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All Journal J@TI (TEKNIK INDUSTRI) Jurnal Ilmiah Teknologi dan Rekayasa 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 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 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 Jurnal Ekonomi Manajemen Sistem Informasi 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 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 Teknosains : Jurnal Sains,Teknologi dan Informatika Journal of Computer Networks, Architecture and High Performance Computing Unilib: Jurnal Perpustakaan Jurnal Pemerintahan dan Kebijakan (JPK) BIOLOVA Journal of Technology and Informatics (JoTI) 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 Jurnal Ilmu Multidisplin Malcom: Indonesian Journal of Machine Learning and Computer Science 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 Jurnal Informatika
Imam Yuadi
Departemen Ilmu Informasi Dan Perpustakaan, Fakultas Ilmu Sosial Dan Ilmu Politik, Universitas Airlangga
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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
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.
Klasifikasi Penyakit Daun Padi Menggunakan Convolutional Neural Network (CNN) Berbasis Pengolahan Citra Digital untuk Mendukung Ketahanan Pangan Nasional Dwisusilo, Aditya; Yuadi, Imam
Jurnal Ilmu Multidisiplin Vol. 4 No. 6 (2026): Jurnal Ilmu Multidisplin (Februari - Maret 2026)
Publisher : Green Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/jim.v4i6.1617

Abstract

Penelitian ini mengkaji penyakit daun padi sebagai faktor utama yang menyebabkan penurunan hasil panen di Indonesia, di mana pemeriksaan manual oleh petani atau petugas lapangan sering berlangsung lambat, bersifat subjektif, dan rentan salah identifikasi. Kondisi ini menunjukkan perlunya sistem deteksi yang cepat, konsisten, dan akurat berbantuan teknologi digital. Penelitian ini bertujuan mengembangkan model klasifikasi penyakit daun padi menggunakan Convolutional Neural Network dengan arsitektur EfficientNetB0 yang disesuaikan dalam kerangka pengolahan citra digital. Metode meliputi ekstraksi ciri otomatis, pembagian data secara proporsional menjadi kelompok pelatihan dan pengujian, serta optimasi model menggunakan prosedur komputasi. Kinerja model dinilai melalui akurasi, ketepatan, sensitivitas, skor F1, dan analisis matriks kebingungan. Hasil penelitian menunjukkan bahwa model mencapai tingkat akurasi tinggi pada kisaran 93–96 persen dengan performa yang stabil di seluruh kategori penyakit. Temuan ini menegaskan bahwa model mampu menangkap karakter visual kompleks penyakit daun padi dan memiliki potensi kuat untuk diintegrasikan dalam sistem deteksi dini otomatis. Sistem tersebut dapat meningkatkan ketepatan pemantauan penyakit dan mendukung ketahanan pangan nasional melalui pengurangan kehilangan hasil panen serta peningkatan kualitas pengambilan keputusan budidaya padi.
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.
Cyberloafing Analytics: Predicting Causes Using Machine Learning Models Ferdiansah, Gilang; Yuadi, Imam
JRST (Jurnal Riset Sains dan Teknologi) Volume 10 No. 1, March 2026: JRST
Publisher : Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/jrst.v10i1.25997

Abstract

Cyberloafing refers to the practice of employees utilizing internet access for non-job-related activities during work hours. Cyberloafing poses a dilemma for organizations, as it is deemed aberrant conduct that might impact overall performance. Consequently, organizations must ascertain the determinants of cyberloafing. This study seeks to identify a suitable predictive model for the determinants of cyberloafing behavior in the workplace using a machine learning methodology. The employed methodology utilizes the conventional data mining cycle, namely the Cross-Industry Standard Process for Data Mining (CRISP-DM), with Orange Data Mining as the application tool. The findings indicate that Logistic Regression is the most effective model for forecasting cyberloafing. Logistic Regression yields performance scores of 90.5% Precision and 88.9% Recall. Conversely, the Naïve Bayes model had the lowest metrics, with a Precision of 64.8% and a Recall of 51.9%. This study serves as a reference demonstrating that Logistic Regression effectively predicts cyberloafing. This study enables firms to examine the factors contributing to cyberloafing, facilitating the development of policies aimed at mitigating its adverse effects.
Knowledge landscape of open access in academic libraries through bibliometric analysis 2020-2025 Bestari, Melati Purba; Yuadi, Imam; Albigaeri, Syahruly Nizar
Jurnal Kajian Informasi & 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.  
Klasifikasi Cacat Permukaan Keramik Menggunakan Logistic Regression dan SVM Berbasis CNN Inggrid Nindia Aprila Palupi; Budiyan Mariyadi; Imam Yuadi; Taufik Roni Sahroni
Jurnal Ekonomi Manajemen Sistem Informasi Vol. 7 No. 4 (2026): Jurnal Ekonomi Manajemen Sistem Informasi (Maret - April 2026)
Publisher : Dinasti Review

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/jemsi.v7i4.7551

Abstract

Klasifikasi dalam mendeteksi cacat permukaan pada ubin keramik merupakan langkah penting dalam memastikan kualitas produk di industri manufaktur. Klasifikasi yang akurat sangat diperlukan untuk meningkatkan kualitas hasil produksi dan mengurangi kesalahan faktor manusia. Penelitian ini bertujuan untuk deteksi dan klasifikasi secara akurat pada jenis cacat baik yang bertekstur 2D dan 3D. Metode yang diusulkan dengan menggunakan Logistic Regression dan dibandingkan dengan Support Vector Machine. Dalam Penelitian ini menggunakan 133 data jenis cacat yang diambil menggunakan kamera smartphone dengan sudut 45˚. Proses pelatihan menggunakan 66% data yang dilatih dengan model Inception V3, VGG-16 dan VGG-19 kemudian 34% data jenis cacat untuk pengujian. Logistic Regression dan Support Vector Machine dengan Inception V3 memberikan hasil klasifikasi terbaik dengan akurasi dan presisi 0,99 dengan kemampuan untuk klasifikasi 100% jenis cacat seperti gompal, lubang, terkelupas, retak dengan tekstur 2D. Sedangkan VGG-19 dapat melakukan klasifikasi 100% pada jenis cacat gelembung dengan tekstur 3D. Waktu pelatihan dan pengujian Logistic Regression dengan Inception V3 6,9 dan 2,1 detik dan VGG-19 membutuhkan waktu pelatihan dan pengujian 53,8 dan 5,36 detik. Sedangkan Support Vector Machine dengan Inception V3 membutuhkana waktu pelatihan dan pengujian 6,6 dan 4,7 detik, sedangkan VGG-19 membutuhkan waktu pelatihan dan pengujian 10,1 dan 4,7 detik.
Co-Authors AA Sudharmawan, AA Achmad Djunawan Albigaeri, Syahruly Nizar Alifka Cellina Velby Anastasya, Diva Berta Andini, Aulia Rizqi Anggraini, Pramudya Galuh Suci Artha Rachma Widiastuti Azmi, Muhammad Izharul Baihaqie, Owen Berliani, Kezia Putri Budiyan Mariyadi Cahyani, Retno Tri Christia, Tifani Dewi Condro Rahino Mustikaning Pawestri Dama Putri, Kania Dewanty, Alifia Kaltsum Dwisusilo, Aditya Endang Gunarti Enny Mar’atus Sholihah Erika Putri Fadilia Rinarwastu, Fadilia Febriano, Rizki Dwi Ferdiansah, Gilang Fitri Mutia, Fitri Fitria Wulandari, Martina Gunarti, Endang Halim, Yunus Abdul Handari Niken Anggraini Hapsari, Ratih Addina Hardevianty, Melissa Yunda Hasna, Dhia Alifia Izdihar Hendrawati, Lucy Dyah Inggrid Nindia Aprila Palupi Ira Puspitasari Ira Puspitasari Ismi Choirunnisa Prihatini Kartika Sari, Della Kezia Rahmawati Santosa Koko Srimulyo Lathifah, Lathifah Lestari, Santi Dwi Desy Lifindra, Stevanie Aurelia M Kafi Maulana M. Fariz Fadillah Mardianto Mahardika, Synthia Amelia Putri Margono, Hendro Mariyadi, Budiyan Marsaa Salsabiila Maulidah, Nofiyah Mayasari, Sentri Indah Melati Purba Bestari, Melati Purba Mochammad Edris Effendi Muhammad Rafi Raihan Nabilla Salsabil Damayanti Zahraa Nainunis, Mas Akhmad Nazikhah, Nisak Ummi Niken Ayu Pratiwi, Bertha Novia, Asradiani Nur Muhammad, Rizqi Nurahman, Yeni Fitria Nurul Firdausy Palupi, Inggrid Nindia Aprila Pradhana, Andrea Thrisiawan Prasetyo Yuwinanto, Helmy Prasyesti Kurniasari, Meinia Prayitna, Thomas Wigung Aji Purba, Trie Dinda Maharani Putra, Dwi Permana Putra, Nawwaf Faruq Adina 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 Salsabila, Chyntia Shafa Santoso, Yuniawan Heru Sari, Tri Kartika Setiadi, Yusuf Sheva Alana Brilianty Sinta Rahmadani Soesantari, Tri Sufryanto, Sukma Sugihartati, Rahma Suhada, Hofur Taufik Roni Sahroni Tikamidia, Sonia Tri Hadi Wicaksono Triandari, Ayu Ullin Nihaya Unas, Frisca Maria Vilosa, Bias Vivia Adriyanti, Elvetta Wardani, Hesti Ari Wettebossy, Anita Elizabeth Wildan Habibi Yuwinanto, Helmy Prasetyo Zidny, Irvan