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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
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Classification of Red Foxes: Logistic Regression and SVM with VGG-16, VGG-19, and Inception V3 Sabayu, Brian; Yuadi, Imam
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 3 (2025): June 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i3.6356

Abstract

Deep learning models demonstrate a high degree of accuracy in image classification. The task of distinguishing between various sources of red fox images—such as authentic photographs, game-captured images, hand-drawn illustrations, and AI-generated images—raises important considerations regarding realism, texture, and style. This study conducts an evaluation of three deep learning architectures: Inception V3, VGG-16, and VGG-19, utilizing images of red foxes. The research employs Silhouette Graphs, Multidimensional Scaling (MDS), and t-Distributed Stochastic Neighbor Embedding (t-SNE) to assess clustering and classification efficiency. Support Vector Machines (SVM) and Logistic Regression are utilized to compute the Area Under the Curve (AUC), Classification Accuracy (CA), and Mean Squared Error (MSE). The MDS plots and t-SNE data clearly demonstrate the capability of the three deep learning models to distinguish between the image categories. For game-captured images, VGG-16 and VGG-19 demonstrate quite outstanding performance with silhouette scores of 0.398 and 0.315, respectively. This study explores the enhancement of classification accuracy in logistic regression and support vector machines (SVM) through the refinement of decision boundaries for overlapping categories. Utilizing Inception V3, an artificial intelligence-generated image silhouette score of 0.244 was achieved, demonstrating proficiency in image classification. The research highlights the challenges posed by diverse datasets and the efficacy of deep learning models in the classification of red fox images. The findings suggest that integrating deep learning with machine learning classifiers, such as logistic regression and SVM, may improve classification accuracy.
Pemetaan Konseptual Kajian Feminisme melalui Analisis Bibliometrik Visual terhadap Literatur Tahun 2015–2025 Wardani, Hesti Ari; Yuadi, Imam
Populis : Jurnal Sosial dan Humaniora Vol. 10 No. 1 (2025)
Publisher : Universitas Nasional

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

Abstract

This study aims to map the conceptual structure of feminist scholarship through a visual bibliometric approach using data from Google Scholar, collected via the Publish or Perish software and analyzed with VOSviewer. A total of 1,000 academic articles published between 2013 and 2023 were analyzed. Three types of visualizations—density, overlay, and network—were employed to identify thematic density, temporal trends, and keyword co-occurrence within the literature on feminism. The results show that terms such as second wave feminism, radical feminism, and popular feminism dominate the field and serve as the foundation of contemporary feminist discourse. Meanwhile, terms such as white feminism, transnational feminism, and methodology have recently emerged, indicating a shift in research interest toward more reflective, intersectional, and global feminist frameworks. The network visualization reveals distinct thematic clusters that illustrate the complexity and diversity of feminist approaches. These findings suggest that feminist scholarship is evolving from ideological roots toward more methodological and transnational reflexivity. This study contributes to the intellectual mapping of feminism and provides a basis for future interdisciplinary and context-specific feminist research.
Tren Publikasi Tentang Model Kepemimpinan dalam Pelayanan Publik: Suatu Analisis Bibliometrik Condro Rahino Mustikaning Pawestri; Imam Yuadi
Jurnal Wacana Kinerja: Kajian Praktis-Akademis Kinerja dan Administrasi Pelayanan Publik Vol 26, No 2 (2023)
Publisher : Center fo State Civil Apparatus Training and Development and Competency Mapping

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31845/jwk.v26i2.834

Abstract

Research related to leadership models that focus on public service is currently experiencing an increase. This is based on the urgency of leadership in public service. With the phenomenon of the development of research on leadership models in public services, the question arises of how to apply the right leadership model for public services. On the basis of this problem formulation, this study aims to describe a leadership model in public services in the 2015-2020 period using bibliometric analysis which in the process uses the VOSViewer and Biblioshiny applications. The research results show that there are 54 keywords related to the topics discussed. Of the 54 keys found, 1,259 relationships were created between the subject and the keywords. Of the 1,259 linkages, there are 4,367 total link strengths. Based on the analysis of one of the most widely cited studies, it was found that the leadership model influences employees' innovative behavior by increasing the emotional approach. A leader can also influence his employees' behavior with the leadership model he applies. Therefore, selecting the right leadership model is necessary to create quality public services.
Evaluating Logistic Regression and SVM for Image Analysis Using VGG-16, VGG-19, and Inception V3 Features Habibi, Wildan; Yuadi, Imam
Jurnal Ilmiah Teknologi dan Rekayasa Vol 30, No 2 (2025)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/tr.2025.v30i2.14056

Abstract

This paper presents a comparison of the classification accuracy of Logistic Regression (LR) and Support Vector Machine (SVM) classifiers on facial expression classification based on image embeddings obtained from pre-trained models like VGG-16, VGG-19, and Inception V3. Facial expression classification has relevance in emotion analysis, human-computer interaction, and security. The dataset consisted of five expressions: Angry, Fear, Happy, Neutral, and Sad. Feature embeddings were extracted by using CNN models, which are said to learn spatial features, and were classified using LR and SVM. Performance metrics like accuracy, precision, recall, and F1-score were evaluated. Inception V3 topped with 89.3% accuracy on SVM, followed by VGG-19 (87.6%) and VGG-16 (85.4%). Inception V3 was best in discriminating fine-grained expressions, as confirmed through confusion matrix analysis and visualization techniques like MDS and t-SNE. In contrast to earlier works on individual models or conventional approaches, this work emphasizes the merits of fusing powerful CNNs with strong classifiers. Limitations encompass a limited dataset and just five expressions, indicating that future research should address larger, varied datasets and real-time responsiveness for enhanced system robustness.
Batik Pattern Classification Using Logistic Regression, SVM, and Deep Learning Features Hapsari, Ratih Addina; Yuadi, Imam
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/inf.v12i2.25855

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.
SENTIMENT ANALYSIS ON TRAINING IMPLEMENTATION’S FEEDBACK IN PT XYZ Rinarwastu, Fadilia; Yuadi, Imam
Jurnal Pilar Nusa Mandiri Vol. 21 No. 2 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v21i2.6641

Abstract

Customer satisfaction is an important aspect in building a company's image, both for employees and external parties. In order to improve employee satisfaction and performance, training that organized by the company needs to receive feedback so that the training organizers can continue to provide the best service to employees who participate in the training. The large volume of feedback that must be processed in text form, leads to prolonged identification of comments and the omission of certain training programs from further analysis. This study applies text mining using sentiment analysis and Word Cloud visualization to evaluate the effectiveness of training methods and identify areas for improvement based on employee feedback on training programs at PT XYZ. The amount of data used after preprocessing was  48,910 open feedback responses from 4,314 training sessions consisting of three forms: classroom training, digital learning, and hybrid learning. The evaluation for clustering used the K-Means method, which turned out to use two optimal clusters based on the silhouette. Overall satisfaction with the training was determined through key points such as stable internet connection, overlapping of training schedule, and poor learning environment. Issues frequently that identified in the Word Cloud analysis revealed keywords describing positive and negative aspects of the situation that are requiring further improvement. This identification is useful for developing recommendations to enhance the implementation of the training and participants' experience. Further research may also involve advanced sentiment analysis and more accurate classification methods.
Mapping Sentiment towards Danantara: A Combined Clustering and Text- Based Predictive Model Lestari, Santi Dwi Desy; Yuadi, Imam
Journal of Law, Politic and Humanities Vol. 5 No. 6 (2025): (JLPH) Journal of Law, Politic and Humanities
Publisher : Dinasti Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/jlph.v5i6.2295

Abstract

Research aims to map public sentiment towards Danantara with the integration of clustering and text-based predictive models from social media data. Clustering using K-means obtained three clusters namely political criticism, neutral and prositive support. Linear SVM model performed best with 96% accuracy, followed by random forest (93%), Logistic Regression (90%) and Naïve Bayes (83%). The findings confirm that the public is highly sensitive to issues of transparency and governance in the establishment of Danantara, and the need for a responsive, data-driven public communication strategy. This research contributes to the public opinion monitoring system for national strategic policies.
PELATIHAN PENULISAN ARTIKEL BUKU BUNGA RAMPAI SEBAGAI PENINGKATAN KINERJA PUSTAKAWAN DI BALAI LAYANAN PERPUSTAKAAN DAERAH ISTIMEWA YOGYAKARTA Tri Atmi, Ragil; Abdul Halim, Yunus; Margono, Hendro; Srimulyo, Koko; Mutia, Fitri; Sugihartati, Rahma; Gunarti, Endang; Yuadi, Imam; Prasetyo Yuwinanto, Helmy; Niken Ayu Pratiwi, Bertha
Martabe : Jurnal Pengabdian Kepada Masyarakat Vol 8, No 8 (2025): MARTABE : JURNAL PENGABDIAN KEPADA MASYARAKAT
Publisher : Universitas Muhammadiyah Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31604/jpm.v8i8.%p

Abstract

 Publikasi artikel menjadi salah satu unsur peningkatan kompetensi dan kinerja bagi para Pustakawan di Indonesia. Berdasarkan Permenpan-RB Nomor 9 Tahun 2014, pustakawan akan mendapatkan nilai tambah pada angka kredit mereka setelah berhasil melakukan publikasi karyanya. Namun, dalam menulis publikasi artikel buku bunga rampai, pustakawan masih memiliki keterbatasan. Kondisi tersebut juga terjadi di Balai Layanan Perpustakaan Daerah Istimewa Yogyakarta (BLPDIY). Keterbatasan dalam penulisan karya tulis ilmiah yang terjadi di Balai Layanan Perpustakaan Daerah Istimewa Yogyarakarta (BLPDIY) disebabkan oleh rendahnya motivasi, kurangnya pengalaman, dan kurangnya manajemen waktu. Departemen Informasi dan Perpustakaan Universitas Airlangga memberikan edukasi yang membantu pustakawan mengatasi kendala tersebut. Tujuan dari kegiatan ini antara lain, yang pertama meningkatkan pengetahuan dan kemampuan pustakawan dalam menulis dan mempublikasikan karya tulis ilmiah kedua, meningkatkan pengetahuan pustakawan dalam mencegah dan mendeteksi plagiarism dalam penulisan karya tulis ilmiah, ketiga, dapat membuat karya tulis ilmiah yang berkualitas, keempat, karya tulis ilmiah terpublikasi, kelima, produktivitas pustakawan semakin meningkat. Kegiatan Pengabdian Masyarakat ini berakhir dengan lancer dan menghasilkan sebuah buku bunga rampai yang ditulis secara kolaboratif dengan pustakawan dari Balai Layanan Perpustakaan Daerah Istimewa Yogyakarta (BLPDIY), dosen, dan Mahasiswa Program Studi Ilmu Informasi dan Perpustakaan.
DIGITAL SELLING SKILL PADA PEDAGANG BUNGA DI PASAR BUNGA TENGGILIS MEJOYO SURABAYA Margono, Hendro; Sugihartati, Rahma; Yuadi, Imam; Srimulyo, Koko; Tri Atmi, Ragil; Dama Putri, Kania; Maulidah, Nofiyah; Vivia Adriyanti, Elvetta
Martabe : Jurnal Pengabdian Kepada Masyarakat Vol 8, No 7 (2025): MARTABE : JURNAL PENGABDIAN KEPADA MASYARAKAT
Publisher : Universitas Muhammadiyah Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31604/jpm.v8i7.2803-2812

Abstract

Pedagang bunga di Pasar Tenggilis Mejoyo, Surabaya, mengalami penurunan penjualan akibat ketatnya persaingan, terutama dengan pedagang yang telah memanfaatkan media digital. Sebagian besar pedagang masih menggunakan metode penjualan konvensional dan belum optimal dalam menggunakan platform digital untuk meningkatkan penjualan. Pengabdian masyarakat ini bertujuan untuk meningkatkan kemampuan pedagang bunga di pasar tersebut dalam menggunakan media digital sebagai sarana penjualan. Kegiatan pengabdian ini meliputi sosialisasi penggunaan media sosial, pendampingan strategi penjualan digital, serta monitoring dan evaluasi hasil pelatihan. Dari 17 pedagang, hanya 9 yang berhasil mendapatkan sosialisasi, dengan sebagian besar masih enggan beralih ke metode digital karena kekhawatiran terhadap keamanan bertransaksi online. Hasil kegiatan ini menunjukkan peningkatan keterampilan digital selling bagi sebagian pedagang, meskipun tantangan dalam partisipasi pedagang masih cukup besar.
Klasifikasi Kepribadian Karyawan Menggunakan Machine Learning Ferdiansah, Gilang; Yuadi, Imam
Riwayat: Educational Journal of History and Humanities Vol 8, No 4 (2025): October
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jr.v8i4.49440

Abstract

Pemahaman terhadap tipe kepribadian menjadi mutlak pada kondisi digitalisasi dan hybrid working. Tipe kepribadian yang umum dikenal saat ini adalah introver dan ekstrover. Organisasi yang tidak mampu memahami tipe kepribadian karyawan, akan berdampak pada penurunan motivasi dan kinerja karyawan. Salah satu cara mengklasifikasikan tipe kepribadian pegawai adalah dengan pendekatan machine learning. Evaluasi terhadap beberapa hasil pendekatan machine learning, akan memberikan model dengan kinerja terbaik yang mampu mengklasifikasikan tipe kepribadian. Model Nave Bayes menjadi model terbaik pada klasfikasi tipe kepribadian ini dengan nilai accuracy sebesar 93,41%, lebih tinggi dibandingkan model lainnya. Penelitian ini diharapkan menambah wawasan ilmu pengetahuan pada human resources analitik dan memberikan informasi klasifikasi tipe kepribadian karyawan bagi organisasi.
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