobile application ecosystem, enhancing user experience on the Google Play Store has become a critical challenge due to the vast number of available applications. This study proposes an integrated approach combining Logistic Regression, Min-Max Scaling, and the Term Frequency–Inverse Document Frequency (TF-IDF) Vectorizer to classify applications and generate personalized recommendations. The dataset, obtained from the Google Play Store, includes numerical features such as ratings, size, and installs, as well as textual data from user reviews. Min-Max Scaling was applied to normalize numerical attributes, ensuring balanced feature contributions during model training. TF-IDF was employed to convert textual reviews into meaningful numerical representations, enabling the model to capture the semantic importance of terms. The classification and recommendation system was evaluated using accuracy, precision, and recall as performance metrics. Experimental results demonstrated a substantial improvement compared to the baseline model, with accuracy, precision, and recall reaching 99.8%, compared to the previous 22.8% baseline performance. The system effectively recommended relevant applications based on user preferences, as measured through cosine similarity in feature space. These results indicate that the proposed method not only improves classification accuracy but also enhances the quality of app recommendations, thereby significantly improving user experience. The findings contribute to the field of computer science by demonstrating an effective integration of feature scaling and text vectorization into a classical machine learning model, offering a scalable and interpretable solution for large-scale recommendation systems in digital marketplaces. This approach can be further adapted to other domains requiring hybrid processing of numerical and textual data for predictive analytics.]]>
<![CDATA[Classification of Date Types Using Gray Level Co-occurrence Matrix Red Green Blue and Convolutional Neural Network ]]>
<![CDATA[Rahmawati, Lailia]]>;
<![CDATA[Erviana, Irma]]>;
<![CDATA[Budiman, Budiman]]>;
<![CDATA[Khairunnisa, Khairunnisa]]>;
<![CDATA[Sutriawan, Sutriawan]]>
<![CDATA[ Jurnal Ilmu Komputer dan Informatika Vol 5 No 2 (2025): JIKI - December 2025 ]]>
Publisher : <![CDATA[CV Firmos ]]>
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DOI: 10.54082/jiki.290
<![CDATA[Classifying date fruit varieties is a challenging task due to their high visual similarity in terms of texture and color. This study aims to address this issue by developing an automated classification model that combines handcrafted Gray Level Co-occurrence Matrix (GLCM) texture features and average RGB color channels with Convolutional Neural Network (CNN) classifiers. The dataset comprises 1,658 images from nine varieties of date fruits, divided into 70% training and 30% testing subsets. The proposed workflow includes image preprocessing (resizing, normalization, grayscale conversion), extraction of GLCM features (contrast, energy, homogeneity, correlation), computation of average RGB channels, feature fusion, and CNN training using VGG16 and VGG19 architectures with Adam and Adadelta optimizers. The model performance is evaluated using accuracy, precision, recall, F1-score, and confusion matrix. Experimental results demonstrate that VGG19 with the Adam optimizer achieved the highest validation accuracy of 91%, slightly outperforming VGG16 (90%) but remaining below the 96% accuracy reported in prior studies using MobileNetV2. The integration of handcrafted features enhanced sensitivity to subtle color and texture variations, although it introduced potential feature redundancy. In conclusion, the hybrid GLCM–RGB–CNN with VGG19 and Adam achieved 91% accuracy, proving the benefit of combining handcrafted and deep features while highlighting opportunities for further enhancement through data augmentation and architectural optimization.]]>
<![CDATA[Analisis Tingkat Kesiapan Siswa Terhadap Proses Pembelajaran Berbasis AI di SMK Negeri 2 Salatiga ]]>
<![CDATA[Salemban, Rica]]>;
<![CDATA[Krismiyati, Krismiyati]]>
<![CDATA[ Jurnal Ilmu Komputer dan Informatika Vol 5 No 1 (2025): JIKI - Juni 2025 ]]>
Publisher : <![CDATA[CV Firmos ]]>
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DOI: 10.54082/jiki.260
<![CDATA[The rapid advancement of Artificial Intelligence (AI) challenges vocational education to ensure students are ready for technology-based learning. Yet, the level of readiness among vocational high school students remains underexplored. This research analyzes the readiness of students at SMK Negeri 2 Salatiga to engage in AI-based learning using the Technology Readiness Index (TRI) 2.0 framework. A total of 63 eleventh-grade Computer and Network Engineering students completed a structured questionnaire. Data were examined through descriptive statistics with validity and reliability tests to confirm instrument accuracy. Findings reveal an overall TRI score of 2.44, indicating a moderate level of readiness. Among the four TRI dimensions, optimism recorded the highest mean (2.93), reflecting students’ positive outlook toward AI benefits, while insecurity showed the lowest mean (2.13), highlighting concerns over security and dependability. These results underscore the importance of educational strategies that enhance not only technical competence but also students’ confidence and sense of safety when using AI. The study enriches the discourse on AI readiness in vocational education and provides a foundation for curriculum improvements and AI-oriented training programs that better equip students for the digital era.]]>
<![CDATA[Analisis Komparasi Algoritma Machine Learning dalam Prediksi Performa Akademik Mahasiswa: Literature Review ]]>
<![CDATA[Azis, Abdur Rahman]]>
<![CDATA[ Jurnal Ilmu Komputer dan Informatika Vol 4 No 2 (2024): JIKI - Desember 2024 ]]>
Publisher : <![CDATA[CV Firmos ]]>
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DOI: 10.54082/jiki.212
<![CDATA[Perkembangan teknologi dan data dalam bidang pendidikan membuka peluang baru untuk memahami dan memprediksi performa akademik mahasiswa. Literatur ini mengkaji efektivitas berbagai algoritma machine learning, seperti Support Vector Machine (SVM), Random Forest, Naïve Bayes, dan Deep Learning, dalam memprediksi performa akademik mahasiswa berdasarkan atribut seperti nilai akademik, kehadiran, serta interaksi dalam platform pembelajaran. Hasil analisis menunjukkan bahwa SVM mencatat akurasi tertinggi hingga 94,4% pada dataset dengan margin data yang jelas, sementara Random Forest unggul dalam menangani dataset besar dan kompleks dengan akurasi konsisten sebesar 85%. Naïve Bayes, dengan kesederhanaannya, mencapai akurasi 87,6% untuk dataset dengan atribut independen, sedangkan Deep Learning menunjukkan potensi untuk dataset besar namun terbatas pada akurasi 72,84% karena keterbatasan data. Penelitian ini menekankan pentingnya pemrosesan data yang lebih baik serta penggunaan algoritma yang sesuai untuk meningkatkan kualitas pembelajaran berbasis data. Implementasi machine learning memungkinkan intervensi dini untuk mendukung keberhasilan akademik mahasiswa, meskipun tantangan seperti kualitas data dan kebutuhan sumber daya komputasi tetap menjadi perhatian utama. Penelitian ini dapat mendukung pengembangan sistem pembelajaran berbasis data di perguruan tinggi, memungkinkan pengambilan keputusan berbasis data yang lebih efektif untuk meningkatkan kualitas pendidikan dan hasil akademik mahasiswa.]]>
<![CDATA[Penerapan Data Mining dalam Mendukung Sistem Penunjang Keputusan Penerima Beasiswa di Universitas: Literature Review ]]>
<![CDATA[Alviansyah, Muhammad Dafa]]>
<![CDATA[ Jurnal Ilmu Komputer dan Informatika Vol 4 No 2 (2024): JIKI - Desember 2024 ]]>
Publisher : <![CDATA[CV Firmos ]]>
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DOI: 10.54082/jiki.214
<![CDATA[Pengelolaan data yang besar untuk pengambilan keputusan dalam proses pemberian beasiswa di universitas menjadi tantangan tersendiri, terutama untuk memastikan bahwa keputusan yang diambil efektif dan efisien. Penelitian ini dilakukan untuk memberikan informasi terkait penerapan data mining dalam mendukung sistem penunjang keputusan (Decision Support System) sebagai solusi untuk membantu proses penentuan penerima beasiswa. Penelitian dilakukan dengan menganalisis artikel dan jurnal yang relevan melalui Google Scholar, IEEE Xplore, Springer, Science Direct dan database elektronik lainnya. Kata kunci yang digunakan meliputi “data mining”, “sistem penunjang keputusan” dan “beasiswa”. Hasil analisis yang telah dilakukan menunjukan bahwa data mining dengan pemilihan algoritma yang sesuai dengan ukuran dataset akan berdampak pada performa algoritma tersebut. Penelitian ini menunjukan bahwa penerapan data mining dengan algoritma artificial neural network (ANN) memiliki performa terbaik dibanding dengan algoritma pembandingnya dengan hasil diatas 79%. Dengan hasil tersebut penggunaan data mining disimpulkan dapat memberikan kemudahan dalam pengelolaan data yang besar serta dapat meningkatkan efisiensi dan efektivitas dalam pengambilan keputusan. Selain itu, teknologi ini juga berpotensi untuk diterapkan pada bidang lain seperti pertanian, kesehatan, dan keuangan untuk mendukung pengambilan keputusan yang lebih baik berdasarkan data yang dimiliki oleh setiap bidang ilmu.]]>
