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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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PREDICTION PERFORMANCE OF AIRPORT TRAFFIC USING BILSTM AND CNN-BI-LSTM MODELS Willy Riyadi; Jasmir Jasmir
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 9 No. 1 (2023): JITK Issue August 2023
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i1.4191

Abstract

The COVID-19 pandemic has had a significant and enduring impact on the aviation industry, necessitating the accurate prediction of airport traffic. This study compares the predictive accuracy of biLSTM (Bidirectional Long Short-Term Memory) and CNN-biLSTM (Convolutional Neural Network-Bidirectional Long Short-Term Memory) models using various optimization techniques such as RMSProp, Stochastic Gradient Descent (SGD), Adam, Nadam, and Adamax. The evaluation is based on Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) indices. In the United States, the biLSTM model utilizing the Nadam optimizer achieved an MAPE score of 9.76%. On the other hand, the CNN-biLSTM model utilizing the Nadam optimizer demonstrated a slightly improved MAPE score of 9.62%. For Australia, the biLSTM model using the Nadam optimizer obtained an MAPE score of 31.52%. However, the CNN-biLSTM model employing the RMSprop optimizer had a marginally higher MAPE score of 33.33%. In Chile, the biLSTM model using the Adam optimizer obtained an MAPE score of 44.04%. Conversely, the CNN-biLSTM model using the RMSprop optimizer had a slightly higher MAPE score of 44.09%. Lastly, in Canada, the biLSTM model using the Nadam optimizer achieved a comparatively low MAPE score of 14.99%. Similarly, the CNN-biLSTM model utilizing the Adam optimizer demonstrated a slightly better MAPE score of 14.75%. These results highlight that the choice of optimization technique, model architecture, and balanced dataset can significantly influence the prediction accuracy of airport traffic.
SENTIMENT LABELING AND TEXT CLASSIFICATION MACHINE LEARNING FOR WHATSAPP GROUP Susandri Susandri; Sarjon Defit; Muhammad Tajuddin
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 9 No. 1 (2023): JITK Issue August 2023
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i1.4201

Abstract

The use of WhatsApp Group (WAG) for communication is increasing nowadays. WAG communication data can be analyzed from various perspectives. However, this data is imported in the form of unstructured text files. The aim of this research is to explore the potential use of the SentiwordNet lexicon for labeling the positive, negative, or neutral sentiment of WAG data from "Alumni94" and training and testing it with machine learning text classification models. The training and testing were conducted on six models, namely Random Forest, Decision Tree, Logistic Regression, K-Nearest Neighbors (KNN), Linear Support Vector Machine (SVM), and Artificial Neural Network. The labeling results indicate that neutral sentiment is the majority with 7588 samples, followed by 324 negative and 1617 positive samples. Among all the models, Random Forest showed better precision and recall, i.e., 83% and 64%. On the other hand, Decision Tree had slightly lower precision and recall, i.e., 80% and 66%, but exhibited a better f-measure of 71%. The accuracy evaluation results of the Random Forest and Decision Tree models showed significant performance compared to others, achieving an accuracy of 89% in classifying new messages. This research demonstrates the potential use of the SentiwordNet lexicon and machine learning in sentiment analysis of WAG data using the Random Forest and Decision Tree models
RAINFALL PREDICTION USING MULTIPLE LINEAR REGRESSION ALGORITHM Mulia Sulistiyono; Acihmah Sidauruk; Budy Satria; Raditya Wardhana
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 9 No. 1 (2023): JITK Issue August 2023
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i1.4203

Abstract

Indonesia is a tropical region with ever-changing weather changes. It is necessary to conduct a research on weather prediction as a decision making regarding weather information that will occur in the future. Rainfall is one of the factors that cause changes in weather in an area. This research was conducted on the climate in the Yogyakarta region in the form of mountains and lowlands causing differences in rainfall. The variables that are used to make predictions are several parameters that affect rainfall, namely temperature, humidity, wind speed and duration of solar radiation. These 5 variables are processed through the data obtained then carried out research and comparisons with the previous data. Multiple linear regression is the algorithm used. This algorithm is one of the machine learning techniques by making rainfall data as the dependent variable and other parameters as independent variables. This study uses Yogyakarta City, Central Java climate data for 2010-2020. The results obtained are an R2 score of 12.99%. Prediction of rainfall is obtained at 14.41778516. Then the RMSE evaluation resulted in a deviation between predicted rainfall and actual rainfall of 14.78316110508722. Based on these results, it shows that there is light rain because it is in the intensity category of 5 mm – 20 mm/day.
CLASSIFICATION OF CORN PLANT DISEASES USING VARIOUS CONVOLUTIONAL NEURAL NETWORK Aditya Yoga Pratama; Yoga Pristyanto
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 9 No. 1 (2023): JITK Issue August 2023
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i1.4258

Abstract

Based on data from the East Java Badan Pusat Statistik (BPS) in 2020, corn production in 2019 decreased by 622,403 tons. The decrease in production was caused by a disease that attacked corn plants identified from the corn leaves' physical appearance. This study aims to obtain an architectural model with good performance between AlexNet, LeNet, and MobileNet in detecting diseases of maize plants. The dataset used in this study came from Kaggle, with 4188 images divided into four disease classes: Common Rust, Gray Leaf Spot, Blight, and Healthy. Agricultural experts from Bantul have confirmed the appearance of each class of corn plant diseases. The preprocessing process is carried out to prepare the data so that the amount of data for each class is balanced. The image data used in this study totaled 4000 images which were divided into training data and testing data with a ratio of 80:20. Based on the experimental results, it was found that the MobileNet architecture has better performance than AlexNet and LeNet with an accuracy value of 83.37%, average precision of 0.8337, and g-mean of 0.8298. These results have been validated by agricultural experts in Bantul Regency and corn farmers experienced in corn farming.
PREDICTING MARKET SEGMENTS FROM TWITTER DATA USING ARIMA TIME SERIES ANALYSIS Septi Andryana; Ben Rahman; Aris Gunaryati
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 9 No. 1 (2023): JITK Issue August 2023
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i1.4275

Abstract

Twitter data on social media can be used to predict potential market segments in the future. The continuous nature of Twitter data and data collection sequentially over a certain period uses a text mining process with time series data that matches the actual data. The problem to be solved is to forecast market segments using Twitter data with greater accuracy. Various market segments that are of business interest through the tweets of individual Twitter users have not been utilized optimally. Based on the Twitter data pattern, the research shows that the data pattern is not stationary, so to analyze the data, it is necessary to use the Autoregressive Integrated Moving Average (ARIMA) method. This study aims to analyze time series data from Twitter data and predict market segment predictions using the ARIMA method. The ARIMA method is a method that has advantages in flexibility, the ability to handle stationary and non-stationary data, as well as short-term forecasting with a statistical approach in various time series data forecasting applications. Prediction results using the ARIMA method with an accuracy rate of 94.88%. There are several ways to measure model validation including MAD, MSE, RMSE, F1-Score, and MAPE. In this study, MAD, MSE, and MAPE were used with an accuracy rate of 5.22%. This study succeeded in applying the ARIMA method to time series data from Twitter to forecast market segments with high accuracy, opening opportunities for utilizing Twitter data in business strategy.
ANALYSIS OF EFFECTS OF APP PERMISSION CONCERNS ON INTENTIONS TO DISCLOSE PERSONAL INFORMATION: A CASE STUDY OF MONEY TRANSFER SERVICE APP Azis Amirulbahar; Yova Ruldeviyani
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 9 No. 1 (2023): JITK Issue August 2023
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i1.4316

Abstract

Data growth increased alongside the rise of mobile app users in financial services. In Indonesia, the number of financial services application downloads reached 24 million by the end of 2022, with a 28.72 percent increase in transactions. However, this growth also brings issues regarding the potential misuse of personal information, although according to the Personal Data Protection Act (UU PDP) in Indonesia, personal data is protected and kept confidential when accessed by another party. This prompts users to be more cautious in disclosing personal information. On the other hand, users are faced with risks to personal data that can be accessed by service providers, one of which is through app permissions. This research focuses on the influence of App Permission Concerns on users' intentions to disclose their personal information, with a case study of a money transfer services app in Indonesia, namely Flip, that received numerous negative reviews about users' data privacy concerns, especially when verifying using an identity card. The study uses a quantitative approach with PLS-SEM for data analysis. Convenience sampling was used, and data were collected via a questionnaire distributed through Google Forms on social media from May 9 to May 21, 2023 and a total of 224 respondents were obtained. The results of this study indicate that App Permission Concerns have a significant influence on Privacy Fatigue, Privacy Awareness, Privacy Concern and Trust. Trust significantly influences Intention to Disclose. This research is expected to contribute to future studies on app permissions and mobile app feature development.
MUSIC RECOMMENDATION SYSTEM BASED ON COSINE SIMILARITY AND SUPERVISED GENRE CLASSIFICATION Jamie Mayliana Alyza; Fandy Setyo Utomo; Yuli Purwati; Bagus Adhi Kusuma; Mohd Sanusi Azmi
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 9 No. 1 (2023): JITK Issue August 2023
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i1.4324

Abstract

Categorizing musical styles can be useful in solving various practical problems, such as establishing musical relationships between songs, similar songs, and finding communities that share an interest in a particular genre. Our goal in this research is to determine the most effective machine learning technique to accurately predict song genres using the K-Nearest Neighbors (K-NN) and Support Vector Machine (SVM) algorithms. In addition, this article offers a contrastive examination of the K-Nearest Neighbors (K-NN) and Support Vector Machine (SVM) when dimensioning is considered and without using Principal Component Analysis (PCA) for dimension reduction. MFCC is used to collect data from datasets. In addition, each track uses the MFCC feature. The results reveal that the K-Nearest Neighbors and Support Vector Machine offer more precise results without reducing dimensions than PCA results. The accuracy of using the PCA method is 58% and has the potential to decrease. In this music genre classification, K-Nearest Neighbors (K-NN) and Support Vector Machine (SVM) are proven to be more efficient classifiers. K-Nearest Neighbors accuracy is 64,9%, and Support Vector Machine (SVM) accuracy is 77%. Not only that, but we also created a recommender system using cosine similarity to provide recommendations for songs that have relatively the same genre. From one sample of the songs tested, five songs were obtained that had the same genre with an average accuracy of 80%.
THE FUNCTION OF SOCIAL MEDIA AS A SUPPORTING TOOL FOR LECTURER CAREER DEVELOPMENT IN UNIVERSITIES IN THE INDUSTRY 4.0 ERA Ali Ibrahim; Rahmat Izwan Heroza; Iredho Fani Reza; Mira Afrina; Yadi Utama
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 9 No. 1 (2023): JITK Issue August 2023
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i1.4329

Abstract

Lectures have many duties. The most important one is to implement the tri-dharma of higher education (teaching, research and community service). One of the efforts to support these duties in the Industry 4.0 era is the use of social media to develop professors' careers. A lack of understanding of the role of social media sometimes only sees the dark side of social media, which is actually very useful in developing lecturers' careers. This study uses a qualitative research method with a narrative research design. The subjects of this study were 92 individuals identified through purposive sampling technique. Data collection was done through an online survey. Data analysis in this study used coding techniques consisting of open coding, axial coding and selective coding stages. The findings of this study show that social media is a professional development tool for teachers and can enhance professional competence, which can contribute to teachers' professional development. Professional development that enhances the performance of the Tri Dharma of Higher Education in the areas of teaching, research and community service.
SKYLINE QUERY BASED ON USER PREFERENCES IN CELLULAR ENVIRONMENTS Ruhul Amin; Taufik Djatna; Annisa Annisa; Imas Sukaesih Sitanggang
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 9 No. 1 (2023): JITK Issue August 2023
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i1.4192

Abstract

The recommendation system is an important tool for providing personalized suggestions to users about products or services. However, previous research on individual recommendation systems using skyline queries has not considered the dynamic personal preferences of users. Therefore, this study aims to develop an individual recommendation model based on the current individual preferences and user location in a mobile environment. We propose an RFM (Recency, Frequency, Monetary) score-based algorithm to predict the current individual preferences of users. This research utilizes the skyline query method to recommend local cuisine that aligns with the individual preferences of users. The attributes used in selecting suitable local cuisine include individual preferences, price, and distance between the user and the local cuisine seller. The proposed algorithm has been implemented in the JALITA mobile-based Indonesian local cuisine recommendation system. The results effectively recommend local cuisine that matches the dynamic individual preferences and location of users. Based on the implementation results, individual recommendations are provided to mobile users anytime and anywhere they are located. In this study, three skyline objects are generated: soto betawi (C5), Mie Aceh Daging Goreng (C4), and Gado-gado betawi (C3), which are recommended local cuisine based on the current individual preferences (U1) and user location (L1). The implementation results are exemplified for one user located at (U1L1), providing recommendations for soto betawi (C5) with an individual preference score of 0.96, Mie Aceh Daging Goreng (C4) with an individual preference score of 0.93, and Gado-gado betawi (C3) with an individual preference score of 0.98. Thus, this research contributes to the field of individual recommendation systems by considering the dynamic user location and preferences.
ACCOUNTING APPLICATION FOR CONSTRUCTION GOODS PROCUREMENT RECORDING (CASE STUDY PT. PLN (PERSERO) UPT CIREBON) Widya Jati Lestari; Agus Sevtiana; Alitia Rachmawati
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 8 No. 1 (2022): JITK Issue August 2022
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v8i1.2277

Abstract

PT. PLN (Persero) UPT Cirebon is a state-owned company that takes care of the electricity aspects in ULTG Cirebon, Garut, Ciamis, and Jatibarang. Inventories of existing materials in this is not for resale. Inventories of existing materials at PT. PLN (Persero) UPT Cirebon is shown to support in carrying out operational programs and investment programs to generate or generate electricity. In this electricity company, there are problems and obstacles in recording the procurement of construction goods, because there are some activities that still use the manual system. To assist the power company in managing information in recording the procurement of construction goods so that it can be searched more quickly and accurately, it must have a system that can be used to help solve existing problems. This research produces a Construction Goods Procurement Recording Application that can manage any required information. The reports generated from this application are Material Mutation Data, Contract Letters for Routine SPPL and Non-routine SPPL, Proof of Cash Out and Cash Disbursement Journal. System procedures that apply in PT. PLN UPT Cirebon includes related parts, internal control system, and documents used. The analysis and system design stages are described manually using a flowchart accompanied by an explanatory narration, then computerized into context diagrams, relationship diagrams between entities, and relationships between tables used. After this stage is complete, the next stage is the design of the software system. The design uses Visual Basic 6.0 as the programming language and Microsoft Access 2003 as the database.

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