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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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DEVELOPMENT A DAILY NUTRITIONAL ADEQUACY BALANCE IDENTIFICATION SYSTEM AS AN EFFORT TO PREVENT MALNUTRITION Abidatul Izzah; Daniel Swanjaya; Kunti Eliyen
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 1 (2024): JITK Issue August 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i1.5188

Abstract

Malnutrition is a deficiency, excess or imbalance in a person's energy and nutritional intake. Malnutrition can occur when a person has too much or too little food and important nutrients in their body. The Ministry of Health, Indonesia, has campaigned for food consumption that complies with balanced nutrition guidelines under the slogan "Isi Piringku". However, the guidelines regarding this matter are still not properly understood by the public. Even if implemented, the nutritional levels contained in one portion of food consumed cannot yet be measured. Thus, to identify the fulfillment of balanced nutritional, a device is needed to easily detect how much calories is consumed. Therefore, this research aims to develop a system which can identify whether the portion of food consumed meets balanced nutrition or not. It is developed in Django framework, Python programming language, and MySQL database. It has been evaluated using black box testing, white box testing, and system usability scales. The result shows that all system requirements have been run well. Meanwhile, system usability testing result shows that the identification system has been tested with a score of 82 and categorized in Excellent.
BRAIN TUMOR CLASSIFICATION USING INCEPTIONRESNET-V2 AND TRANSFER LEARNING APPROACH Vallent Austin Theasar Kurniawan; Elan Cahya Niswary; christian s.k.aditya; Didih Rizki Chandranegara
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 1 (2024): JITK Issue August 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i1.5223

Abstract

Brain, a highly intricate organ within the central nervous system, plays a fundamental role in information processing, cognition, motor control, and consciousness. Brain tumors pose severe threats to brain function and overall human well-being. Timely detection of these tumors is imperative for life-saving interventions. A dataset comprising four categories: no tumors, meningioma tumors, glioma tumors, and pituitary tumors was regarded in this research. The employed of the InceptionResNet-V2 architecture combined with Transfer Learning and data augmentation proposed to obtain optimal results on brain tumor classification types. Transfer learning act as fine tuning, enabling the model to acquire fundamental low-level image features from a comprehensive dataset. It then leverages higher-level features to become more tailored to the specific training data. This method is employed to improve the model's adaptability to the training data. The InceptionResNet-V2 architecture utilized in the evaluation using test data, in Scenario 1, achieved 94.18% accuracy. Scenario 2, which combined augmentation with InceptionResNetV2, showed an improvement in accuracy to 95.10%. Furthermore, in Scenario 3, the combination of InceptionResNetV2 with Transfer Learning and augmentation resulted in an impressive accuracy of 96.63%, demonstrating its effectiveness in brain tumor classification. Transfer learning aligns the model by acquiring low-level image features and utilizing higher-level features to improve adaptability to the training data.
MODEL OF INDONESIAN CYBERBULLYING TEXT DETECTION USING MODIFIED LONG SHORT-TERM MEMORY Mariana Purba; Paisal Paisal; Cahyo Pambudi Darmo; Handrie Noprisson; Vina Ayumi
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 1 (2024): JITK Issue August 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i1.5239

Abstract

Cyberbullying, in its essence, refers to the deliberate act of exploiting technological tools to inflict harm upon others. Typically, this offensive conduct is perpetuated repeatedly, as the perpetrator takes solace in concealing their true identity, thereby avoiding direct exposure to the victim's reactions. It is worth noting that the actions of the cyberbully and the responses of the individual being cyberbullied share an undeniable interconnection. The main objective of this study was to identify and analyze Instagram comments that contain bullying words using a model of WLSTML2 which is an optimization of a long short-term memory network with word-embedding and L2 regularization. This experiment using dataset with negative labels as many as 400 data and positive as many as 400 data. In this study, a comparison of 70% training data and 30% testing data was used. Based on experimental results, the WLSTMDR model obtained 100% accuracy at the training stage and 80% accuracy at the testing stage. The WLSTML2 model received an accuracy of 99.25% at the training stage and an accuracy of 83% at the testing stage. The WLSTML1 model obtained an accuracy of 97.01% at the training stage and an accuracy of 80% at the testing stage. Based on the experimental results, the WLSTML2 model gets the best accuracy at the training and testing stages. At the testing stage of 132 data, it was found that the positive label data predicted to be correct was 56 data and the negative label data that was predicted to be correct was 53 data.
IMPLEMENTATION OF FISHER-YATES SHUFFLE ALGORITHM IN ANDROID-BASED JAVANESE BATIK CULTURE EDUCATION GAME Ben Rahman; Mochamad Naufal Shofy; Septi Andryana
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 1 (2024): JITK Issue August 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i1.5256

Abstract

Batik, as one of Indonesia's precious cultural heritages, has a variety of motifs in the art of batik in this country. The preservation of batik is important and the success of educational games in supporting this goal has been proven. Understanding the complex meaning and philosophy of batik is difficult given the variety of motifs. Therefore, this research creates an Android game that incorporates elements of batik culture to introduce the meaning and philosophy of Indonesian batik to the next generation. Android technology makes learning more flexible, allowing unrestricted access to information. By following the Game Development Life Cycle (GDLC) method and integrating the Fisher-Yates Shuffle algorithm and Finite State Machine (FSM), this game takes players on an adventure against Non-Playable Character (NPC) characters using the FSM model. The Fisher-Yates Shuffle algorithm is used to randomize 10 questions, making each game session unique. The algorithm test results showed an average question execution time of about 35.6 microseconds, indicating stable performance despite variations in each trial. The alpha test results showed an average score of 87%, covering aspects of information readability, responsiveness, player motivation, combat experience, educational benefits, as well as satisfaction, and game design that showed good performance. Thus, this research succeeded in creating an educational game that is entertaining and educational, as well as helping to maintain and introduce batik cultural heritage to the next generation.
ACTIVATION FUNCTION IN LSTM FOR IMPROVED FORECASTING OF CLOSING NATURAL GAS STOCK PRICES Budimann Budiman; Nur Alamsyah; R. Yadi Rakhman Alamsyah
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 1 (2024): JITK Issue August 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i1.5258

Abstract

The closing price of natural gas stocks greatly influences investment decisions and the energy industry. Predicting prices correctly can greatly help investors, market participants, and all parties involved, as it allows for making better decisions and optimizing investment portfolios. By using deep learning methods to role model various LSTM activation functions, such as Sigmoid, ReLU, and Tanh, this exploration will hopefully help understand complex patterns in time series data. By finding an appropriate forecasting method, all parties involved can reduce the environmental impact. The experimental results show that the model with ReLU activation function has the highest R2 value of 0.960 in both the training and test sets, and the model with Tanh activation function is also successful, with R2 values of 0.950 in the training set and 0.949 in the test set, and an MSE of 0.002. The model with the sigmoid activation function was slightly lower, with R2 values of 0.931 in the training set and 0.943 in the test set, and an MSE of 0.003. These findings indicate that the LSTM model with the ReLU activation function is considered better for predicting the closing price of natural gas stocks. These findings may help investors, stakeholders, and market participants choose the most accurate model to predict the closing price of natural gas stocks.
COMPARISON OF SVM AND NAÏVE BAYES CLASSIFIER ALGORITHMS ON STUDENT INTEREST IN JOINING MSIB Amira Aida Rashifa; Hendra Marcos; Pungkas Subarkah; Siti Alvi Sholikhatin
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 1 (2024): JITK Issue August 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i1.5270

Abstract

Machine learning (ML) is a branch of artificial intelligence (AI) that deals with the development of systems capable of learning from data to make predictions or decisions without being explicitly programmed. In this study, we conducted an analysis of students' interest in the Internship and Certified Independent Study Program (MSIB) in the context of the Independent Campus Learning policy. The method used is a survey by distributing questionnaires to students of Amikom Purwokerto University in the MSIB batch 5 in year 2023. The results of this study can provide understanding and predictions about students' interest in the MSIB program based on relevant variables, such as study program, semester, cumulative grade point average (GPA), semester credit system (SKS), and previous work experience. The research results indicate that GPA and Study Program greatly influence students' interest in MSIB. The Naïve Bayes algorithm yielded an accuracy of 0.6875 on the training data and 0.25 on the testing data, with a confusion matrix of (0, 1, 0; 0, 1, 2; 0, 0, 0). Meanwhile, the Support Vector Machine (SVM) algorithm yielded an accuracy of 0.4375 on the training data and 0.75 on the testing data, with a confusion matrix of (0, 1; 0, 3). The machine learning model developed in this study is expected to help predict students interest based on new data provided, thus supporting decision-making in optimizing the MSIB program.
PERFORMANCE OF ROBUST SUPPORT VECTOR MACHINE CLASSIFICATION MODEL ON BALANCED, IMBALANCED AND OUTLIERS DATASETS Muhammad Ardiansyah Sembiring; Herman Saputra; Riki Andri Yusda; Sutarman Sutarman; Erna Budhiarti Nababan
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 1 (2024): JITK Issue August 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i1.5272

Abstract

In the realm of machine learning, classification models are important for identifying patterns and grouping data. Support Vector Machine (SVM) and Robust SVM are two types of models that are often used. SVM works by finding an optimal hyperplane to separate data classes, while Robust SVM is designed to deal with uncertainty and noise in the data, making it more resistant to outliers. However, SVM has limitations in dealing with class imbalance and outliers in the dataset. Class imbalance makes the model tend to predict the majority class, and outliers can interfere with model formation. This research compares the performance of SVM and Robust SVM on normal, unbalanced and outlier datasets. The software uses Python and Scikit-learn for implementation and comparison of the two models. Key features include automatic data preprocessing, model training, and evaluation with metrics such as accuracy, precision, recall, and F1 score. The results show that Robust SVM is superior in accuracy on normal datasets and is very effective in dealing with class imbalance, achieving a maximum accuracy of 100%. On datasets with outliers, Robust SVM maintains stable accuracy, demonstrating its robustness to outliers. This research contributes to correspondence management by providing more reliable classification models, improving data processing accuracy, and supporting more informed decision making in software development
ENTERPRISE ARCHITECTURE TRENDS OVER A DECADE: A BIBLIOMETRIC ANALYSIS Albi Firmansyah; Grandys Frieska Prassida
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 1 (2024): JITK Issue August 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i1.5279

Abstract

Enterprise Architecture (EA) has recently become significantly essential for every organization in ensuring the alignment of information technology implementation with the organization's strategy and goals. However, its diverse application to organizations can uprise interesting trends that can be reviewed further. Therefore, this research, through a systematic literature review approach, shows the importance of paying attention to the context and scope of EA which has evolved in the last decade. Bibliometric analysis methods are used to show existing correlations, based on journal article data obtained from 2013 to 2023. This research provides a valuable contribution to the development of EA literature by identifying topics that are frequently discussed and those that have the potential to be discussed in the future and who examine them and their relationship to each other. Furthermore, this research can also provide practitioners and stakeholders with a better understanding of the latest EA implementation trends
COMPARISON OF DEEP LEARNING METHODS ON SENTIMENT ANALYSIS USING WORD EMBEDDING Rizal Gibran Aldrin Pratama; nuri cahyono
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 1 (2024): JITK Issue August 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i1.5280

Abstract

According to ICW, corruption cases in Indonesia in the last 5 years have increased and the amount of losses suffered by the state from 2012-2022 reached Rp138.39 trillion. According to Transparency International, Indonesia's CPI ranking decreased in 2023 to 115 compared to 2022 at 110 out of 180 countries. These results show that the response to corruption is still slow and continues to deteriorate due to a lack of support from stakeholders. The purpose of this study is to test and compare the performance of deep learning model algorithms (RNN/LSTM/GRU/Bi-GRU/Bi-LSTM) on sentiment classification using word embedding, and obtain a model architecture that can determine the polarity of a sentence about public sentiment related to corruption in Indonesia, which can help governments, researchers, and practitioners in designing more effective anti-corruption strategies. The dataset used amounted to 1793 derived from crawling Twitter with 3 classes namely positive, negative and neutral. This research starts from data collection, preprocessing, word embedding, splitting the dataset which is divided into 80% training data and 20% test data, deep learning model testing, model evaluation and result representation. Word embedding uses word2vec with a dimension of 300. Based on the experimental results obtained, Bi-GRU has better performance than other architectural models with an accuracy value of 88%, precision 88.07%, recall 86.97% and f1-score 87.51%. The data used in this research is relatively small, it is recommended that future research can overcome it
TICKER SYMBOL IDENTIFICATION WITH CIMA ON NON-STATIONARY STOCK PRICE DATASET Aji Gautama Putrada; Maman Abdurohman; Doan Perdana; Hilal Hudan Nuha
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 1 (2024): JITK Issue August 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i1.5349

Abstract

Ticker symbol identification based on stock price data in investor decisions has been proven to be pivotal. Though research exists on stock price forecasting, ticker symbol identification is still a research opportunity. Meanwhile, some temporal-sequential classification methods are available, such as classification-integrated moving average (CIMA) and recurrent neural network (RNN)-based deep learning such as long short-term memory (LSTM), and gated recurrent unit (GRU). Our research aim is to prove that CIMA can perform ticker symbol identification on non-stationary stock price datasets. This research collects ten most well-known stock price dataset from Kaggle and performs pre-processing. Then it designs CIMA with non-stationary data and the benchmark deep learning methods. Both methods are optimized with hyperparameter tuning and model selection between adaptive boosting (AdaBoost) and legacy k-nearest neighbors (KNN). The test results show five non-stationary features in the stock price dataset must go through a differentiation process. Then, AdaBoost has an accuracy of 0.9967 ± 0.001, while KNN has an accuracy of 0.9971 ± 0.001, with no significant difference based on t-test. Meanwhile, AdaBoost has a significantly smaller model size and testing and prediction time than KNN. In benchmarking, CIMA+AdaBoost is superior to the three other methods for accuracy, precision, recall, and f1-score, all of which have a value of 0.996. Our research contribution is ticker symbol identification based on stock price using CIMA on multiple-class sequential classification with non-stationary data. For future research, we advice to perform this method on other stock price data.

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