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All Journal International Journal of Electrical and Computer Engineering Program Kreativitas Mahasiswa - Karsa Cipta Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Technomedia Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) EDUMATIC: Jurnal Pendidikan Informatika JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) eProceedings of Engineering Community Service Seminar and Community Engagement (COSECANT) LOGIC: Jurnal Penelitian Informatika
Gamma Kosala
Telkom University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Other

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ALAT PENDETEKSI DINI GANGGUAN SALURAN PERNAFASAN TERINTEGRASI CLOUD STORAGE Noor, Latifah; Kosala, Gamma; Nugraha, Muhammad Mukhlis; Fitriyani, Ulfah; Imaduddin, Ilham
Program Kreativitas Mahasiswa - Karsa Cipta PKM-KC 2014
Publisher : Ditlitabmas, Ditjen DIKTI, Kemdikbud RI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (253.12 KB)

Abstract

Technology advances very rapidly and currently can be implemented into every aspect of human life needs. This research seeks to take advantage of advances in technology to help meet human needs in the field of health. This tool uses the flowmeter sensor to measure the human breath that passes the sensor as input values will be processed by the system. Human breath volume value unit searched the time by using the principle of Bernoullis law of physical to detect any abnormalities in respiratory function in humans. With the possibility of it detects early these abnormalities will increase the effectiveness of treatments that can be given to sufferers. Measuring instrument is then integrated with database systems with cloud computing using the web server, so that the data collected through such tools can be sent directly to the database server and displayed through a Web page. Thus, parties concerned who have access rights to view this data can access that data anytime and anywhere. Making it easier to do preparation handling or even do prevention because it is an indication of the risk of disease can be detected early. The data can also be used as a record of statistical data for penilitian and logging that can be beneficial in the field of health. The results of this research is the integration of the two subsystems for measuring air flow and a data base of cloud computing. Hardware that is designed in such a way that it can ease of use and datas can be accessed any where are expected to provide a useful innovation which connects science in health and technology student works as a creation of Gadjah Mada UniversityKeywords : ashtma, flowmeter, cloud computingdatabase, Bernoulli’s law
CLASSIFICATION OF DOG BREEDS FROM SPORTING GROUPS USING CONVOLUTIONAL NEURAL NETWORK Naufal Harsa Pratama; Ema Rachmawati; Gamma Kosala
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 7, No 4 (2022)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v7i4.3208

Abstract

The use of convolutional neural networks has been applied to various applications. Such as image clas-sification, object detection and recognition, and others. One of the most popular uses for neural networks is image classification. Image classification mainly identifies and categorizes images according to the specified group. One application is to distinguish between one type of dog to another. Classification of dog breeds has its challenges because several kinds of dogs have similar physical characteristics, espe-cially those that belong to the same group. This study explains how to develop a dog breed classification system from a sporting group using a residual neural network (ResNet). The system's goal is to make it simpler for people to identify the dog breed. Five types of dog breeds were used, which were obtained from the Tsinghua Dogs dataset. In its implementation, two variants of CNN are used to be compared, ResNet 50 and ResNet 101, using the same configuration. Based on the research results, ResNet 101 shows better macro-average f1-score results while maintaining high accuracy. The ResNet 50 produces an f1-score of 84%, while ResNet 101 makes an f1-score of 86%.
Fire Detection on Video Using ViBe Algorithm and LBP-TOP Kurniawan Nur Ramadhani; Febryanti Sthevanie; Gamma Kosala; Ketut Sudyatmika Putra
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 6 (2022): Desember 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i6.4164

Abstract

In this research, we built a system to detect fire using the ViBe (Visual Background Extractor) algorithm to extract dynamic targets. The ViBe algorithm is better at detecting moving target objects such as flame combustion. In this research we combined the ViBe algorithm with three frame differencing to gain better results on movement object. The HSI color space model was applied after the movement object was obtained. We used Local Binary Pattern-Three Orthogonal Planes to obtain the feature extraction to be classified with Support Vector Machine. Our result has shown that the proposed system were able to detect the fire using the LBP-TOP and ViBe algorithm methods with an average accuracy rate of 88.10%, and the best accuracy was 90.37%. The parameters used to achieve this accuracy in the feature extraction process were T=120, Radius=2, and frame gap=15, then the threshold value parameter for three-frame difference parameter was 25.
DUNIA BARU PENDIDIKAN DI ERA METAVERSE UNTUK GURU SMA MUHAMMADIYAH CILEUNGSI Rifki Wijaya; Gamma Kosala; Tito Waluyo
Prosiding COSECANT : Community Service and Engagement Seminar Vol 2, No 2 (2022)
Publisher : Universitas telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (225.399 KB) | DOI: 10.25124/cosecant.v2i2.18681

Abstract

Metaverse adalah salah satu kata yang sering didengar akhir-akhir ini. Metaverse telah merambah ke semua bidang termasuk diantaranya dunia pendidikan. Metaverse menjadi salah satu cara untuk melakukan pembelajaran interaktif. Media pembelajaran interaktif semakin berkembang di era sekarang menuju metaverse. Beberapa tools sudah banyak dikembangkan untuk mengemas pembelajaran semakin menarik untuk siswa. Salah satu aplikasi web yang cukup menarik adalah gather town. Aplikasi web ini menciptakan sebuah ruang digital dimana masingmasing individu baik siswa maupun guru dapat menciptakan avatarnya sendiri. Aplikasi ini pun memiliki banyak feature diantaranya video conference, chat, bahkan papan tulis digital. Guru, siswa dan semua yang terlibat dalam kegiatan belajar mengajar perlu mempersiapkan diri menghadapi teknologi ini. Teknologi ini sudah lama digunakan akan tetapi kemunculan covid19 menjadi waktu yang tepat dalam mengembangkan berbagai metode pembelajaran jarak jauh. Guru dan siswa perlu memiliki pemikiran yang sama mengenai metode pembelajaran metaverse ini sehingga bisa memiliki pemikiran yang sama dalam menghadapi era baru ini.Kata Kunci: Metaverse, Pembelajaran Interaktif, Pembelajaran Jarak jauh
Glove Detection System on Laboratory Members Using Yolov4 Abdul Khaliq Al Bari; Ema Rachmawati; Gamma Kosala
Journal of Information System Research (JOSH) Vol 4 No 4 (2023): Juli 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i4.3806

Abstract

The use of gloves by laboratory workers has become mandatory in laboratory work intending to maintain the safety of workers from the spread or side effects carried out in the laboratory, but there are still workers who violate the rules by not using gloves when workers are in the laboratory room. This study aims to detect the use of gloves by laboratory workers. The method used in this research is You Only Look Once (YOLO) version 4. YOLOv4 has a system that can complete computer visual tasks in detecting and detecting objects quickly in real time. Based on the results of experiments and testing conducted, the model obtain an Average IoU of 55.56%.
Safety Helmet Detection on Field Project Worker Using Detection Transformer Muhammad Rayhan Subhi; Ema Rachmawati; Gamma Kosala
Journal of Information System Research (JOSH) Vol 4 No 4 (2023): Juli 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i4.3852

Abstract

There have been many cases of work accidents caused by not complying with safety standards at work, especially in the use of safety helmets. This study is able to make regular observations in identifying project personnel using safety helmets at work, this aims to reduce the risk of accidents at work, namely in the use of helmet attributes at work. Some previous studies, have proposed the use of image detection-based models using the Detection Transformer (DeTr) method for obtaining object detection, group prediction, and combining methods, using the Intersection over Union (IoU) method for obtaining object detection results, to achieve the best performance, namely to get convergence results. Based on the combination of these two methods, the results value of average IoU is 0.50 from 500 identified project personnel data were obtained.
Deteksi Logo Kendaraan dengan MSER-Vertical Sobel Gamma Kosala; Agus Harjoko; Sri Hartati
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 5 (2023): October 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i5.5034

Abstract

Detecting a vehicle logo is the first step before realizing the identity of the logo. However, the detection of logos can pose difficulties due to various factors, including logo variations, differing scales and orientations, background interference, varying lighting conditions, and partial obstruction. This paper presents a vehicle logo detection method using hand-crafted features. We used a combination of Maximally Stable Extremal Region (MSER) and Vertical Sobel. We combine vertical Sobel with MSER to overcome MSER's limitation in recognizing objects of different sizes. These two features are merged using a closing morphology operation to form blobs selected as logo candidate areas. Moreover, a Support Vector Machine (SVM) is implemented to choose a logo area by analyzing each candidate's Histogram of Oriented Gradient (HOG). The proposed method was compared with other methods by implementing them on the same dataset. The significant advantage of using MSER-Vertical Sobel is its fast computation time. It is faster than other approaches that use non-handcrafted features. The test results show that the MSER-Vertical Sobel can achieve high accuracy and the fastest computation time.
Integrating Heart Rate, Gyro, and Accelero Data for Enhanced Emotion Detection Post-Movie, Post-Music, and While-Music Setyawan, Hendy; Wijaya, Rifki; Kosala, Gamma
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.v8i2.7634

Abstract

The study investigates the integration of heart rate, gyro, and accelerometer data to enhance emotion recognition across different scenarios like post-movie, post-music, and during music listening. Recognizing the limitations of solely using heart rate data, the research combines gyroscopic and accelerometer data to provide a more comprehensive understanding of emotional responses. Employing machine learning algorithms, notably support vector machines, the aim is to develop robust models for real-time emotion recognition. Conducting rigorous experimental protocols involving motion sensor data from smartwatches, a user study with 50 participants examines emotional responses during activities such as movie-watching and music listening. The dataset includes data from accelerometers, gyroscopes, and heart rate sensors, with additional evaluation metrics to assess the effectiveness of the proposed method in detecting emotional states. The findings demonstrate significant effectiveness of an innovative neural network (NN) method in determining post-activity emotional states, with accuracies ranging from 59.0% to 83.4%, depending on the activity and context. Although NN accuracy is slightly lower compared to other methods like random forest and logistic regression, the differences are not significant, especially when compared to logistic regression. Overall, the research aims to advance emotion recognition technology for applications in human-computer interaction contexts.
Pengoptimasian Pengukuran Kepadatan Jalan Raya Dengan Cctv Menggunakan Metode Yolov8 : Optimizing Highway Density Measurement with CCTV Using the Yolov8 Method ​ Gibran, Hilal; Purnama, Bedy; Kosala, Gamma
Technomedia Journal Vol 9 No 1 Juni (2024): TMJ (Technomedia Journal)
Publisher : Pandawan Incorporation, Alphabet Incubator Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/tmj.v9i1.2216

Abstract

The development and growth of society according to research on the website dataindonesia.id as of December 31, 2022, the number was 126.99 million units by the end of last year. Increasing transportation density has become a serious problem in Indonesia. The relationship between speed and traffic flow (volume) can be used as a guide in determining the mathematical value of road capacity under ideal conditions. The proposed system requires CCTV to run properly. In each input frame, the system will perform data preprocessing to determine the vehicle object that will be segmented in the image. When the frame enters and preprocessing is done, the data will be rezoned to adjust to the system's compatibility. Then the data will go through the image processing stage. Image processing uses RGB color and is converted to grayscale in order to distinguish blobs in the frame. When the blob is detected, the number of objects will be counted and calculated to output the number of vehicles. The results of the number of vehicles will be used for datasets in vehicle density optimization. Motion detection that can be applied to measure highway density with CCTV using YOLO.
Stress detection through wearable sensors: a convolutional neural network-based approach using heart rate and step data Wijaya, Rifki; Kosala, Gamma
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1880-1888

Abstract

With the current technological advancements, particularly in sensing technologies, monitoring various health aspects, including heart rate, has become feasible. The problem addressed in this study is the need for effective stress detection methods to mitigate the significant consequences of high-intensity or long-term stress, which impacts safety and disrupts normal routines. We propose a stress detection system developed based on the convolutional neural network (CNN) method to address this. The study involves university students aged 20–22, focusing on mental stress. The dataset encompasses parameters such as heart rate, footsteps, and resting heart rate recorded through a smartwatch with 149,797-row data. Our results indicate that the CNN model achieves an 84.5% accuracy, 80.9% precision, 79.8% recall, and an 80.4% F1-score, confirming its efficacy in stress classification. The confusion matrix further validates the model’s accuracy, particularly for classes 1 and 2. This research contributes significantly to the development of an effective and practical stress detection method, holding promise for enhancing well-being and preventing stress-related health issues.
Co-Authors Abdul Khaliq Al Bari Agus Harjoko Al’Fattah, Bimayudha Bedy Purnama Deny Haryadi Ema Rachmawati Ema Rachmawati Ema Rachmawati Febryanti Sthevanie Gibran, Hilal Ilham Imaduddin, Ilham Kamil, Muhammad Najmi Ketut Sudyatmika Putra Kurniawan Nur Ramadhani Latifah Noor, Latifah Mahmud Dwi Sulistiyo Muhammad Mukhlis Nugraha, Muhammad Mukhlis Muhammad Rayhan Subhi Naufal Harsa Pratama Purnama , Bedy Rahman, Rahadian Yusuf Abdul Rifki Wijaya Safarin, Arva Adwitya Setyawan, Hendy Sri Hartati Tito Waluyo Ubaidillah, Rifky Fahrizal Ulfah Fitriyani, Ulfah Wirasakananda, Dewa Made Aditya