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All Journal Indonesian Journal of Electronics and Instrumentation Systems

Muhammad Idham Ananta Timur

Departemen Ilmu Komputer Dan Elektronika, FMIPA UGM, Yogyakarta

Author-ID : 2898916

Electrical & Electronics Engineering

Published : 7 Documents Claim Missing Document

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Articles

Model Tracking Pembicara Dalam Perekaman Video Otomatis Pada Kelas Cendekia Elga Ridlo Sinatriya; Muhammad Idham Ananta Timur; Ika Candradewi
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 9, No 1 (2019): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

The requisite of intelligent classroom’s saving the information from speakers inside the class using ubiquitous computing concept. It said the most profound technologies are those that disappear, and they weave themselves into fabric of everday life until they are indistinguishable from it. It requires a few capability such as tracking the speaker and record it. Therefore it will be require the system that can tracking the speaker in real time, ignore the other speaker, and recording speaker’s activity. The system consumes 168.02 ms in one move, like detection using statis camera, send the centroid to microcontroller, second detection using dinamis camera, and record it. The system had an accuracy of 93.37 % to fits the speaker at the middle of frame record. The system is also had an accuracy of 98% to detecting the correct speaker.

Collaborative Filtering Recommender System pada Virtual 3D Kelas Cendekia Angga Setia Wardana; Muhammad Idham Ananta Timur
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 8, No 1 (2018): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Intelligent Clasrooms is a concept of modern learning process where users can perform collaborative learning wherever and whenever. With learning in Intelligent Classroom, users can get different learning experience where learning process is expected to run more effectively and efficiently. One application of the Intelligent Classrooms concept is learning by utilizing the virtual world. The information collected in the Intelligent Classroom will increase so that a system is needed. The recommendation system of collaborative filtering is the most appropriate system with the intellectual class. With the sparsity of training rate of 80%, it is implemented a collaborative filtering recommendation system with error rate which if calculated with RMSE is 1.060709 or it can be said that the accuracy level is 78.79%.

Hand-Raise Detection Pada Kelas Cendekia Menggunakan Kamera RGB Dan Depth Muhammad Fajar Khairul Auni; Muhammad Idham Ananta Timur; Ika Candradewi
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 8, No 1 (2018): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

The requisite of intelligent classroom’s to perform the quickest speaker lift determination of speakers in the classroom using the concept of ubiquitous computing where the technology exists but does not feel around. The classroom concept requires several capabilities such as knowing the ideal distance from the camera, performing real-time hand-lifted movements from the speaker using the AdaBoost method, and determining the fastest hand lift from the speaker in real-time. The camera's ideal distance to speakers is about 250 cm. the system has a detection accuracy of 97.485497% and accuracy using coordinates joint point of 98%. The system is also capable of determining the fastest time using AdaBoost with 93.5% accuracy and the accuracy of the fastest hands lifting using coordinates joint point of 95%.

Kelas Cendekia Versi Mobile yang Terintegrasi dengan Sistem Rekomendasi Nur Ridho Abdurrahmansyah; Muhammad Idham Ananta Timur
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 8, No 2 (2018): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Urgency usefulness of online learning system based on social constructivism which is the mobile virtual classroom learning philosophy is of concern, because the system is built on the pattern of reciprocity between users in order to produce the most quality materials see the absence of a system that provides online learning for it. Content of lecture materials that have been divided into certain categories are processed into virtual versions and delivered lightly. The recommendation system is designed to respond users who have rated it by providing good quality course material. Software is created with Unity Engine and incorporated the recommended system protocol with data stored in a scholarly research database. The recommendation system implemented is the items based collaborative filtering with the specification of training data used are 401 rating data, 51 records and 17 users. With sparsity data training amounted to 53.74%, tested the prediction accuracy resulted RMSE 0.91523 and the accuracy of 81.69%. The mobile version of virtual class that has been planted with recommendation system is tried and tested on several brands of android smartphone. Results obtained on the questionnaire resulted in a rating of 4,762 on performance and 4,572 against the intellectual class software interface. Whereas the level of user enthusiasm for the virtual class reaches 4,0588 on a scale of 1 to 5.

Sistem Deteksi Orang Jatuh Dengan Menggunakan Sensor Kamera Kinect Dengan Metode AdaBoost Satria Perwira; Muhammad Idham Ananta Timur; Agus Harjoko
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 11, No 2 (2021): Oktober
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijeis.49974

Fall cases of elderly people aged 65 or above put their health at risk because it could lead to hip bone fracture, concussion, even death. Immediate help is needed if fall happened which is why an automatic and unobtrusive fall detection system is needed. There are three approaches in fall detection system; wearable, ambience, and vision-based. Wearable approach has the drawback of its obtrusive nature while ambience approach is prone to high false positive value. Vision-based approach is chosen because its unobtrusive nature and low false positive value. This study uses Kinect camera because of its ability on extracting skeletal data. The methods that are used in the fall detection system are AdaBoost method and joint velocity thresholding method. Thresholding method is used as a comparison to AdaBoost method. Both methods use skeletal data from the subject recorded by the Kinect camera. AdaBoost method compares the skeletal data with model that was made before while thresholding method compares the joint velocity value with the threshold value. System test is done using training data, test data, and real-time data. The average accuracy obtained from the system test with AdaBoost method is 91.75% and with thresholding method is 68.22%.

Peningkatan Akurasi Deteksi Jatuh Menggunakan Sensor Akselerometer dan Giroskop pada Smartphone Widagdo, Muhammad Luthfi Arya; Timur, Muhammad Idham Ananta
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 14, No 1 (2024): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijeis.93068

The aging population is a global concern, partly because as the body ages, physical conditions weaken, increasing the likelihood of falls. Falls are particularly dangerous for the elderly as they can lead to serious problems and even death. Detecting falls quickly and accurately is crucial to implement preventive measures and timely intervention when a fall occurs.This research focuses on designing a human physical activity classification system, primarily used for fall detection. Seven model architectures are proposed using a novel approach involving the variant of recurrent neural network (RNN) methods, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Simple Recurrent Neural Network (SimpleRNN). Additionally, variations with Convolutional Neural Network (CNN) are explored, specifically 1D Convolutional Neural Network (1D CNN).Validation results of the classification show that the experimented methods for the classes of sitting, standing, and falling achieved perfect scores, while the falling class showed varying scores for each designed model architecture. For the overall classes, the lowest performance is observed in the combination of 1D CNN and SimpleRNN architecture with an accuracy of 95.6%, whereas the highest performance is attributed to the SimpleRNN architecture and the combined CNN and GRU architecture with an accuracy reaching 99.0%.

Pengembangan Kemampuan Model Autonomous Car Terhadap Aspek Keselamatan Berkendara Saat Kondisi Ekstrem Menggunakan Carla Simulator Hernanda, Muhammad Fadli Fadli; Timur, Muhammad Idham Ananta
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 15, No 1 (2025): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijeis.104937

The advancement of automation technology, particularly in autonomous vehicles, has rapidly progressed with the integration of machine learning. However, these systems still face challenges in environments with dense traffic and dynamic conditions, making safety a primary concern. Traffic accident data indicate that the implementation of autonomous vehicles remains far from optimal, especially under extreme conditions such as severe weather and unpredictable traffic congestion. This study aims to develop an autonomous vehicle system model that can operate not only under normal conditions but also adapt to extreme situations. The model is developed using the CARLA Simulator, which enables testing in various realistic environmental scenarios. Simulations involving severe weather and high traffic density are conducted to evaluate the model’s resilience and responsiveness across different scenarios. The results show that the developed model enhances driving safety under extreme conditions with high effectiveness in obstacle avoidance and dynamic decision-making. Thus, this approach is expected to contribute to the development of more adaptive and safer autonomous vehicles for real-world applications

Co-Authors Agus Harjoko Angga Setia Wardana Elga Ridlo Sinatriya Hernanda, Muhammad Fadli Fadli Ika Candradewi Muhammad Fajar Khairul Auni Nur Ridho Abdurrahmansyah Satria Perwira Widagdo, Muhammad Luthfi Arya

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