cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
semdejafet1908@gmail.com
Editorial Address
-
Location
Kota adm. jakarta timur,
Dki jakarta
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 394 Documents
 34   35   36   37   38 
DETECTION OF FRAUDULENT ATM TRANSACTIONS USING RULE-BASED CLASSIFICATION TECHNIQUES Deni Ekel Ramanda Sembiring Pelawi; Ahmad Saikhu
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 4 (2025): JITK Issue May 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i4.6401

Abstract

The significant rise in ATM fraud—reflected in 130,472 suspicious transactions reported in Indonesia in 2022—highlights the urgent need for accurate and efficient real-time fraud detection systems. This study evaluates two complementary detection approaches using a dataset of 20,000 anonymized ATM transactions collected from XYZ Bank between January and December 2022, each labeled by internal fraud analysts as fraud or non-fraud. The models compared are a Rule-Based Classifier and a Decision Tree classifier. The Decision Tree demonstrates strong overall performance, achieving 98% accuracy, 75% precision, 79% recall, and a 77% F1-score, indicating a reliable ability to detect diverse fraud patterns. In contrast, the Rule-Based Classifier yields 60% accuracy, 97% precision, 60% recall, and a 74% F1-score, showing high precision with fewer false alarms but a limited ability to detect varied fraud cases. These results emphasize the trade-off between specificity and sensitivity in static versus adaptive models. To address this, a hybrid detection framework is proposed—combining rule-based screening to filter obvious non-fraud cases, followed by Decision Tree analysis to handle more complex patterns. This approach aims to reduce unnecessary transaction holds and improve detection reliability. This study contributes to the limited comparative research on fraud detection methods using real ATM transaction data within the Indonesian banking context. Future research will focus on adaptive learning models to maintain performance against evolving fraud behaviors in dynamic financial systems.
THE ROLE OF L1 REGULARIZATION IN ENHANCING LOGISTIC REGRESSION FOR EGG PRODUCTION PREDICTION Nur Alamsyah; Budiman Budiman; Elia Setiana; Valencia Claudia Jennifer
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 4 (2025): JITK Issue May 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i4.6409

Abstract

Poultry egg productivity is strongly influenced by various environmental factors, such as air and water quality. However, accurately predicting productivity remains a challenge due to the complex interplay of multiple environmental variables and the risk of overfitting in predictive models. This study improves egg productivity prediction using Logistic Regression with L1 regularization, which enhances model generalization by performing automatic feature selection. The research methodology includes data collection of key environmental indicators—Air Quality Index (AQI), Water Quality Index (WQI), and Humidex—followed by data preprocessing, exploratory data analysis (EDA), and model training using L1-regularized Logistic Regression. Model evaluation was performed using classification metrics and learning curve analysis to assess stability and effectiveness. Experimental results indicate that Logistic Regression without regularization achieved an accuracy of 90.7%, with misclassification occurring in the lower production categories. By applying L1 regularization, accuracy increased significantly to 97%, demonstrating its ability to reduce overfitting while improving classification performance. This study also compares its findings with previous research, such as De Col et al. (wheat epidemic prediction, 80–85% accuracy) and Jia Q1 et al. (heart disease prediction, 92.39% accuracy), confirming that our approach outperforms prior Logistic Regression models in similar predictive tasks. These findings suggest that L1 regularization is an effective solution for improving egg productivity prediction, particularly in scenarios with complex environmental influences. Future work will explore advanced ensemble learning techniques and real-time IoT-based monitoring to further enhance prediction accuracy and practical applicability.
APPLICATION OF MACHINE LEARNING MODELS FOR FRAUD DETECTION IN SYNTHETIC MOBILE FINANCIAL TRANSACTIONS Imam Mulyana; Muhamad Bahrul Ulum
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 4 (2025): JITK Issue May 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i4.6420

Abstract

The financial industry faces challenges in detecting fraud. The 2023 Basel Anti-Money Laundering (AML) Index report shows a worsening money laundering risk trend over the last five years in 107 countries. And according to the Financial Action Task Force (FATF) in 2023, this is exacerbated by financial institutions which have problems with low reporting of suspicious financial transactions (Suspicious Transaction Report). Limited access to confidential financial transaction data is an obstacle in developing machine learning-based fraud detection models. To overcome this challenge, the research uses PaySim synthetic datasets that mimic real financial transaction patterns. The CRISP-DM approach is used, including the Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation and Deployment stages. The algorithms used are Decision Tree, Random Forest, and XGBoost. Model evaluation is carried out using accuracy, precision, recall, F1-score, specificity, cross-validation and ROC-AUC metrics. The results show that the Random Forest algorithm has the best performance with 99% accuracy, followed by XGBoost (98.9%) and Decision Tree (97%). Data analysis shows that cash-out and transfer transactions have the highest risk of fraud. This model has proven effective in detecting suspicious financial transactions with a high level of accuracy. This research makes a significant contribution to mitigating financial risks, supporting anti-fraud policies, and encouraging innovation in fraud detection using synthetic data.
DEEP GATED RECURRENT UNITS PARAMETER TRANSFORMATION FOR OPTIMIZING ELECTRIC VEHICLE POPULATION PREDICTION ACCURACY Jeni Sugiandi; Solikhun Solikhun; Anjar Wanto
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 4 (2025): JITK Issue May 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i4.6429

Abstract

The development of electric vehicles is an important innovation in reducing greenhouse gas emissions while reducing dependence on fossil fuels. The main problem in developing electric vehicles is the lack of adequate infrastructure. Inaccurate predictions regarding the number of electric vehicles hinder adequate infrastructure planning and development. This research proposes the use of the Gated Recurrent Units (GRU) algorithm to improve the accuracy of electric vehicle population predictions by carrying out GRU parameter transformations. This parameter transformation involves searching and adjusting the parameters of the GRU model in more depth to increase its ability to handle uncertainty in electric vehicle population data. After carrying out the training and testing process, the result was that the hyperparameter model using RandomizedSearchCV was the best model because it had the highest accuracy compared to other models tested with a combination of GRU_unit 64 and 128, dropout 0.5 and 0.6, batch size 64 and the number of epochs was 100 which had MAE results: 257.94, MSE: 66655.087, RMSE: 258.176, and Accuracy of 100%.
IMPLEMENTATION MEAN IMPUTATION AND OUTLIER DETECTION FOR LOAN PREDICTION USING THE RANDOM FOREST ALGORITHM Nimatul Mamuriyah; Richard; Haeruddin
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 4 (2025): JITK Issue May 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i4.6437

Abstract

Loans and credit are among the most in-demand banking products, making accurate loan prediction systems essential for minimizing bank credit risks and boosting profitability. This study proposed a loan prediction model using the Random Forest algorithm, with mean imputation and 3 outlier detection (Boxplot, Z-score, and Interquartile Range (IQR)) as data pre-processing methods. Using Lending Club loan data from 2014-2021 (466,285 records, split 70/30 for training/testing), model performance was assessed using accuracy, recall, and F1 Score. The proposed approach achieved a 95% prediction accuracy, outperforming previous models at 83%. The best results were obtained using mean imputation with IQR-based outlier detection. However, the determination of the mean imputation mean can be a limitation of this study. This highlights the importance of thorough pre-processing in enhancing prediction accuracy. The study underscores the role of machine learning and financial technology (fintech) in informing credit decisions and support incorporating imputation and outlier handling as standard steps in financial modeling pipeline
OPTIMIZATION CVRP WITH MACHINE LEARNING FOR IMPROVED CLASSIFICATION OF IMBALANCED DATA FOOD DISTRIBUTION Muhammad Syahputra Novelan; Solly Aryza
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 4 (2025): JITK Issue May 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i4.6467

Abstract

The classification of imbalanced data in food delivery distribution is an important issue that needs to be considered to ensure fairness and efficiency in the food distribution system. This research answers these problems by improving the accuracy of the classification of delivery locations that have imbalanced demand data, so that high priority areas are not neglected. Generating more efficient and cost-effective distribution routes, taking into account vehicle capacity and delivery urgency. Reducing delivery time and potential food waste due to delays or non-optimal route allocation. This study addresses the problem of improving the accuracy of delivery location classification that has imbalanced demand data, so that high priority areas are not neglected. Generate more efficient and cost-effective distribution routes, taking into account vehicle capacity and delivery urgency. Reduce delivery time and potential food wastage due to delays or non-optimal route allocation. This study uses the research stages of data collecting, data preprocessing, and implementation of K-Means and K-NN methods. The results of CVRP testing with K-Means show the value of cluster 7 acc=80, precc=85, recall=84. cluster 9 acc=85, precc=90, recall=91. cluster 11 acc=88, precc=93, recall=94. While the results of CVRP testing with K-NN show the value of K 7 acc=89, precc=88, recall=85. value of K 9 acc=87, precc=90, recall=91. value of K 11 acc=95, precc=97, recall=94. The optimization results show that this approach not only improves operational efficiency but also increases the accuracy of food delivery, which will affect the availability of traditional markets.
ENHANCING HERBAL PLANT LEAF IMAGE DETECTION ACCURACY THROUGH MOBILENET ARCHITECTURE OPTIMIZATION IN CNN Anan Wibowo; Rahmat Zulpani; Agus Perdana Windarto; Anjar Wanto; Sundari Retno Andani
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 4 (2025): JITK Issue May 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i4.6498

Abstract

Herbal plants have various health benefits, but their type identification remains challenging for the general public. This study aims to improve the accuracy of herbal plant leaf classification using Convolutional Neural Network (CNN) based on MobileNetV2 architecture. To enhance model performance, various optimization techniques including fine-tuning, batch normalization, dropout, and learning rate scheduling were implemented. The experimental results showed that the proposed optimized model achieved an accuracy of 100%, significantly outperforming previous studies that used standard MobileNet with an accuracy of 86.7%. While these perfect results warrant additional validation with more diverse datasets to confirm generalizability, this study contributes to the development of a more accurate herbal plant classification system that is readily accessible to the general public. Future work should explore model performance under varying environmental conditions and with expanded plant species datasets.
DEVELOPMENT OF SKIN CANCER PIGMENT IMAGE CLASSIFICATION USING A COMBINATION OF MOBILENETV2 AND CBAM Juni Ismail; Lili Tanti; Wanayumini Wanayumini
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 4 (2025): JITK Issue May 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i4.6541

Abstract

Skin cancer is one of the most common types of cancer worldwide, making early detection a crucial factor in improving patient recovery rates. This study compares three classification methods for pigmented skin cancer images using a combination of VGG16 with CBAM, MobileNetV2 with CBAM, and a hybrid VGG16-MobileNetV2 approach with transfer learning. The dataset used in this study is the Skin Cancer ISIC - The International Skin Imaging Collaboration (HAM10000) from Kaggle, which consists of 10,015 images covering seven types of skin cancer. After balancing, the dataset was reduced to 2,400 images with three main classes: Actinic Keratosis (AKIEC), Basal Cell Carcinoma (BCC), and melanoma (MEL), each containing 800 images. This study involves data preprocessing stages such as augmentation, normalization, and image resizing to ensure optimal data quality. The model training process was conducted using the Adam optimizer, a batch size of 16, and an Early Stopping mechanism to prevent overfitting. Evaluation results indicate that the MobileNetV2 with CBAM model achieved the best performance with a validation accuracy of 86%, followed by the VGG16-MobileNetV2 combination at 77%, while VGG16 with CBAM experienced overfitting with an accuracy of 54%. Additionally, the best-performing model demonstrated a precision of 86.53% and a recall of 86.46%, highlighting its superior stability in detecting skin cancer compared to previous single-model approaches. With these results, the developed system can serve as an effective tool for medical professionals in performing early and more accurate skin cancer diagnoses
OPTIMIZATION OF THE INCEPTIONV3 ARCHITECTURE FOR POTATO LEAF DISEASE CLASSIFICATION Khairun Nisa Arifin Nur; Nazlina Izmi Addyna; Agus Perdana Windarto; Anjar Wanto; Poningsih Poningsih
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 4 (2025): JITK Issue May 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i4.6554

Abstract

Potato leaf diseases can cause significant yield losses, making early detection crucial to prevent major damages. This study aims to optimize the Inception V3 architecture in a Convolutional Neural Network (CNN) for potato leaf disease classification by applying Fine Tuning Pre-Trained. This method leverages weights from a pre-trained model on a large-scale dataset, enhancing accuracy while reducing the risk of overfitting. The training process involves adjusting several final layers of Inception V3 to better adapt to specific features of potato leaf diseases. The results show that this approach improves classification performance, achieving an accuracy of 97.78%, precision of 98%, recall of 98%, and an F1-score of 98%. With better computational efficiency compared to previous architectures, this model is expected to be widely applicable in plant disease detection systems, particularly for farmers or institutions with limited resources.
COMPARATIVE OF LSTM AND GRU FOR TRAFFIC PREDICTION AT ADIPURA INTERSECTION, BANDAR LAMPUNG Ilham Firman Ashari; Verlina Agustine; Aidil Afriansyah; Nela Agustin Kurnianingsih; Andre Febrianto; Eko Dwi Nugroho
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 4 (2025): JITK Issue May 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i4.6569

Abstract

The Tugu Adipura intersection in Bandar Lampung is a vital traffic hub connecting four major roads. Rapid population growth and increasing vehicle numbers challenge traffic flow and urban quality of life. Despite its importance, there is limited research using predictive models to analyze traffic patterns at complex intersections in mid-sized Indonesian cities. This study addresses that gap by examining traffic growth on four connected roads using deep learning models. Traffic data were collected hourly from June 1, 2021, to July 31, 2023. A comparative analysis of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models was conducted, with SGD and Adam as optimizers. Results show the GRU model with Adam achieved the lowest RMSE (0.23) on road section 1, indicating its superior ability to model short-term fluctuations and non-linear growth in traffic volume. The study offers practical implications for traffic management by highlighting GRU’s capacity to capture seasonal trends and rapid growth, supporting proactive infrastructure planning and congestion mitigation strategies. These findings demonstrate the value of data-driven approaches in enhancing transportation systems in growing urban areas.

Page 36 of 40 | Total Record : 394

 34   35   36   37   38 

Filter by Year

2016 2025


Reset
Filter By Issues
All Issue Vol. 11 No. 2 (2025): JITK Issue November 2025 Vol. 10 No. 4 (2025): JITK Issue May 2025 Vol. 10 No. 3 (2025): JITK Issue February 2025 Vol. 10 No. 2 (2024): JITK Issue November 2024 Vol. 10 No. 1 (2024): JITK Issue August 2024 Vol. 9 No. 2 (2024): JITK Issue February 2024 Vol. 9 No. 1 (2023): JITK Issue August 2023 Vol. 8 No. 2 (2023): JITK Issue February 2023 Vol. 8 No. 1 (2022): JITK Issue August 2022 Vol 8 No 1 (2022): JITK Issue August 2022 Vol 7 No 2 (2022): JITK Issue February 2022 Vol 7 No 1 (2021): JITK Issue August 2021 Vol 6 No 2 (2021): JITK Issue February 2021 Vol 6 No 1 (2020): JITK Issue August 2020 Vol 5 No 2 (2020): JITK Issue February 2020 Vol 5 No 1 (2019): JITK Issue August 2019 Vol 4 No 2 (2019): JITK Issue February 2019 Vol 4 No 1 (2018): JITK Issue August 2018 Vol 3 No 2 (2018): JITK Issue February 2018 Vol 3 No 1 (2017): JITK Issue August 2017 Vol 2 No 2 (2017): JITK Issue February 2017 Vol 2 No 1 (2016): JITK Issue August 2016 More Issue