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INDONESIA
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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EVALUATION OF REAL-TIME SPEECH RECOGNITION ACCURACY IN INTERACTIVE VIDEO MEDIA FOR DEAF STUDENTS Hilman Nuril Hadi; Adnan Zulkarnain
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.v11i4.7933

Abstract

Deafness is a type of disability characterized by partial or complete hearing loss in one or both ears. Deaf students in higher education face several critical challenges: (1) dependence on oral communication they cannot directly access, (2) limited sign language interpreters in regular classrooms, (3) the absence of media that converts speech into real-time text while displaying the speaker's facial expressions. These conditions cause deaf students to struggle with following explanations, engaging in discussions, and participating actively in the learning process. However, individuals with hearing impairment tend to rely on visual learning, whereas the majority of instructional information is delivered through oral communication. This research aims to develop interactive media based on speech recognition and real-time video as a solution to improve communication in the learning process of deaf students. The novelty of this research lies in the integration of web-based speech recognition with a multi-actor interface (instructor, student, and general user) specifically designed for inclusive education in higher education settings, distinguishing it from conventional solutions.  The method used is Research and Development (R&D) with the stages of needs analysis, system design, implementation, and functional testing and performance testing using Word Error Rate (WER). The overall average WER was 19.70%, with the range of WER being 14.05% (from the minimum of 13.22% to the maximum of 27.27%). The results showed that all system features performed as required, and an average WER indicated a good level of accuracy for interactive educational contexts.
ABSTRACTIVE SUMMARIZATION FOR INDONESIAN HUMAN TRAFFICKING COURT DECISIONS USING TRANSFORMER MODELS Faradhita Eka Septiana; Febri Bagus Triwibowo; Galih Wasis Wicaksono
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.v11i4.8221

Abstract

Indonesian Supreme Court decisions on Human Trafficking (TPPO) are lengthy and structurally complex, rendering manual review inefficient for legal practitioners. Existing abstractive summarization research for Indonesian text concentrates on news and social media, while no publicly benchmarked XSum-style dataset exists for the Indonesian legal domain. This study has two explicit objectives: (i) an XSum-structured legal summarization dataset is constructed from 404 TPPO decisions, and (ii) four fine-tuned Transformer models (T5 Base Indonesia, mT5 Small, DistilBART CNN, BART Large XSum) are benchmarked against extractive and classical abstractive baselines. The method couples an n8n-based PDF extraction pipeline with CSV-sourced verdict statements as reference summaries, followed by fine-tuning and evaluation using ROUGE-1/2/L and BERTScore F1, complemented by paired bootstrap significance testing (n=10,000). Results show T5 Base Indonesia attains the highest ROUGE-L of 39.49 and BERTScore F1 of 74.82, while mT5 Small achieves the highest ROUGE-1 of 44.97, all significantly outperforming Seq2Seq+Attention (ROUGE-1 27.31) and First-2-Sentences (ROUGE-1 10.86) with p<0.001. The contributions are: an XSum-formatted TPPO dataset, an automated extraction pipeline, and a comprehensive benchmark spanning extractive, classical abstractive, and Transformer-based methods. These findings offer practical benefits for legal document analysis and judicial information retrieval in Indonesia.
ENHANCING ACCURACY OF WEATHER CLASSIFICATION USING DEEP FEATURES AND SUPPORT VECTOR MACHINE Raden Sumiharto; Faisal Dharma Adhinata
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.v11i4.8251

Abstract

Weather is a determinant of farmers' planting calendar. Farmers usually start planting rice in the rainy season because rice requires sufficient water to produce optimal harvests. The weather is almost unpredictable in certain months, so farmers now look at cloud conditions to predict the season. Seasonal predictions based on cloud imagery can be assisted using Artificial Intelligence methods. Previous research used deep learning via transfer learning, but the results were not optimal. This research dataset is sourced from Kaggle and consists of five classes, namely cloudy, foggy, rainy, shine, and sunrise with a total data of 1500 images. This research proposes that a hybrid deep features and machine learning approach be used to increase the accuracy of the results. The MobileNet deep learning method is used at the feature extraction stage, then for classification using the Support Vector Machine (SVM) method. Experimental results with the Radial Basis Function (RBF) kernel on SVM produced an accuracy of 0.9500 for training data. The evaluation results using testing data produced an accuracy of 0.9667. This result also saw an increase of 4.2% in training data compared to previous research. Through these results, MobileNet-SVM is proven to be able to improve classification accuracy when using a small dataset with 1500 images.
EARLY DETECTION OF STUNTING IN CHILDREN USING HSV AND GLCM FEATURES WITH CNN-BASED CLASSIFICATION Ade Cristian Silalahi; Bonatio Vincent E Hutagalung; Sandy Walfredo Ritonga; Marlince NK Nababan
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.v11i4.8258

Abstract

Stunting is a chronic nutritional issue in children that can have long-term impacts on physical growth and cognitive development. Early detection is therefore important to support timely intervention. This study develops an image-based stunting detection approach that integrates HSV color descriptors, Gray Level Co-occurrence Matrix (GLCM) texture descriptors, and CNN features extracted with MobileNetV2. Images are preprocessed through resizing and Region of Interest (ROI) cropping based on bounding-box annotations. The handcrafted HSV and GLCM features are fused with CNN features at the feature level through vector concatenation before classification into stunting and non-stunting categories. This design was selected to preserve complementary low-level color-texture information and high-level semantic representations in a single classifier input. The hybrid model achieved a test accuracy of 84.39% with a stunting recall of 83%. Although the results indicate that multimodal visual descriptors can improve classification performance compared with single-feature approaches, the model still shows mild overfitting and was evaluated on a relatively limited dataset. In addition, inference efficiency and robustness to variations in imaging conditions were not yet quantitatively measured. Therefore, the present system should be interpreted as a proof of concept for objective, early, image-based stunting screening by healthcare personnel.
COMPARATIVE EVALUATION OF YOLOV5–YOLOV11 MODELS FOR DETECTING NUTRIENT DEFICIENCY IN CHILI SEEDLINGS Rangga Pebrianto Rangga; Agus Buono; Heru Sukoco; Aziz Kustiyo; Muhamad Syukur
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.v11i4.8263

Abstract

Nutrient deficiencies during the seedling stage of chili plants can reduce crop productivity, while conventional identification methods remain subjective and costly. This study compares YOLOv5 to YOLOv11 object detection models for detecting nutrient deficiency symptoms in Bonita chili seedling leaves, including complete nutrition, nitrogen deficiency, phosphorus deficiency, potassium deficiency, and NPK deficiency. The final dataset comprised 4,173 images derived from 1,739 original annotated leaf images through controlled dataset preparation, including split-before-augmentation, laboratory validation of nutrient conditions, and expert-reviewed labeling. All YOLO models were trained and evaluated using the same dataset partition and comparable experimental settings. Performance was assessed using mAP@0.5, computational complexity (FLOPs), inference speed, and model size. The results show that all evaluated models achieved high detection performance, with differences mainly appearing in computational efficiency and the balance between accuracy and speed. YOLOv10s and YOLOv11s obtained the highest mAP@0.5 in this experiment, whereas YOLOv8s showed a competitive balance between accuracy, inference speed, and model compactness. These findings indicate that recent YOLO developments are promising for fine-grained nutrient deficiency detection in computer vision–based precision agriculture.

Page 51 of 51 | Total Record : 505

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