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All Journal International Journal of Advances in Applied Sciences Tekno : Jurnal Teknologi Elektro dan Kejuruan Jurnal Visi Ilmu Pendidikan 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) ILKOMNIKA: Journal of Computer Science and Applied Informatics Sains, Aplikasi, Komputasi dan Teknologi Informasi JPP (Jurnal Pendidikan dan Pembelajaran) Frontier Energy System and Power Engineering Jurnal JEETech 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

Author-ID : 512535

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 Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Neuroscience Physics Social Sciences Other

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IMPLEMENTASI DECISION TREE PADA SARUNG TANGAN PINTAR PENERJEMAH SISTEM ISYARAT BAHASA INDONESIA GUNA MEMBANTU KOMUNIKASI PENYANDANG DISABILITAS TUNARUNGU Rasidy, Ahmad Himawari; Zaeni, Ilham Ari Elbaith
Jurnal Media Elektro Vol 13 No 2 (2024): Oktober 2024
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jme.v13i2.18322

This research implements the Decision Tree algorithm in a Smart Glove to classify hand signals representing numbers 1 to 5 in the Indonesian Sign Language System (SIBI). The system utilizes flex and gyro sensors to capture hand movements, which are then processed and classified using the Decision Tree algorithm. Training data was collected from multiple trials, resulting in an accuracy of 79% across 50 trials. The model's performance evaluation yielded precision, recall, and F1-score values ranging between 80% and 90% for each number class. The best performance was achieved with number 1, reaching 90% in precision, recall, and F1-score. However, areas for improvement were identified in precision and recall for numbers 2 and 4. Although the results are adequate, this study highlights the need for further development in enhancing model accuracy, particularly by increasing training data and refining the algorithm. The Smart Glove is expected to aid communication for individuals with hearing disabilities and holds potential for future expansion to recognize more complex gestures.

EEG-Based Lie Detection Using Autoencoder Deep Learning with Muse II Brain Sensing Hermawan, Arya Tandy; Zaeni, Ilham Ari Elbaith; Wibawa, Aji Prasetya; Gunawan, Gunawan; Hartono, Nickolas; Kristian, Yosi
International Journal of Robotics and Control Systems Vol 4, No 3 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i3.1497

Detecting deception has significant implications in fields like law enforcement and security. This research aims to develop an effective lie detection system using Electroencephalography (EEG), which measures the brain's electrical activity to capture neural patterns associated with deceptive behavior. Using the Muse II headband, we obtained EEG data across 5 channels from 34 participants aged 16-25, comprising 32 males and 2 females, with backgrounds as high school students, undergraduates, and employees. EEG data collection took place in a suitable environment, characterized by a comfortable and interference-free setting optimized for interviews. The research contribution is the creation of a lie detection dataset and the development of an autoencoder model for feature extraction and a deep neural network for classification. Data preparation involved several pre-processing steps: converting microvolts to volts, filtering with a band-pass filter (3-30Hz), STFT transformation with a 256 data window and 128 overlap, data normalization using z-score, and generating spectrograms from power density spectra below 60Hz. Feature extraction was performed using an autoencoder, followed by classification with a deep neural network. Methods included testing three autoencoder models with varying latent space sizes and two types of classifiers: three new deep neural network models, including LSTM, and six models using pre-trained ResNet50 and EfficientNetV2-S, some with attention layers. Data was split into 75% for training, 10% for validation, and 15% for testing. Results showed that the best model, using autoencoder with latent space size of 64x10x51 and classifier using the pre-trained EfficientNetV2-S, achieved 97% accuracy on the training set, 72% on the validation set, and 71% on the testing set. Testing data resulted in an F1-score of 0.73, accuracy of 0.71, precision of 0.68, and recall of 0.78. The novelty of this research includes the use of a cost-effective EEG reader with minimal electrodes, exploration of single and 3-dimensional autoencoders, and both non-pretrained classifiers (LSTM, 2D convolution, and fully connected layers) and pretrained models incorporating attention layers.

Modelling Naïve Bayes for Tembang Macapat Classification Wibawa, Aji Prasetya; Ningtyas, Yana; Atmaja, Nimas Hadi; Zaeni, Ilham Ari Elbaith; Utama, Agung Bella Putra; Dwiyanto, Felix Andika; Nafalski, Andrew
Harmonia: Journal of Arts Research and Education Vol 22, No 1 (2022): June 2022
Publisher : Department of Drama, Dance and Music, FBS, Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/harmonia.v22i1.34776

The tembang macapat can be classified using its cultural concepts of guru lagu, guru wilangan, and guru gatra. People may face difficulties recognizing certain songs based on the established rules. This study aims to build classification models of tembang macapat using a simple yet powerful Naïve Bayes classifier. The Naive Bayes can generate high-accuracy values from sparse data. This study modifies the concept of Guru Lagu by retrieving the last vowel of each line. At the same time, guru wilangan’s guidelines are amended by counting the number of all characters (Model 2) rather than calculating the number of syllables (Model 1). The data source is serat wulangreh with 11 types of tembang macapat, namely maskumambang, mijil, sinom, durma, asmaradana, kinanthi, pucung, gambuh, pangkur, dandhanggula, and megatruh. The k-fold cross-validation is used to evaluate the performance of 88 data. The result shows that the proposed Model 1 performs better than Model 2 in macapat classification. This promising method opens the potential of using a data mining classification engine as cultural teaching and preservation media.

Improving Efficiency and Effectiveness of Wheeled Mobile Robot Pathfinding in Grid Space Using a Genetic Algorithm with Dynamic Crossover and Mutation Rates Lestari, Dyah; Sendari, Siti; Zaeni, Ilham Ari Elbaith; Arifin, Samsul; Sari, Rina Dewi Indah
International Journal of Robotics and Control Systems Vol 5, No 1 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i1.1573

Incorrect parameter tuning of crossover and mutation rates in Genetic Algorithms (GA) can negatively impact their effectiveness and efficiency in mobile robot pathfinding. This study focuses on improving the performance of wheeled mobile robots in grid-based environments by introducing a Dynamic Crossover and Mutation Rates (DCMR) strategy within the GA framework. The primary contribution of this research is enhancing the efficiency and effectiveness of mobile robot pathfinding, resulting in shorter average path lengths and faster convergence times. Additionally, this method addresses the challenge of selecting appropriate GA parameters while increasing the algorithm's adaptability to different phases of the search process. The DCMR approach involves linearly increasing the crossover rate by 10% (from 0% to 100%) and decreasing the mutation rate by 10% (from 100% to 0%) over every 10 generations during the GA's evolution. Unlike fixed parameter tuning or exponential and sigmoid parameter tuningâ€”both of which require trial and error to determine optimal valuesâ€”the DCMR method provides a systematic and efficient solution without additional computational cost. Experiments were conducted across eight scenarios featuring varying distances between the start and target points, with two obstacles randomly placed in the robot's environment. The results showed that implementing the DCMR method consistently identified the optimal path, reduced average path lengths by 0.99%, and accelerated algorithm convergence by 48.39% compared to fixed parameter tuning. These findings demonstrate that the DCMR method significantly enhances the performance of GAs for mobile robot pathfinding, offering a reliable and efficient approach for navigating complex environments.

Meningkatkan Keterlibatan Dan Hasil Belajar Peserta Didik Melalui Active Learning Berbantuan Quizizz Dengan TaRL Faozan; Ilham Ari Elbaith Zaeni; Ronny Afrian
Kaisa: Jurnal Pendidikan dan Pembelajaran Vol. 5 No. 1 (2025): Kaisa: Jurnal Pendidikan dan Pembelajaran
Publisher : STAIN Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56633/kaisa.v5i1.1073

This study was conducted with the aim of optimizing academic achievement and enhancing the active engagement of Grade VII-E students at a public junior high school in Malang City through the implementation of an active learning model assisted by Quizizz, integrated with the Teaching at the right level (TaRL) approach. The research employed a Classroom Action Research (CAR) design carried out in two cycles. Data were collected through learning outcome tests (pre-test and post-test), engagement observation sheets, and perception questionnaires. Quantitative data analysis using paired t-tests showed a statistically significant improvement in learning outcomes (p < 0.05) following the intervention. Descriptive analysis of qualitative data also revealed an increase in student engagement, as reflected in active participation in discussions, enthusiasm in completing Quizizz quizzes, and interaction during lessons. Adjustments made between cycles—such as adapting questions based on TaRL and optimizing Quizizz gamification—contributed to the effectiveness of the method. It is concluded that the integration of active learning assisted by Quizizz and the TaRL approach positively influences students' academic performance and engagement, and represents an innovative strategy in technology-enhanced learning.

SIMON-PELASIS DENGAN METODE SIMPLE MULTI ATTRIBUTE RATING TECHNIQUE (SMART) SEBAGAI SOLUSI RAMAH TATIB DI SMK Elmunsyah, Hakkun; Elbaith Zaeni, Ilham Ari; Wibisono, M. Nurwiseso; Fahreza Al Rafi, Muhammad Alif; erinda, hayyu
Jurnal Visi Ilmu Pendidikan Vol 17, No 3 (2025): Oktober 2025
Publisher : Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jvip.v17i3.86982

Permasalahan yang sering terjadi pada dunia pendidikan hingga saat ini adalah kegagalan siswa dalam bersikap disiplin, meskipun sikap tersebut memiliki peranan yang sangat penting dalam keberhasilan proses pembelajaran. Untuk mendukung kinerja guru kesiswaan dalam menetapkan tindakan kepada siswa yang memiliki masalah di sekolah, diperlukan sebuah sistem yang bertujuan untuk memudahkan pengelolaan terhadap pelanggaran siswa. Tujuan penelitian ini adalah untuk mengembangkan sebuah sistem informasi kesiswaan yang menggunakan metode Simple Multi Attribute Rating Technique (SMART) untuk menentukan penanganan pelanggaran siswa di tingkat Sekolah Menengah Kejuruan (SMK). Penggunaan metode SMART dalam sistem ini membantu dalam pengambilan keputusan terkait penanganan pelanggaran siswa berdasarkan atribut relevan seperti tingkat keparahan pelanggaran dan poin pelanggaran siswa. Penelitian dan pengembangan sistem dilakukan menggunakan model FourD (4D) yang mencakup tahapan define, design, develop, dan disseminate. Model pengembangan 4D digunakan karena memiliki tahapan yang sistematis dan sesuai untuk penelitian pengembangan. Hasil pengujian oleh validator ahli perangkat lunak menunjukkan efektivitas sistem dengan hasil 100% pada aspek fungsionalitas dan 92.26% pada aspek usabilitas. Implementasi penggunaan produk dilakukan oleh tujuh guru tatib SMKN 2 Malang. Hasil uji coba pengguna menunjukkan persentase sebesar 92.69% dengan kriteria sangat valid. Oleh karena itu, produk dianggap layak dan dapat digunakan sebagai solusi penanganan pelanggaran siswa di SMK.

Journal ClassificationÂ UsingÂ Cosine SimilarityÂ MethodÂ on Title and Abstract withÂ Frequency-BasedÂ Stopword RemovalÂ Nurfadila, Piska Dwi; Wibawa, Aji Prasetya; Zaeni, Ilham Ari Elbaith; Nafalski, Andrew
International Journal of Artificial Intelligence Research Vol 3, No 2 (2019): December 2019
Publisher : Universitas Dharma Wacana

Classification of economic journal articles has been done using the VSM (Vector Space Model) approach and the Cosine Similarity method. The results of previous studies are considered to be less optimal because Stopword Removal was carried out by using a dictionary of basic words (tuning). Therefore, the omitted words limited to only basic words. This study shows the improved performance accuracy of the Cosine Similarity method using frequency-based Stopword Removal. The reason is because the term with a certain frequency is assumed to be an insignificant word and will give less relevant results. Performance testing of the Cosine Similarity method that had been added to frequency-based Stopword Removal was done by using K-fold Cross Validation. The method performance produced accuracy value for 64.28%, precision for 64.76 %, and recall for 65.26%. The execution time after pre-processing was 0, 05033 second.

Analysis of feature reduction for identifying stress levels electroencephalogram signal based Setyorini, Setyorini; Zaeni, Ilham Ari Elbaith; Elmusyah, Hakkun
International Journal of Advances in Applied Sciences Vol 14, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i4.pp1137-1145

Stress identification based on electroencephalogram (EEG) signals has become a rapidly growing research topic, with the main approaches utilizing features from the frequency domain and time-frequency domain. This research aims to combine principal component analysis (PCA) and independent component analysis (ICA) for feature extraction to improve the accuracy of stress identification. Additionally, PCA+ICA features are reduced from 64 to 32 columns to optimize computational efficiency without losing important information from the EEG signal. The stress identification models used in this research include Ensemble, naive Bayes, and support vector machine (SVM). The data used are from the SAM-40 task Stroop color trials 1, 2, and 3. Experimental results indicate that the combination of PCA+ICA features improves accuracy only in the ensemble method. Reducing PCA+ICA features from 64 to 32 columns led to an improvement in accuracy only for Stroop trial 2 data with the naive Bayes method.

Penerapan Problem Base Learning Berbantuan Quizizz untuk Meningkatkan Hasil Belajar Siswa pada Mata Pelajaran Informatika Febi Elvara Aprilia; Zaeni, Ilham Ari Elbaith; Afrian, Ronny
Kaisa: Jurnal Pendidikan dan Pembelajaran Vol. 5 No. 2 (2025): Kaisa: Jurnal Pendidikan dan Pembelajaran
Publisher : STAIN Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56633/kaisa.v5i2.1110

This study aims to improve students' learning outcomes in the Informatics subject through the application of Problem Based Learning (PBL) supported by the Quizizz media. The method used is Classroom Action Research (CAR) referring to the Kemmis and McTaggart model, which consists of two cycles. Each cycle includes planning, action implementation with observation, and reflection. The research subjects involved 27 students. Data were collected using observation techniques as well as pre-test and post-test assessments. The results showed an improvement in students' learning outcomes at each cycle stage. In the first cycle, students scoring ≥80 increased from 52.38% to 61.90%, while students scoring <60 decreased from 23.81% to 14.29%. In the second cycle, after improvements such as anticipating network issues by utilizing students' personal devices, rescheduling quiz sessions, applying a blended learning approach, and deepening the understanding of problems using more relevant case studies related to students' daily lives, a significant improvement was recorded: all students (100%) scored ≥80, and no students scored <60. Based on these results, it can be concluded that the implementation of the PBL model supported by Quizizz is effective in enhancing students' learning outcomes.

Integration of Knowledge-Based CNN Model for Breast Cancer Histopathology Image Classification Badri, Fawaidul; Patmanthara, Syaad; Zaeni, Ilham Ari Elbaith
ILKOMNIKA Vol 7 No 3 (2025): Volume 7, Number 3, December 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v7i3.801

This study examines the integration of a knowledge-based Convolutional Neural Network (CNN) model for breast cancer histopathology image classification through ontological and epistemological perspectives. Ontologically, the research focuses on the digital representation of histopathological breast tissue images as entities representing benign and malignant conditions, establishing a stable and comprehensive mapping of tissue morphological characteristics. Epistemologically, the study employs a deep learning approach using a CNN model to acquire and validate knowledge about cancer cell morphology patterns from image data, constructing robust epistemic claims regarding tissue differentiation. The BreakHis dataset comprises 7,909 images resized to 224×224 pixels that underwent preprocessing normalization and image augmentation to enhance data quality. The CNN model was designed with Adam and SAM optimizers, learning rates of 0.0001 and 0.003, and a three-epoch warm-up phase to maintain training stability. Experimental results achieved training accuracy of 0.8432, testing accuracy of 0.8481, AUC of 0.8318, precision of 0.8124, and recall of 0.8966, demonstrating excellent model performance in recognizing cancer tissue patterns without overfitting. The integration of this knowledge-based CNN model contributes theoretically to the advancement of artificial intelligence and biomedical science, while demonstrating practical relevance as a reliable decision-support system for breast cancer diagnosis.

Co-Authors A.N. Afandi Adam Rachmawan Adib Nur Sasongko Aditama Yudha Atmanegara Adjie Rosyidin Afifah Salim Afnan Habibi, M. Afrian, Ronny Agung Bella Putra Utama Aji Prasetya Wibawa Aji Wibawa Akhmad Afrizal Rizqi Amalia Sufa Andrew Nafalski Andy Hermawan Anggraeni Budiarti Anik N. Handayani Anik Nur Handayani Arengga Wibowo, Danang Arifin, Samsul Aripriharta - Aripriharta 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 Elmusyah, Hakkun Erinda, Hayyu Fahreza Al Rafi, Muhammad Alif Fanani, Erianto Faozan Fauzi, Rochmad Fawaidul Badri Febi Elvara Aprilia Felix Andika Dwiyanto Felix Andika Dwiyanto Ferdiansyah, Dodik Septian Ferdinand, Miftakhul Anggita Bima Fitriana Kurniawati Gunawan Gunawan Gunawan Gwinny Tirza Rarastri Hakkun Elmunsyah 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 Iqbal Akbar Muhammad Khusairi Osman Muhammad Rifai Muhammad Syauqi Muhammad Usman Mursyit, Mohammad Nafalski, Andrew Ningtyas, Yana Nurfadila, Piska Dwi Nusantar, Alrizal Akbar Nusantar Akbar 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 Setyorini Setyorini 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 Wibisono, M. Nurwiseso Yandhika Surya Akbar Gumilang Yogi Dwi Mahandi Yosi Kristian Zafifatuz Zuhriyah

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