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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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SIMPLE ARTIFICIAL INTELLIGENCE APPLICATION FOR CLASSIFYING HOUSEHOLD WASTE AT THE NEIGHBORHOOD WASTE BANK Irmawati Carolina; Mari Rahmawati; Al Ghoni Achmed. J; Arifin Salam; M. Arif Budiman; M. Daffa Ramadhani
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.v11i4.8297

Abstract

Waste management remains a critical environmental issue globally, including in Indonesia, where increasing household waste generation creates significant environmental and social challenges, particularly at the neighborhood level. In community-based Waste Banks, manual sorting processes are often inconsistent due to limited human resources and varying levels of public understanding of waste categories. This study aims to develop and evaluate a lightweight, web-based real-time waste detection and classification system to support community-level waste management. The proposed system utilizes the YOLOv8 object detection architecture implemented through the Ultralytics framework with PyTorch as the deep learning backend, integrated with OpenCV for real-time video processing and Streamlit for web-based deployment. The dataset consists of approximately 9,200 annotated images across 24 waste categories, divided into training, validation, and testing sets, with data augmentation applied to improve robustness. Model performance was evaluated using precision, recall, and mean Average Precision at IoU 0.5 (mAP@0.5). The results demonstrate high detection performance, achieving 99.5% mAP@0.5, 99.4% precision, and 100.0% recall, while maintaining stable real-time detection under varying lighting conditions. However, these results are obtained under relatively controlled dataset conditions; therefore, further evaluation in more diverse real-world environments is necessary to ensure generalization capability. The system enables multi-object detection without requiring specialized hardware, making it accessible for neighborhood-level Waste Banks and providing a practical solution for community-based waste management.
THE IMPACT OF COLOR AND CONTRAST ENHANCEMENT FOR DIAGNOSING GASTROINTESTINAL DISEASES BASED DEEP LEARNING Gregorius Guntur Sunardi Putra; Chastine Fatichah; Shintami Chusnul Hidayati; Rusdiyana Ekawati
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 1 (2025): JITK Issue August2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i1.6229

Abstract

Endoscopy is a crucial tool for diagnosing digestive tract diseases—colon cancer and polyps using a camera with LED lighting, but often results in low-quality images with poor contrast and luminance. This study evaluates the performance of two contrast-based image quality enhancement—Contrast Limited Adaptive Histogram Equalization (CLAHE) and Improved Adaptive Gamma Correction with Weighting Distribution (IAGCWD)—along with various color space transformations (RGB, HSV, YCbCr, CIELAB, Grayscale) in deep learning-based digestive tract diseases detection system. The detection system using EfficientNetV2S model and Quadratic Weighted Kappa (QWK) loss function to obtain the balance of prediction results for each class. The experiment shows that CLAHE is able to achieve 79% accuracy which is superior in clarifying important information in endoscopy images. CLAHE performs well due to its ability to reduce noise and enhance contrast. The classification model with HSV and CLAHE on KVASIR is able to recognize all classes well. RGB, HSV, and YCbCr color spaces have stable performance in most tests. This study contributes insights for enhancing endoscopic image quality to support both computer-aided and clinical diagnosis.
ENHANCING SENTIMENT ANALYSIS ACCURACY WITH BERT AND SILHOUETTE METHOD OPTIMIZATION Kelvin Kelvin; Frans Mikael Sinaga; Wulan Sri Lestari; Sunaryo Winardi; Khairul Hawani Rambe; Ronsen Purba
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 1 (2025): JITK Issue August2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i1.6392

Abstract

This research is based on the emergence of ChatGPT technology, which has significant implications in various fields. This research aims to design a model that improves sentiment analysis classification accuracy. The methods applied include the use of the Silhouette Coefficient to determine the best cluster parameters before performing data grouping with the Self-Organizing Map (SOM) method. Additionally, the Bidirectional Encoder Representations from Transformers (BERT) model is utilized to perform precise and convergent sentiment classification. The research methodology encompasses several phases, including data preprocessing through natural language processing techniques. Textual data is converted into vector representations, which are then processed using the Silhouette Coefficient to identify the optimal cluster parameters. These parameters are subsequently applied in the Self-Organizing Map method to cluster data, while the Bidirectional Encoder Representations from Transformers model determines public sentiment, categorized as positive, negative, or neutral. The findings of this study indicate that the best cluster parameter is 9, using a batch size of 64 and a maximum sequence length of 128. The highest accuracy achieved using the confusion matrix is 92.06%. Further tests with varying parameters confirm that the Silhouette Coefficient method significantly enhances the convergence and accuracy of classification outcomes. The conclusion of this research is that integrating the Silhouette Coefficient and Bidirectional Encoder Representations from Transformers is effective in optimizing sentiment analysis on large datasets, achieving both accurate and reliable results.
SCHOLAR AI: INNOVATION IN SCHOLARSHIP SELECTION CLUSTERING BASED ELIGIBILITY CLASSIFICATION BASED ON MACHINE LEARNING Tutik Lestari; Achmad Farouq Abdullah; Oddy Virgantara Putra; Onno Widodo Purbo
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.v11i4.7196

Abstract

Scholarship allocation is a crucial process that ensures financial support for students based on academic performance, potential, and financial need. However, the scholarship selection process at Pondok Pesantren Darunnajah has faced challenges in capturing the holistic characteristics of applicants. This research proposes a machine learning model that integrates clustering and predictive techniques to improve the scholarship selection process. The dataset consists of 300 student samples with attributes such as academic scores, tahfidz (Qur'an memorization), family income, and extracurricular activities. These features help determine if a student qualifies for one of three scholarship schemes: Beasiswa Tahfidz, Beasiswa Prestasi, or Beasiswa Ashabunnajah, or if they are deemed "not eligible." The model follows the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework and utilizes machine learning algorithms for classification. To ensure the model's robustness, its performance is evaluated using K-fold cross-validation, with 5-fold validation employed to validate the model's predictions. The results show a high mean validation accuracy of 90.61% and an F1-score of 0.9311, indicating strong generalization capabilities. These findings highlight the model's potential to improve the scholarship allocation process, ensuring scholarships are awarded to the most deserving students based on academic performance, leadership potential, and financial need. Despite its high performance, the study acknowledges limitations such as potential biases in the dataset and challenges in capturing all relevant factors. These issues may affect the overall effectiveness of the model, suggesting room for improvement in addressing the complexity of the selection process.
SOLAR-POWERED IOT-BASED BEHAVIORAL VALIDATION SYSTEM FOR SUSTAINABLE RAT PEST CONTROL IN RURAL RICEFIELDS Willy Prihartono; Ade Rizki Rinaldi; Cep Lukman Rohmat; Odi Nurdiawan
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.v11i4.7247

Abstract

Rice-field rats (Rattus argentiventer) continue to cause substantial rice yield losses in Indonesia, reaching up to 30% per season. This study presents a solar-powered IoT-based ultrasonic deterrent system designed for autonomous operation in off-grid rural environments. The system integrates PIR motion detection, PWM-controlled ultrasonic emission (16–20 kHz; 85–95 dB), and a solar-battery energy subsystem to ensure continuous nocturnal functionality. Field validation involving 21 rats demonstrated measurable short-term behavioral disruption, with 42.9% avoidance and 33.3% panic responses. Electrical testing confirmed stable night-time performance, with an average power output of 26.8 W during peak rodent activity. Statistical analysis showed χ²(2, N = 21) = 2.38, p = 0.30. While statistical significance was not achieved, the observed effect size (Cramer’s V = 0.24) indicates a moderate behavioral association, supporting practical deterrent potential under field conditions. Unlike prior studies that evaluate sensing or energy components separately, this research integrates renewable energy autonomy, real-field behavioral validation, and IoT-based automation within a single operational framework. The findings establish a foundation for adaptive, machine-learning-driven pest control systems to enhance sustainable rice-field management
MACHINE LEARNING WITH LIGHTWEIGHT CNN (RESNET-18) FOR EARLY DETECTION OF RICE LEAF DISEASES Poningsih; Suhendra; Ahmad Zamsuri
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.v11i4.7358

Abstract

Rice leaf diseases such as blast and brown spot significantly threaten rice productivity, especially in agrarian countries like Indonesia. Manual diagnosis methods remain subjective, slow, and inconsistent across field conditions, highlighting the need for an automated and reliable detection system. This study presents a lightweight deep learning framework for the automatic classification of rice leaf diseases from image data. To assess its effectiveness, four Convolutional Neural Network (CNN) architectures ResNet-18, VGG-16, Inception V3, and MobileNetV2 were evaluated. The dataset, obtained from Kaggle, consists of three classes healthy, blast-infected, and brown spot with all images preprocessed through normalization and augmentation before being split into training and validation sets. Experimental results show that ResNet-18 achieves the best overall performance, with 96.94% accuracy, 100% precision, 95.45% recall, an F1-score of 96.18%, and an AUC of 1.0000. Compared to the other architectures, ResNet-18 demonstrates higher stability, stronger generalization, and lower overfitting tendencies while maintaining computational efficiency. The findings indicate that ResNet-18 is a promising lightweight model for practical deployment in mobile or IoT-based agricultural monitoring systems, supporting early disease detection and enhancing local food security efforts
IMPACT OF ROI SEGMENTATION METHODS ON ANTHRACNOSE DETECTION IN PAPAYA LEAVES USING RESNET-50 Shinta Siti Sundari; Ruuhwan; Evi Dewi Sri Mulyani
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.v11i4.7445

Abstract

Early detection of anthracnose disease on papaya leaves is important for mitigating crop yield losses, but manual methods are inefficient and prone to subjectivity; this study evaluates the effect of region of interest (ROI) extraction strategies on deep learning-based classification performance. The objective of this study is to compare four classification pipelines: ResNet-50 without segmentation, (M1) ExG+Otsu + ResNet-50, (M2) U-Net + ResNet-50, and (M3) RCNN + ResNet-50, in detecting anthracnose on papaya leaf images. The methods included the use of the public BDPapayaLeaf dataset, pre-processing and augmentation, and evaluation using stratified K-fold cross-validation with evaluation metrics including precision, recall, and F1. The results show that the semantic segmentation-based pipeline, U-Net + ResNet-50 (M2), provides the best performance with an F1-score (macro/weighted) ≈ 0.961 and precision–recall balance in both classes; M0 showed the highest recall for the anthracnose class (≈0.99) while M1 based on color thresholding provided the lowest performance due to sensitivity to lighting variations; M3 (RCNN) was in the middle. The findings recommend the use of segmentation and classification pipelines for field applications, noting the need for dataset expansion and inference optimization for deployment.
PERFORMANCE EVALUATION OF RECENT YOLO VERSIONS FOR CLASSROOM STUDENT BEHAVIOR DETECTION Mahendra Adiastoro; Febry Putra Rochim; Syahroni Hidayat
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.v11i4.7773

Abstract

The increasing adoption of smart classroom systems underscores the need for automated, objective, and real-time  monitoring of student behavior to support effective teaching and learning. Computer vision–based object detection, particularly the You Only Look Once (YOLO) family, has shown strong potential for this task. However, existing studies predominantly evaluate YOLO models in isolation or across different frameworks, resulting in biased comparisons. To address this gap, this study presents a controlled intra-family comparative evaluation of four recent YOLO generations YOLOv8, YOLOv10, YOLOv11, and YOLOv12 across three weight variants (nano, small, and medium), yielding 12 model configurations. All experiments were conducted under a uniform training pipeline and computing environment using an NVIDIA T4 GPU to ensure fair benchmarking. Model performance was assessed using Precision, Recall, F1-Score, mean Average Precision (mAP), inference speed (FPS), and computational complexity. The results reveal a consistent trade-off between detection accuracy and inference speed: YOLOv12m achieves the highest detection accuracy but the lowest FPS due to increased architectural complexity. At the same time, YOLOv10n offers the fastest inference at the cost of reduced reliability for subtle behaviors. Within the scope of the evaluated dataset and controlled classroom setting, YOLOv8s and YOLOv11s demonstrate the most balanced accuracy–speed performance, making them suitable candidates for real-time  classroom monitoring under similar conditions. This study provides practical insights for researchers and developers by offering an objective benchmark and model-selection guidance tailored to smart classroom applications, while accounting for dataset and environmental constraints.
COMPETITIVE COLLABORATION BETWEEN SVM AND NUTCRACKER OPTIMIZATION ALGORITHM FOR CARDIOVASCULAR DISEASE CLASSIFICATION Florentina Yuni Arini; Itsna Sabila Hidayati; Januar Pancaran Nur Fajri; Muhammad Alvin Adinata; Bhanu Rizqi Marzaki; Rangga Wicaksana; Poomin Duankhan
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.v11i4.7826

Abstract

Cardiovascular disease (CVD) remains the leading cause of death worldwide, emphasizing the need for accurate and reliable diagnostic models. Support Vector Machine (SVM) has demonstrated strong performance in medical data classification due to its ability to handle complex and high-dimensional data; however, its effectiveness depends heavily on appropriate hyperparameter selection. To address this limitation, this study integrates the Nutcracker Optimization Algorithm (NOA), a population-based metaheuristic inspired by squirrel foraging behavior, to optimize SVM hyperparameters for cardiovascular disease prediction. Using a standardized heart disease dataset, three models were evaluated: a baseline SVM, an NOA-optimized model, and an integrated SVM–NOA model. The proposed SVM–NOA approach achieved the best performance, improving accuracy from 83.61% to 88.52%, precision from 78.12% to 86.21%, and F1-score from 83.33% to 87.72%, while maintaining a recall of 89.29%. Although NOA incurs additional one-time optimization cost, the final optimized SVM required only 0.0095 s for training, compared to 0.0205 s for the baseline SVM. These results demonstrate that SVM–NOA provides a robust and computationally practical approach for enhancing cardiovascular disease prediction.
COMPARATIVE ANALYSIS OF YOLOV5SM, YOLOV8, AND YOLOV11 FOR IMAGE-BASED TEMPEH QUALITY RECOGNITION Isa Mahfudi; Mila Kusumawardani; Moechammad Sarosa; Chandrasena Setiadi
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.v11i4.7930

Abstract

Tempeh is a traditional Indonesian fermented food whose quality is influenced by fermentation and environmental conditions. Quality assessment is still commonly performed manually, leading to subjectivity and inconsistency. This study compares three modern object detection models—YOLOv5sM, YOLOv8, and YOLOv11—for digital image–based tempeh quality recognition. A dataset of 1,000 images (500 good and 500 defective) was collected using a Logitech C270 camera under controlled lighting conditions. YOLOv5sM was trained with data augmentation (Mosaic, flip, rotation), while YOLOv8 and YOLOv11 were trained without augmentation to isolate architectural differences. All models were trained for 100 epochs using identical hyperparameters and evaluated on a 10% test set. Results show that YOLOv11 achieved the highest accuracy (98%), outperforming YOLOv8 (94%) and YOLOv5sM (88%). Although mAP@0.5 reached 99.5% across models, stricter evaluation using mAP@0.5:0.95 revealed performance differences (96.2%, 96.9%, and 97.0%, respectively). The superior performance of YOLOv11 is attributed to its C3K2 and C2PSA modules, which enhance fine-grained feature extraction and localization precision. These findings indicate that YOLOv11 is the most suitable architecture for automated tempeh quality inspection

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