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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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UTILIZING TEXT MINING FOR ENCRYPTION ALGORITHM RECOMMENDATION USING CONTENT-BASED FILTERING Mukti Qamal; Muhammad Iqbal; Yesy afrillia
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.v11i4.7663

Abstract

The selection of an appropriate encryption algorithm is crucial in ensuring data security, as each algorithm has distinct advantages and disadvantages in terms of speed, efficiency, and security level. Many users struggle to determine the most suitable algorithm due to limited technical knowledge and the vast amount of literature that must be reviewed. Therefore, this study proposes a recommendation system based on Content-Based Filtering (CBF) integrated with text mining to facilitate faster, more accurate, and data-driven algorithm selection. The objective of this research is to develop a recommendation system capable of analyzing the technical characteristics of encryption algorithms from scientific literature and providing relevant suggestions according to user needs. The methodology includes collecting 300 articles from the Garuda Kemdikbud portal using web scraping, performing data preprocessing such as tokenization, stop word removal, and case folding, representing text with TF-IDF, and calculating similarity using Cosine Similarity. The results indicate that the most frequently discussed algorithms are RSA (52 articles), AES (40 articles), and RC4 (25 articles), reflecting research trends focusing on modern public-key and symmetric cryptography. The evaluation results show that the system achieved Precision@3 of 1.0000 and Average Precision (AP) of 0.0583, indicating that the top recommendations generated are highly relevant to user needs. The developed system successfully generated recommendations tailored to specific needs, such as suggesting AES as the primary choice for “fast encryption of sensitive data.” This study demonstrates that combining text mining and CBF is effective in assisting the selection of encryption algorithms through literature-based analysis.
ENHANCED QR CODE BASED DIGITAL SIGNATURES FOR SECURE DOCUMENT MANAGEMENT TOWARD SUSTAINABLE GREEN INVESTMENT Bagas Dwi Yulianto; Wisnu Wendanto
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.v11i4.7728

Abstract

This study proposes a secure QR Code–based digital signature framework to support paperless legal document management and sustainable green investment. The method integrates SHA-256 hashing and enhanced Multi-Factor RSA encryption, combined with a block-based hexadecimal processing approach to improve computational efficiency while maintaining strong cryptographic security. The generated encrypted signature is embedded into a QR Code, enabling real-time document verification through decryption and hash comparison. The main contribution of this study lies in the integration of optimized Multi-Factor RSA with QR-based authentication, providing both high security and practical verification capability within a unified system. Experimental results demonstrate successful authentication across all test cases, with robust performance under image distortions such as rotation, sharpening, Poisson noise, and Gaussian noise. The proposed method achieves an average Avalanche Effect of 85.78%, indicating strong diffusion and resistance to cryptographic attacks. Furthermore, a sustainability assessment involving 100 authorized respondents from education, corporate, and government sectors produced average scores above 4.0 across green computing, legal reliability, technology adoption, and digital security dimensions. These findings confirm that the proposed framework enhances digital document security while supporting environmentally sustainable and legally compliant paperless transformation.
LEVERAGING CONTINUAL FINE-TUNING FOR EMOTION CLASSIFICATION IN PRODUCT REVIEWS ON MSME SUSTAINABILITY SUPPORT Galih Setiawan Nurohim; Heribertus Ary Setyadi; Pudji Widodo; Yusuf Sutanto
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.v11i4.7729

Abstract

Automatic analysis of consumer product reviews is essential for understanding granular customer perceptions beyond basic sentiment. While transformer-based models are prevalent in Indonesian sentiment analysis, their adaptation for multi-emotion classification shifting from broad polarities to specific affective states remains underexplored. This study addresses this gap by proposing a Continual Fine-Tuning (CFT) approach to adapt a pre-trained IndoBERTweet model from three sentiment categories into five distinct emotion classes: Happiness, Sadness, Fear, Love, and Anger. The novelty lies in the strategic repurposing of sentiment-oriented weights to capture nuanced emotional representations in Indonesian e-commerce discourse. Experimental results on the PRDECT-ID dataset demonstrate that the proposed CFT model achieves an accuracy of 0.8157 and a weighted F1-score of 0.8118, significantly outperforming traditional neural networks and multilingual baselines. The CFT model demonstrates a 2.13% improvement in accuracy compared to the base IndoBERTweet without continual tuning and a substantial 59.54% lead over the multilingual BERT (mBERT) baseline. Despite limitations concerning the dataset scale (5,400 samples) and inherent subjectivity in emotion labeling, this research provides a robust conceptual framework for model adaptation in the Indonesian NLP ecosystem. These findings suggest that CFT is an efficient strategy for enhancing the emotional intelligence of transformer models, especially in domain-specific tasks where high-quality labeled data is constrained.
REAL-TIME VIDEO-BASED VISITOR COUNTING FOR SMART TOURISM DESTINATIONS USING YOLOV11 AND BYTETRACK Feriantano Sundang Pranata; Arif Adrian; Khairani Saladin
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.v11i4.7759

Abstract

Accurate and real-time visitor data are needed to support smart tourism management. However, conventional counting methods still have limitations in dynamic outdoor tourism environments. This study develops and evaluates a real-time video-based visitor counting system by integrating YOLOv11 for person detection and ByteTrack for multi-object tracking. This approach extends visitor counting evaluation to uncontrolled open-air tourist destinations, where lighting variation, background complexity, visitor movement, and crowd density may affect detection and tracking performance. The system was evaluated using nine Full HD videos from five tourist destinations in West Sumatra, recorded under daylight and afternoon conditions with low to medium visitor densities. The YOLOv11–ByteTrack system achieved an average counting accuracy of 84.02%, MAE of 7.22 visitors per video, MAPE of 15.98%, and an average processing speed of 36.23 FPS. The average accuracy exceeded those of YOLOv3 and YOLOv8, which achieved 75.71% and 77.15%, respectively. These findings suggest that YOLOv11–ByteTrack has practical potential as a real-time visitor counting approach in smart tourism management, particularly for monitoring visitor flows, assessing site capacity, controlling visitor density, and supporting data-driven infrastructure planning.
PERFORMANCE EVALUATION OF TRANSFER LEARNING MODELS BASED ON OPTIMIZATION IN AGRICULTURAL PEST CLASSIFICATION Attarik Mohammad; Sugiyarto Surono; Aris Thobirin
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.v11i4.7800

Abstract

Pests in agriculture lower crop yields and jeopardize the world’s food security. Thus, quick and precise pest identification is crucial for successful pest management. Convolutional Neural Networks (CNN) and other deep learning techniques have made it possible to automatically classify pests thanks to developments in digital image processing and artificial intelligence (AI). Using three optimization algorithms, Adam, RMSprop, and SGD, this study assesses three transfer learning architectures, ResNet50V2, Xception, and EfficientNetB0. This study’s primary contribution is a comparative analysis of CNN architectures and optimization techniques to determine the best configuration for classifying agricultural pests. The dataset, which includes 5494 pest photos from 12 classes, was acquired via Kaggle. A ratio of  80%, 10%, and 10% was used to separate the data into training, validation, and testing sets. The performance of feature extraction and classification was enhanced by applying transfer learning with fine-tuning. According to findings, Xception with Adam and RMSprop has the highest accuracy of 94%. Adam and EfficientNetB0 both achieved competitive results with the same precision. These results suggest that the performance of agricultural pest classification models is influenced by both optimizer and architecture choices.
AGILE REQUIREMENTS MANAGEMENT CHALLENGES AND STRATEGIC RECOMMENDATIONS IN INDONESIA’S NATIONAL SINGLE WINDOW Dencaswo Purnomo; Teguh Raharjo; Ni Wayan Trisnawaty
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.v11i4.7818

Abstract

Agile methodology is widely adopted by organizations with dynamic user needs, particularly in complex environments involving multiple stakeholders such as ministries and agencies in the public sector. The Indonesia National Single Window Agency (LNSW), which facilitates electronic data exchange for national exports, imports, and logistics, faces this condition. This study aims to identify challenges in requirements management and provide recommendations for LNSW. This study uses three stages: (1) a Systematic Literature Review (SLR) of 15 main articles, (2) validation and categorization of challenges through interviews and questionnaires analyzed using the Content Validity Index (CVI) method (S-CVI/Ave = 1, S-CVI/UA = 1), and (3) development of practical recommendations. This study identifies 12 challenges in the requirements management process at LNSW, which are categorized based on the 4P framework: People (lack of expertise in respective fields, lack of stakeholder problem identification), Process (uncertainty and dynamics of requirements, inability to set requirement priorities, organizational bureaucracy, poor documentation practices), Project (difficulty in estimating time and cost, unrealistic targets, lack of clarity in roles and responsibilities), and Product (lack of product vision, issues in user story development, and technical issues). Based on these challenges, 12 recommendations are proposed, along with operational guidance in the form of an Agile Requirements Playbook tailored for public sector organizations with multi-stakeholder environments. This study contributes to both theory and practice by providing a context-specific Agile Requirements Playbook with validated recommendations to support requirements management in complex public sector environments.
CNN MODEL OPTIMIZATION USING MULTI-STAGE DATA AUGMENTATION FOR LOCAL PLANT LEAF DISEASE CLASSIFICATION Verdi Yasin; Timbo Faritcan P. Siallagan
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.v11i4.7845

Abstract

Plant leaf diseases are a major factor in reducing agricultural productivity, particularly for local commodities that often lack adequate artificial intelligence-based disease detection systems. This study aims to optimize the performance of a Convolutional Neural Network (CNN) model using the Inception V3 architecture through the application of multi-stage data augmentation to improve the classification accuracy of local plant leaf diseases. The dataset used is PlantifyDR from Kaggle, which has limited data volume and visual variation, requiring an effective augmentation strategy to improve the model's generalization ability. The proposed multi-stage augmentation approach consists of three stages—geometric, photometric, and texture-noise augmentation—that systematically enrich the diversity of training images. Evaluation results show that the proposed model provides significant performance improvements compared to the baseline model. The Inception V3 model with multi-stage augmentation achieved an accuracy of 0.762, an F1-score of 0.727, and a perfect AUC (1.00) across all classes, while the baseline model only achieved an accuracy of 0.595 and an average AUC of 0.877. Accuracy, loss, ROC curve, and confusion matrix analyses confirmed that multi-stage augmentation reduced overfitting and enhanced the model's ability to differentiate disease symptoms across leaf types. Therefore, this study concludes that multi-stage data augmentation is an effective approach for optimizing deep learning models on small and complex datasets, while also providing a significant contribution to the development of more accurate and reliable AI-based plant disease detection systems.
EXPERT SYSTEM FOR CLASSIFYING AUTISM CHILDREN’S INDEPENDENCE LEVEL FROM DAILY ACTIVITY USING FORWARD CHAINING Indah Werdiningsih; Fachrizal Fikri; Nania Nuzulita; Barry Nuqoba; Sigit Dani Perkasa
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.v11i4.7866

Abstract

Children with Autism Spectrum Disorder (ASD) require early intervention during their developmental stages. Currently, the availability of experts capable of accurately classifying the independence levels of children with ASD remains limited. Determining these independence levels is crucial, as it serves as the basis for establishing appropriate early interventions. The system aims to assist specialists in conducting more consistent and efficient assessments. This study contributes a novel application of a forward chaining–based expert system for classifying ASD children’s independence levels, integrating rule-based reasoning with user-centered evaluation, which distinguishes it from previous studies that primarily focus on diagnosis rather than functional independence assessment. Data were collected from three institutions: two Public Special Need Schools and a Regional Technical Implementation Unit of Children with Special Needs in East Java. The dataset consists of 400 records encompassing five daily activities: eating, drinking, brushing teeth, dressing, and taking off clothes. The independence levels are classified into three categories: independent, partially independent, and dependent. This research consists of seven stages, namely data collection, rule based system using forward chaining, database design using CDM and PDM, user interface development, implementation of the Next.js framework system and PostgreSQL database, system testing, and system evaluation. The results of the study showed that the accuracy was 98.5% and the user satisfaction score was 80.85%. These results indicate that the proposed method is effective in supporting therapists in determining the level of independence of children with ASD based on rules established by experts.
STUNTING CLASSIFICATION IN CHILDREN USING VIOLA-JONES AND MULTI-FEATURE FUSION WITH PRE-TRAINED MODELS Maylani Kusuma Wardhani; Garin Muhammad Akbar; Christian Sri Kusuma Aditya
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.v11i4.7890

Abstract

Stunting remains a critical public health issue, particularly in developing countries, where early detection plays a vital role in prevention and intervention. Previous studies have generally relied on single-feature approaches, either using handcrafted descriptors or convolutional neural networks (CNNs) alone, which often fail to capture subtle craniofacial differences associated with stunting. This study proposes an image-based classification system for detecting stunting in children using facial analysis. The proposed method integrates Viola–Jones face detection with facial landmarks, Gray Level Co-occurrence Matrix (GLCM), Color Co-occurrence Matrix (CCM), and local descriptors such as SIFT–FAST/ORB, combined with deep features extracted from a pre-trained EfficientNet model. Feature fusion was performed by concatenating handcrafted and deep features before classification using a fully connected layer with Softmax activation. Experimental results demonstrated that the proposed fusion model achieved superior performance compared to single-feature baselines, reaching 98% accuracy, 0.98 precision, 0.97 recall, and an F1-score of 0.98. These findings indicate that the integration of geometric, texture, color, and deep semantic cues effectively enhances sensitivity toward the stunting class and improves model interpretability. The novelty of this study lies in the combination of classical computer vision and deep learning techniques for robust, interpretable, and clinically relevant stunting detection. This approach offers strong potential for developing digital health tools that enable early, non-invasive stunting screening in children.
ELECTRICITY CONSUMPTION PREDICTION AND INFLUENTIAL FACTORS ANALYSIS USING MACHINE LEARNING REGRESSION Evita Fitri; Siti Nurhasanah Nugraha; Muji Ernawati
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.v11i4.7914

Abstract

The increase in electricity demand in line with population growth and economic activity requires an accurate and reliable electricity consumption forecasting system. Short-term electricity consumption predictions are an important component in energy system planning and management, particularly to support grid stability and operational efficiency. This study aims to model electricity consumption predictions using a machine learning regression approach and analyze the factors that most influence electricity consumption based on historical data. The dataset used consists of smart meter data with a 30-minute time interval that has undergone data cleansing, data transformation, and feature engineering, including the formation of lag features and temporal features. Three regression algorithms were used, namely Linear Regression, Random Forest Regression, and Gradient Boosted Trees Regression. Model evaluation was performed using the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R²) metrics. The results show that Linear Regression provides the best performance on the test data with an RMSE value of 0.156, MAE of 0.125, and R² of 0.140, and demonstrates stable generalization capabilities. The analysis of influencing factors reveals that historical consumption variables, particularly Avg_Past_Consumption and electricity consumption lag features, are dominant factors in the prediction, while environmental variables contribute relatively less. These findings provide practical implications for short-term energy demand planning by enabling more accurate load estimation and supporting data-driven decision-making through interpretable electricity consumption patterns.

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