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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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OPTIMIZING MSME PRODUCT AUTHENTICITY VERIFICATION IN DECENTRALIZED MARKETS USING BLOCKCHAIN Adnan Zulkarnain; Mukhlis Amien
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 3 (2025): JITK Issue February 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i3.6010

Abstract

Blockchain technology offers a solution for ensuring product authenticity in decentralized digital marketplaces. However, Micro, Small, and Medium Enterprises (MSMEs) face barriers such as limited infrastructure, high costs, and data interoperability challenges. This study develops a hybrid blockchain-based application architecture tailored to MSME needs, integrating on-chain and off-chain storage. Critical security data, such as product hashes, is stored on-chain, while non-sensitive data, like product descriptions, is managed off-chain using a cloud-based MySQL database. This design reduces storage costs and computational load while maintaining data integrity. Ethereum smart contracts manage product registration and verification, linked to QR code-based authentication for end-users. A realistic simulation environment using server-based infrastructure and cloud databases evaluated system performance, including transaction throughput, latency, resource utilization, and scalability. The results show significant improvements compared to conventional centralized methods, achieving a transaction throughput of 391 TPS for 1 million transactions while maintaining low latency and resource efficiency. This research addresses a theoretical gap by optimizing blockchain for small-scale decentralized markets, tackling resource limitations and interoperability issues unique to MSMEs. Practically, it provides a scalable and cost-effective solution for product authenticity verification, enhancing consumer trust and reducing counterfeiting in MSME digital markets. While real-world testing remains a limitation, the findings underline the system’s potential to support sustainable MSME digital marketplaces and build consumer confidence.
DENTAL CARIES SEVERITY DETECTION WITH A COMBINATION OF INTRAORAL IMAGES AND BITEWING RADIOGRAPHS Jennifer Jennifer; Winni Setiawati; Gabriella Adeline Halim; Tony Tony
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 3 (2025): JITK Issue February 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i3.6042

Abstract

Dental caries is a multifactorial oral disease caused by plaque due to bacterial sugar fermentation. Quite a number of dentists have misdiagnosed caries due to the subjective nature of visual examination and radiograph in early-stage lesions. Thus, research on the implementation of deep learning technology is expected to improve the accuracy of diagnosis. However, caries detection with deep learning has accuracy problems. This problem makes researchers interested in developing a deep learning method that combines Faster R-CNN algorithm and texture feature extraction to more accurately detect carious teeth from bitewing radiography datasets and intraoral images. The overall performance of the model to detect the radiographic class was slightly better than the intraoral class. Overall, the classification accuracy of the model was 88.95% which is better than previous research that only used one or the other type of images. GLCM (Gray-Level Co-Occurrence Matrix) is effective in detecting contrast areas, but it still cannot specifically distinguish normal anatomical contrast from caries. The Faster R-CNN model learned well and was able to differentiate between each caries type and was successfully integrated with the GLCM matrix for radiographic image pre-processing to facilitate caries detection. This approach could have the potential of assisting dental professionals in reducing diagnostic errors and increasing patient care.
COMPARISON OF ACTIVATION AND OPTIMIZER PERFORMANCE IN LSTM MODEL FOR PURE BEEF PRICE PREDICTION Dasril Aldo
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 3 (2025): JITK Issue February 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i3.6115

Abstract

One of the primary factors impacting the economy is the ability to forecast the prices of commodities such as beef. This paper aims to evaluate the effectiveness of various activation functions and optimization strategies when integrated into the LSTM (Long Short-Term Memory) architecture model in predicting the price of lean beef in Aceh. The data sample utilized was obtained from the Indonesian National Food Agency panel, which shows daily prices for beef within the time frame of July 14th, 2022, to July 31st, 2024. As for the conducted research, the process of preparation data preprocessing, partitioning data into training, validation and test sets and the actual execution of the LSTM model which was trained using four different types of activation functions: tanh, ReLU, sigmoid and PReLU together with three different optimizers: Adam, Nadam and RMSprop for 50, 70, 100 and 200 training iterations. The evaluation metrics employed were Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R-squared). The best performance was recorded at 200 epochs with the combination of PReLU activation function and Nadam optimizer, which had the best performance with RMSE = 2.56, MAPE = 0.65% and R² = 0.104. This combination was more effective than others since it depicted better overall performance in identifying complex non-linear relationships that existed in the price data. Further on, Nadam seems to have benefits in terms of allowing the model to converge faster and making the training more stable. This work stresses the selection of activation functions and optimization methods when building LSTM models aimed at forecasting prices of commodities with large volatility. It will be very helpful in developing better predictive models and decision-making processes in the agro-business. Another way to enhance predictive performance could be changing the model architecture or using different techniques, such as attention mechanisms.
CAUSAL MODELING OF FACTORS IN STUNTING USING THE PETER-CLARK AND GREEDY EQUIVALENCE SEARCH ALGORITHMS Yohani Setiya Rafika Nur; Aminatus Sa’adah; Dasril Aldo; Bidayatul Masulah
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 3 (2025): JITK Issue February 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i3.6184

Abstract

Stunting is one of the nutritional problems that can hinder the growth and development process in toddlers. Untreated stunting can lead to fatal outcomes. Previous research on the factors that exist in the incidence of stunting mostly used multivariate analysis. Previous research on stunting factors has primarily used multivariate or correlation analyses. However, this study uniquely focuses on establishing causal relationships between these factors, a crucial step in improving early diagnosis for stunting prevention and treatment. The data used in this research was 83 data on stunting incidents and consisted of eight parameters. The purpose of this study is to model the causal relationship between factors that represent the incidence of stunting. This study uses two simple causal approaches, namely the Peter-Clark (PC) algorithm to obtain the initial concept of a graph model of the relationship between variables and the Greedy Equivalence Search (GES) algorithm to refine the model by obtaining the direction of the causal relationship. There are six bi-directed relationships that have been found, namely from food variables to support; maternal knowledge with sanitation; Height/Age and Weight/Age with Child Nutrition; height/age with weight/age and stunting. In addition, both algorithms in this study have successfully obtained a causal model, by comparing performance using directional and causal densities that the GES algorithm was able to identify a relationship of 0.66 compared to the PC algorithm.
APPLICATION OF NON-PREEMPTIVE PRIORITY SCHEDULING METHOD FOR WORK ORDER SCHEDULING SYSTEM Suratun Suratun; Novita Br Ginting; Zulkarnaen Noor Syarif; Ryan Abdul Gofur
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 3 (2025): JITK Issue February 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i3.5610

Abstract

Work order allocation is one of the problems experienced by PT Indomobil Trada Nasional. Companies need tools to make it easier to allocate work orders effectively, namely an optimal work order scheduling system. Work order allocation data for the last three months was 3,817, with 15 technicians. This work order exceeds the company's target, namely to have a difference of 1.2 work orders per technician daily. These work orders have a priority order in their processing. The work order scheduling method used in this research is the non-preemptive priority scheduling method. The non-preemptive priority scheduling method is used because it can determine which work orders are in the queue and ready to be allocated according to the priority order without disturbing work orders that are being worked on when new work orders arrive. The work order scheduling system that was built provides adequate scheduling time and produces a smaller average waiting time, namely 12.97 minutes.   The average waiting time in the scheduling system without priority non-preemptive scheduling is 52.18, and the difference in average waiting time for the 34 existing work orders is 39.12 minutes. Applying the non-preemptive priority scheduling method helps companies allocate work orders optimally.
DIGITALLY FILE EXTRACTION OPTIMISED WITH GPT-4O BASED MOBILE APPLICATION FOR RELEVANT EXERCISE PROBLEM GENERATION Syanti Irviantina; Hernawati Gohzaly; Dustin Lionel; Peter Fomas Hia
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 3 (2025): JITK Issue February 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i3.6101

Abstract

This research studies the creation of an AI-driven question extraction system using the GPT-4o model to improve the accessibility and variety of practice questions for students. The study tackles the difficulties in sourcing relevant practice materials and aims to transform educational technology by integrating mobile learning. A mobile application was built with Dart and Flutter, designed to extract questions from PDF files. The system is capable of generating both multiple-choice and essay questions across different difficulty levels. The quality and relevance of the generated questions were assessed using ROUGE metrics. The results indicated strong performance for multiple-choice questions, especially in single-answer and true/false formats. However, the system encountered difficulties in producing complex essay questions, highlighting the need for further improvements in understanding intricate contextual relationships. Key findings reveal effective generation of multiple-choice questions with high precision and recall; inconsistent performance in essay question generation, with simpler questions yielding better results; and ROUGE-1 metrics surpassing ROUGE-2 and ROUGE-L, indicating a stronger ability to generate straightforward questions. The research concludes that while the developed system shows potential in enhancing educational resources, additional research is necessary to refine complex question generation. Recommendations include broadening the training dataset and creating specialized models for question generation tasks to enhance the effectiveness of AI-assisted learning tools.
DEVELOPMENT OF GRAPH GENERATION TOOLS FOR PYTHON FUNCTION CODE ANALYSIS Bayu Samodra; Vebby Amelya Nora; Fitra Arifiansyah; Gusti Ayu Putri Saptawati Soekidjo; Muhamad Koyimatu
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 3 (2025): JITK Issue February 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i3.6177

Abstract

The increasing complexity of programs in software development requires understanding and analysis of code structure, especially in Python, which dominates machine learning and data science applications. Manual static analysis is often time-consuming and prone to errors. Meanwhile, static analysis tools for Python, like PyCG and Code2graph, are still limited to generating call graphs without including dependency and control flow analysis. This research addresses these shortcomings by proposing the development of a web-based tool that integrates the generation of function call graphs, function dependency graphs, and control flow graphs using Abstract Syntax Tree (AST), Graphviz, and Streamlit. With an iterative SDLC methodology, this tool was developed gradually to visualize Python function code as a heterogeneous graph. Evaluation of 11 Python function codes showed a success rate of 95.45% in analyzing and visualizing Python function codes with various levels of complexity. The limitations of Graphviz present an opportunity for future research to focus on improving scalability and Python code analysis.
COMBINATION OF LEARNING VECTOR QUANTIZATION AND LINEAR DISCRIMINANT ANALYSIS FOR TEA LEAF DISEASE CLASSIFICATION Mutasar Mutasar; Chaeroen Niesa
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 3 (2025): JITK Issue February 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i3.6013

Abstract

Tea farming, one of the key pillars of Indonesia's economy, faces productivity challenges due to diseases affecting tea leaves. Manual identification of tea leaf diseases requires significant time and cost, making an automated solution necessary. This research develops an innovative model for classifying tea leaf diseases by synergizing Learning Vector Quantization (LVQ) and Linear Discriminant Analysis (LDA). By leveraging LVQ’s prototype-based classification and LDA’s dimensionality reduction, the model ensures accurate and efficient disease identification. During preprocessing, tea leaf images were converted to the CIELAB color space to enhance segmentation using Otsu’s Thresholding. Features such as Mean Color and texture attributes based on Gray Level Co-occurrence Matrix (GLCM) were extracted, reduced via LDA, and classified using LVQ. Tested on five tea leaf disease classes, the model achieved 94.1% accuracy. This performance underscores its potential to significantly assist farmers in early detection and management of tea leaf diseases, while also providing researchers with a robust tool for advancing agricultural technology.
BEYOND ALGORITHMS: AN INTEGRATED APPROACH TO FAKE NEWS DETECTION USING MACHINE LEARNING TECHNIQUES Bimantyoso Hamdikatama
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 3 (2025): JITK Issue February 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i3.6061

Abstract

The internet has become a major source of information, but it also facilitates the rapid spread of fake news, which can significantly influence public opinion and social decisions. While various techniques have been developed for detecting fake news, many studies focus on individual algorithms, which often result in suboptimal performance. This study addresses this gap by comparing machine learning models, including Support Vector Classification (SVC), XGBoost, and a Stacking Ensemble that combines both SVC and XGBoost, to determine the most effective approach for fake news detection. Text preprocessing was performed using IndoBERT, which provides context-aware and semantically rich text representations specifically for the Indonesian language. The evaluation results demonstrate that the Stacking Ensemble outperforms the individual models, achieving an accuracy of 82%, compared to 79% for XGBoost and 78% for SVC. This superior performance is attributed to the complementary strengths of the base models: SVC excels in handling high-dimensional data, while XGBoost effectively manages imbalanced datasets and captures complex feature interactions. The use of IndoBERT further enhances model performance by improving text representation through contextual embeddings. These findings highlight the effectiveness of ensemble learning in enhancing predictive performance and robustness for fake news detection, demonstrating the potential of combining different machine learning techniques with advanced preprocessing methods to achieve more reliable results.
DESIGN OF FIRE EXTINGUISHER ROBOT USING IOT WITH ANDROID APPLICATION CONTROL Budy Satria; Syarif Hidayatullah; Fitra Yuda; Leonard Tambunan; Siti Sahara Lubis; Irzon Meiditra
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 3 (2025): JITK Issue February 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i3.6135

Abstract

Fire is an unsupervised incidental disaster. This disaster has a detrimental impact on living and non-living things in the surrounding environment. This study was conducted to design an intelligent firefighting robot using Arduino Mega 2560 and Android-based IoT technology. This firefighting robot uses several Node MCU ESP8266 components as additional devices to connect to wifi. The L298N module regulates the speed and direction of the DC motor rotation, followed by the L9110 fan as hardware to extinguish the fire. The mobile robot prototype uses a DC motor as its driver. In addition, an Android application has been programmed to control the firefighting robot. This application has features that allow the robot to move in various directions and adjust the fan speed when extinguishing fires, all through an internet network connection. The study results showed that the application can be connected within a distance of 1-8 meters with good network quality. The test results showed that at a distance of 1-28 cm, the fan worked very well according to its function, and the Android application also worked optimally. In that range, the fan can extinguish the simulated fire source. The results of this study obtained a new approach to autonomous fire detection and extinguishing using IoT and robotic technology. In addition, it is able to integrate an Android-based IoT controller to enable remote control with real-time monitoring to overcome problems in previous research.

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