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All Journal Humanika : Kajian Ilmiah Mata Kuliah Umum JIPI (Jurnal Ilmu Perpustakaan dan Informasi) Masyarakat, Kebudayaan dan Politik Journal of Economic, Bussines and Accounting (COSTING) Martabe : Jurnal Pengabdian Kepada Masyarakat Fair Value: Jurnal Ilmiah Akuntansi dan Keuangan JURNAL GOVERNANSI Khazanah: Jurnal Pengembangan Kearsipan Journal of Urban Sociology Building of Informatics, Technology and Science BALANCE: Economic, Business, Management and Accounting Journal JENIUS (Jurnal Ilmiah Manajemen Sumber Daya Manusia) MUKADIMAH: Jurnal Pendidikan, Sejarah, dan Ilmu-ilmu Sosial Bubungan Tinggi: Jurnal Pengabdian Masyarakat Journal of Soft Computing Exploration ABDI PUSTAKA: Jurnal Perpustakaan dan Kearsipan Literatify : Trends in Library Development Bidik : Jurnal Pengabdian kepada Masyarakat Malcom: Indonesian Journal of Machine Learning and Computer Science Pustakaloka: Jurnal Kajian Informasi dan Perpustakaan MILRev: Metro Islamic Law Review Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) The Indonesian Journal of Computer Science Madania: Jurnal Kajian Keislaman
Hendro Margono
Universitas Airlangga
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Social Sciences Other

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Membangun kepercayaan publik: visualisasi data interaktif capaian kinerja Kantor Regional IX BKN Jayapura Setiadi, Yusuf; Margono, Hendro; Yuadi, Imam
Jurnal Governansi Vol 11 No 1 (2025): Jurnal Governansi Volume 11 Nomor 1, April 2025
Publisher : Universitas Djuanda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30997/jgs.v11i1.15091

Abstract

This research aims to build public trust in the performance of XYZ Regional Office through interactive data visualization using Power BI. Public trust in government agencies is often influenced by transparency and openness in delivering performance information. In this context, interactive and easy-to-understand data visualization is important to improve public understanding of agency performance achievements. The urgency of this research lies in the need for transparency and public accountability, which can encourage active public participation in monitoring and supporting government performance. This research uses a descriptive method with a quantitative approach. The data used is the performance data of XYZ Regional Office, which is then processed and visualized using Power BI. Interactive data visualization is designed to facilitate users in exploring relevant performance information, with a focus on key indicators of agency achievement. By interpreting data findings, this research identifies factors that influence staffing dynamics, such as the total number of civil servants, retirement proposals, and promotion proposals. The implications of the findings are also discussed to provide relevant recommendations for stakeholders related to strategic staffing decision-making. The results showed that the interactive data visualization created was able to increase public understanding and positive perception of the performance of XYZ Regional Office. The novelty of this research lies in the use of Power BI as an interactive visualization tool in the context of government agencies, which has not been widely applied. This research is expected to be a reference for other government agencies to increase public trust through a data-driven approach and performance transparency.
Testing Smoker Detection Using Google Cloud Services and Infrastructure Muhammad Mustajib; Sri Gunawan; Aldo Lovely Arief Suyoso; Hendro Margono; Muhammad Rafi Solakhudin
Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) Vol 11 No 2 (2024): Jurnal Ecotipe, October 2024
Publisher : Jurusan Teknik Elektro, Universitas Bangka Belitung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33019/jurnalecotipe.v11i2.4499

Abstract

Smoking remains a significant public health challenge globally, contributing to a wide range of detrimental health outcomes including cardiovascular diseases, cancer, and respiratory disorders. Despite concerted efforts to curb smoking rates through policy interventions, effective monitoring and enforcement remain complex and resource-intensive tasks for health authorities and organizations. Innovative approaches leveraging advanced technologies such as visual detection systems powered by deep learning offer promising solutions to enhance smoking behavior detection and monitoring. Integrating the Google Cloud Vision API enables real-time identification of smoking indicators and discrimination from complex visual backgrounds. This capability not only supports proactive health monitoring but also strengthens the enforcement of public health policies aimed at reducing smoking prevalence. The research methodology utilizes a dataset of 600 images sourced from the Kaggle platform, encompassing diverse scenarios to optimize model training. Techniques such as image segmentation, feature extraction, and machine learning-based classification are employed to achieve high levels of precision and recall in identifying smokers and cigarette smoke. Despite the advantages of scalability, robust infrastructure, and high availability facilitated by cloud computing, the study acknowledges challenges such as bandwidth constraints and security risks associated with handling sensitive health data. Nevertheless, technological innovations in visual detection systems and cloud services are underscored as pivotal in mitigating the health impacts of smoking and advancing public health initiatives.
Strategies for Enhancing Graduate Employability in Islamic and State Universities: A Comparative Study in Indonesia Adriansyah, Muhammad Ali; Handoyo, Seger; Margono, Hendro; Tondang, Edoardo; Julian, Antoni
Madania: Jurnal Kajian Keislaman Vol 29, No 1 (2025): JUNE
Publisher : Universitas Islam Negeri (UIN) Fatmawati Sukarno Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29300/madania.v29i1.7818

Abstract

This study explores strategies to enhance graduate employability in Indonesia’s Islamic-based universities and state universities. Employing a descriptive qualitative approach, data were collected through in-depth interviews with university managers, lecturers, students, and alumni, supported by documentation studies and participatory observations. Thematic coding and data triangulation were used to analyze the data, ensuring the depth and validity of findings. The results reveal distinct institutional approaches: Islamic universities emphasize character development and work ethics rooted in spiritual values through programs like tahfiz-based leadership training and religious mentoring, while state universities focus on strengthening technical competencies and industry networks through structured internships and career development centers. Despite these differences, both institutions actively align higher education with labor market needs. This research contributes to the theoretical discourse on employability by introducing a new conceptual framework that integrates spiritual values with professional skills. The framework includes three core components: values-based curriculum integration, collaborative industry engagement, and holistic graduate profiling. These findings offer insights for policymakers and educators in developing inclusive and contextually relevant higher education models in pluralistic societies. Penelitian ini mengkaji strategi peningkatan daya saing lulusan di perguruan tinggi berbasis Islam dan perguruan tinggi negeri di Indonesia. Dengan menggunakan pendekatan kualitatif deskriptif, data dikumpulkan melalui wawancara mendalam dengan pimpinan universitas, dosen, mahasiswa, dan alumni, serta didukung oleh studi dokumentasi dan observasi partisipatif. Analisis data dilakukan melalui teknik pengkodean tematik dan triangulasi untuk menjamin kedalaman dan validitas temuan. Hasil penelitian menunjukkan adanya pendekatan kelembagaan yang berbeda: perguruan tinggi Islam menekankan pada pembentukan karakter dan etos kerja yang berlandaskan nilai-nilai spiritual melalui program seperti pelatihan kepemimpinan berbasis tahfiz dan pembinaan keagamaan, sedangkan perguruan tinggi negeri lebih menitikberatkan pada penguatan kompetensi teknis dan jaringan profesional melalui program magang terstruktur dan pusat pengembangan karier. Meskipun memiliki perbedaan karakteristik, kedua jenis institusi sama-sama menunjukkan upaya aktif dalam menghubungkan pendidikan tinggi dengan kebutuhan dunia kerja. Secara akademik, penelitian ini berkontribusi dalam pengembangan diskursus teoretis mengenai employability dengan menawarkan kerangka konseptual baru yang mengintegrasikan nilai-nilai spiritual dengan keterampilan profesional. Kerangka ini mencakup tiga komponen utama: integrasi kurikulum berbasis nilai, kolaborasi strategis dengan dunia industri, dan profil lulusan yang holistik. Temuan ini memberikan wawasan bagi para pengambil kebijakan dan pendidik dalam merancang model pendidikan tinggi yang inklusif dan kontekstual di masyarakat yang pluralistik.
A Simulation of Student Study Group Formation Design Using K-Means Clustering Putra, Yudistira Ardi Nugraha Setyawan; Margono, Hendro
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 2 (2025): MALCOM April 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i2.1795

Abstract

This research focuses on developing a simulation model for forming student study groups using an enhanced K-Means algorithm, addressing the challenge of optimizing group dynamics to improve learning outcomes. By analyzing the effectiveness of the formed study groups through RMSE (Root Mean Square Error) after dimensionality reduction with various regression models—including Linear Regression, Ridge Regression, Lasso Regression, Elastic Net, Random Forest Regressor, Gradient Boosting Regressor, and XGBoost Regressor—we aim to provide educators with a robust tool for assessing group configurations. The study identifies four distinct clusters, revealing that "Previous_Score" and "Attendance" are critical variables, achieving a highest Silhouette Score of 0.64 with five selected features. The ridge regression model also yielded a low RMSE of 0.045, explaining 72.39% of the variance in "Exam_Score." The findings suggest that targeted interventions tailored to each cluster—yellow, purple, blue, and green—can enhance academic outcomes by addressing specific student needs. This data-driven approach optimizes group dynamics and fosters a more inclusive learning environment, enhancing academic performance and cultivating essential social skills. The study underscores the potential of machine learning techniques in education and suggests avenues for future research into alternative clustering methods and their long-term impact on student engagement and success.
Instructor Performance Analysis in Educational Contexts Based on Learner Evaluation Data: Integration of Clustering and Predictive Model Lestari, Santi Dwi Desy; Margono, Hendro
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.v9i2.11772

Abstract

This study aims to analyze instructor performance in educational contexts by classifying instructors based on learner evaluation data through the K-Means clustering algorithm and developing a predictive model to support effective and targeted instructor development programs. The data were derived from learners’ evaluations of instructors, covering aspects such as discipline and professionalism, mastery of subject matter, and pedagogical skills in delivering content. The results indicate that k=3 is the optimal cluster, producing three categories: Superior Instructor, Potential Instructor, and Developing Instructor. Furthermore, the predictive model demonstrates that the Naive Bayes algorithm outperforms XGBoost in performance prediction, achieving higher accuracy, recall, precision, and F1-scores. The integration of clustering and prediction proves effective in enabling faster, objective, and data-driven decisions for instructor development. These findings provide significant implications for educational institutions in establishing adaptive and sustainable systems of instructor evaluation and management.‎
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.
Predictive Sales Analysis in Coffee Shops Using the Random Forest Algorithm Windrasari, Shella Norma; Margono, Hendro; Putra, Yudistira Ardi Nugraha Setyawan
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 3 (2025): MALCOM July 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i3.2023

Abstract

The coffee shop industry has experienced significant growth, evolving into a highly competitive marketplace demanding specialty coffee and personalized experiences. While data-driven strategies are crucial for optimizing operations, many owners still struggle to effectively leverage their sales data to understand dynamic customer behavior and enhance decision-making. Addressing this gap, this study explores the application of machine learning (ML) techniques, specifically the Random Forest Regressor model, to predict sales performance within the coffee shop business environment. By analyzing factors such as transaction timing, store location, product type, and day of the week, this research aims to uncover patterns that can enhance inventory management and customer engagement. The Random Forest model was evaluated through cross-validation, yielding a mean Mean Squared Error (MSE) of 80.97, which indicates moderate predictive accuracy and represents an improvement over traditional forecasting methods commonly employed in the industry. Feature importance analysis revealed that Premium Beans is the most influential predictor, followed by seasonal trends (month), time of day, and weekend sales patterns. These findings underscore the importance of incorporating temporal and contextual factors into forecasting models. 
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.
The Urgency of Disability Services in the Library with Literature Review Rifa, Syafira Dila; Rosyani, Widha; Margono, Hendro
Pustakaloka Vol. 16 No. 1 (2024)
Publisher : IAIN Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21154/pustakaloka.v16i1.8524

Abstract

Penyandang disabilitas mengalami keterbatasan yang membuat akses ke berbagai layanan, termasuk di perpustakaan, menjadi sulit. Untuk itu, diperlukan layanan khusus di perpustakaan agar mereka dapat mengakses informasi dengan lebih mudah. Peraturan Pemerintah Nomor 23 Tahun 2014 mengatur pelaksanaan Undang-Undang Nomor 43 Tahun 2007 tentang perpustakaan, yang menekankan pentingnya layanan non-diskriminatif bagi penyandang disabilitas. Penelitian ini bertujuan mengatasi kurangnya artikel jurnal mengenai layanan disabilitas di perpustakaan dengan menggunakan metode literature review, yang melibatkan pencarian jurnal terkait di Google Scholar. Data dari jurnal-jurnal tersebut menunjukkan bahwa beberapa perpustakaan masih memiliki layanan disabilitas yang kurang memadai, sementara yang lain sudah mulai memperhatikan pentingnya keamanan dan kenyamanan bagi penyandang disabilitas. Artikel ini membahas pengenalan kebutuhan pengguna disabilitas, pelatihan petugas perpustakaan, aksesibilitas fasilitas, dan inovasi digital untuk aksesibilitas universal di era digital bagi penyandang disabilitas.
Co-Authors Adriansyah, Muhammad Ali Aestikani Mahani Aldo Lovely Arief Suyoso Aldo Lovely Arief Suyoso Aliffatin, Ety Subekti Anastasya, Diva Berta Anggraini, Handari Niken Arief Suyoso, Aldo Lovely Arum Karisma Nadya Lakshita Bastian, Devi Br Sihaloho, Dwi Paskah Marito Cahyani, Retno Tri Caspari, Andrean Dama Putri, Kania Dian Dwi Nur Rahmah Dwi Permana Putra Faiza, Nova Auliatul Fauziah, Salsa Fenny Nur Aprillia Fitri Mutia, Fitri Gunarti, Endang Halim, Yunus Abdul Hary Supriyatno Imam Yuadi Inriani, Sefania Wenny Eka Julian, Antoni Khithobi, Arya Teges Koko Srimulyo Lestari, Nur Laili Indah Fuji Lestari, Santi Dwi Desy Maulidah, Nofiyah Meilinda Putri, Ulfa Muchammad Toyib Muhammad Mustajib Muhammad Rafi Solakhudin Nadya Ananda Tri Anugrah Putri Nawwaf Faruq Adina Putra Niken Ayu Pratiwi, Bertha Nurul Jamila Hariani Prasetyo Yuwinanto, Helmy Prasyesti Kurniasari, Meinia Putra, Yudistira Ardi Nugraha Setyawan Ragil Tri Atmi, Ragil Tri Rahma, Nadhifia Shabrina Nur Rasyid, Rizka Adristi Rifa, Syafira Dila Rinanda, Tiara Disti Ronal Kamim Rosyani, Widha Seger Handoyo Setiadi, Yusuf Setiyarini, Melina Dewi Sheikh Adnan Ahmed Usmani Soegiono, Agie Nugroho Soesantari, Tri Solly Aryza Sugihartati, Rahma Suharbi, Muhammad Akbar Syaidatul Fiza Ma'arif Tondang, Edoardo Vivia Adriyanti, Elvetta Windrasari, Shella Norma Yuwinanto, Helmy Prasetyo