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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 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 Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Pustaka Ilmiah 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) 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 Jurnal Ilmu Multidisplin 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
Imam Yuadi
Departemen Ilmu Informasi Dan Perpustakaan, Fakultas Ilmu Sosial Dan Ilmu Politik, Universitas Airlangga
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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.
HR Analytics for Predicting Job Satisfaction in Hybrid Work Hardevianty, Melissa Yunda; Yuadi, Imam
MUKADIMAH: Jurnal Pendidikan, Sejarah, dan Ilmu-ilmu Sosial Vol 9, No 2 (2025)
Publisher : Prodi Pendidikan Sejarah Fakultas Keguruan dan Ilmu Pendidikan Universitas Islam Sumatera

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30743/mkd.v9i2.12015

Abstract

This research investigates the application of machine learning classification models for predicting employee job satisfaction, considering demographic, professional, and psychosocial aspects. With a secondary dataset obtained from Kaggle, five supervised learning techniques were applied: Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, and Gradient Boosting. The best model was considered to be Gradient Boosting as it achieved the highest accuracy and F1 score. The model's explainability was enhanced with LIME. LIME enhanced the model's explainability. Stress, work-life balance, and job tenure were identified as the primary factors of job satisfaction. These results support the Job Demands-Resources (JD-R) theory and highlight the model's effectiveness in the hands of HR professionals. The study highlights the need to achieve a balance between predictive accuracy and explainability to ethically align the use of AI in HR analytics, aiming to enhance the well-being of employees and the effectiveness of organizations.
Predicting Book Return Delays in Airlangga University Library: A Machine Learning Approach Triandari, Ayu; Yuadi, Imam
Journal of Economics and Management Scienties Volume 8 No. 1, December 2025
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/jems.v8i1.254

Abstract

This research aims to predicting the delay of book return in the Airlangga University library by using machine learning algorithms. With the consideration of approximately 1600 circulation documents from January to December 2023, several algorithms including Naïve Bayes, Support Vector Machine (SVM), Random Forest, Logistic Regression, Neural Networks, and Gradient Boosting are utilized. The Naïve Bayes model prove to be the most effective model by 92.7% accuracy and 97.7% precision in predicting return delay. The analysis of feature importance has demonstrated that a handful of features, especially days overdue, loan duration, and return date, are the main predictive variables for delay prediction in book returns outcomes. From this study, the Naïve Bayes can be an effective predictor of book return delays in Airlangga University library in order to improve user satisfaction, potentially notifying user in advance and offering alternatives. This study provided a promising picture about machine learning applications in library management systems for practical resource allocation and service quality improvement related to book return delays.
From Comments to Insight: Predictive Classification of Organizational Cultural Entropy Using SBERT, K-Means, and Logistic Regression Mayasari, Sentri Indah; Yuadi, Imam
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 4 (2025): MALCOM October 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i4.2157

Abstract

This study aims to develop a machine learning-based predictive model based on clustered data to identify cultural entropy in organizations through the analysis of open-ended comments on employee perception surveys of superiors. energy used for unproductive activities in a work environment. Entropy shows the level of conflict, friction and frustration in the environment. With a text mining approach, answers to open-ended questions in the cultural entropy survey were processed with Sentence-BERT and clustered using the K-Means algorithm into two categories, namely cultural entropy and non-cultural entropy. The dataset that already has labels from the clustering results is used to develop a classification model. The algorithms used are Random Forest, Logistic Regression, and Support Vector Machine (SVM), which are evaluated through accuracy, precision, recall, and F1-score metrics and a confusion matrix. The results show that Logistic Regression provides the best performance with an accuracy of 0.985, a precision of 1.00, and an F1-score of 0.978 without any classification errors. These findings indicate that the clustering approach followed by machine learning-based predictive is effective in identifying organizational cultural entropy. This can be used to design appropriate interventions and as an early detection system for cultural entropy in human resource management
Analisis Pengelompokan Laporan Panggilan untuk Perencanaan Respons Berbasis Data: Clustering Analysis of Call Reports for Data-Driven Response Planning Cahyani, Retno Tri; Yuadi, Imam; Margono, Hendro
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 4 (2025): MALCOM October 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i4.2168

Abstract

Setiap tahun, Call Center 112 Kabupaten Sidoarjo menerima ribuan laporan dari masyarakat, yang mencakup berbagai kejadian seperti kebakaran, kecelakaan lalu lintas, darurat medis, kabel menjuntai, pohon tumbang, dan masalah PJU. Penelitian ini menganalisis 6.207 laporan berfokus pada koordinat lokasi kejadian dengan tujuan untuk mengelompokkan pola spasial laporan sehingga dapat mendukung tata Kelola pelayanan publik yang lebih responsif. Untuk mencapai tujuan tersebut digunakan dua algoritma pembelajaran yaitu K-Means dan K-Medoids. Metode Elbow digunakan untuk menentukan jumlah klaster (k=3). Metode ini menunjukkan titik optimum ketika nilai inertia mulai menurun secara linier. Analisis menggunakan Google Colab dan ada dukungan pustaka untuk visualisasi seperti scikit-learn, pyclustering, dan matplotlib. Hasil visualisasi menunjukkan bahwa K-Medoids membentuk klaster yang lebih terstruktur secara geografis, sedangkan K-Means menghasilkan klaster yang tumpang tindih. Silhouette Score 0,479, yang lebih tinggi dari K-Means hanya 0,193, K-Medoids terbukti lebih unggul dalam membentuk klaster yang kompak dan konsisten. K-Medoids berhasil mengelompokkan wilayah yang rawan insiden (Waru, Gedangan) dan wilayah infrastruktur dominan (Sidoarjo, Candi) ke dalam klaster yang sesuai secara spasial. Analisis ini mengidentifikasi fitur tiap klaster berdasarkan jenis laporan, mulai dari darurat medis hingga masalah PJU. Penemuan ini berguna untuk mendukung alokasi sumber daya dan layanan publik yang lebih efisien saat membangun kota pintar.
Analisis bibliometrik tentang tren penerapan kebijakan kota hijau (green city) Vilosa, Bias; Yuadi, Imam
Jurnal Pengelolaan Lingkungan Berkelanjutan (Journal of Environmental Sustainability Management) JPLB, Vol 8, No 2 (2024)
Publisher : Badan Kerjasama Pusat Studi Lingkungan (BKPSL) se-Indonesia bekerjasama dengan Pusat Penelitian Lingkungan Hidup IPB (PPLH-IPB)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36813/jplb.8.2.162-178

Abstract

Environmental pollution continues to occur due to the times, so it is necessary to make improvements through sustainable development that is environmentally friendly. One form of support for sustainable development can be realized in the implementation of green city policies. To find out more about the implementation of green city policies, it can be done through analyzing trends in the application of green city policies from 2013 - 2023 in literature review research that has been published in the Web of Science (WoS) and Scopus databases using bibliometric analysis. From the search for scientific articles on green city policies, the data was visualized with the VOSviewer and RStudio applications using the biblioshiny method. The results of the research analysis are; the scope of the most popular topics with 6 (six) green city discussions, topic trends namely urban planning, sustainability, and green city policy, the high average number of citations in 2015 due to world commitment and the green city phenomenon, and the most countries that publish the USA, China, and the UK. Research trends in the implementation of green city policies produce positive data and collaborate with each other.
Analisis Bibliometrik: Perkembangan Kebijakan Pengelolaan Limbah Usaha Mikro Kecil Menengah (UMKM) berbasis Circular Economy Sabrina Hartianingrum, Hikmah; Yuadi, Imam
HIGIENE: Jurnal Kesehatan Lingkungan Vol 9 No 3 (2023): Kesehatan Lingkungan
Publisher : Public Health Department, Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/higiene.v9i3.38144

Abstract

The activities of small medium enterprises (SMES) are assessed to have a significant impact on environmental conditions if not balanced by post management of post-production waste that can lead to environmental degradation. The circular economy model is considered to be a new model to replace the linear economic model. The purpose of this study was conducted to determine the development of the economic circular model used in the management policy of SMES waste. Bibliometric research method by using Vosviewer and RSTUDIO software. Sampling used in this study is a journal article with subjects related to public policy. Data are collected using the Scopus database then the data is analyzed manually to generate the relevant articles. The results showed that research has been developed massively in 2018, 7 years since the first time the research was conducted. The focus of research has been in the simplification of production and policy flow. Later, a study to achieve the innovation of the economic circular concept on the management of the SMES waste. Finally, regulations or policies in waste management significantly have linkages with attempts to achieve development goals.
ANALISIS TEXT CLUSTERING MASYARAKAT DI TWITTER TENTANG SEPAK BOLA INDONESIA MENGGUNAKAN ORANGE DATA MINING Baihaqie, Owen; Yuadi, Imam
Jurnal Khatulistiwa Informatika Vol 11, No 2 (2023): Periode Desember 2023
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/jki.v11i2.16072

Abstract

Twitter saat ini menjadi sebuah platform media sosial yang sangat menarik untuk diamati. Topik yang sedang trend di Twitter seringkali memunculkan komentar dan opini dari masyarakat Indonesia. Salah satu topik yang menarik untuk dianalisis adalah sepak bola Indonesia yang saat ini banyak disoroti. Penelitian ini bertujuan untuk menganalisis tanggapan masyarakat terhadap topik tersebut melalui komentar di Twitter, menggunakan metode analisis Vader, tweet profiler, dan visualisasi distribution. Proses analisis dilakukan dengan menggunakan aplikasi Orange Data Mining, yang melibatkan tahapan preprocess text, seperti transformation, tokenization, normalization, dan filtering untuk memastikan teks bisa dianalisis. Hasil dari penelitian ini menunjukkan bahwa terdapat enam jenis respon dari masyarakat terhadap sepak bola Indonesia, dengan respon tertinggi adalah rasa terkejut dan takut.
Training Evaluation Analysis Using Text Mining Nainunis, Mas Akhmad; Yuadi, Imam
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 1 (2024): March 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i1.27607

Abstract

Training evaluation is an evaluation of the results of training that has been carried out. This evaluation includes technical and non-technical factors which are very important for the company to pay attention to when implementing training in the future. Many companies only implement training evaluations as a formality and only include evaluations that are limited by choice, such as closed questionnaires, training evaluations using open questionnaires can provide the freedom to provide positive or negative input that can be of concern to the company. This research aims to find out the words or topics that appear most frequently in open comments on training evaluation results by using the FP Growth algorithm and association rules to find out the relationship between topics or words from the training evaluation results. They are applied to 516 open-ended comments submitted via the post-training questionnaire. The research results showed that 15 association rules were created using Rapidminer using the FP-Growth algorithm with a minimum support of 0.02 and a minimum confidence of 0.5. All rules have a lift value>1 which indicates that all rules are valid or have a strong association relationship. This research can determine the pattern of comments or suggestions given by workers regarding training evaluation.
Co-Authors AA Sudharmawan, AA Achmad Djunawan Albigaeri, Syahruly Nizar Alifka Cellina Velby Alyusi, Shiefti Dyah Anastasya, Diva Berta Andini, Aulia Rizqi Anggraini, Pramudya Galuh Suci 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 Denaldy Oktavian Noor Rizki Dewanty, Alifia Kaltsum Dwisusilo, Aditya Endang Gunarti Enny Mar’atus Sholihah Erika Putri Fadilia Rinarwastu, Fadilia Febriano, Rizki Dwi Ferdiansah, Gilang Fitri Mutia, Fitri Gunarti, Endang Halim, Yunus Abdul Handari Niken Anggraini Hapsari, Ratih Addina Hardevianty, Melissa Yunda Hary Supriyatno Hasna, Dhia Alifia Izdihar Hendrawati, Lucy Dyah Hendro Margono Inggrid Nindia Aprila Palupi 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 M Kafi Maulana M. Fariz Fadillah Mardianto Mahardika, Synthia Amelia Putri Mariyadi, Budiyan Marsaa Salsabiila Martina Fitria Wulandari 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 Noviana Wahyu Basuki Nur Muhammad, Rizqi Nurahman, Yeni Fitria Nurul Firdausy Palupi, Inggrid Nindia Aprila Pradhana, Andrea Thrisiawan Prasetya Triputra Nugraha 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, Muthia Andriana Putri, Selviana Azzira Ragil Tri Atmi, Ragil Tri Rahardian, Dwiky 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 Saputra, Aditya Cahya Sari, Tri Kartika Setiadi, Yusuf Sherly Deasy Anjuwita Gultom Sheva Alana Brilianty Sinta Rahmadani Siswahyudianto Soesantari, Tri Sonia Tikamidia Sufryanto, Sukma Sugihartati, Rahma Suhada, Hofur Taufik Roni Sahroni 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 Yuwinanto, Helmy Prasetyo