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All Journal International Journal of Advances in Applied Sciences Tekno : Jurnal Teknologi Elektro dan Kejuruan Jurnal Visi Ilmu Pendidikan The Journal of Experimental Life Sciences (JELS) 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 Frontier Energy System and Power Engineering Indonesian Journal of Data and Science Science in Information Technology Letters International Journal of Robotics and Control Systems Jurnal Pengabdian Kepada Masyarakat 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 Jurnal JEETech

Zaeni, Ilham Ari Elbaith

Universitas Negeri Malang

Author-ID : 512535

Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities 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 Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Veterinary Other

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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.

Metode Migrasi Lebah Madu Ratu untuk Meningkatkan Deteksi Fibrilasi Atrium dari Sinyal Detak Jantung Muhammad Hafiizh; Aripriharta Aripriharta; Ilham Ari Elbaith Zaeni
JURNAL INFOTEL Vol 17 No 2 (2025): May
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i2.1362

Atrial Fibrillation (AF) is a common cardiac arrhythmia characterized by rapid and irregular electrical activity of the atrium. AF significantly increases the risk of ischemic stroke and mortality. With the increasing prevalence of cardiovascular risk factors, early detection of AF is crucial for effective intervention. Traditional electrocardiogram (ECG)-based detection methods face limitations, especially in asymptomatic patients or those with sporadic episodes of AF. This paper proposes a novel approach using the Queen Honey Bee Migration (QHBM) algorithm to detect AF from heartbeat signals. The dataset comprises both normal and AF heartbeat signals. The data undergoes preprocessing steps, including noise reduction and feature extraction. The system then classifies the signals using the QHBM algorithm. Key features such as heart rate variability (HRV), amplitude, and RR intervals are extracted for analysis. The QHBM algorithm achieved an accuracy of 95.2%, with a precision of 96.1%, a recall of 94%, and an F1 score of 95%. It outperformed traditional classifiers such as Random Forest, Support Vector Machine (SVM), and Naive Bayes across all performance metrics. In addition, QHBM demonstrated a superior ability to distinguish between normal sinus rhythm and AF, showing a significant improvement over the conventional method. Although the results are promising, challenges remain, including data imbalance and false positive and negative classifications. Oversampling techniques and further optimization of feature selection can enhance model performance. The QHBM algorithm presents a highly effective solution for automatic and real-time AF detection, offering a promising alternative to improve cardiac health monitoring systems.

Deep Learning Convolutional Neural Networks on Multi Label Image Classification of Torajanese Buffalo Ramadhan, Aslan Poetra; Handayani, Anik Nur; Zaeni, Ilham Ari Elbaith
ILKOM Jurnal Ilmiah Vol 17, No 2 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i2.2905.162-169

Convolutional Neural Networks (CNNs) represent the primary methodology in the advancement of intelligent systems and technologies. The capacity to transition from prediction to categorization establishes CNNs as the primary benchmark in the advancement of deep artificial intelligence. This study use CNN implementation to categorize photos of Torajanese buffalo. The Torajanese buffalo is a distinctive animal species belonging to the Bos bubalis family, integral to the lives and culture of the Torajanese people residing in northern South Sulawesi. This species is integral to the culture, deeply intertwined with several traditional practices of the community. This renders the species distinctive for more investigation. The distinctiveness of the buffalo's style, coloration, and form differentiates them from one another. This study use Convolutional Neural Networks (CNNs) as the primary method to categorize Torajanese buffalo species using head photos and markers derived from local knowledge. This research employs InceptionV3, DenseNet, and Xception as primary architectures, each with variations corresponding to 10, 50, and 100 epochs, therefore enhancing the study. The findings of this investigation indicate that the InceptionV3 architecture has commendable performance across both versions, achieving an average AUC-ROC score of 0.96, although with increased execution time. Nonetheless, the DenseNet architecture demonstrates superior performance in its optimal configuration, achieving flawless accuracy; nonetheless, it incurs the most processing time for the frontal view of the Torajanese buffalo head test case.

Optimalisasi Energi Pada Lift Berdasarkan Gerak Vertikal pada Lift Menggunakan Hybrid Naive Bayes Adika Prana Ihsanuddin; Siti Sendari; Ilham Ari Elbaith Zaeni; M. Afnan Habibi; Danang Arengga Wibowo
Jurnal JEETech Vol. 6 No. 2 (2025): Nomor 2 November
Publisher : Universitas Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32492/jeetech.v6i2.6203

Penelitian ini bertujuan untuk mengoptimalkan penggunaan energi pada sistem lift berdasarkan gerak vertikal menggunakan algoritma Hybrid Naive Bayes. Proses optimalisasi didasarkan pada pengumpulan data dilakukan di Gedung B11 Fakultas Teknik Universitas Negeri Malang selama periode waktu tertentu, dalam upaya mengurangi konsumsi energi pada gedung bertingkat, efisiensi energi lift menjadi salah satu fokus utama. Dengan memanfaatkan data penggunaan lift yang meliputi pola pergerakan vertikal, waktu operasional, serta beban muatan, penelitian ini melakukan klasifikasi dan prediksi efisiensi energi. Algoritma Hybrid Naive Bayes dipilih karena kemampuannya dalam menangani ketidakpastian data serta keandalannya dalam klasifikasi, terutama saat dikombinasikan dengan metode optimisasi lainnya. Hasil prediksi efisiensi energi yang akurat juga memungkinkan manajemen gedung untuk menerapkan strategi operasional yang lebih hemat energi dan ramah lingkungan. Dengan demikian, penelitian ini diharapkan memberikan kontribusi signifikan dalam pengelolaan energi yang lebih efisien pada sistem lift di gedunggedung tinggi.

Co-Authors A.N. Afandi Adam Rachmawan Adib Nur Sasongko Adika Prana Ihsanuddin 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 Ashar, Muhammad Atmaja, Nimas Hadi Azlan Mohd Zain Danang Arengga Wibowo Dessy Rif’a Anzani Dian Candra Lestari Didik Dwi Prasetya Dony Setiawan Dwiyanto, Felix Andika Dyah Lestari Eko Pambagyo Setyobudi Elmusyah, Hakkun Enggie Hendrawan Saputra Erinda, Hayyu Fahreza Al Rafi, Muhammad Alif Fanani, Erianto Faozan Fauzi, Rochmad Fawaidul Badri Fawaidul Badri Fawaid Febi Elvara Aprilia Felix Andika Dwiyanto Felix Andika Dwiyanto Ferdiansyah, Dodik Septian Ferdinand, Miftakhul Anggita Bima Fithri, Hidayah Kariima Fitriana Kurniawati Gunawan Gunawan Gunawan Gwinny Tirza Rarastri Hakkun Elmunsyah Hanny Prasetya Hariyadi Hari Putranto Harits Ar Rosyid Hariyadi, Hanny Prasetya Hartono, Nickolas Hendrawan, William Hartanto Heru Wahyu Herwanto Heru Hidayah Kariima Fithri Hsien-I Lin I Made Wirawan Irvan, Mhd Ismail, Amelia Ritahani Ivatus Sunaifah Kartika Kirana Kevin Raihan Khafit Zaman Kotaro Hirasawa Lestari, Dian Candra Liliek Rahayu M. Adib Nursasongko M. Afnan Habibi 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 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 Ramadhan, Aslan Poetra Rasidy, Ahmad Himawari Renaldi Primaswara Prasetya Retno Indah Rokhmawati Revanza Akiella Jihan Putra Ridwan Shalahuddin Rina Dewi Indahsari Riris Andriani Rizal Kholif Nurrohman Ronny Afrian Samsul Arifin Setumin, Samsul Setyorini Setyorini Shandy Darmawan Simbolon, Triyanti Siti Sendari Soenar Soekopitojo Sugiono, Bhima Satria Rizki Sujito Sujito Suyono Suyono Syaad Patmanthara Syafaat, Mokhammad Tri Atmadji Sutikno Utama, Agung Bella Putra Welly Antonius Wibisono, M. Nurwiseso Yandhika Surya Akbar Gumilang Yogi Dwi Mahandi Yosi Kristian Zafifatuz Zuhriyah

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