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All Journal J@TI (TEKNIK INDUSTRI) Jurnal Ilmiah Teknologi dan Rekayasa Jurnal Ilmu Perpustakaan Techno.Com: Jurnal Teknologi Informasi MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Forum Ilmu Sosial Jurnal Adabiya Edulib Lentera Pustaka Jurnal Kajian Informasi & Perpustakaan JIPI (Jurnal Ilmu Perpustakaan dan Informasi) Jurnal Tamaddun Populis : Jurnal Sosial dan Humaniora Publication Library and Information Science Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Informatika Jurnal Khatulistiwa Informatika HIGIENE: Jurnal Kesehatan Lingkungan JBMP (Jurnal Bisnis, Manajemen dan Perbankan) Jurnal Pilar Nusa Mandiri Jurnal Penelitian Pendidikan IPA (JPPIPA) JURNAL YAQZHAN: Analisis Filsafat, Agama dan Kemanusiaan Indonesian Journal of Artificial Intelligence and Data Mining JRST (Jurnal Riset Sains dan Teknologi) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Management and Economics Journal (MEC-J) Jurnal Manajemen Kesehatan Yayasan RS.Dr. Soetomo Angkasa: Jurnal Ilmiah Bidang Teknologi Martabe : Jurnal Pengabdian Kepada Masyarakat International Journal of Community Service Learning JURNAL GOVERNANSI Cakrawala: Jurnal Litbang Kebijakan Tibanndaru : Jurnal Ilmu Perpustakaan dan Informasi JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat J-Dinamika: Jurnal Pengabdian Kepada Masyarakat Transparansi Jurnal Ilmiah Ilmu Administrasi Jurnal Kesehatan Medical Technology and Public Health Journal Applied Technology and Computing Science Journal Journal of Information Systems and Informatics Dinasti International Journal of Education Management and Social Science Journal of Economics, Business, and Government Challenges MUKADIMAH: Jurnal Pendidikan, Sejarah, dan Ilmu-ilmu Sosial Jurnal Informasi dan Teknologi Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jatilima : Jurnal Multimedia Dan Teknologi Informasi Responsive: Jurnal Pemikiran dan Penelitian Administrasi, Sosial, Humaniora dan Kebijakan Publik Bubungan Tinggi: Jurnal Pengabdian Masyarakat J-3P (Jurnal Pembangunan Pemberdayaan Pemerintahan) Info Bibliotheca: Jurnal perpustakaan dan ilmu Informasi Jurnal Penelitian Pendidikan, Psikologi Dan Kesehatan (J-P3K) Journal of Computer Networks, Architecture and High Performance Computing Unilib: Jurnal Perpustakaan Jurnal Teknik Informatika (JUTIF) Jurnal Pemerintahan dan Kebijakan (JPK) Dialogue: Jurnal Ilmu Administrasi Publik BIOLOVA Journal La Multiapp Journal of Technology and Informatics (JoTI) International Journal of Social Science, Educational, Economics, Agriculture Research, and Technology (IJSET) Az-Zahra: Journal of Gender and Family Studies Media Pustakawan Pustaka Karya : Jurnal Ilmiah Ilmu Perpustakaan dan Informasi Bidik : Jurnal Pengabdian kepada Masyarakat Journal of Law, Poliitic and Humanities Malcom: Indonesian Journal of Machine Learning and Computer Science Research and Development in Education (RaDEn) MIMBAR INTEGRITAS Journal of Governance and Social Policy Eduvest - Journal of Universal Studies SATIN - Sains dan Teknologi Informasi Journal of Economics and Management Scienties Riwayat: Educational Journal of History and Humanities (Journal of Environmental Sustainability Management) Indonesian Governance Journal : Kajian Politik-Pemerintahan Jurnal Wacana Kinerja: Kajian Praktis-Akademis Kinerja dan Administrasi Pelayanan Publik Al Maktabah Jurnal kajian Ilmu dan Perpustakaan Jurnal Informatika TEKNOSAINS: Jurnal Sains, Teknologi dan Informatika
Imam Yuadi
Departemen Ilmu Informasi Dan Perpustakaan, Fakultas Ilmu Sosial Dan Ilmu Politik, Universitas Airlangga
Religion 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 Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Other

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Multi-Device Image Dataset With Manual And Python-Based Augmentations For Cross-Device Robustness In Image Classification Research Erika Putri; Imam Yuadi
Jurnal Multimedia dan Teknologi Informasi (Jatilima) Vol. 7 No. 04 (2025): Jatilima : Jurnal Multimedia Dan Teknologi Informasi
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jatilima.v7i04.1887

Abstract

This study presents a multi-device image collection and a reproducible VSCode/Python pipeline for analyzing image classification and the effects of data augmentation under real hardware variation. Images were captured at the Galeri Inovasi Institut Teknologi Sepuluh Nopember (GRITS) using four devices Infinix Note 4, LG G6, Samsung S23+, and Xiaomi Pad 6s Pro with 31 images per device. We applied manual and Python-based augmentations (rotation, flips, brightness, sharpening, contrast) and organized outputs by device and augmentation type for controlled comparisons. Using stratified 80:20 splits, we evaluated Logistic Regression (LR), SVM (RBF), and KNN. Results: LR reached accuracy 0.90 (macro-F1 0.88; weighted-F1 0.90), SVM 0.89 (macro-F1 0.88; weighted-F1 0.89), and KNN 0.67 (macro-F1 0.65; weighted-F1 0.68). Augmentation enhanced robustness and cross-device generalization, though Xiaomi Pad 6s Pro remained the most challenging class, indicating a persistent device-specific shift. The dataset and scripts provide a transparent, baseline-ready testbed for research on image classification, cross-device variability, and the impact of augmentation.
Pemetaan Bibliometrik Tren Penelitian Artificial Intelligence dalam Bidang Pendidikan Tahun 2015–2025 Salsabila, Chyntia Shafa; Yuadi, Imam
Populis : Jurnal Sosial dan Humaniora Vol. 10 No. 2 (2025)
Publisher : Universitas Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47313/ppl.v10i2.4260

Abstract

Penelitian ini bertujuan untuk memetakan tren penelitian tentang Artificial Intelligence (AI) dalam bidang pendidikan pada kurun tahun 2015–2025 dengan menggunakan metode bibliometrik. Analisis dilakukan secara deskriptif berbasis data publikasi yang diperoleh dari basis data Scopus melalui distribusi publikasi per tahun, identifikasi penulis, jurnal, institusi, serta pemetaan keywoard co-occurance dan kolaborasi antar penulis dengan memanfaatkan perangkat VOSViewer. Hasil penelitian menunjukkan bahwa terdapat pertumbuhan publikasi yang signifikan dari tahun ke tahun terutama di tahun 2020 disertai lonjakan yang tinggi pada tahun 2023 – 2025 yang dipicu dengan adanya generative AI seperti ChatGPT. Analisis kata kunci mengungkapkan tiga kluster utama, yaitu pengembangan teknologi (machine learning, natural language processing, intelligent tutoring systems), isu sosial dan etika (AI ethics, student perceptions), serta aspek pedagogis yang menekankan peran guru dan pengalaman belajar. Jejaring kolaborasi memperlihatkan dominasi peneliti dari Tiongkok, Amerika Serikat, dan Eropa, dengan beberapa tokoh berperan sebagai penghubung lintas negara. Pada temuan ini juga menyoroti bahwa penelitian AI tidak hanya fokus pada aspek teknis namun juga menyoroti etika, sosial, dan pedagogis. Abstract This research aims to map the research trends on Artificial Intelligence (AI) in the field of education during the period 2015–2025 using a bibliometric method. The analysis is conducted descriptively based on publication data obtained from the Scopus database through the distribution of publications per year, identification of authors, journals, institutions, and mapping of keyword co-occurrence and collaboration between authors using the VOSViewer tool. The results show a significant growth in publications from year to year, especially in 2020, accompanied by a high spike in 2023 and 2025, triggered by the presence of generative AI such as ChatGPT. Keyword analysis revealed three main clusters: technology development (machine learning, natural language processing, intelligent tutoring systems), social and ethical issues (AI ethics, student perceptions), and pedagogical aspects that emphasize the role of teachers and the learning experience. Collaboration networks show the dominance of researchers from China, the United States, and Europe, with several figures acting as cross-border liaisons. This finding also highlights that AI research does not only focus on technical aspects but also highlights ethical, social, and pedagogical aspects.
Penerapan Rapidminer dengan Metode Decision Tree Pada Tingkat Motivasi Berkunjung Pemustaka di Perpustakaan UIN Sunan Ampel Surabaya Hary Supriyatno; Imam Yuadi
AL Maktabah Vol 10, No 1 (2025): JUNI
Publisher : Pusat Publikasi Ilmiah UIN Fatmawati Sukarno Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29300/mkt.v10i1.7086

Abstract

Pasca pandemi Covid-19, perpustakaan memiliki tugas berat dalam upaya peningkatan kunjungan fisik pemustaka. Salah satu penyebabnya ialah adanya pergeseran budaya akses informasi dari cetak ke digital. Kondisi ini menyebabkan turunnya angka kunjungan onsite di perpustakaan, tidak terkecuali UIN Sunan Ampel Surabaya. Salah satu strategi yang dilakukan perpustakaan adalah melalui inovasi penyediaan layanan, seperti koleksi corner. Tujuan peneltian untuk mengetahui tingkat motivasi berkunjung pemustaka di Perpustakaan UIN Sunan Ampel Surabaya berdasarkan teori ERG (Existence, Relatedness, Growth) Clayton Alderfer. Instrumen yang digunakan adalah kemudahan akses wifi, koleksi yang relevan, fasilitas lengkap, tempat nyaman untuk berkegiatan, diskusi, penyelesaian tugas akademik, dan sumber inspirasi. Metode penelitian menggunakan decision tree melalui aplikasi RapiMiner untuk menentukan aturan atau rule motivasi pemustaka. Hasil analisis penelitian menggunakan Decision Tree menunjukkan tingkat akurasi pada angka 98%. Sedangkan untuk tingkat motivasi berkunjung pemustaka di Layanan Koleksi Corner Perpustakaan UIN Sunan Ampel Surabaya menciptakan tiga rule dalam tiga kategori, yakni cukup, tinggi, dan sangat tinggi. Rule pertama, jika Layanan Koleksi Corner memenuhi kebutuhan pemustaka sebagai tempat yang nyaman untuk penyelesaian tugas akademik dan memiliki fasilitas lengkap maka motivasi berkategori cukup. Kedua, jika ekspektasi kebutuhan sebagai tempat penyelesaian tugas akademik, adanya fasilitas lengkap, dan koleksi relevan dapat dipenuhi oleh layanan, maka berdampak pada motivasi tinggi. Ketiga, jika layanan koleksi corner memiliki spesifikasi sebagai tempat penyelesaian tugas akademik, adanya dukungan fasilitas lengkap, kemudahan akses wifi, dan dilengkapi koleksi relevan maka motivasi berkategori sangat tinggi.
Comparative Analysis of Foot Sole Classification Models: Evaluating Logistic Regression, SVM, and Random Forest Purba, Trie Dinda Maharani; Yuadi, Imam
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11550

Abstract

Accurate sole classification and types can aid applications in healthcare, sports, and biometrics such as diagnosis of high arch or flat foot disease, as well as in improved design of custom orthotics and enhanced gait analysis to improve sports performance. When applied to large-scale datasets, traditional methods for foot sole classification are inefficient as they are often manual, time-consuming and prone to human error. Machine learning has the ability to significantly improve accuracy and efficiency in automating this process. The proposed method uses Logistic Regression model compared to Support Vector Machines (SVM), and Random Forest using Orange Data Mining. The performance of these algorithms changes depending on the complexity of the data and model parameters. There are three types of feet that will be processed in this image analytics namely normal arch, flat foot and high arch. The pre-trained models used are Inception V3, VGG-19 and SqueezeNet. Logistic Regression model showed the best overall performance with superior parameter values such as AUC of 0.973, Classification Accuracy (CA) of 0.933, and MCC of 0.902, and demonstrated reliability and balance between precision and recall.
STUDI KOMPARATIF MODEL MACHINE LEARNING DALAM MEMPREDIKSI KETERLAMBATAN PEGAWAI: LOGISTIC REGRESSION, SVM, DAN RANDOM FOREST Palupi, Inggrid Nindia Aprila; Mardianto, M Fariz Fadillah; Yuadi, Imam; Mariyadi, Budiyan
J@ti Undip: Jurnal Teknik Industri Vol 21, No 1 (2026): Januari 2026
Publisher : Departemen Teknik Industri, Fakultas Teknik, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jati.21.1.76-87

Abstract

Keterlambatan karyawan adalah salah satu jenis pelanggaran terhadap disiplin kerja yang dapat berdampak pada produktivitas dan efektivitas organisasi. Penelitian ini bertujuan untuk mengembangkan serta membandingkan performa dari tiga algoritma machine learning Regresi Logistik, SVM, dan Random Forest dalam memprediksi keterlambatan pegawai dengan menggunakan data keterlambatan dan karakteristik individu. Dataset yang digunakan terdiri dari 1902 data, yang dibagi 80% data training dan 20% data testing dengan enam variabel, mencakup usia, lama bekerja, status pernikahan, jarak tempat tinggal ke kantor, jenis kendaraan yang digunakan, dan gaya hidup. Hasil analisis menunjukkan bahwa Random Forest memberikan kinerja prediktif yang paling baik dalam mengenali pegawai yang memiliki potensi untuk terlambat, dengan nilai akurasi tertinggi sebesar 0.82, presisi sebesar 0.93, recall sebesar 0.84, dan F1-score sebesar 0.88. Model ini terbukti dapat menunjukkan kemampuan klasifikasi yang andal dan seimbang. Analisis feature importance mengidentifikasi usia dan masa kerja sebagai faktor paling berpengaruh terhadap prediksi keterlambatan. Temuan ini tidak hanya memberikan wawasan baru dalam pengelolaan kedisiplinan pegawai, tetapi juga membuka peluang implementasi sistem peringatan dini yang dapat diintegrasikan ke dalam sistem kehadiran digital organisasi. Penelitian ini merekomendasikan perluasan variabel untuk studi lanjutan dan pemanfaatan hasil model sebagai dasar penyusunan kebijakan SDM yang lebih adaptif dan berbasis data. Abstract[Comparative Study of Machine Learning Models in Predicting Employee Delay: Logistic Regression, SVM, and Random Forest] Employee tardiness is one type of violation of work discipline that can impact organizational productivity and effectiveness. This study aims to develop and compare the performance of three machine learning algorithms Logistic Regression, SVM, and Random Forest in predicting employee tardiness using tardiness data and individual characteristics. The dataset used consists of 1902 data, which is divided into 80% training data and 20% with six variables, including age, length of service, last education level, marital status, distance from residence to office, type of vehicle used, and lifestyle. The results of the analysis show that Random Forest provides the best predictive performance in identifying employees who have the potential to be late, with the highest accuracy value of 0.82, precision of 0.93, recall of 0.84, and F1-score of 0.88. This model is proven to be able to demonstrate reliable and balanced classification capabilities. Feature importance analysis identifies age and length of service as the most influential factors in predicting tardiness. These findings not only provide new insights into employee discipline management but also open up opportunities for the implementation of an early warning system that can be integrated into the organization's digital attendance system. This study recommends expanding the variables for further studies and utilizing the model results as a basis for formulating more adaptive and data-based HR policies.Keywords: sustainability industry; developing strategy; MCDM
Batik Pattern Classification Using Logistic Regression, SVM, and Deep Learning Features Ratih Addina Hapsari; Imam Yuadi
Jurnal Informatika Vol. 12 No. 2 (2025): October
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/

Abstract

This study presents the integration of deep learning-based feature extraction with conventional machine learning classifiers for automatically categorizing Indonesian batik patterns. The research utilizes five traditional motifs: Alas Alasan, Kokrosono, Semen Sawat Gurdha, Sido Asih, and Sido Mulyo. Feature extraction was conducted using three deep learning models: Inception V3, VGG16, and VGG19, followed by classification through Logistic Regression and Support Vector Machines (SVM), with data processing performed in Orange. Experimental results show that Inception V3 combined with Logistic Regression achieved the highest classification performance, reaching 99.2% classification accuracy and an F1-score of 0.992. These results confirm the effectiveness of deep feature embeddings in improving the automatic classification of batik motifs. The study contributes to developing intelligent classification frameworks, offering a scalable approach to cultural heritage preservation through technology. Future work will focus on enhancing feature extraction methods and expanding the dataset to address motif overlap challenges.
Integrating generative AI into Society 5.0: A paradigm for sustainable education Sherly Deasy Anjuwita Gultom; Toetik Koesbardiati; Imam Yuadi
Research and Development in Education (RaDEn) Vol. 5 No. 1 (2025): July
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/raden.v5i1.40665

Abstract

In the era of Society 5.0, the integration of generative technologies such as artificial intelligence (AI) into the education system is very important to achieve sustainable learning outcomes. This educational paradigm aims to harness the potential of AI in creating a learning environment that is adaptive and responsive to the needs of individual students. By using generative technology, educators can develop more interactive and personalized teaching materials, and provide faster and more accurate feedback. Qualitative research methods will be used to gain an in-depth understanding of the experiences, perceptions, and challenges faced by educators and learners in this integration process. The subjects of the study will consist of: 1). Educators who use AI technology in teaching. 2). Students involved in AI-based learning. 3). Educational institution managers who implement policies related to the use of generative technology. The research will be conducted at SMA Gloria, one of them, and several educational institutions that have implemented AI technology in their curriculum, both at elementary, middle, and high levels. Data collection techniques using semi-structured interviews will be carried out with educators and students to explore their experiences related to the use of AI technology in the teaching and learning process. In addition, the application of AI in education also allows big data analysis to understand student learning patterns and identify areas that require more attention. With the results of this approach, it is hoped that the learning process will not only be more efficient but also more inclusive, so that all students can reach their maximum potential. Thus, the integration of generative technologies in education will contribute to the achievement of the goals of Society 5.0 which focuses on human well-being and sustainability.
Knowledge landscape of open access in academic libraries through bibliometric analysis 2020-2025 Bestari, Melati Purba; Yuadi, Imam; Albigaeri, Syahruly Nizar
Jurnal Kajian Informasi dan Perpustakaan Vol 13, No 2 (2025): Accredited by Ministry of Education, Culture, Research and Technology of the Re
Publisher : Universitas Padjadjaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24198/jkip.v13i2.65064

Abstract

Background: The digital era transformation has changed the role of academic libraries, which initially served as repositories for physical collections and have evolved into facilitators of digital information access and initiators of change in open-access management. The COVID-19 pandemic accelerated the adoption of open access due to the urgent need for unrestricted access to scientific information. Purpose: This study aimed to map the knowledge landscape of open access in academic libraries through a comprehensive bibliometric approach for 2020-2025, identifying dominant themes, intellectual structures, collaboration patterns, and emerging trends. Methods: Data were collected from the Scopus database using the TITLE-ABS-KEY search strategy ("open access" AND "academic library"). Analysis was conducted using Bibliometrix in R Statistical Software version 4.3.0 and Biblioshiny, covering Conceptual Structure Analysis, Multiple Correspondence Analysis, Intellectual Structure Analysis, Social Structure Analysis, and Thematic Evolution Analysis. Results: The analysis showed that 118 documents from 57 publication sources were dominated by collaborative research (72.1%), with limited international collaboration (6.78%). Publication productivity peaked in 2020 (26 articles) and then declined continuously. The United States dominated with 114 citations, followed by Pakistan (36) and South Africa (33). Institutional repositories, digital libraries, and scholarly communication have emerged as central themes connecting various aspects of research. Conclusion: The open-access knowledge landscape has evolved from a focus on technical infrastructure to a strategic, holistic approach. Implications: This research provides practical guidance for librarians and policymakers to develop more effective strategies in the digital transformation era.  
Comparative Study of the Performance of Naïve Bayes, SVM, and K-NN Algorithms for Sentiment Analysis and Topic Modeling of #KaburAjaDulu Hashtags Sonia Tikamidia; Imam Yuadi
Dinasti International Journal of Education Management and Social Science Vol. 7 No. 1 (2025): Dinasti International Journal of Education Management and Social Science (Octob
Publisher : Dinasti Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/dijemss.v7i1.5119

Abstract

The #KaburAjaDulu hashtag phenomenon that has been widely discussed on platform X reflects the increasing anxiety of Indonesia's younger generation towards socio-economic conditions and the direction of state policy. This research aims to assess public perception of the hashtag through sentiment analysis and topic modeling approaches. Data was collected from X users' tweets from May to June 2025. The methods used include text preprocessing, sentiment classification using Naïve Bayes, SVM, and K-NN algorithms, and topic modeling with Latent Dirichlet Allocation (LDA). The analysis results show that SVM performs best with 98.93% accuracy and optimal precision-recall balance. The Naïve Bayes model also shows competitive results but tends to favour positive classes. In contrast, K-NN showed the lowest performance due to its inability to overcome the curse of dimensionality in TF-IDF representation. LDA topic modeling identified three main themes: the employment crisis, distrust of institutions due to corruption, and the nationalism vs. migration dilemma. These three topics indicate deep psychological conflicts experienced by youth. The findings support the Self-Determination Theory, which emphasizes the importance of autonomy, competence, and social connection for individual attachment to the environment. Lack of fulfilment of these needs triggers migration intentions as a form of escape or adaptive strategy. This research provides a practical contribution to designing HR policies based on social data. In addition, this approach can be used as the basis for a real-time public perception monitoring system.
Bahasa Inggris Irvan Zidny; Ira Puspitasari; Imam Yuadi
Jurnal Penelitian Pendidikan IPA Vol 9 No 8 (2023): August
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v9i8.4531

Abstract

This study presents a novel approach to assist learning analysts in identifying suitable learning pathways based on historical training data through the utilization of text mining techniques. The dataset utilized in this research comprises training data from the year 2021 and the Course Development Management Program (CDMP) catalogue. The BERT 'bert-base-nli-mean-tokens' model is employed for encoding purposes. By comparing the training data names from 2021 with the CDMP catalogue using cosine similarity and dot score, valuable insights are obtained. The findings indicate that cosine similarity is a more effective measure for interpreting the data, thereby simplifying the process for learning analysts and managers in identifying appropriate learning paths for their employees. This research provides a practical solution that leverages text mining techniques to optimize the analysis and decision-making processes in learning and development domains, enabling organizations to enhance the effectiveness and efficiency of their training programs.
Co-Authors AA Sudharmawan, AA Achmad Djunawan Aditya Cahya Saputra Albigaeri, Syahruly Nizar Alifka Cellina Velby Anastasya, Diva Berta Andini, Aulia Rizqi Anggraini, Pramudya Galuh Suci Ardian Mohib Artha Rachma Widiastuti Arum Karisma Nadya Lashita Azmi, Muhammad Izharul Baihaqie, Owen Berliani, Kezia Putri Bondan Ari Wijaya Cahyani, Retno Tri Christia, Tifani Dewi Chyntia Shafa Condro Rahino Mustikaning Pawestri Dama Putri, Kania Dea Roseliana Putri Dewanty, Alifia Kaltsum Dwiky Rahardian Endang Gunarti Enny Mar’atus Sholihah Erika Putri Erika Putri Fadilia Rinarwastu Fadilia Rinarwastu, Fadilia Fairus Faqih Febri Ari Wicaksono Febriano, Rizki Dwi Ferdiansah, Gilang Fitri Mutia, Fitri Gilang Ferdiansah Gunarti, Endang Halim, Yunus Abdul Handari Niken Anggraini Hapsari, Ratih Addina Hardevianty, Melissa Yunda Hary Supriyatno Hasna, Dhia Alifia Izdihar Hendro Margono Ira Puspitasari Ira Puspitasari Ira Puspitasari Irvan Zidny Ismi Choirunnisa Prihatini Kartika Sari, Della Kezia Rahmawati Santosa Koko Srimulyo Lathifah, Lathifah Lestari, Santi Dwi Desy Lifindra, Stevanie Aurelia Lucy Dyah Hendrawati M Kafi Maulana M. Fariz Fadillah Mardianto Mahardika, Synthia Amelia Putri Marsaa Salsabiila Martina Fitria Wulandari Maulidah, Nofiyah Mayasari, Sentri Indah Melati Purba Bestari, Melati Purba Mochammad Edris Effendi Muhammad Rafi Raihan Muhammad Rafi Raihan Muthia Andriana Putri Nabilla Salsabil Damayanti Zahraa Nainunis, Mas Akhmad Nawwaf Faruq Adina Putra Niken Ayu Pratiwi, Bertha Nisak Ummi Nazikhah Noor Rizki, Denaldy Oktavian Novia, Asradiani Noviana Wahyu Basuki Nur Muhammad, Rizqi Nurahman, Yeni Fitria Nurul Firdausy Palupi, Inggrid Nindia Aprila Parenda Rizkya Permata Pradhana, Andrea Thrisiawan Prasetya Triputra Nugraha Prasetyo Yuwinanto, Helmy Prasyesti Kurniasari, Meinia Purba, Trie Dinda Maharani Purwaningtyas, Aris Putra, Dwi Permana Putri Kinanti, Novrianti Putri, Selviana Azzira Ragil Tri Atmi, Ragil Tri Rahmadani, Sinta Raihanzaki, Raka Gading Ratih Addina Hapsari Rosiana, Lidya Rosyani, Widha Sabayu, Brian Sabrina Hartianingrum, Hikmah Sabrina Nur Amalia Safina Innaf Mia Ardelia Salsabiila, Marsaa Salsabila, Chyntia Shafa Sari, Tri Kartika Setiadi, Yusuf Sherly Deasy Anjuwita Gultom Sheva Alana Brilianty Shiefti Dyah Alyusi Sinta Rahmadani Siswahyudianto Soesantari, Tri Sonia Tikamidia Sugihartati, Rahma Suhada, Hofur Sukma Sufryanto Tikamidia, Sonia Toetik Koesbardiati Tri Hadi Wicaksono Triandari, Ayu Ullin Nihaya Unas, Frisca Maria Vilosa, Bias Vivia Adriyanti, Elvetta Wardani, Hesti Ari Wettebossy, Anita Elizabeth Wildan Habibi Yuniawan Heru Santoso Yusi Dyah Patriani Yuwinanto, Helmy Prasetyo