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JITK (Jurnal Ilmu Pengetahuan dan Komputer)
Published by STMIK Nusa Mandiri
ISSN : -     EISSN : 25274864     DOI : -
Core Subject : Science,
Computer Science & IT
Kegiatan menonton film merupakan salah satu cara sederhana untuk menghibur diri dari rasa gundah gulana ataupun melepas rasa lelah setelah melakukan aktivitas sehari-hari. Akan tetapi, karena berbagai alasan terkadang seseorang tidak ada waktu untuk menonton film di bioskop. Dengan bantuan media internet, berbagai macam aplikasi nonton film android sangat mudah dicari. Hanya bermodalkan smartphone saja para penonton film dapat streaming berbagai macam jenis film di mana saja dan kapan saja mereka inginkan. Akan tetapi, karena banyaknya pilihan aplikasi nonton film android yang bisa digunakan, terkadang seseorang bingung memilihnya. Untuk itu, diperlukan suatu sistem pendukung keputusan yang dapat digunakan para pengguna sebagai alat bantu pengambilan keputusan untuk memilih dengan berbagai macam kriteria yang ada. Salah satu metode yang digunakan adalah metode Analytical Hierarchy Process (AHP). AHP melakukan perankingan dengan melalui penjumlahan antara vector bobot dengan matrik keputusan dengan tujuan agar hasil yang diberikan lebih baik dalam menentukan alternatif yang akan dipilih. Berdasarkan hasil penelitian yang dilakukan oleh 36 sampel responden didapatkan kriteria konten menjadi prioritas pertama pengguna untuk memilih aplikasi nonton film android dengan nilai bobot sebesar 0,224. Sedangkan Netflix menjadi alternatif dengan prioritas pertama keputusan pengguna dalam memilih aplikasi nonton film android dengan nilai bobot sebesar 0,352.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 505 Documents
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DIABETIC RETINOPATHY SEVERITY CLASSIFICATION USING GAMMA CORRECTION-BASED IMAGE ENHANCEMENT AND BN-VGG ARCHITECTURE Indri Ramayanti; Karnadi; Septiani Nadra Indawaty; Muhammad Umar Abdussalam; Malika Zilda; Anita Desiani
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 4 (2026): JITK Issue May 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i4.8094

Abstract

Diabetic retinopathy (DR) is a diabetes-related condition that can cause vision impairment or vision loss. Accurately identifying the level of DR from retinal fundus images is crucial for early detection. However, poor image quality often degrades classification performance. This study proposes an approach that integrates gamma correction-based image enhancement with a Batch Normalization–Visual Geometry Group (BN-VGG) architecture for multiclass DR severity classification. Gamma correction is applied to improve image contrast, while BN-VGG enhances training stability and feature representation. The proposed method categorizes DR into five classifications: normal, mild, moderate, severe, and proliferative. The enhanced images achieved PSNR of 30.85 and SSIM above 0.86, indicating improved visual quality. The model achieved accuracy at 0.97, sensitivity at 0.92, specificity at 0.98, F1-score at 0.92, Cohen's Kappa at 0.90, and G-Mean at 0.97. The innovative aspect of this study is the incorporation of gamma correction with BN-VGG architecture, demonstrating that image enhancement can significantly improve multiclass DR classification performance without increasing model complexity. The study's results indicate the proposed method's effectiveness for accurate & reliable DR severity classification
COMPARISON OF THE COMPLEXITY OF SEARCH ALGORITHMS IN DIGITAL PAPUAN LANGUAGE DICTIONARIES Nur Fitrianingsih Hasan; Vera Wati; Nurfadillah Bima Julianto Mambobo; Bima Julianto Mambobo
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 4 (2026): JITK Issue May 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i4.5948

Abstract

Regional-language digital dictionaries play a strategic role in supporting Tourism 4.0 by enabling communication between local communities and tourists. However, inefficient search mechanisms can substantially degrade their usability, while research on the computational complexity of search algorithms for low-resource languages such as those of Papua remains scarce. This study presents a comparative analysis of three search algorithms binary search, tree-based search, and n-gram search benchmarked against an unoptimized linear search baseline in a Papuan regional-language digital dictionary containing 5,597 lemmas. Each algorithm was evaluated on both time and space complexity through controlled experiments executed on five heterogeneous computing devices. The experimental results show that the tree-based search algorithm achieves the best overall performance, with the lowest average search time of 1.28 seconds and the smallest average memory usage of 3.73 kB. These findings provide an empirical basis for selecting efficient search algorithms in regional-language digital dictionaries and contribute to the Tourism 4.0 digital infrastructure by enabling fast, scalable access to local-language information.
HYBRID SAW-TOPSIS DECISION SUPPORT SYSTEM FOR EXEMPLARY RELIGIOUS AFFAIRS OFFICES SELECTION Muhdi; Sharipuddin; Joni Devitra
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 4 (2026): JITK Issue May 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i4.6881

Abstract

The selection of the Exemplary Religious Affairs Office (KUA) in Muaro Jambi Regency is currently hindered by manual, subjective assessments that lack transparency and objective benchmarking. This study addresses this gap by developing a web-based Decision Support System (DSS) that integrates Simple Additive Weighting (SAW) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Developed using the waterfall model, the system aims to enhance objectivity and efficiency in evaluating institutional performance. The primary scientific contribution lies in the proposed hybrid Multi-Criteria Decision-Making (MCDM) architecture: SAW is utilized to establish transparent initial criteria weights, which are then processed through TOPSIS to resolve complex trade-offs by identifying the shortest distance to the positive ideal solution. Results indicate that this integrated framework significantly improves ranking consistency and provides a robust validation mechanism compared to traditional manual evaluations. Beyond its practical application, this study contributes to the theoretical discourse on hybrid MCDM integration, offering a validated framework for enhancing accountability and objective governance within public sector institutional evaluations.
AI-BASED CLASSIFICATION OF SCHOOL STUDENT NEATNESS USING CONVOLUTIONAL NEURAL NETWORK (CNN) Andi Saryoko; Hanna Rizkia
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 4 (2026): JITK Issue May 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i4.7182

Abstract

The neatness of students in Scouting uniforms is a form of implementation of discipline that reflects compliance with school rules. At SDIT Ajimutu Global Insani, the uniform attributes assessed include the completeness of the Scout uniform. The neatness in wearing these attributes greatly supports the creation of an orderly and conducive learning environment. This study aims to classify the level of neatness of Scouting uniforms automatically using an artificial intelligence-based approach. The data used are in the form of student images recorded using a Canon EOS 60D DSLR camera, with a total data of 510 images, consisting of 88 female students and 82 male students. The method used in this study is Convolutional Neural Network (CNN) with a transfer learning approach using the MobileNetV2 architecture with Transfer Learning and data augmentation techniques to improve model accuracy. The system was developed to classify uniform neatness into two categories: neat and untidy. The test results show that the model is able to classify with an accuracy level of 73%, with precision, recall, and f1-score having the same results with an accuracy of 73%. These findings indicate that the developed system can help teachers and schools in evaluating student discipline objectively and continuously. Thus, this study contributes to improving the quality of education through the habituation of orderly behavior integrated with technology.
K-MEANS-BASED TRAINING DATA PROCESSING FOR IMPROVING TOURISM RECOMMENDATION ACCURACY Candra Agustina; Purwanto Purwanto; Farikhin Farikhin; Eka Rahmawati
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 4 (2026): JITK Issue May 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i4.7274

Abstract

This study investigates the enhancement of tourism destination recommendation systems through the use of K-Means clustering to improve training data quality and model accuracy. The rapid advancement of information technology has increased the demand for personalized and accurate recommendation systems within the tourism industry. Despite this, achieving high prediction accuracy remains a significant challenge. This study employs K-Means clustering to segment training data into homogeneous clusters, thereby improving data representation and enhancing the predictive accuracy of recommendation models. The research methodology includes a comprehensive literature review, data collection, preprocessing, clustering, and model testing using K-Nearest Neighbors (KNN), Decision Tree, and Naive Bayes algorithms. The results show that after applying K-Means clustering, KNN's accuracy increased by 2.27%, and its kappa and precision values also improved, indicating enhanced reliability and prediction accuracy. Naive Bayes exhibited substantial improvements with a 9.09% increase in accuracy, alongside significant enhancements in kappa and precision metrics. Conversely, the Decision Tree algorithm experienced a decline in performance after clustering. Therefore, clustering techniques are not suitable for application to the Decision Tree algorithm.
THE EMOTIONAL ANALYSIS OF SONG LYRICS AND VIDEO COMMENTS ON YOUTUBE USING DEEP LEARNING Nova Noor Kamala Sari; Widiatry Widiatry; Inda Fitria Maharisty
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 4 (2026): JITK Issue May 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i4.7321

Abstract

Digital media platforms shape public perception of music through song lyrics and audience comments. This study analyzes emotions expressed in the lyrics and YouTube comments of Taylor Swift’s “Fortnight” using deep learning models. The dataset consists of 42 lyric lines and 13,406 user comments collected from April to December 2024. Emotion labeling was manually performed based on Plutchik’s eight basic emotions with an additional neutral category. This research applies two models: Long Short-Term Memory (LSTM) and DistilRoBERTa, with random oversampling to address class imbalance. Performance was evaluated using accuracy, precision, recall, F1-score, and confusion matrices. As a single-song case study, this research provides a focused comparison of sequential and transformer-based architectures for simultaneous emotion analysis of lyrics and audience responses. The results show that DistilRoBERTa achieved higher accuracy (94.07%) than LSTM (90.75%), indicating the advantage of contextual transformer models in capturing nuanced emotional expressions within this dataset. However, the findings are limited to the thematic characteristics of this single-song dataset and should be interpreted within this contextual scope.
MULTI-ARCHITECTURE DEEP LEARNING FOR SUBJECT INDEPENDENT FACIAL EXPRESSION RECOGNITION Taufiq; Muhammad; Asran; Ezwarsyah; Muchlis Abdul Muthalib
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 4 (2026): JITK Issue May 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i4.7404

Abstract

Facial Expression Recognition (FER) remains a challenging problem in computer vision, particularly under subject-independent conditions in which models must generalize to individuals not seen during training. This study reports a controlled comparative evaluation of three Convolutional Neural Network (CNN) architectures — MobileNetV3-Large, EfficientNet-B3, and ResNet50 — using the Extended Cohn-Kanade (CK+) dataset (981 apex-frame images, 118 subjects, seven emotion classes). All models were trained and tested under identical experimental conditions with a subject-disjoint partition (72/23/23 subjects for training, validation, and testing), so that observed performance differences may be attributed primarily to architectural design. The results indicate that MobileNetV3-Large attains the highest test accuracy of 95.16%, exceeding EfficientNet-B3 (93.01%) and ResNet50 (91.94%), while requiring the fewest parameters (~5.4M) and the shortest inference latency (~8.2 ms per image). A multi-dimensional evaluation covering per-class metrics and computational cost is also reported. These observations provide preliminary architectural guidance for FER deployment in resource-constrained environments; however, because they are derived from a single dataset and a single subject split, broader claims should be confirmed on more diverse benchmarks.
DEVELOPMENT OF THE SWARA AUGMENTED REALITY APPLICATION FOR WASTE SORTING EDUCATION IN ELEMENTARY SCHOOLS Syahbaniar Rofiah; Endang Retnoningsih
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 4 (2026): JITK Issue May 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i4.7459

Abstract

The prevailing issue of suboptimal waste management significantly contributes to environmental pollution. While early environmental education is crucial for instilling the habit of waste sorting, conventional methods often fail to engage elementary school students. To address this, this study developed an innovative learning medium integrating Augmented Reality (AR) to provide a more interactive and enjoyable learning experience. This research employed using the ADDIE model (Analysis, Design, Development, Implementation, Evaluation). Initial Analysis identified user needs, leading to the Design of an application incorporating AR for interactive visualization for waste classification and marker creation. This development resulted in the "Smart Waste Sorting Augmented Reality" (SWARA) learning medium. Implementation involved trials in elementary schools in Bekasi Regency, with sampling determined by simple random sampling and the Slovin formula. The developed SWARA application features three waste marker categories: organic, Inorganic, and hazardous waste. Trial results demonstrated that the application successfully improved students' waste sorting accuracy and enhanced their knowledge of recycling. Evaluation using the User Experience Questionnaire (UEQ) across six main scales (Attractiveness, Perspicuity, Efficiency, Dependability, Stimulation, Novelty) showed an overall average result in the "Good" category. The highest scores were for Perspicuity (1.78) and Efficiency (1.74), while the lowest was Novelty (1.15), though still within the good range. The findings demonstrate that AR based learning significantly enhances students’ conceptual understanding, engagement, and practical skills in waste sorting compared to conventional learning methods.
HYBRID RESAMPLING METHOD AND HYPERPARAMETER OPTIMIZATION FOR HIV/AIDS PREDICTION: EVIDENCE FROM EIGHT MACHINE-LEARNING MODELS Lydia Nur Sa'adah; Fatkhurokhman Fauzi; Prizka Rismawati Arum; M Al Haris; Yan Nazala Bisoumi
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 4 (2026): JITK Issue May 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i4.7533

Abstract

HIV/AIDS remains a global health challenge with continuously increasing infection rates, highlighting the importance of accurate prediction models to support prevention and early detection. However, the development of such models is often constrained by class imbalance and irrelevant features. This study aims to improve HIV/AIDS infection prediction by integrating feature selection, data balancing techniques, and eight machine learning algorithms. Feature selection was performed using Mutual Information and Chi-Square to identify the most relevant features. The dataset used was the HIV/AIDS Infection Prediction Dataset from Kaggle, consisting of 2,139 instances and 23 features, with an imbalanced distribution of 1,618 non-infected and 521 infected cases. The dataset was divided into 80% training data and 20% testing data, with resampling applied only to the training set to prevent data leakage. Three resampling scenarios were evaluated: no sampling, SMOTE, and SMOTE-ENN. Hyperparameter tuning was conducted using Bayesian Optimization integrated with 5-fold Cross-Validation to improve model robustness and reliability. Eight machine learning algorithms were evaluated, including Decision Tree, Random Forest, AdaBoost, Gradient Boosting, XGBoost, LightGBM, K-Nearest Neighbors, and Logistic Regression. The results show that SMOTE-ENN combined with hyperparameter optimization significantly improved model performance. The best model, Gradient Boosting + SMOTE-ENN, achieved 96.1% accuracy, 94.8% precision, 98.4% recall, and 96.5% F1-score. These findings indicate that the proposed integrated framework is highly effective for predicting HIV/AIDS infection and has strong potential to support early diagnosis and data-driven decision-making in healthcare.
COMPARATIVE ANALYSIS OF COGNITIVE DIAGNOSTIC MODELS IN POMDP-BASED ADAPTIVE TESTING Mohamad Ridho Mubarok; Fandy Setyo Utomo
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 4 (2026): JITK Issue May 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i4.7601

Abstract

Recent progress in learning analytics and educational data mining has accelerated the development of adaptive learning systems, where Cognitive Diagnostic Computerized Adaptive Testing (CD-CAT) has emerged as a significant approach. CD-CAT employs Cognitive Diagnosis Models (CDMs) to generate detailed evaluations of student competencies. Nevertheless, increasing numbers of attributes and test items introduce challenges related to the complexity and uncertainty of adaptive policies. This research investigates and compares four cognitive diagnosis models, namely GD-DINA, MIRT, MCD, and KaNCD, within an adaptive testing framework based on a Partially Observable Markov Decision Process (POMDP). The evaluation was conducted using the ASSISTments dataset with Accuracy, AUC, and expected reward as performance metrics. The findings indicate that KaNCD achieved the best overall performance, obtaining the highest diagnostic accuracy (0.7503) and AUC score (0.7410), while also maintaining stable results in POMDP-based adaptive testing (expected reward = 0.792). Although GD-DINA produced the highest expected reward (0.901), its accuracy was comparatively lower. Meanwhile, MCD demonstrated a balanced performance, with high accuracy (0.7464) and strong adaptability of policy (reward = 0.873). Overall, these results suggest that KaNCD offers the most effective balance among accuracy, interpretability, and efficiency in POMDP-based adaptive testing systems.

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