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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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THE ROLE OF INFORMATION SYSTEMS IN ADVANCING SMART VILLAGES: A RURAL TOURISM CASE STUDY Agus Trihandoyo; Rizki Hesananda; Kushandajani Kushandajani; Firman Muhksin
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 9 No. 2 (2024): JITK Issue February 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i2.5044

Abstract

Recent studies highlight the need for a deeper understanding of the ways in which information systems, local government policy and community involvement affect the development of rural tourism. By using Structural Equation Modelling and Partial Least Squares (SEM-PLS), the current study aims to analyze the role of information systems, local government policy and local community engagement in rural tourism development. Using data from 69 participants in Watesjaya village, Bogor regency, the study analyzes multiple relationships among latent constructs. The data, encompassing variables such as system quality, information quality, local government policy, local community engagement, destination branding, and rural tourism development, undergoes meticulous reliability and validity assessments. Results from the SEM-PLS analysis unveil significant relationships and insights. Local community engagement emerges as a pivotal factor, positively influencing tourist satisfaction (0.499) and moderately affecting destination branding (0.239). However, local government policy exhibits a less pronounced positive impact on tourist satisfaction (0.069847) and a notable negative influence on destination branding (-0.300460), underscoring the need for policy realignment. Information quality paradoxically influences tourist satisfaction negatively (-0.185) and destination branding (-0.158), highlighting areas for strategic improvement. Meanwhile, information system quality positively affects tourist satisfaction (0.055) and significantly contributes to rural tourism development (0.783). This study provides a better understanding of stakeholders about rural tourism development by focusing on information system quality, information quality, local government policies, and local community engagement The study indicates that information system quality and local community engagement can be valuable indicators for boosting rural tourism development and improving tourist satisfaction.
IMPROVING TRAFFIC DENSITY PREDICTION USING LSTM WITH PARAMETRIC ReLU (PReLU) ACTIVATION Nur Alamsyah; Titan Parama Yoga; Budiman Budiman
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 9 No. 2 (2024): JITK Issue February 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i2.5046

Abstract

In the presence of complex traffic flow patterns, this research responds to the challenge by proposing the application of the Long Short-Term Memory (LSTM) model and comparing four different activation functions, namely tanh, ReLU, sigmoid, and PReLU. This research aims to improve the accuracy of traffic flow prediction through LSTM model by finding the best activation function among tanh, relu, sigmoid, and PReLU. The method used starts from the collection of traffic flow datasets covering the period 2015-2017 used to train and evaluate the LSTM model with the four activation functions. Tests were conducted by observing the Train Mean Squared Error (MSE) and Validation MSE. The experimental results show that PReLU provides the best results with a Train MSE of 0.000505 and Validation MSE of 0.000755. Although tanh, ReLU, and sigmoid provided competitive results, PReLU stood out as the optimal choice to improve the adaptability of the model to complex traffic flow patterns.
ENHANCING SENTIMENT ANALYSIS ACCURACY IN DELIVERY SERVICES THROUGH SYNTHETIC MINORITY OVERSAMPLING TECHNIQUE Setia Sri Anggraeni; Septi Andryana
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 9 No. 2 (2024): JITK Issue February 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i2.5058

Abstract

Paxel is one of the delivery services that use the application. On Google Play, there are more than 10 thousand users leaving reviews. From this review data, a sentiment analysis was then carried out to determine the level of user satisfaction with Paxel's services. The methods used in this study are Random Forest (RF) and Support Vector Machine (SVM), as well as applying Synthetic Minority Oversampling Technique (SMOTE) to overcome data imbalance. The results showed that the method testing by dividing the data into two, namely training data and testing data by 80:20, stated that by applying the SMOTE, a higher accuracy value was obtained, where the accuracy of the RF method reached 91%, and the SVM method reached 87%. The level of user satisfaction with Paxel services tends to be neutral. This can be seen in the classification of the RF method with F1-Score values for the Positive class 89%, Neutral class 93%, and Negative class 92%.
EXPERT SYSTEM FOR DISEASE IDENTIFICATION BASED ON HEMATOCHEZIA SYMPTOMS WITH NAÏVE BAYES METHOD Dasril Aldo; Alwendi Alwendi; Adanti Wido Paramadini; Ilwan Syafrinal; Sapta Eka Putra
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 9 No. 2 (2024): JITK Issue February 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i2.5064

Abstract

Hematochezia is a common clinical symptom in various gastrointestinal diseases, requiring accurate diagnosis for effective treatment. This study aims to develop an expert system for the rapid and precise identification of hematochezia-causing diseases. The expert system is designed to assist patients in efficiently recognizing diseases, minimizing treatment failure risks. It employs the Naïve Bayes method, a data calculation approach involving summing combinations and frequencies of each dataset. The expert system methodology begins with training using a dataset comprising hematochezia symptoms and corresponding disease diagnoses. The dataset is input into a database as training data. Subsequently, it undergoes classification and training stages. Symptom data can then be processed using the Naïve Bayes method. The system's end result displays probability values for each disease based on provided symptoms. This analysis relies on specific symptoms selected by the user, such as Rectal Pain, Hematochezia, Constipation, Fatigue, and Abdominal Cramps. It yields a Hemorrhoids diagnosis with a posterior probability of 0.514738. In testing with 35 sample cases, the expert system exhibited a remarkable accuracy rate of 94.29%. This expert system efficiently supports disease diagnosis based on hematochezia symptoms, aiding in swift and accurate identification.
E-GOVERNMENT MATURITY ANALYSIS USING THE LAYNE AND LEE, HILLER AND BELANGER, AND SPBE MODELS Eltyasar Putrajati Noman; Andi Wahju Rahardjo Emanuel
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 9 No. 2 (2024): JITK Issue February 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i2.5110

Abstract

This research aims to analyze the level of e-government maturity in Kupang City using the Layne and Lee and Hiller and Belanger models and combine them with the SPBE model to provide a more comprehensive approach. The method used to develop an audit model involves a literature study to understand the e-government maturity model, identification of specific objectives for analysis of the Kupang City Population and Civil Registration Service (DUKCAPIL) website, determination of scope based on the SPBE model, determination of design audit criteria and benchmarks, collection and data synthesis from the Kupang City DUKCAPIL e-Government site, as well as analysis of audit findings, and using the GT Metrix tool for performance analysis and evaluation of the Kupang City DUKCAPIL website. The research results show that the lowest rating is F out of six, which indicates poor service performance. The Layne and Lee Model assessment gives a score of 18, indicating the technology's lack of integration and complexity. Hiller and Belanger's Model assessment gives a value of 13, indicating an immature level. These findings highlight significant gaps between the models evaluated. Recommendations based on research are to increase citizen participation by improving the Kupang City DUKCAPIL website based on a maturity model and the need for regular audits and ongoing evaluations to improve public services in Kupang City in e-government maturity. In conclusion, this research provides a new contribution to the field of e-government by highlighting the need for audits using several e-government maturity models to improve public services in Kupang City.
HERBAL LEAF CLASSIFICATION USING DEEP LEARNING MODEL EFFICIENTNETV2B0 Rakha Pradana Susilo Putra; Christian Sri Kusuma Aditya; Galih Wasis Wicaksono
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 9 No. 2 (2024): JITK Issue February 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i2.5119

Abstract

Science regarding plants has experienced significant progress, especially in the study of medicinal plants. Medicinal plants have been used in medicine and are still an important component in the world of health today. Among the various parts of the plant, the leaves are also one that can be used as medicine. However, not many people can recognize these herbal leaves directly. This is because the herbal leaves at first glance look almost the same, so it is difficult to differentiate them. The aim of this research is to classify herbal leaf images by identifying the structural features of the leaf images. The dataset in this study uses 10 classes of leaf images, namely, starfruit, guava, lime, basil, aloe vera, jackfruit, pandan, papaya, celery, and betel, where each class uses 350 images with a total of 3500 images of data. The EfficientNetV2B0 model was chosen because it has a minimalist architecture but has high effectiveness. Based on the results of research using the EffiecientNetV2B0 model, the accuracy was 99.14% and the loss value was 1.95% using test data.
MACHINE LEARNING FOR PREDICTING SPREAD OF COVID-19 IN INDONESIA Nur Hayati; Eri Mardiani; Fauziah Fauziah; Toto Haryanto; Viktor Vekky Ronald Repi
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 9 No. 2 (2024): JITK Issue February 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i2.5174

Abstract

In previous research, we carried out an analysis using the FBProphet model to predict the COVID-19 outbreak in Indonesia. The application of the FBProphet model to time series data is considered quite good because it produces a MAPE of 22.60% with a linear distribution. Additionally, based on the pattern in the previous dataset and the total number of active cases currently stands at 2,606, in this research we tried to use the Linear Regression (LR) model as a comparison with the FBProphet model by using additional data from the same data source, KAWALCOVID19 website. Data collection started from March 2, 2020 to December 19, 2021. The aim of this research is the same as previous research, namely predicting the spread of COVID-19. The analysis process is carried out by preprocessing the data by validating missing data and validating the format of the data variables. Then carry out descriptive analysis and data visualization so that it can be seen that in this 657 data there is a fluctuates data that non-periodically from July to August 2021. Next, model analysis is carried out using FBProphet and LR and validating the results of each model. The research results are in the form of evaluation metrics where the LR model gets better RMSE, MAE and MAPE values compared to FBProphet, namely 292.91; 178, 81 and 12.79%.
DATA QUALITY ASSESSMENT: A CASE STUDY ON ASSET VALUATION COMPARISON DATA I Gusti Ngurah Adi Wicaksana; Achmad Nizar Hidayanto; Handini Mekkawati; Rizha Febriyanti
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 9 No. 2 (2024): JITK Issue February 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i2.5184

Abstract

To realize a data-driven organization, good data quality is needed as a foundation for solving various problems related to data management. The case study used in this research is asset valuation comparison data. The purpose of this research is to define dimensions, measure and analyze data quality on asset valuation comparison data. There are three dimensions used in measuring data quality in this study which are adjusted based on existing regulations at Ministry X, namely accuracy, completeness, and validity. This research uses the stages in the Total Data Quality Management (TDQM) framework to measure data quality. The results of measuring all dimensions, 29 out of 58 business rules cannot be fulfilled completely. The business rules that can be fulfilled in each dimension are 47.06% in the completeness dimension, 60% in the validity dimension, and 44.44% in the accuracy dimension. The main factor causing the existence of data attributes that have not met the data quality business rules is because the asset valuation comparison data comes from various data sources. In addition, there are methods or standards for recording data from data source units that are not uniform, so an evaluation of the uniformity of data standardization and the implementation of data governance is needed. The results of this study can be used as material for organizational consideration to be more aware of the current state of data quality. In addition, it can be used by organizations to design strategies and steps to improve data quality so that it can support leaders in making the right decisions.
SENTIMENT ANALYSIS USING CONVOLUTIONAL NEURAL NETWORK (CNN) AND PARTICLE SWARM OPTIMIZATION ON TWITTER Regina Anatasya Rudiyanto; Erwin Budi Setiawan
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 9 No. 2 (2024): JITK Issue February 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i2.5201

Abstract

Over time, social media has always changed quickly. People can voice their ideas on various topics and communicate with each other through social media. One social media platform that allows users to express their ideas through tweets is Twitter. Sentiment is the route via which each person can express their ideas on a variety of subjects. The sentiment can be positive or negative. Sentiment analysis can be used to determine how Twitter users feel about particular subjects. Sentiment analysis on popular subjects in 2023, specifically the 2024 presidential contenders, will be done in this research. The dataset used in this research consists of 37,391 entries with 5 keywords. The research aims to understand how Twitter users respond and express their sentiments towards the presidential candidate through the use of deep learning classification techniques with Convolutional Neural Network (CNN), feature extraction using Term Frequency-Inverse Document Frequency (TF-IDF) method, and feature expansion with Word2Vec. Furthermore, this study employs Particle Swarm Optimization as an optimization technique to enhance the sentiment analysis model's performance. The test's results demonstrate a high degree of accuracy, offering a comprehensive picture of Twitter users' sentiments and perspectives toward the 2024 presidential contenders. This research helps to understand the dynamics of public opinion in the political context. Based on the evaluation results of the research, it yielded an accuracy of 78.2%, showcasing an improvement of 10.07% compared to the baseline.
OPTIMIZATION OF LIVESTOCK MONITORING SYSTEM IN OUTDOOR BASED ON INTERNET OF THINGS (IOT) Andi Chairunnas; Agung Prahujana Putra
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 9 No. 2 (2024): JITK Issue February 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i2.5312

Abstract

Livestock businesses are often underestimated by the public because they are associated with less hygienic working environments. However, the demand for livestock products such as meat and milk is increasing, providing significant business opportunities. Several obstacles, such as livestock loss and the capital required for cage construction, are barriers to starting a livestock business. Livestock losses, especially in outdoor farms, often occur because of the lack of proper monitoring and data collection. Therefore, technology is required to overcome this problem. The application of IoT technology is an effective solution for overcoming this problem. By utilizing sensors, such as GPS, temperature, and heart rate, farmers can monitor farm animals remotely using Android applications. In this study, a U-blox Neo6m GPS sensor was used to track the location of farm animals, a temperature sensor was used to monitor the temperature conditions of farm animals, and a heart rate sensor was used to determine the health of farm animals that had been tested. The use of a 1500 mAh LI-ION LITHIUM battery as a power source proved to be sufficient for 7 h. The results showed that this IoT-based Outdoor Livestock Monitoring System can provide information on the last location of livestock as well as real-time heart rate and temperature data in the database. This innovation opens opportunities for farmers to improve livestock management and monitoring efficiently, minimize losses, and increase the productivity of their livestock business

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