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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Simantec Jurnal Teknologi Informasi dan Ilmu Komputer JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Knowledge Engineering and Data Science ILKOM Jurnal Ilmiah SENTIA 2017 Kumawula: Jurnal Pengabdian Kepada Masyarakat Jurnal Teknologi Informasi dan Terapan (J-TIT) Buletin Ilmiah Sarjana Teknik Elektro Indonesian Journal of Electrical Engineering and Computer Science JAMI: Jurnal Ahli Muda Indonesia International Journal of Engineering, Science and Information Technology Jurnal Pengabdian pada Masyarakat Ilmu Pengetahuan dan Teknologi Terintegrasi Jurnal Teknik Ilmu dan Aplikasi Jurnal Informatika Polinema (JIP) Jurnal Pengabdian Kepada Masyarakat
Rosa Andrie Asmara
Politeknik Negeri Malang
Agriculture, Biological Sciences & Forestry Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Real-Time BISINDO Alphabet Recognition via Faster R-CNN Incorporating Skin Tone Diversity as a Classification Feature Lilis Nur Hayati; Anik Nur Handayani; Wahyu Sakti Gunawan Irianto; Rosa Andrie Asmara; Dolly Indra; Nor Salwa Damanhuri
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 3 (2026): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i3.15587

Abstract

Indonesian Sign Language (Bahasa Isyarat Indonesia/BISINDO) enables communication for deaf individuals through hand gestures, yet limited public awareness creates significant barriers between deaf and hearing communities. Existing recognition systems often fail to generalize across diverse skin tones, reducing their effectiveness in inclusive real-world deployment. The contribution of this research is a BISINDO alphabet recognition system that integrates skin color features - extracted via HSV-based skin segmentation - as an additional preprocessing layer within the Faster R-CNN framework, explicitly improving detection robustness across varied skin tones. The dataset consists of 8,000 images from ten adult actors representing light, medium-brown, and dark skin tones, augmented through flipping and brightness variation, with a 90:10 training-to-testing ratio. The model was trained over 15,000 steps with a batch size of 24, selected through empirical validation to balance convergence stability and dataset size. Experimental results show that indoor conditions outperform outdoor settings due to controlled lighting. Light-skinned and dark-skinned participants achieved the highest accuracy of 87.5% and F1-score of 85.71%, while medium-brown-skinned participants showed slightly lower performance, likely attributed to greater variability in reflectance under mixed lighting. The system achieves 24 frames per second, demonstrating potential for real-time communication support. These findings confirm that Faster R-CNN with skin color feature integration is effective for BISINDO alphabet recognition, with skin tone diversity being a critical performance factor. Future work will explore larger participant pools and dynamic gesture recognition under varied real-world lighting scenarios.
Basketball Activity Recognition Using Supervised Machine Learning Implemented on Tizen OS Smartwatch Rosa Andrie Asmara; Nofrian Deny Hendrawan; Anik Nur Handayani; Kohei Arai
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 8 No. 3 (2022): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i3.23668

Abstract

Basketball Activity Recognition (BAR) in sports teams, especially in basketball, to make statistical analysis of player activity data is currently a very important thing. BAR is one part of sports science that recognizes the movement of players in each activity, such as dribbling, passing, etc. Sport science in the sports business is used as one of the factors of coaches and management to determine strategy, starter line-up, check the condition of players after injury, etc. the current technology to recognize player activity only depends on the object detection method of players' through video recordings of players is considered lacking because it only sees the perspective of the coach to reduce players as starter line-up and there is no logical calculation of why players are not installed as starter line-up. One method for recognizing player activity is using a wearable device that has an accelerometer and gyroscope sensor with high accuracy. The values from those sensors will be classified and recognize their activity, i.e., Dribbling, Passing, and Shooting. Smartwatch is one of those wearable devices that meet those criteria. For the activity classification process, the use of the K-NN classification method is the most appropriate because it has a low computational level that is in accordance with the smartwatch specifications. The results of the classification using accelerometer sensor data and gyroscopes with K-NN as an activity recognition method have an accuracy of 81.62%, and player activity recognition applications using accelerometer and gyroscope sensors can also record the results of player movements for further analysis by management and coaches. This is the advantage of this BAR application compared to the recognition of player activity using object detection on video recordings.
Convolutional Neural Network in Motion Detection for Physiotherapy Exercise Movement Laistulloh, Dika Fikri; Handayani, Anik Nur; Asmara, Rosa Andrie; Taw, Phillip
Knowledge Engineering and Data Science
Publisher : citeus

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Physiotherapy focuses on movement and optimal utilization of the patient's potential. Exercise Therapy is a physiotherapy procedure that specifically focuses exercises on active and passive movements. Cerebral Palsy (CP) patients are one of the sufferers of motor disorders of the upper extremities. Cerebral Palsy (CP) patients suffer from disorders in motor functions of the upper extremities. Physiotherapy Exercise Movement has 4 categories of movement exercises for the therapy of people with upper extremity body disorders: Elbow flexor strengthening in sitting using free weights, lifting an object up, reaching diagonally in sitting, and reaching from a low surface to a high surface. By taking 4 categories of motion movements in exercise therapy, data were taken using normal child subjects as standard movements, which then became a reference for CP child therapy. The limitations of therapy in physical care prompted researchers to investigate the use of image processing as input to Human Computer Interaction (HCI) in the process of motion detection-based therapy. In research using Deep learning as a classifier, namely using the CNN Model (Inception V3, Resnet152, and VGG16 architectural models). The results obtained by the CNN (Inception V3) model have the best performance with an accuracy percentage of 98%.
Traffic Density Prediction using IoT-based Double Exponential Smoothing Asmara, Rosa Andrie; Noprianto, Noprianto; Ilmy, Muhammad Ainur, State Polytechnic of Malang; Arai, Kohei
Knowledge Engineering and Data Science
Publisher : citeus

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Abstract

The number of vehicles and currents that tend to increase causes traffic density. A system is proposed to calculate the number of vehicles and predict real-time traffic density. This research uses Haar Cascade to detect the number of cars and motorcycles and the Double Exponential Smoothing (DES) for forecasting the number of vehicles on the road. MAPE describes forecasting accuracy as a base for selecting the best smoothing constant (Alpha). The best test results from June 13 to 20, 2020, are cars on June 14, 2020 (alpha 0.5, MAPE 0%) and Motorcylecycles on June 18, 2020 (alpha 0.5, MAPE 0.1134% ). The most significant MAPE results of the car were on June 15, 2020, with alpha 0.5 and MAPE 2.1073%. The 3 minutes haar cascade detects 72.58% of cars and 81.90% of motorcycles.
Efficiency and Comparative Performance of LBP-Based Random Forest and SVM for Toraja Buffalo Classification Abdul Rachman Manga'; Anik Nur Handayani; Heru Wahyu Herwanto; Rosa Andrie Asmara; Syamsul Bahri
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026 (in progress)
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7536

Abstract

Toraja buffalo holds significant cultural and economic value, yet automated classification remains challenging due to subtle visual differences between types. This study evaluates the efficiency of Local Binary Pattern (LBP) combined with Random Forest (RF) and Support Vector Machine (SVM) for classifying six Toraja buffalo types: Balian, Lotong Boko, Pudu, Saleko, Todi, and Ulu. Unlike complex deep learning approaches, this research focuses on a computationally efficient framework by extracting texture features from multiple body parts: head, eyes, horns, body, and tail to capture distinctive patterns. The methodology involves multi-part feature fusion and a comparative analysis of ensemble versus kernel-based learners. Experimental results demonstrate that the LBP-Random Forest model significantly outperforms SVM, achieving a superior accuracy of 92.08% compared to 64.17%. The findings highlight that the proposed LBP-RF integration provides a robust and resource-efficient alternative for livestock image classification, balancing high diagnostic accuracy with lower computational requirements.
Co-Authors Abdul Rachman Manga' Abdurrahman, Raka Admiral Adhiati Kusuma Wardani Adhitama, Dodik Widya Adzikirani Adzikirani Adzikirani, Adzikirani Agustina, Reza Alfan Hadi Permana Aliza Rizqi Fitriana Almira Rahma Sabita Alviana, Vita Andjani, Bella Sita Angga Aditya Indra Wiratmaka Anik Nur Handayani Ariadi Retno Tri Ariadi Retno Tri Hayati Ririd Arie Rachmad Syulistyo Arief Prasetyo Arief Prasetyo Arinda, Vivid Ichtarosa Astiningrum, Mungki Astuti, Ely Setyo Atiqah Nurul Asri Awan Setiawan Bella Sita Andjani Burhanuddin, Mohd Aboobaider Candra Bella Vista Choirina, Priska Christine, Anastasia Merry Citra Nurina Prabiantissa Citra Nurina Prabiantissa Damanhuri, Nor Salwa Damayanti, Farradila Ayu Deddy Kusbianto P. A Deddy Kusbianto Purwoko Aji Dhika Ainul Luthfi Dika Rizky Yunianto Dimas Wahyu Wibowo Dolly Indra Dwi Puspitasari Dwi Puspitasari DWI PUSPITASARI Eka Larasati Amalia Ekojono, Ekojono Elok Nur Hamdana Era Chalis Kurniangesti Erfan Rohadi Faisal Rahutomo Farida Ulfa Fitriana Nur’Aini D Galang Audi Pramasha Gunawan Budi P Habibie Ed Dien Hapsari, Ratih Indri Hendrawan, Muhammad Afif Heru Wahyu Herwanto Ilmy, Muhammad Ainur, State Polytechnic of Malang Imam Fahrur Rozi Indra Wiratmaka, Angga Aditya Kasmira, Kasmira Kohei Arai Kohei Arai Kohei Arai Kristinanti Charisma Kurniangesti, Era Chalis Kusbianto P. A, Deddy Kusbianto P. A Kusumaningtyas, Sella Laistulloh, Dika Fikri lilis nurhayati M. Rahmat Samudra M. Unggul Pamenang Manga', Abdul Rachman Moch Zawaruddin Abdullah Mochamad Faisal Rahman Muhammad Ainur Ilmy Muhammad Ridwan Muhammad Rizqi Ardiansyah Musthafa, Muhammad Bisri Mustika Mentari Nadhifatul Laeily Nalendra, Adimas Ketut Ngatmari, Ngatmari Noprianto, Noprianto Noprianto, Noprianto Nor Salwa Damanhuri Nur’Aini D, Fitriana Nurudin Santoso Odhitya Desta Triswidrananta Permana, Alfan Hadi Pramasha, Galang Audi Primadhana, Yoga Andri Qonitatul Hasanah Rahmad, Cahya Rahmanto, Anugrah Nur Rahmat Samudra Anugrah, Muhammad Rakhmat Arianto Reza Agustina Robertus Romario Rokhman, Syaiful Ronilaya, Ferdian Rudy Ariyanto Ryan Rifqi Arista Santoso, Nurudin Sari, Irawati Nurmala Sella Kusumaningtyas Shoumi, Milyun Ni’ma Siradjuddin, Indrazno Siska Stevani Siti Romlah Stevani, Siska Syaiful Rokhman Syamsul Bahri Taw, Phillip Tri, Ariadi Retno Triswidrananta, Odhitya Desta Ulla Delfana Rosiani Usman Nurhasan Veithzal Rivai Zainal Vita Alviana Vivin Ayu Lestari Wahyu Sakti Gunawan Irianto Wardani, Adhiati Kusuma Wilda Imama Sabilla Yan Watequlis Syaifudin Yoga Andri Primadhana Yoppy Yunhasnawa Yudha Islami Sulistya