Article Per Year (5 Year)
p-Index From 2021 - 2026
13.543
P-Index
This Author published in this journals
All Journal International Journal of Informatics and Communication Technology (IJ-ICT) 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) Jurnal Abdi Insani 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 Journal of Business, Social and Technology 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 Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Other

Published : 112 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 6 Documents
Search
Journal : malcom indonesian journal of machine learning and computer science

 1 
Klasifikasi Jenis Jerawat Berdasarkan Convolutional Neural Network: Classification of Acne Type Based on Convolutional Neural Network Andini, Aulia Rizqi; Yuadi, Imam
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 1 (2025): MALCOM January 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i1.1678

Abstract

Jerawat biasanya dimulai pada masa awal pubertas dengan meningkatnya produksi minyak pada wajah. Penelitian ini merupakan implementasi dari klasifikasi dan pendeteksian jenis jerawat menggunakan Image Processing. Jerawat dapat diklasifikasikan kedalam beberapa jenis, yaitu komedo hitam, komedo putih, pustula, dan papula. Klasifikasi menggunakan Deep Learning metode CNN menggunakan pustaka Tensforflow Keras. Berdasarkan penelitian yang telah dilakukan, tujuan penelitian ini untuk mengklasifikasikan berbagai macam jenis jerawat dengan data gambar yang dimiliki setiap jenis jerawat. Hasil penelitian yang didapat dari hasil pengujian menghasilkan nilai akurasi yang tinggi 96.57% dan Loss 24.78%. Menggunakan Deep Learning terbukti bekerja cukup efisien karena telah menghasilkan nilai akurasi yang tinggi.
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.
Implementation of Machine Learning to Predict The Timeliness of Graduation of Employees on Study Assignment at Company X Parenda Rizkya Permata; Imam Yuadi
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 2 (2026): MALCOM April 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i2.2474

Abstract

The energy transition requires workers in the energy sector who have relevant skills that can be applied in the future. Company X implements a study assignment program to improve its employees' skills, but delays in completing their studies hinder their readiness to enter the workforce. Identifying the factors that influence graduation timeliness can improve the program's effectiveness. This study aims to develop a predictive model to determine whether employees in Company X's work-study program will graduate on time. The main purpose of this model is to provide early warnings about employees at risk of delays, enabling more targeted interventions to improve human resource management. We applied the CRISP-DM framework and used Machine Learning to analyze data from 317 employees who participated in the study program. Four machine learning algorithms were tested, namely Gradient Boosting, Decision Tree, Random Forest, and Naive Bayes. 17 factors were trained to cover academic, demographic, and administrative aspects to predict timely graduation. Among the algorithms tested, Gradient Boosting showed the best performance with an AUC of 0.956 and an accuracy of 0.909. These results were supported by high ROC and confusion matrix values, indicating the model's excellent predictive ability. 
Unsupervised Text Mining of Employee Feedback for Identifying Organizational Strengths and Improvement Areas Febri Ari Wicaksono; Imam Yuadi
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 2 (2026): MALCOM April 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i2.2482

Abstract

Employee feedback provides rich signals about organizational performance, yet its free-text format makes systematic analysis at scale difficult. This study proposes an unsupervised text mining workflow in Orange Data Mining to extract actionable themes from continuous employee comments by separating two semantic polarities: strength feedback (“What went well?”) and improvement feedback (“What could be improved?”). After cleaning and Indonesian-language preprocessing (Sastrawi stemming, custom stopwords), 3,406 strength and 3,172 improvement entries were represented using TF–IDF. Improvement feedback was clustered using K-Means and assessed with silhouette-based validation, while both feedback types were explored using LDA topic modeling supported by topic coherence checks for interpretability. The results reveal recurring organizational themes related to goal execution and performance, supervision, communication/coordination, and motivation, with notable vocabulary overlap between strengths and areas for improvement. Scientifically, this work demonstrates how polarity-aware unsupervised analytics improves interpretability compared to treating feedback as a single corpus, and practically, it provides a scalable way for managers to transform unstructured feedback into structured insights for targeted improvement initiatives.
Klasifikasi Kondisi Isolator Menggunakan Logistic Regression dan Support Vector Machine dengan Analisis Citra Berbasis VGG-16, VGG-19, dan Inception V3: Classification of Insulator Conditions Using Logistic Regression and Support Vector Machine with Image Analysis Based on VGG-16, VGG-19, and Inception V3 Purwaningtyas, Aris; Yuadi, Imam
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 3 (2026): MALCOM July 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i3.2652

Abstract

Keandalan sistem ketenagalistrikan sangat bergantung pada kondisi isolator sebagai komponen yang mencegah kebocoran arus listrik, karena kerusakannya dapat memicu gangguan hingga pemadaman berskala luas. Meskipun inspeksi menggunakan Unmanned Aerial Vehicle (UAV) semakin banyak diterapkan, analisis visual masih dilakukan secara manual sehingga cenderung subjektif, tidak konsisten, dan memerlukan waktu yang lama. Penelitian ini mengembangkan sistem klasifikasi kondisi isolator berbasis citra menggunakan platform Orange Data Mining. Model pre-trained Deep Convolutional Neural Network (DCNN), yaitu VGG-16, VGG-19, dan Inception V3, dimanfaatkan sebagai feature extractor, sedangkan Logistic Regression dan Support Vector Machine (SVM) digunakan sebagai algoritma klasifikasi untuk membedakan isolator normal dan terkontaminasi. Dataset terdiri atas 168 citra isolator keramik dan kaca yang diperoleh melalui inspeksi lapangan menggunakan drone. Evaluasi dilakukan menggunakan 5-fold stratified cross-validation untuk menjaga distribusi kelas pada setiap fold. Hasil menunjukkan bahwa kombinasi VGG-19 dan Logistic Regression memberikan performa terbaik dengan akurasi 96,4%, AUC 0,986, dan Matthews Correlation Coefficient (MCC) 0,929, serta menurunkan false positive menjadi 3,6%. Temuan ini menunjukkan efektivitas pendekatan hybrid DCNN–machine learning untuk deteksi dini kondisi isolator secara otomatis.
Co-Authors AA Sudharmawan, AA Achmad Djunawan Aditya Cahya Saputra 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 Berlian Savitri Berliani, Kezia Putri Billy Hiskia Sigalingging Bondan Ari Wijaya Caecilia Eva Martina Korompis 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 Hilwah Hilwah Indah Nur Maulidya 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 Naufal Zaafarani 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 Ollyvia Pujasari Anggraini 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 Stanislaus Junanta Sugihartati, Rahma Suhada, Hofur Sukma Sufryanto Tikamidia, Sonia Toetik Koesbardiati Tri Hadi Wicaksono Triandari, Ayu Ulfa Meilinda Putri Ullin Nihaya Unas, Frisca Maria Vilosa, Bias Vivia Adriyanti, Elvetta Wardani, Hesti Ari Wettebossy, Anita Elizabeth Wildan Habibi Yasmina Sholihatunnisa Yuniawan Heru Santoso Yusi Dyah Patriani Yuwinanto, Helmy Prasetyo