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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 543 Documents
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FORECASTING STOCK MARKET MODEL: A SYSTEMATIC LITERATURE REVIEW Elia Setiana; Kusrini Kusrini; Tonny Hidayat; Dhani Ariatmanto
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 12 No. 1 (2026): JITK Issue August 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v12i1.7991

Abstract

The increasing digitisation of stock markets and the growing diversity of financial data sources have intensified the need for accurate, robust, and risk-aware stock market forecasting. This systematic literature review synthesises recent evidence to examine the effectiveness of forecasting methods under different data and market conditions, the characteristics of commonly used benchmark datasets, the contribution of preprocessing strategies, and the evaluation and validation practices applied in stock market forecasting. Following the PRISMA framework, 71 peer-reviewed studies retrieved from the Scopus database were systematically screened, classified, and analysed. The evidence mapping shows that sequence-based deep learning models, including LSTM, GRU, and CNN–LSTM, represent the largest methodological group at approximately 41%, followed by transformer- and attention-based approaches at around 16%. Volatility-oriented econometric and classical statistical models account for approximately 18% and 14%, respectively, while probabilistic and quantile-based approaches remain limited. The findings indicate that forecasting performance is strongly context-dependent: classical models remain effective for relatively stationary univariate series, volatility-oriented models are particularly relevant when clustering and spillover effects are present, and deep learning and transformer-based approaches are more suitable for multivariate, nonlinear, and feature-rich settings. Overall, the review highlights the need for greater integration of uncertainty-aware evaluation, regime-sensitive validation, and risk-oriented forecasting frameworks.
MULTI-OBJECTIVE OPTIMIZATION OF RESNET50 ARCHITECTURE USING GENETIC ALGORITHM FOR ENHANCED BATIK MOTIF CLASSIFICATION P.A.M. ZIDANE R.W.P.P ZER ZER; Dedy Hartama; Agus Perdana Windarto
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 12 No. 1 (2026): JITK Issue August 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v12i1.8050

Abstract

Automatic batik motif classification remains challenging due to the high visual similarity among many motifs, making manual differentiation difficult. This study proposes a weighted-sum multi-objective Genetic Algorithm (GA) to optimize a pre-trained ResNet50 model for batik motif classification. The novelty of this study lies in optimizing the top-layer architecture and transfer-learning hyperparameters of ResNet50 by jointly considering classification accuracy, trainable parameter count, and inference latency. Unlike many previous GA-based CNN studies that mainly focus on accuracy or construct architectures from scratch, this study employs GA as a resource-efficient optimizer for practical deployment. The research used a quantitative experimental design with a secondary dataset of 3,550 batik images from five motif classes, namely Kawung, Megamendung, Parang, Sidomukti, and Truntum. The optimization process searched for the best configuration of four hyperparameters, namely the number of neurons in the dense layer, dropout rate, learning rate, and fine-tuning depth, while Adam was used as a fixed optimizer throughout the experiments. The results show that the proposed model improved classification accuracy from 84.00% to 90.00%, reduced trainable parameters by 94.4% from 23.59 million to 1.31 million, and decreased inference time by 29.4% compared with the baseline ResNet50 model. These findings indicate that the proposed method can achieve a favorable balance between predictive performance and computational efficiency for cultural heritage recognition on resource-constrained devices.
COMPARING OPTUNA AND HYPEROPT FOR MOBILENETV3 HYPERPARAMETER OPTIMIZATION IN CORN LEAF DISEASE CLASSIFICATION Nur Rachmat; Anugerah Widi
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 12 No. 1 (2026): JITK Issue August 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v12i1.8228

Abstract

Corn leaf diseases pose a significant threat to agricultural productivity, necessitating accurate and efficient detection methods. While extensive deep learning research has been applied to plant disease classification, comparative studies of Hyperparameter Optimization (HPO) frameworks on lightweight architectures such as MobileNetV3 remain scarce. This study aims to compare the performance of Optuna and Hyperopt in optimizing the hyperparameters of MobileNetV3 for corn leaf disease classification. The Kaggle Corn Leaf Disease dataset, comprising 4,000 images across four classes (leaf blight, leaf spot, rust, and healthy), was split 80:10:10 for training, validation, and testing. The hyperparameter search space encompassed learning rate, optimizer type, batch size, and dropout rate, with each framework limited to 15 trials. Results indicate that for MobileNetV3-Small, Optuna achieved an accuracy of 96.75% (F1: 96.66%), marginally outperforming Hyperopt (96.50%; F1: 96.39%). Conversely, for MobileNetV3-Large, Hyperopt demonstrated superior performance with an accuracy of 97.00% (F1: 96.96%) compared to Optuna (96.25%; F1: 96.14%). The Wilcoxon signed-rank test revealed no statistically significant difference between the two frameworks (p > 0.05). These findings suggest that both frameworks are statistically equivalent, and MobileNetV3-Small offers the most favorable balance between classification accuracy and computational efficiency for mobile-based plant disease detection applications.
METAHEURISTIC HYPERPARAMETER OPTIMIZATION FOR DEEP LEARNING: A COMPARATIVE STUDY Zulfahmi Syahputra; Romi Fadillah Rahmat
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 12 No. 1 (2026): JITK Issue August 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v12i1.8269

Abstract

One of the primary challenges in developing effective deep learning models lies in identifying optimal hyperparameter configurations, particularly within high-dimensional and complex search spaces. Traditional tuning strategies, including grid search and random search, are widely known to be computationally inefficient and frequently yield suboptimal outcomes. To address this limitation, this study presents a comparative analysis of two metaheuristic algorithms — Particle Swarm Optimization (PSO) and Dwarf Mongoose Optimization (DMO) — as advanced alternatives for hyperparameter tuning in deep learning models trained on the CIFAR-10 dataset. Both algorithms were rigorously assessed using a comprehensive set of performance metrics, namely accuracy, precision, recall, and F1-score, supplemented by confusion matrix analysis to capture class-level behavior. Experimental findings confirm that both approaches yield substantial improvements in model performance. Notably, PSO demonstrated superior results, achieving a validation accuracy of 89.15% and a test accuracy of 87.82%, while DMO reached a final accuracy of 86.38%. In terms of optimization behavior, PSO exhibited greater convergence stability and more uniform class-level performance, whereas DMO proved more effective in broadly exploring the search space. Overall, this study reinforces the potential of metaheuristic-based optimization as a robust framework for hyperparameter tuning and underscores the critical role of optimization stability in achieving reliable model generalization.
PSO-OPTIMIZED XGBOOST FOR MAIL DELIVERY DELAY PREDICTION AND LOGISTICS SLA COMPLIANCE Syafrial Fachri Pane; Bargana Kukuh Raditya
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 12 No. 1 (2026): JITK Issue August 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v12i1.8282

Abstract

The Indonesian postal logistics sector continues to face challenges in maintaining letter delivery timeliness and Service Level Agreement (SLA) compliance under dynamic operational conditions. Conventional predictive approaches often rely on static representations and are limited in capturing temporal risk patterns in mail delivery processes. This study proposes a data-driven machine learning framework to predict delivery delay risk in postal letter services by integrating temporal feature engineering, class imbalance handling, and metaheuristic-based hyperparameter optimization. The framework applies the Synthetic Minority Over-sampling Technique (SMOTE) and evaluates multiple classification models using stratified cross-validation. Among the evaluated algorithms, XGBoost optimized using Particle Swarm Optimization (PSO) demonstrates the strongest predictive performance. The PSO-optimized configuration (n = 96, lr = 0.1718, d = 3) achieves an accuracy of 0.6605, ROC–AUC of 0.6883, and F1-score of 0.6059, indicating improved class-sensitive prediction. Model interpretability is examined using Mean Decrease in Impurity (MDI), which identifies posting day as the dominant contributor to delivery delays, followed by SLA commitment and intra-day posting patterns. The final framework generates probabilistic risk scores from 0 to 100 percent, with the highest observed value reaching 99.44, enabling early warning and prescriptive operational interventions for potential SLA violations. These results indicate that the proposed PSO–XGBoost framework supports proactive logistics risk management. However, this study is limited to historical data from a single postal operational environment and does not incorporate external factors such as weather, traffic, or regional delivery variations.
CONTEXTUAL FEATURE NORMALIZATION ON THE PERFORMANCE OF HEART DISEASE CLASSIFICATION MODELS Adi Suwondo; Kusrini Kusrini; Ema Utami; Kumara Ari Yuawan
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 12 No. 1 (2026): JITK Issue August 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v12i1.8299

Abstract

Heart disease classification models commonly employ statistical normalization techniques that standardize features according to data distribution but do not explicitly incorporate clinically meaningful cardiovascular information. This study evaluates Clinical Contextual Normalization (CCN) as an alternative feature representation strategy for heart disease classification. Using the Cleveland Heart Disease dataset (303 records), standard numerical representation and CCN were evaluated across five classifiers: Logistic Regression (LR), Support Vector Classifier (SVC), Random Forest (RF), Multilayer Perceptron (MLP), and Naïve Bayes (NB). Model performance was assessed using repeated stratified 10-fold cross-validation with five repetitions (50 evaluation folds), with recall as the primary metric because false negatives may delay clinical screening. The results revealed a classifier-dependent response to CCN. Random Forest showed a small numerical recall increase (ΔRecall = +0.0059), but the difference was not statistically significant (p = 0.6434). MLP produced the largest positive numerical recall change (ΔRecall = +0.0143) and produced 10 more aggregated true-positive predictions while false positives decreased by two, although its recall difference was also not statistically significant (p = 0.2195). In contrast, Logistic Regression showed a statistically significant recall decrease (ΔRecall = −0.0115, p = 0.0186), while Naïve Bayes exhibited the largest significant reduction (ΔRecall = −0.0333, p < 0.001). These findings demonstrate that clinically informed feature representation does not uniformly improve predictive performance but produces classifier-dependent effects. Further validation using larger and more diverse datasets is required before clinical deployment.
OPTIMISING EXPLAINABLE AI IN EDUCATION: A DSPY-BASED FRAMEWORK WITH CHAIN-OF-THOUGHT REASONING FOR ADAPTIVE LEARNING Ben Rahman
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 12 No. 1 (2026): JITK Issue August 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v12i1.8319

Abstract

The application of artificial intelligence (AI) in education is often constrained by limited reasoning transparency, computational demands, and reproducibility challenges. This study proposes and conducts an exploratory evaluation of a modular AI education framework based on Declarative Structured Programming (DSPy) and Chain-of-Thought reasoning. The framework integrates typed input–output signatures with structured inference to support transparent question generation and adaptive feedback. A pilot study involved 10 participants with computer-science or education backgrounds; each completed three sessions, yielding 30 session-level observations. The framework used GPT-4o mini and was compared with prompt-based and rule-based baselines. It achieved 92.4% session-level learning accuracy and higher observed reasoning-clarity ratings than the baselines, with an average response time of 1.2 s. Because the sample was small, technically oriented, and evaluated over short sessions, the findings constitute preliminary evidence and should not be generalized to diverse learners or sustained learning outcomes. Larger, heterogeneous, longitudinal, and resource-instrumented studies are require.
IMPACT OF TEXT AUGMENTATION ON INDOBERT PERFORMANCE FOR HOSPITAL REVIEW SENTIMENT ANALYSIS Yoga Anugrah Pratama.SY; Ali Ibrahim
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 12 No. 1 (2026): JITK Issue August 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v12i1.8327

Abstract

Sentiment analysis of hospital patient reviews plays a critical role in evaluating healthcare service quality. However, limited labeled data and class imbalance often affect classification performance and reduce minority-class detection. This study empirically investigates the impact of text augmentation techniques on improving IndoBERT performance for sentiment classification of patient reviews at Dr. Mohammad Hoesin Palembang General Hospital. A total of 1,464 reviews were collected, preprocessed, and weakly labeled using a VADER-based approach, resulting in 1,168 positive and 296 negative instances. To address class imbalance, augmentation was applied exclusively to the training set using back-translation and Easy Data Augmentation (EDA), including synonym replacement, random insertion, random swap, and random deletion. IndoBERT was fine-tuned under consistent hyperparameter settings and evaluated using accuracy, precision, recall, F1-macro, and AUC. The baseline model achieved an F1-macro of 70.2%, indicating limited minority-class sensitivity. After augmentation, the random swap technique achieved the highest observed performance within the experimental setup, reaching 96.8% accuracy, 95.3% F1-macro, and 98.9% AUC. These results suggest improved performance within the experimental setting, particularly in minority-class detection. However, it should be noted that the labels were generated through a translation-based weak labeling approach, which may introduce noise and affect the accuracy of the labels. Therefore, the findings should be interpreted within the scope of this experimental setting.
AN INTELLIGENT LEARNING-DRIVEN FOR DYNAMIC WASTE COLLECTION ROUTING USING LSTM AND EVOLUTIONARY CVRP OPTIMIZATION Muhammad Amin; Muhammad Iqbal; Irvanizam Irvanizam
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 12 No. 1 (2026): JITK Issue August 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v12i1.8357

Abstract

Inefficient waste collection routes result in significant operational costs and environmental impacts. Traditional static routes based on historical averages often deviate substantially from actual requirements. This study proposes an intelligent framework integrating Long Short-Term Memory (LSTM) networks for dynamic time-series forecasting with an Evolutionary Capacitated Vehicle Routing Problem (CVRP) optimizer. The LSTM model captures temporal waste generation patterns using a 7-day sliding window; these patterns are fed into a metaheuristic optimizer that minimizes travel distance to disposal sites while eliminating redundant trips. Experimental results demonstrate high prediction accuracy, with the Mean Squared Error (MSE) converging at 0.0001 during the validation phase. Furthermore, the optimization process achieved a 10.95% reduction (22.5 km/day) in average travel distance compared to the baseline model. In high-density scenarios, the framework improved route efficiency by up to 15.99%. The study concludes that combining deep learning memory capabilities with evolutionary optimization provides a reliable decision-support system for smart city waste management..
PULMONARY EDEMA CLASSIFICATION USING CLASSICAL AND QUANTUM CONVOLUTIONAL NEURAL NETWORKS Adri Sopiana; Tony Sumaryada; Sitti Yani
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 12 No. 1 (2026): JITK Issue August 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v12i1.8446

Abstract

This study develops a pulmonary edema detection model based on chest x-ray images using both classical Convolutional Neural Network (CNN) and Quantum Convolutional Neural Network (QCNN) approaches. The dataset consists of chest x-ray images labeled as positive and negative for pulmonary edema and is divided into training and testing sets with an 80:20 ratio. To obtain the best performance, both models were optimized through hyperparameter tuning. The classical CNN model employed 3×3 filters, four convolutional layers, and was trained for 10 epochs. The QCNN model was designed with a comparable architecture incorporating quantum gate modifications and was also trained for 10 epochs. The performances of these models were assessed based on accuracy, sensitivity, and precision in order to measure their classification capacities. The QCNN was developed under the same experimental conditions to enable a fair performance comparison with the optimized classical CNN model. The results show that the classical CNN achieved better performance than the QCNN. This lower QCNN performance is likely due to the current limitations of quantum architectures and hardware, which are not yet able to extract and process image features as effectively as classical CNNs. However, this study was conducted using low-resolution medical images and a preliminary QCNN framework under limited computational resources.

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