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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 394 Documents
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INTEGRATED SYSTEM-BASED SMART APPLICATION (SIPATIN) FOR STRENGTHENING FISHERIES GOVERNANCE IN LEBAK REGENCY, BANTEN Karyaningsih, Dentik; Wajdi, Farid; Huddin, Muhammad Nurhaula; Susandi, Diki; Suhendar, Akip; Anharudin, Anharudin; Annur, Shohifah
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.7124

Abstract

The Fisheries Office of Lebak Regency is responsible for the management of capture fisheries, aquaculture, resource monitoring, and the marketing of fishery products. However, geographical challenges, the difficulty of obtaining real-time data, and the use of conventional monitoring and reporting methods hinder effective and sustainable fisheries governance. In addition, limited market reach—primarily targeting only local areas—further restricts the region’s economic potential. This study aims to address issues related to monitoring, reporting, and marketing through the development of an Integrated Fisheries Information System (SIPATIN), a smart mobile-based fisheries governance application integrated with a web-based monitoring platform. SIPATIN features include fishery area mapping, real-time reporting, an E-Commerce marketing platform, and a recommendation system that provides detailed information on products, seller locations, prices, reviews, estimated delivery times, and proximity-based suggestions for users. The system was developed using a prototyping method, consisting of needs analysis, design, development, testing, and evaluation based on user feedback. The application was evaluated using the System Usability Scale (SUS), which scored 74.26 (Good), and User Acceptance Testing (UAT), involving 246 respondents and resulting in a score of 79.13 (Acceptable). The results of this study show that SIPATIN effectively supports integrated fisheries governance, enhances service efficiency at the Lebak Regency Fisheries Office, and empowers fishery business actors including fishers, fish farmers, and small and medium enterprises (SMEs) in processed fish products. Furthermore, this research also produces a data-based fishery system for sustainable economic development
CRYPTOGRAPHIC FRAMEWORK FOR CLOUD-BASED DOCUMENT STORAGE USING AES-256 AND SHA-256 HYBRID SYSTEMS Junaidi Surya, junaidi; Louis, Ahmad; Rini, Faiza; Mulyati, Sri; Elzas, Elzas
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.7132

Abstract

Cloud-based document storage offers significant flexibility but faces security challenges such as the risk of data leaks and illegal modifications. The study proposes a cryptographic framework using a combination of Advanced Encryption Standard (AES)-256 for confidential encryption and Secure Hash Algorithm (SHA)-256 for cloud storage-based document integrity verification. The system was developed with an experimental approach, implemented in application prototypes, and tested on a wide range of file sizes from as small as < 1 mb, 10 mb to 100 mb showing greater efficiency than Rivest-Shamir-Adleman (RSA) and elliptical curve cryptography (ECC). To improve security, a distributed key management scheme and password-based user authentication were added.  The encryption system will be tested on Google Drive, One Drive, and mega cloud platforms and evaluated through a series of performance and security tests combined with on-premises personal computer (PC) systems. This framework provides a practical solution for secure document storage in the cloud with a balance between security, performance, and ease of use. This research reinforces the urgency of applying modern cryptography in dealing with the risk of data leakage in public cloud services, and can be adopted as a security and efficiency model and solution for individuals, as well as government and private offices that use cloud storage as a storage base for important documents such as Decrees, Securities, certificates, diplomas and other important data
APPLICATION OF RANDOM FOREST ALGORITHM FOR ARRHYTHMIA DETECTION BASED ON ELECTROCARDIOGRAM DATA Turnip, Mardi; Situmorang, Fransido; William, David; Patterson, Jennifer; Ardila, Niki
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.7136

Abstract

Arrhythmia is a common cardiac disorder that requires early detection to prevent serious complications. This study applied the Random Forest algorithm to enhance electrocardiogram (ECG) analysis and enable accurate arrhythmia classification. Unlike prior studies that focused primarily on resting ECG signals, this research incorporated dynamic data collected from 26 participants performing three physical activities for three minutes each, capturing physiological variations across multiple activity states. The Random Forest model was constructed and evaluated using ECG-derived temporal and morphological features to detect potential arrhythmias. Experimental results showed that the model achieved an accuracy of 97.4%, with precision, recall, and F1-score each reaching 98%, and an AUC of 0.97. However, several limitations remain, including the relatively small and homogeneous sample, as well as the short recording duration. Nonetheless, the proposed approach demonstrates strong potential to support early cardiac screening and real-time monitoring, particularly in portable and resource-limited healthcare applications
OPTIMIZATION OF EFFICIENTNET-B0 ARCHITECTURE TO IMPROVE THE ACCURACY OF GLAUCOMA DISEASE CLASSIFICATION Akbari, Imam; Hartama, Dedy; Wanto, Anjar
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.7140

Abstract

Glaucoma is a chronic eye disease that can potentially cause permanent blindness if not detected early. This study aims to improve the generalization capability and reliability of glaucoma classification by optimizing the EfficientNetB0 architecture based on a Convolutional Neural Network (CNN). Optimization was carried out by applying double dropout (0.4 and 0.3) and adding a Dense layer with 128 ReLU-activated neurons to reduce overfitting and strengthen non-linear feature representation. The dataset used consists of 1,450 fundus images (899 glaucoma and 551 normal) obtained from IEEE DataPort. Model performance evaluation was performed using accuracy, precision, recall (sensitivity), specificity, F1 score, and Area Under the Curve (AUC) metrics, complemented by confusion matrix analysis to assess overall classification performance. The results showed that the optimized EfficientNetB0 model consistently outperformed the baseline comparison model with the highest accuracy, precision, recall (sensitivity), specificity, F1 score, and AUC values ​​of 95%. Based on the system performance results obtained, the Proposed model can be used as an aid for medical personnel in classifying glaucoma conditions so that they can provide appropriate medical treatment and reduce the risk of permanent blindness due to glaucoma.
SENTIMENT ANALYSIS OF IT WORKERS ON NO CODE AND LOW CODE TRENDS: COMPARISON OF LSTM AND SVM MODELS Agustin, Yoga Handoko; Nabil Nur Afrizal
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.7166

Abstract

This research explores the sentiment of IT professionals toward the growing trend of No Code and Low Code technologies by comparing the performance of Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) algorithms. Using the SEMMA methodology and automatic labeling with ChatGPT, a total of 4,238 comments were collected from Reddit and Twitter and categorized into positive, neutral, and negative sentiments. The analysis showed that neutral sentiment dominates on both platforms (47.9% on Reddit and 48.8% on Twitter), followed by positive sentiment (41.3% and 43.1%, respectively), indicating cautious but optimistic attitudes toward LCDPs. In terms of model performance, SVM outperformed LSTM with 87% accuracy and a weighted F1-score of 0.87, compared to LSTM’s 80% accuracy and a weighted F1-score of 0.80. These findings confirm that classical machine learning methods remain highly effective for short-text sentiment analysis in social media, particularly when combined with TF-IDF feature representation, SMOTE balancing, and LLM-based automatic labeling, while also offering new insights into IT community perceptions of disruptive technologies
PREDICTIVE MODEL FOR COOPERATIVE LOAN RECIPIENT ELIGIBILITY USING SUPERVISED MACHINE LEARNING Rajunaidi, Rajunaidi; Yuliansyah, Herman; Sunardi, Sunardi; Murinto, Murinto
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.7235

Abstract

Non-performing loans remain a critical challenge for cooperatives as they can undermine financial stability, erode member trust, and impede institutional growth. This study develops a predictive model for cooperative loan eligibility using supervised machine learning techniques and a novel three-class classification framework, Approved, Consideration, and Rejected, to support more objective and transparent decision-making. A dataset of 1,000 borrower records containing demographic and financial attributes was analyzed using Naive Bayes, Decision Tree, and Random Forest algorithms implemented in RapidMiner. The Random Forest algorithm achieved the best predictive performance with an accuracy of 96.02%, demonstrating its robustness and reliability compared to the other models. The proposed three-class system differentiates this study from conventional binary classification approaches, enabling finer distinctions among borrower categories and promoting fairness in cooperative credit evaluations. The findings provide practical guidance for cooperatives to adopt data-driven, transparent, and accountable decision-making systems that reduce manual bias and strengthen financial inclusion. Overall, the proposed three-class model built through a supervised learning framework offers a reliable, fair, and scalable solution to support sustainable lending practices and enhance risk management in cooperative institutions.
COMPARISON OF PRINCIPAL COMPONENT ANALYSIS AND RANDOM FOREST ALGORITHM FOR PREDICTING HOUSING PRICES Susilo, Dahlan; Diyah Ruswanti; Supriyanta; Wawan Nugroho
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.7256

Abstract

House price predictions are an important thing in the property industry and are useful for buyers in making decisions. Principal Component Analysis (PCA) and Random Forest (RF) methods were used for accuracy analysis in predicting housing prices. Purpose of this research is to measure the accuracy of both methods also to compare RF method optimized with PCA and the one that has not been optimized. The data used is house prices in Karanganyar city based on data scraping results on the rumah123.com site. The analysis reveals that Jaten has the highest number of house sales, and sales of houses with land ownership certificates are also the highest. Of the 10 variables used, land area and buildings have the most influence on selling prices. The model training results show that the RF and PCA methods combination has more optimal value than only using the RF method. The error rate of the PCA method is smaller, averaging 0.0257, making its value more consistent than using only the RF method, which has a larger error value with an average of 0.0332. The model training time using PCA is faster (5005.75) than only using the RF method (6099.25)
REFORMULATION OF MULTI-ATTRIBUTE UTILITY THEORY NORMALIZATION TO HANDLE ASYMMETRIC DATA IN MADM Puspaningrum, Ajeng Savitri; Susanto, Erliyan Redy; Hendrastuty, Nirwana; Setiawansyah, Setiawansyah
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.7273

Abstract

Multi-Attribute Utility Theory (MAUT) is a widely used multi-attribute decision-making (MADM) method due to its ability to integrate multiple criteria into a single utility value. However, conventional MAUT faces limitations when handling asymmetric data, where standard normalization processes often lead to value distortion and less representative rankings. This study aims to reformulate the normalization function in MAUT to improve adaptability to non-symmetric data distributions and to enhance ranking validity in decision-making. A modification approach called MAUT-A was developed by applying an adaptive normalization mechanism capable of accommodating extreme distributions and outliers by adding Z-score normalization. The performance of MAUT-A was evaluated by comparing the correlation of its ranking results with reference rankings, and the outcomes were benchmarked against conventional MAUT. The experimental findings indicate that conventional MAUT achieved a correlation value of 0.9688 with the reference ranking, while the proposed MAUT-A method achieved a higher correlation of 0.9792. This improvement represents that MAUT-A has better suitability, stability, and reliability in managing asymmetric data. The study contributes by offering a reformulated MAUT framework through adaptive normalization, providing more accurate, stable, and fair ranking outcomes. This approach enhances the validity of MADM applications, particularly in contexts involving asymmetric data distributions
UNVEILING SPATIAL PATTERNS OF LAND CONVERSION THROUGH MACHINE LEARNING AND SPATIAL DISTRIBUTION ANALYSIS Mufida Fauziah Faiz; Achmad Fauzan
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.7281

Abstract

Kayu Agung District in Ogan Komering Ilir (OKI) Regency, South Sumatra, has undergone rapid population growth, resulting in notable land-use transformations. This study examines land-use change dynamics from 2019 to 2023 and identifies their spatial distribution using satellite imagery. Satellite imagery classification was performed using three machine learning algorithms—K-Nearest Neighbors (KNN), Naïve Bayes, and Logistic Regression—with KNN achieving the highest accuracy. Spatial analysis employing the Variance-to-Mean Ratio (VMR) revealed that land-use changes are spatially clustered, indicating concentrated land conversion in specific areas. These findings emphasize potential environmental risks, including declining green open spaces and increasing urban pressure. The study contributes by integrating machine learning and spatial statistical analysis (VMR) as a comprehensive framework for understanding land-use conversion, providing scientific insights to support adaptive spatial planning and the achievement of Sustainable Development Goal (SDG) 11: Sustainable Cities and Communities.
ANALYZING CLIMATE IMPACTS ON RICE PRODUCTION IN SUMATRA THROUGH SPATIOTEMPORAL MACHINE LEARNING MODELS Zaqi Kurniawan; Rizka Tiaharyadini; Puguh Jayadi; Windhy widhyanty
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.7344

Abstract

Climate variability poses a major challenge to rice production in Sumatra, a key contributor to Indonesia’s food security. This study aims to analyze spatiotemporal climate impacts on rice yields by integrating climatic, geographical, and agricultural datasets. Historical records from 1993–2024, including rainfall, temperature, humidity, and rice production statistics, were collected from BMKG, BPS, and the Ministry of Agriculture. After preprocessing and feature selection, six machine learning algorithms—Linear Regression, Random Forest, Gradient Boosting, Support Vector Regression, Decision Tree, and K-Nearest Neighbors—were evaluated for predictive performance. Results show significant spatial heterogeneity: rainfall strongly affects yields in Aceh and North Sumatra, while temperature stress is critical in southern provinces. Among the tested models, Random Forest achieved the best accuracy (R² = 0.985), outperforming other algorithms. These findings highlight the importance of localized adaptation strategies and demonstrate the potential of ensemble machine learning to support climate-resilient rice production.

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