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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Kursor JAIS (Journal of Applied Intelligent System) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA JITK (Jurnal Ilmu Pengetahuan dan Komputer) International Journal of Engineering, Technology and Natural Sciences (IJETS) Jurnal TIKOMSIN (Teknologi Informasi dan Komunikasi Sinar Nusantara) Jurnal Teknologi Informasi Cyberku
Ruri Suko Basuki
Universitas Dian Nuswantoro
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science

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Hoax classification and sentiment analysis of Indonesian news using Naive Bayes optimization Heru Agus Santoso; Eko Hari Rachmawanto; Adhitya Nugraha; Akbar Aji Nugroho; De Rosal Ignatius Moses Setiadi; Ruri Suko Basuki
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 2: April 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i2.14744

Abstract

Currently, the spread of hoax news has increased significantly, especially on social media networks. Hoax news is very dangerous and can provoke readers. So, this requires special handling. This research proposed a hoax news detection system using searching, snippet and cosine similarity methods to classify hoax news. This method is proposed because the searching method does not require training data, so it is practical to use and always up to date. In addition, one of the drawbacks of the existing approaches is they are not equipped with a sentiment analysis feature. In our system, sentiment analysis is carried out after hoax news is detected. The goal is to extract the true hidden sentiment inside hoax whether positive sentiment or negative sentiment. In the process of sentiment analysis, the Naïve Bayes (NB) method was used which was optimized using the Particle Swarm Optimization (PSO) method. Based on the results of experiment on 30 hoax news samples that are widely spread on social media networks, the average of hoax news detection reaches 77% of accuracy, where each news is correctly identified as a hoax in the range between 66% and 91% of accuracy. In addition, the proposed sentiment analysis method proved to has a better performance than the previous analysis sentiment method.
QUALITY IMPROVEMENT OF OBJECT EXTRACTION FOR KEYFRAME DEVELOPMENT BASED ON CLOSED-FORM SOLUTION USING FUZZY CMEANS AND DCT-2D Ruri Suko Basuki; Mochamad Hariadi; Mauridhi Hery Purnomo
Jurnal Ilmiah Kursor Vol 7 No 2 (2013)
Publisher : Universitas Trunojoyo Madura

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Abstract

QUALITY IMPROVEMENT OF OBJECT EXTRACTION FOR KEYFRAME DEVELOPMENT BASED ON CLOSED-FORM SOLUTION USING FUZZY CMEANS AND DCT-2D aRuri Suko Basuki, bMochamad Hariadi, cMauridhi Hery Purnomo a,b,cFaculty of Industrial Technology, Dept. of Electrical Engineering Institut Teknologi Sepuluh Nopember, Kampus ITS Keputih, Sukolilo, Surabaya, Jawa Timur, Indonesia a Faculty of Computer Science, Dian Nuswantoro University Jalan Imam Bonjol, Semarang, Indonesia E-mail: a rurisb@research.dinus.ac.id Abstrak Penelitian ini bertujuan untuk meningkatkan kualitas ekstraksi obyek pada citra tunggal hasil pemecahan frame dari video sekuensial yang terkompresi. Kualitas hasil ekstraksi obyek dengan algoritma closed-form solution menurun karena adanya beberapa perubahan nilai intensitas pada channel RGB. Sehingga di sekitar batas tepi obyek hasil ekstraksi terlihat kasar baik secara visual maupun hasil pengukuran dengan Mean Squared Error (MSE) antara obyek hasil ekstraksi dengan ground truth. Untuk meningkatkan kualitas hasil ekstraksi objek, nilai threshold pada unknown region ditentukan melalui adaptive threshold yang diperoleh dengan mengaplikasikan algoritma Fuzzy C-Means (FCM). Pemilihan algoritma FCM karena dalam penelitian sebelumnya algoritma ini menunjukkan hasil yang lebih robust dibandingkan algoritma Otsu untuk mendapatkan nilai threshold yang optimal. Sedangkan untuk menghaluskan obyek di sekitar daerah batas tepi digunakan filter Discrete Cosine Transform (DCT) – 2D. Dari 10 obyek yang digunakan dan dievaluasi dengan MSE menunjukkan peningkatan rata-rata sebesar 31.55%. Namun pendekatan ini tidak begitu robust pada citra yang memiliki kemiripan warna. Penggabungan pendekatan ini dengan optimasi cost function dalam alpha region pada basis spectrum diharapkan mampu meningkatkan kinerja algoritma ekstraksi obyek pada penelitian selanjutnya. Kata kunci: Closed-form Solution, Algoritma Fuzzy C-Means, Discrete Cosine Transform-2D. Abstract The research is aimed to improve the quality of the extraction of the object in a single image resulted from frame’s fragmentation of sequential compressed video. The quality of the extracted objects with closed-form solution algorithm decreased due to some changes in the intensity values on the RGB channel. Thus, the extraction result around the boundary edges of objects visually seemed to be rough and when it was measured with the Mean Squared Error (MSE) beween the object extraction results with ground truth. To improve the quality of the extracted object, the threshold value on unknown region was determined by adaptive threshold obtained by applying the Fuzzy C-Means algorithm (FCM). FCM algorithm is chosen since in the previous research this algorithm gives more robust results than Otsu algorithm to obtain the optimal threshold value. Meanwhile, to eliminate noise around the border area, this research applies Discrete Cosine Transform (DCT) - 2D filters. The result of 10 objects used and evaluated with the MSE showed an average increase of 31.55%. However, this approach is not so robust to images having similar color. Combination of this approach with optimization of the cost function on the alpha region based on spectrum is expected improving the performance of object extraction algorithm for the next research. Key words: Closed-form Solution, Fuzzy C-Means Algorithm, Discrete Cosine Transform-2D
KOORDINASI NONPLAYER CHARACTER FOLLOWER MENGGUNAKAN ALGORITMA POTENTIAL FIELDS BERBASIS MULTIBEHAVIOUR Latius Hermawan; Mochamad Hariadi; Ruri Suko Basuki
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 13 No 1 (2017): Jurnal Teknologi Informasi CyberKU Vol. 13, no 1
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (656.045 KB)

Abstract

Games have become popular among the people, as a form of entertainment, social support interaction between them . NPC behavior modeling is an important issue in realizing the intelligence of computer games . In NPC team - mate, AI needed to help regulate the behavior of team - mate who played alongside or under the command of a human player to assist players in achieving the goal. Potential fields is described as the iron particles are moving towards the object through the magnetic field created by the target object . This movement depends on the existing magnetic field , the particles will be drawn towards the goal , or just the opposite of the iron particles will be rejected by the magnetic field at the time met an obstacle . In this study , the data obtained by reading the references relating to the title to find out the problems faced in coordinating the team in the game . From the study , analyzed the needs of the games that will be made to the AI model that will be used for team coordination . Only then designed a game that can resolve the issue . After the game was made , the game will be tested by several methods , so it will look the difference . The expected outcome of this study is to model the NPC behavior Follower and adjust the position of the player in accordance with the AI have been made . So players will not quickly lose the game and can finish coordinate with the NPC Follower followed by adjusting the movement of NPC Follower to the players during the attacks , NPC Follower still within range radius of the player to protect the player
GAME SCORING NON PLAYER CHARACTER MENGGUNAKAN AGEN CERDAS BERBASIS FUZZY MAMDANI Astrid Novita Putri; Mochamad Hariadi; Ruri Suko Basuki
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 11 No 2 (2015): Jurnal Teknologi Informasi CyberKU Vol.11 no 2
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (582.34 KB)

Abstract

Game are activity most structure, one that ordinary is done in fun and also education tool and help todevelop practical skill, as training, education, simulation or psychological. On its developing currentGame have until 3D. In one Game, include in First Person Shutter necessary scoring one that intent tomotivate that player is more terpacu to solve Game until all through, on scoring Super Mario's GameBoss, Compass does count scoring haven't utilized Artifical Intelligent so so chanted, while player meetwith enemy (Non Player Character) really guns directly dead, so is so easy win. Therefore at needs acount scoring interesting so more terpacu in menyelasaikan problem Scoring accounting point for FirstPerson Shutter's Game .This modelling as interesting daring in one Game, since model scoring one thateffective gets to motivate that player is more terpacu in plays and keep player for back plays. Besidesmodel scoring can assign value that bound up with Game zoom.On Research hits scoring this Game willmake scoring bases some criterion which is health Point, Attack point, Defending point, And Dammagewhat do at miiliki zombie,then in this research do compare two method are methodic statistic and Fuzzy.Result of this research 90 % on testing's examination and on eventually gets to be concluded that fuzzy'smethod in trouble finish time more long time but will player more challenging to railroad
GLCM Based Locally Feature Extraction On Natural Image Edi Faisal; Agung Nugroho; Ruri Suko Basuki; Suharnawi Suharnawi
Journal of Applied Intelligent System Vol 7, No 2 (2022): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v7i2.6569

Abstract

GLCM is a feature extraction method that uses statistical analysis using a gray scale. Contrast, correlation, energy and entropy are feature features whose value will be sought as the basis for finding the threshold which can then be used to find the threshold value in image segmentation. In this study, a local-based GLCM method is used where the image that has been made into grayscale will be divided into 16 parts of the same size. Each section will look for the value of its GLCM features, namely Contrast, correlation, energy and entropy. The calculation of these four features will be applied to 16 parts of the grayscale image, which can then be used to find the threshold value. The results of the four features in the calculation with an angle of 0o are the contrast value = 0.0080, correlation = 0.619, energy : 0.00160 and entropy : 0.05591.
COMPARISON OF SIFT AND ORB METHODS IN IDENTIFYING THE FACE OF BUDDHA STATUE Linda Marlinda; Fikri Budiman; Ruri Suko Basuki; Ahmad Zainul Fanani
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 8 No. 2 (2023): JITK Issue February 2023
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v8i2.4086

Abstract

The statue is part of the heritage facial recognition process which is immobile and artistically stylized. Identifying the similarities between the statues can help provide an important reference for tourism in recognizing the faces of the statues which are different and have almost the same characteristics in every country, especially in Indonesia, among the facial recognition of the statues based on the condition, color, and shape of the face. The purpose of this study is to apply the original images that have characteristics, partially done manually to various types of transformations and calculate matching evaluation parameters such as the number of key points in the image, the level of matching, and the required execution time for each algorithm. To confirm the efficiency of the proposed method, experiments were carried out on private data sets obtained from statues under low light conditions and in different poses. The data was taken based on the image of the Buddha's face and matched with the facial image of the Buddha statue available in the database using comparisons resulting from data processing using the Sift and ORB methods with various types of transformations. The result will be seen in the image that is matched with the best algorithm for each type of distortion. The faces tested are images of the faces of the Buddha statues that are recognized, and photos of some of the original statues that were not saved due to unclear lighting and camera distance factors. The results show that the number of key points generated is the number of key points, the ORB method gives fewer results compared to the SIFT method and the average SIFT recognition and processing time shows better performance for an average of 100% at a SIFT matching rate of 2% with time 0.400285 and the ORB method is 1% for the time 0.400961
Optimization of Yolov5 Hyperparameter Using Adam Optimizer in Vehicle Object Detection Irawan, Bambang; Andono, Pulung Nurtantio; Basuki, Ruri Suko
(JAIS) Journal of Applied Intelligent System Vol. 9 No. 1 (2024): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jais.v9i1.9244

Abstract

Utilization of computer vision can be applied in various aspects of daily life, reducing dependence on human labor. One of its implementations is in industry, such as in the production process of motorized vehicles, to sort or classify parts or goods. The computer vision process involves many stages, such as image capture, image processing, image analysis, image recognition, and decision-making. In the automotive industry, computer vision has been used in autonomous or driverless electric vehicles, as well as in creating intelligent transportation systems. To detect objects in real-time, one of the options that can be used is to use the YOLO algorithm, which can detect objects in one stage with predictions of bounding boxes and class probabilities simultaneously. However, although YOLO has good performance, the architecture has some drawbacks, such as complexity and complicated hyperparameter congurations. To remedy this, the Adam optimization algorithm was introduced, which combines the momentum and RMSprop algorithms to adjust the learning rate adaptively and provide faster convergence in model training. This is evidenced by an increase in the value of mAP on Yolov5. These results prove that the Yolov5 method with Adam`s optimization is better than the Yolov5 method without optimization.
Peningkatan Performa Model Machine Learning XGBoost Classifier melalui Teknik Oversampling dalam Prediksi Penyakit AIDS Wicaksono, Duta Firdaus; Basuki, Ruri Suko; Setiawan, Dicky
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 2 (2024): April 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i2.7501

Abstract

The data shows that HIV (Human Immunodeficiency Virus) has caused tens of millions of global deaths, with 630,000 people dying from HIV-related illnesses in 2022 and 1.3 million people newly infected with HIV. Without treatment, HIV can progress to AIDS (Acquired Immune Deficiency Syndrome), weakening the immune system and increasing the risk of infections and other diseases. Despite advancements in treatment, early detection of AIDS remains a priority. This research develops an AIDS prediction model using machine learning, which proves to be an effective solution in providing future health predictions. However, data imbalance issues challenge the model in predicting rare AIDS cases. To solve this problem, oversampling techniques are employed to balance the distribution of minority classes. This study explores oversampling techniques such as SMOTE, ADASYN, and Random Oversampling, combined with the XGBoost algorithm. The results show that the combination of Random Oversampling technique with the XGBoost Classifier yields the best performance with an accuracy of 94.44%, precision of 90.72%, recall of 98.74%, and an f1_score of 94.65%. This research is expected to provide valuable insights for healthcare practitioners and the public in efforts to control the spread of AIDS globally.
Deep Convolution Neural Network to solve Problems for Appel Leaf Disease Detection Sutriawan; Ahmad Zaniul Fanani; Farrikh Alzami; Ruri Suko Basuki
Jurnal Internasional Teknik, Teknologi dan Ilmu Pengetahuan Alam Vol 5 No 2 (2023): International Journal of Engineering, Technology and Natural Sciences
Publisher : Universitas Teknologi Yogyakarta, Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46923/ijets.v5i2.232

Abstract

Orchardists are now concerned about apple leaf infections since they could result in crop failure. It is challenging for growers to identify the type of illness on apple leaves due to the large variety of diseases that can affect apple leaves. In this study, we cover four different illness categories: healthy, numerous diseased, rusty, and scabby. a deep convolutional neural network method of processing. using a number of suggested methods, including data preprocessing and the pre-configured VGG-16 deep convolutional neural network (CNN) architecture. The Adam optimization model's beta 2 = 0.999 parameter value at Ephoch to 85/100 with an accuracy of 0.7582 and epsilon = 1e-07 parameter value at Ephoch to 32/100 with an accuracy of 0.7582 both produced the best accuracy outcomes.
GLCM-Based Feature Extraction for Alpha Matting on Natural Images Ruri Suko Basuki; Jehad A.H Hammad
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 3 (2024): June 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i3.5644

Abstract

The main objective of this research is to determine the optimal threshold value in the unknown region in the alpha-matting operation of natural images. Alpha-mating serves to draw matte from the image used in segmentation. The alpha value is very influential on the quality of segmentation which is determined by the level of threshold value accuracy. The determination of the threshold begins by breaking the grayscale image into several sub-images using Region of Interest (RoI). Each sub-image was extracted using the Gray Level Co-occurrence Matrix (GLCM) considered by the parameters of contrast, energy, and entropy at angles of 0°, 45°, 90°, and 135 °. Each feature results in extractions, which are then averaged and normalized in each sub-image. The value is determined as the local threshold value used in the alpha matting operation. Experiments were carried out on 12 natural images from the image-mating dataset to evaluate the performance of the proposed algorithm. The increase in accuracy shows up to 63% by the measurements of experiments, compared to the calculation of adaptive threshold by using the fuzzy CMs Algorithm.
Co-Authors Adhitya Nugraha Agung Nugroho Ahmad Zainul Fanani Ahmad Zaniul Fanani Akbar Aji Nugroho Alzami, Farrikh Astrid Novita Putri, Astrid De Rosal Ignatius Moses Setiadi Edi Faisal Eko Hari Rachmawanto Fikri Budiman Heru Agus Santoso Jehad A.H Hammad Latius Hermawan Linda Marlinda Mauridhi Hery Purnomo Mochamad Hariadi Pulung Nurtantio Andono Setiawan, Dicky Suharnawi Suharnawi Suharnawi Sutriawan Sutriawan Tengku Riza Zarzani N Wicaksono, Duta Firdaus