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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Techno.Com: Jurnal Teknologi Informasi Jurnal Teknologi dan Sistem Komputer Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Wahyono Wahyono
Department of Computer Science and Electronics, FMIPA UGM, Yogyakarta
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Peningkatan Kecepatan Algoritma k-NN Untuk Sistem Pengklasifikasian Kendaraan Bermotor Wahyono Wahyono
Techno.Com Vol 19, No 2 (2020): Mei 2020
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (356.251 KB) | DOI: 10.33633/tc.v19i2.3458

Abstract

K-Nearest Neighbor (KNN) merupakan algoritma mesin pembelajaran yang memiliki akurasi yang baik meski sangat sederhana untuk diimplementasikan. Namun, salah satu kelemahan algorima KNN adalah kecepatan komputasi yang sangat tergantung pada jumlah dataset yang dimiliki. Penelitian ini mencoba mengusulkan sebuah strategi untuk meningkatkan kecepatan algoritma KNN tetapi dengan akurasi yang hampir sama dengan standard KNN. Jika pada standar KNN proses hanya dilakukan dengan menyimpan data latih, yang kemudian akan dibandingkan dengan data uji baru dengan cara menghitung jarak satu persatu, sementara strategi yang diusulkan mencoba mengurangi jumlah data latih dengan strategi clustering, sehingga jumlah data yang akan dibandingkan dengan data uji lebih sedikit. Akibatnya, diharapkan waktu prosesnya menjasi lebih cepat. Strategi yang diusulkan akan diterapkan pada kasus klasifikasi jenis kendaraan berbasis pengolahan citra digital. Untuk menghitung tingkat akurasi dan kecepatan, maka metode yang diusulkan akan dievaluasi menggunakan dataset yang dikumpulkan melalui internet.
An Optimal Stock Market Portfolio Proportion Model Using Genetic Algorithm Wahyono Wahyono; Chasandra Puspitasari; Muhammad Dzulfikar Fauzi; Kasliono Kasliono; Wahyu Sri Mulyani; Laksono Kurnianggoro
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 12, No 2 (2018): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.36154

Abstract

To reduce the amount of loss due to investment risk, an investor or stockbroker usually forms an optimal stock portfolio. This technique is done to get the maximum return of investment on shares to be purchased. However, in forming a stock portfolio required a fairly complex calculations and certain skills. This work aims to provide an alternative solution in the problem of forming the optimal and efficient stock portfolio composition by designing a system that can help decision making of investors or stockbrokers in preparing stock portfolio in accordance with the policy and risk investment. In this work, determination of optimal stock portfolio composition is constructed by using Genetic Algorithm. The data used in this work are the 4 selected stocks listed on the LQ45 index in 2017. Meanwhile, the calculation of profit and loss rate utilizes a single index model theory. The efficiency of the algorithm has been examined against the population size and crossover and mutation probabilities. The experimental results show that the proposed algorithm can be used as one of solutions to select the optimal stock portfolio.
Determining Optimal Architecture of CNN using Genetic Algorithm for Vehicle Classification System Wahyono Wahyono; Joko Hariyono
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 13, No 1 (2019): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.42299

Abstract

 Convolutional neural network is a machine learning that provides a good accura-cy for many problems in the field of computer vision, such as segmentation, de-tection, recognition, as well as classification systems. However, the results and performance of the system are affected by the CNN architecture. In this paper, we propose the utilization of evolutionary computation using genetic algorithm to de-termine the optimal architecture for CNN with transfer learning strategy from parent network. Furthermore, the optimal CNN produced is used as a model for the case of the vehicle type classification system. To evaluate the effectiveness of the utilization of evolutionary computing to CNN, the experiment will be conducted using vehicle classification datasets.
An Expert System of Chicken Disease Diagnosis by Using Dempster Shafer Method Yaqutina Marjani Santosa; Suprapto Suprapto; Wahyono Wahyono
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 14, No 3 (2020): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.55632

Abstract

 Chicken is an animal that can provide many benefits for human life, meat and eggs can be used as food to fulfill the needs of human food, the excrement can be made fertilizer, and frequently its be used as a farm animal. Although it can provide many benefits, but for chicken farmers, the maintenance of chicken meet some obstacles that must be faced such as disease, poor environmental sanitation, and the production of eggs are declining. From some of the obstacles that have been mentioned, the most frequently encountered are animals infected with the disease. Based on the results of interviews that have been done to some chicken farmers, it can be said that the knowledge of chicken farmers against chicken disease and its handling is still very lacking. But the number of experts who understand and know about the type of chicken disease and the way of handling is limited, then it takes an expert system that can simulate knowledge and understanding of experts to overcome the problem. Based on the study of the libraries, the method suitable for use in the expert system is the Dempster shafer method by processing the value of belief in a disease. Dempster shafer method is a method used to calculate uncertainty due to the addition or reduction of new facts that will change the existing rules. Based on tests in 40 cases using an expert system applying the Dempster Shafer method, obtained the percentage of diagnostic compatibility result given by experts and system is 95%.
Estimation of Average Car Speed Using the Haar-Like Feature and Correlation Tracker Method Muhammad Dzulfikar Fauzi; Agfianto Eko Putra; Wahyono Wahyono
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 14, No 4 (2020): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.57262

Abstract

The speed of a car traveling on the road can generally be estimated by using a speed gun. Efforts are needed to use CCTV (closed circuit television) as a tool that can be used to estimate the speed of the car so as to ease the burden on the road operator to estimate the speed of the car. This study discusses the estimated average speed of the car with the Haar-like Feature method used to detect the car, then the detection results are tracked using Correlatin Tracker to track the movement of objects that have been detected and calculate the distance of movement from the car, so that the speed of the car detected in video can be estimated. The results of the estimated average speed compared with the results of taking speed with a speed gun so that an error is obtained by MAE testing of 5,55 km / hour and the resulting standard deviation is 4,61 km / hour, thus it can be concluded that the system is made valid and can be used by road organizers to monitor the average speed of a car.
Case-Based Reasoning Using The Nearest Neighbor Method For Detection Of Equipment Damage To PLN Power Plant Riska Amalia Praptiwi; Nur Rokhman; Wahyono Wahyono
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 14, No 4 (2020): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.57434

Abstract

Predictive Maintenance (PdM) at the PLN Power Plant is a periodic monitoring of equipment activities before the equipment is damaged in more severe conditions. According to an expert or PdM owner that maintenance analysis is not appropriate and efficiency has an impact on maintenance costs that are not small. In real conditions, the PdM owner analyzes equipment damage based on previous cases of damage equipment. Then we need a computer-based intelligent system that can help detect damage to equipment.Based on the Literature Review that has been done, Case-Based Reasoning can solve new problems using answers or experiences from old problems such as imitating human abilities. Case-Based Reasoning Process there is the most important step, which is to find the highest similarity value or the level of similarity between new cases and old cases by adapting solutions from old cases that have occurred (Sankar, 2004). In this study the process of similarity or approach using Nearest Neighbor.Testing on the system uses 20 test data and the measurement of system performance uses confusion matrix. Evaluation of testing using confusion matrix can be seen how accurately the system can classify data correctly that is equal to 97.98%. Then the precision value of 95% represents the number of positive categorized data that is correctly divided by the total data classified as positive. Furthermore, the test results of the equipment damage detection test data at the PLN plant with a threshold value of 0.75 using the nearest neighbor, the system has a performance with a 95% sensitivity level.
Perbandingan penghitungan jarak pada k-nearest neighbour dalam klasifikasi data tekstual Wahyono Wahyono; I Nyoman Prayana Trisna; Sarah Lintang Sariwening; Muhammad Fajar; Danur Wijayanto
Jurnal Teknologi dan Sistem Komputer Volume 8, Issue 1, Year 2020 (January 2020)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.8.1.2020.54-58

Abstract

One algorithm to classify textual data in automatic organizing of documents application is KNN, by changing word representations into vectors. The distance calculation in the KNN algorithm becomes essential in measuring the closeness between data elements. This study compares four distance calculations commonly used in KNN, namely Euclidean, Chebyshev, Manhattan, and Minkowski. The dataset used data from Youtube Eminem’s comments which contain 448 data. This study showed that Euclidian and Minkowski on the KNN algorithm achieved the best result compared to Chebycev and Manhattan. The best results on KNN are obtained when the K value is 3.
Penghitung orang pada video CCTV menggunakan metode histogram of oriented gradient dan filter Kalman Faisal Dharma Adhinata; Muhammad Ikhsan; Wahyono Wahyono
Jurnal Teknologi dan Sistem Komputer Volume 8, Issue 3, Year 2020 (July 2020)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.2020.13660

Abstract

CCTV cameras have an important function in the field of public service, especially for convenience. The objects recorded through CCTV cameras are processed into information to support service satisfaction in the community. This study uses the function of CCTV for people counting from objects recorded by a camera. Currently, the process of detecting and tracking people takes a long time to detect all frames. In this study, the frame selection into keyframes uses the mutual information entropy method. The keyframes processing uses the Histogram of Oriented Gradient (HOG) and Kalman filter methods. The proposed method results F1 value of 0.85, recall of 76 %, and precision of 97 % with winStride parameter (12,12), scale 1.05, and the distance of the human object to CCTV 4 meters.
Person Reidentification pada Sistem Surveillance Cerdas Menggunakan Metode Bag of Visual Feature Muhammad Yusuf Manshur; Wahyono
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 9 No 3: Agustus 2020
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1309.377 KB) | DOI: 10.22146/.v9i3.249

Abstract

Camera-based surveillance systems have been widely used to monitor public places for ensuring security and safety. One of the problems in the surveillance process is identifying human objects on different CCTV cameras, which is referred to as person reidentification (Re-ID). Re-ID is the process of identifying whether the images of human objects, captured from two or more images from CCTV cameras with the different viewpoints, are the same person or not. This paper proposes a method based on visual features of the object image, named as Bag of Visual Feature (BOVF). BOVF works by representing image data as a collection of local features that are used with a feature clustering mechanism. BOVF implementation uses the Density Based Spatial Clustering of Applications with Noise (DBSCAN) clustering methods in the Histogram of Oriented Gradient (HOG) features. The results of this study with 70 image frames from iLIDS-VID dataset obtained the best accuracy at R-20 by 88% using DBSCAN with a processing speed of 1.85 seconds.
Automatic Detection of Helmets on Motorcyclists Using Faster - RCNN Aliyyah Nur Azhari; Wahyono Wahyono
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 16, No 4 (2022): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.68245

Abstract

Motorcycles have been a popular choice for a go-to daily means of transportation due to its lower price, making it affordable for high to low-class citizens. Helmets are required for every motorcycle owner so that the rider’s head is protected from accidents. However, not many people follow the rules and tend to not wear helmets and plenty of them underestimate the usage of helmets. For this, it is necessary to implement a system that can detect which rider wears the helmet or not by applying deep learning techniques. This paper aims to implement one of the deep learning techniques, which is Faster R – CNN to detect the helmets and the motorcyclists. After training 400 images using different learning rates, the mean average precision (mAP) achieved the highest with 87% using the learning rate of 0.0001
Co-Authors Agfianto Eko Putra Aliyyah Nur Azhari Chasandra Puspitasari Faisal Dharma Adhinata Fauzi, Muhammad Dzulfikar I Nyoman Prayana Trisna Joko Hariyono Kasliono Kasliono Laksono Kurnianggoro Muhammad Fajar B Muhammad Ikhsan Muhammad Yusuf Manshur Nur Rokhman Riska Amalia Praptiwi Sarah Lintang Sariwening Suprapto Suprapto Wahyu Sri Mulyani Wijayanto, Danur Yaqutina Marjani Santosa