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All Journal Tekno : Jurnal Teknologi Elektro dan Kejuruan TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Informatika Harmonia: Journal of Research and Education International Journal of Artificial Intelligence Research INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Knowledge Engineering and Data Science Jurnal Media Elektro : Journal of Electrical Power, Informatics, Telecommunication, Electronics, Computer and Control System ILKOM Jurnal Ilmiah JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Journal of Electronics, Electromedical Engineering, and Medical Informatics Mobile and Forensics International Journal of Visual and Performing Arts Journal of Robotics and Control (JRC) Sains, Aplikasi, Komputasi dan Teknologi Informasi JPP (Jurnal Pendidikan dan Pembelajaran) Frontier Energy System and Power Engineering Science in Information Technology Letters International Journal of Robotics and Control Systems Kaisa: Jurnal Pendidikan dan Pembelajaran ALINIER: Journal of Artificial Intelligence & Applications Fidelity : Jurnal Teknik Elektro SinarFe7 Jurnal Inovasi Teknologi dan Edukasi Teknik Jurnal INFOTEL Karunia: Jurnal Hasil Pengabdian Masyarakat Indonesia
Zaeni, Ilham Ari Elbaith
Universitas Negeri Malang
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Library & Information Science Mathematics Mechanical Engineering Neuroscience Social Sciences

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Development of Stride Detection System for Helping Stroke Walking Training Ilham Ari Elbaith Zaeni; Dyah Lestari; Anik N. Handayani; Muhammad Khusairi Osman
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 5 No 3 (2023): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeemi.v5i3.306

Abstract

Walking is a popular post-stroke rehabilitation exercise for patients. Stroke walking training is a sort of physical therapy that aims to help people who have had a stroke improve their walking ability. The goal of this research is to classify stride length and include it into a mobile application. The accelerometer sensor on a smartphone can be used to construct a stride detection system to aid in stroke walking training. This application was created for Android-powered smartphones. A binder must be used to secure the smartphone device to the patient's thigh. This application reads the accelerometer sensor included into the smartphone. In this study, a stride detection model is designed to increase the performance of stride length and circumduction detection. The accelerometer is read and saved by the application as the participant walks on the specific path. After the signal has been pre-processed and its feature extracted, the data is used to create the stride detection model. The performance is good, as evidenced by accuracy, precision, recall, and f-measure values of 88.60%, 88.60%, 88.60%, and 88.60%, respectively. When utilized on a stride detection system, the decision tree algorithms function admirably. The model is then loaded into the Android walking app.
Fatigue Detection Using Decision Tree Method based on PPG signal Ilham Ari Elbaith Zaeni; Arya Kusuma Wardhana; Erianto Fanani
JURNAL INFOTEL Vol 15 No 2 (2023): May 2023
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v15i2.935

Abstract

Fatigue is a complex psychophysiological condition marked by sleepiness or fatigue, poor performance, and a range of physiological changes. A decision tree may be used to categorize weariness based on the subject's heart rate data. To begin the experiment, a dataset of the heart rate signal was obtained. The signal has already undergone preprocessing. The feature obtained through preprocessing is then used to construct the decision model. Four traits were discovered. The HF power, LF power, normalized HF power, and normalized LF power are the characteristics. This research has a 75.94% accuracy rating. The precision, recall, and F-measure scores for this study were 0.736, 0.736, and 0.736, respectively.
Genetic algorithm for finding shortest path of mobile robot in various static environments Dyah Lestari; Siti Sendari; Ilham Ari Elbaith Zaeni
JURNAL INFOTEL Vol 15 No 3 (2023): August 2023
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v15i3.961

Abstract

In conducting their work in the industry quickly, precisely, and safely, mobile robots must be able to determine the position and direction of movement in their work environment. Several algorithms have been developed to solve maze rooms, however, when the room is huge with several obstacles which could be re-placed in other parts of the room, determining the path for a mobile robot will be difficult. This can be done by mapping the work environment and determining the position of the robot so that the robot has good path planning to get the optimal path. In this research, a Genetic Algorithm (GA) will be used to determine the fastest route that a robot may take when moving from one location to another. The method used is to design a mobile robot work environment, design genetic algorithm steps, create software for simulation, test the algorithm in 6 variations of the work environment, and analyze the test results. The genetic algorithm can determine the shortest path with 93% completeness among the 6 possible combinations of the start point, target point, and position of obstacles. The proposed GA, it can be argued, can be used to locate the shortest path in a warehouse with different start and end points.
Peningkatan Kualitas Dan Efisiensi Packing Produk Berbasis Continnuous Sealer Machine Automatic Bagi Usaha Mikro Kecil Menengah Di Kecamatan Gondanglegi Kabupaten Malang Ilham Ari Elbaith Zaeni; Sujito; Hari Putranto; Tri Atmadji Sutikno
Karunia: Jurnal Hasil Pengabdian Masyarakat Indonesia Vol. 2 No. 4 (2023): Desember : Jurnal Hasil Pengabdian Masyarakat Indonesia
Publisher : Fakultas Teknik Universitas Maritim AMNI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58192/karunia.v2i4.1215

Abstract

UMKM Odey merupakan pelaku UMKM yang bergerak dalam bidang makanan dan cemilan kering yang telah berdiri dari tahun 2019 yang di bantu oleh sebagaian masyarakat di daerah Jl. Hasyim Ashari II, Rt 3 RW 2 Desa Sepanjang, Kecamatan Gondanglegi, Kabupaten Malang. Dengan produk olahan kripik pisang yang di distribusikan ke sekitar malang, Surabaya dan Sidoarjo sehingga produsen sering menggalami peningkatan permitaan pembeli namun UMKM Odey hanya mampu memproduksi 10 kg tiap harinya sehingga UMKM Odey ini harus meningkatkan proses produksi yang mampu meningkatkan kualitas dan efisiensi sehingga mampu menutupi kebutuhan pasar dengan mengguankan continuous sealer machine automatic dapat meningkatkan kualitas dan efisiensi packing produk UMKM Odey.
A Multi Representation Deep Learning Approach for Epileptic Seizure Detection Hermawan, Arya Tandy; Zaeni, Ilham Ari Elbaith; Wibawa, Aji Prasetya; Gunawan, Gunawan; Hendrawan, William Hartanto; Kristian, Yosi
Journal of Robotics and Control (JRC) Vol 5, No 1 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i1.20870

Abstract

Epileptic seizures, unpredictable in nature and potentially dangerous during activities like driving, pose significant risks to individual and public safety. Traditional diagnostic methods, which involve labour-intensive manual feature extraction from Electroencephalography (EEG) data, are being supplanted by automated deep learning frameworks. This paper introduces an automated epileptic seizure detection framework utilizing deep learning to bypass manual feature extraction. Our framework incorporates detailed pre-processing techniques: normalization via L2 normalization, filtering with an 80 Hz and 0,5 Hz Butterworth low-pass and high-pass filter, and a 50 Hz IIR Notch filter, channel selection based on standard deviation calculations and Mutual Information algorithm, and frequency domain transformation using FFT or STFT with Hann windows and 50% hop. We evaluated on two datasets: the first comprising 4 canines and 8 patients with 2.299 ictal, 23.445 interictal, and 32.915 test data, 400-5000Hz sampling rate across 16-72 channels; the second dataset, intended for testing, 733 icatal, 4.314 interictal, and 1908 test data, each 10 minutes long, recorded at 400Hz across 16 channels. Three deep learning architectures were assessed: CNN, LSTM, and a hybrid CNN-LSTM model-stems from their demonstrated efficacy in handling the complex nature of EEG data. Each model offers unique strengths, with the CNN excelling in spatial feature extraction, LSTM in temporal dynamics, and the hybrid model combining these advantages.  The CNN model, comprising 31 layers, yielded highest accuracy, achieving 91% on the first dataset (precision 92%, recall 91%, F1-score 91%) and 82% on the second dataset using a 30-second threshold. This threshold was chosen for its clinical relevance. The research advances epileptic seizure detection using deep learning, indicating a promising direction for future medical technology. Future work will focus on expanding dataset diversity and refining methodologies to build upon these foundational results.
Implementation of Backpropagation Artificial Neural Network for Electricity Load Forecasting in Jember District Eko Pambagyo Setyobudi; Ilham Ari Elbaith Zaeni
Frontier Energy System and Power Engineering Vol 5, No 1 (2023): January
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um049v5i1p26-31

Abstract

The increase in population and various kinds of human activities in the world has made it possible for changes to increase the need for electrical power with demand that is not the same at any time. Based on this description, this research will propose research on the theme of electricity load forecasting as a preventive measure to determine future electricity load needs. Research was assisted using MATLAB data processing software to process research data. Three forecasting models were carried out, namely day, night and day-night conditions. From these three forecasting models, parameters such as epoch, number of input layers, number of hidden layers, activation function, and etc. The data is divided into two parts, training data and test data with a ratio of 70: 30. Test results using the backpropagation artificial neural network method show the highest MSE values for the three forecasting models, day, night, and day-night, are, 0.0039, 0.0041, and 0.002 while the lowest MSE values were in the three models are, 6.77E-04, 0.001, and 0.0011.
Mining the public sentiment for wayang climen preservation and promotion Aji Prasetya Wibawa; Adjie Rosyidin; Fitriana Kurniawati; Gwinny Tirza Rarastri; Ilham Ari Elbaith Zaeni; Suyono Suyono; Agung Bella Putra Utama; Felix Andika Dwiyanto
International Journal of Visual and Performing Arts Vol 5, No 2 (2023)
Publisher : ASSOCIATION FOR SCIENTIFIC COMPUTING ELECTRICAL AND ENGINEERING (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/viperarts.v5i2.1163

Abstract

Indonesia is a country that has a variety of cultural arts, one of which is shadow puppetry (Wayang). Wayang, in a staged, simple, and minimalist manner, is called Wayang Climen. Wayang Climen has been performed since the COVID-19 pandemic as a solution to keep working while still complying with health protocols. Utilization through YouTube social media attracts people to watch and provide opinions through comments. This opinion is beneficial and can be used as a feasibility study through sentiment analysis information classified as positive, negative, and neutral opinions. Sentiment analysis determines a person's opinion and tendency to opinionated sentences. The methods used are Random Forest (RF), Support Vector Machine (SVM), and Naïve Bayes (NB). The dataset comes from YouTube comments of Dalang Seno and Ki Seno Nugroho. The best accuracy is generated by SVM (70.29%). The positive sentiment shows the public's appreciation for the Wayang Climen performance, which ultimately represents the performance even though it is staged densely. This research contributes to effectively utilizing digital platforms for cultural preservation and audience engagement during challenging times, demonstrating the potential for innovative solutions in traditional arts and entertainment.
Optimization of Machine Learning-Based Automatic Target Detection and Locking System on Robots Syafaat, Mokhammad; Sendari, Siti; Zaeni, Ilham Ari Elbaith; Setumin, Samsul
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 8 No 2 (2024): August 2024
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v8i2.21688

Abstract

Background: In recent years, the world of robotics has made significant progress in improving the operational capabilities of robots through target detection and locking systems. These systems play a crucial role in improving the efficiency and effectiveness of critical applications such as defense, security, and industrial automation. However, the main challenge faced is the limitations of the existing system in adapting to unstable environmental conditions and dynamic changes in targets. Objective: This research aims to overcome these challenges by developing a more adaptive and responsive target detection and locking system by integrating two leading machine learning technologies: Convolutional Neural Networks (CNN) for target detection and Long Short-Term Memory (LSTM) for target tracking. Methods: This study uses a quantitative approach to evaluate the effectiveness of the integration of CNNs and LSTMs in target detection and locking systems. Results: The results of the study showed a detection accuracy rate of 95% and a locking accuracy of 90%. The system is proven to be able to adapt to changing operational conditions in real-time and provide consistent performance in a variety of complex and dynamic scenarios. Conclusion: The conclusion of this study is that the integration of CNN and LSTM technologies in target detection and locking systems in robots significantly improves the performance and efficiency of the system, enabling a wider and more complex application.
Comparison Learning Model AIR and TAI Combined With Cognitive Conflict Strategy Againts Active Learning and Concept Understanding Ferdiansyah, Dodik Septian; Patmanthara, Syaad; Zaeni, Ilham Ari Elbaith
JPP (Jurnal Pendidikan dan Pembelajaran) Vol 27, No 1 (2020)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um047v27i12020p027

Abstract

Abstract:  There are several problems in the learning process of Basic Electricity and Electronics at SMK Negeri 8 Malang, including: (1) When learning takes place students do not pay attention and listen to the teacher when delivering material, (2) The teacher does not focus on learning activities to students, (3) Students are less active in asking and expressing his opinion about the material that has been taught. This study uses a variety of learning models and methods that can improve students' learning activeness and conceptual understanding, namely the Auditory, Intellectual, Repetition (AIR) learning model and the Team Assisted Individualization (TAI) learning model, each of which is combined with cognitive conflict strategies. The research design used a quasi experimental design with a non-equivalent control group design type. The data analysis technique consisted of normality test, homogeneity test, two mean similarity test, and hypothesis testing. The conclusion of this study is that the AIR learning model combined with cognitive conflict strategies is superior to the TAI learning model combined with cognitive conflict strategies
Cloud-Based Realtime Decision System for Severity Classification of COVID-19 Self-Isolation Patients using Machine Learning Algorithm Sugiono, Bhima Satria Rizki; Hadi, Mokh. Sholihul; Zaeni, Ilham Ari Elbaith; Sujito, Sujito; Irvan, Mhd
ILKOM Jurnal Ilmiah Vol 15, No 3 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i3.1945.413-426

Abstract

The global impact of the COVID-19 pandemic has been profound, affecting economies and societal structures worldwide. Indonesia, with a high caseload, has encountered significant challenges across various sectors. Virus transmission primarily occurs through physical contact, and the surge in active cases has strained hospital capacities, leading to the hospitalization of only severe cases. The remaining patients receive home telecare, but some experience sudden health deterioration with fatal consequences. To address this issue, this study proposes a remote outpatient care system utilizing Internet of Things (IoT) technology and medical electronics. This integrated system aims to provide an effective response to the COVID-19 pandemic. The research includes a comparative analysis of three machine-learning algorithms: decision tree, gradient tree boosting, and random forest for the classification of COVID-19 patients. The results reveal that the random forest algorithm outperforms the others with an accuracy rate of 70%, as compared to 67% for the decision tree and 62% for the gradient tree boosting algorithm. This integrated system not only addresses immediate healthcare delivery challenges but also offers data-driven insights for patient classification, thereby enhancing the effectiveness and reach of medical interventions
Co-Authors A.N. Afandi Adam Rachmawan Adib Nur Sasongko Aditama Yudha Atmanegara Adjie Rosyidin Afifah Salim Afnan Habibi, M. Agung Bella Putra Utama Aji Prasetya Wibawa Aji Wibawa Akhmad Afrizal Rizqi Amalia Sufa Andrew Nafalski Andrew Nafalski Andy Hermawan Anggraeni Budiarti Anik N. Handayani Anik Nur Handayani Arengga Wibowo, Danang Arifin, Samsul Aripriharta - Arya Kusuma Wardhana Arya Tandy Hermawan Atmaja, Nimas Hadi Dessy Rif’a Anzani Dian Candra Lestari Dony Setiawan Dwiyanto, Felix Andika Dyah Lestari Eko Pambagyo Setyobudi Fanani, Erianto Faozan Fauzi, Rochmad Felix Andika Dwiyanto Felix Andika Dwiyanto Ferdiansyah, Dodik Septian Ferdinand, Miftakhul Anggita Bima Fitriana Kurniawati Gunawan Gunawan Gunawan Gwinny Tirza Rarastri Hanny Prasetya Hariyadi Hari Putranto Harits Ar Rosyid Hartono, Nickolas Hendrawan, William Hartanto Hidayah Kariima Fithri Hsien-I Lin I Made Wirawan Irvan, Mhd Ismail, Amelia Ritahani Ivatus Sunaifah Kartika Kirana Kevin Raihan Khafit Zaman Kotaro Hirasawa Liliek Rahayu M. Adib Nursasongko Maftuh Ahnan Mahisha Laila Moh. Iqbal Ardiansyah Mohamad Iqbal Mokh Sholihul Hadi Muhammad Arrazy Muhammad Firmansyah muhammad hafiizh, muhammad Muhammad Iqbal Akbar Muhammad Khusairi Osman Muhammad Rifai Muhammad Syauqi Muhammad Usman Mursyit, Mohammad Nafalski, Andrew Ningtyas, Yana Nusantar, Alrizal Akbar Nusantar Akbar Piska Dwi Nurfadila Prana Ihsanuddin, Adika Puji Santoso Pundhi Yuliawati Rasidy, Ahmad Himawari Retno Indah Rokhmawati Ridwan Shalahuddin Rina Dewi Indahsari Riris Andriani Rizal Kholif Nurrohman Ronny Afrian Samsul Arifin Setumin, Samsul Shandy Darmawan Simbolon, Triyanti Siti Sendari Sugiono, Bhima Satria Rizki Sujito Sujito Suyono Suyono Syaad Patmanthara Syafaat, Mokhammad Tri Atmadji Sutikno Utama, Agung Bella Putra Yandhika Surya Akbar Gumilang Yogi Dwi Mahandi Yosi Kristian Zafifatuz Zuhriyah