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All Journal Jurnal Lanskap Indonesia Rotasi Agrifor : Jurnal Ilmu Pertanian dan Kehutanan DiH : Jurnal Ilmu Hukum JAM : Jurnal Aplikasi Manajemen Rekayasa Mesin Sinergi Journal of Educational Science and Technology MALIH PEDDAS JMPM (Jurnal Material dan Proses Manufaktur) Jurnal Pertanian Terpadu SWARNADWIPA: Jurnal Kajian Sejarah, Sosial, Budaya, dan Pembelajarannya Turbo : Jurnal Program Studi Teknik Mesin Indonesian Journal of Educational Counseling GUIDENA: Jurnal Ilmu Pendidikan, Psikologi, Bimbingan dan Konseling JBKI (Jurnal Bimbingan Konseling Indonesia) Jurnal Lentera Pendidikan Pusat Penelitian LPPM UM Metro Agroekoteknologi Tropika Lembab Jurnal Teknik Mesin Indonesia International Journal of Advanced Technology in Mechanical, Mechatronics and Material (IJATEC) Abdi Kami : Jurnal Pengabdian Kepada Masyarakat Teknomekanik Jurnal Pengabdian Kepada Masyarakat (JPKM) Tabikpun Sinar Sang Surya: Jurnal Pusat Pengabdian kepada Masyarakat International Journal of Community Service JIKI (Jurnal Ilmu Komputer dan Informatika) Jurnal Abdimas ADPI Sains dan Teknologi Counseling Milenial (CM) Sinergi Polmed : Jurnal Ilmiah Teknik Mesin Abdimas Indonesian Journal Florona : Jurnal Ilmiah Kesehatan International Journal of Innovation in Mechanical Engineering and Advanced Materials PROSIDING SEMINAR NASIONAL PENELITIAN DAN PENGABDIAN KEPADA MASYARAKAT (SNPPM) UNIVERSITAS MUHAMMADIYAH METRO IRA Jurnal Pengabdian Kepada Masyarakat JIEP: Jurnal Ilmu Ekonomi dan Pembangunan ABDI KAMI: Jurnal Pengabdian Kepada Masyarakat
Hadi Pranoto
Universitas Muhammadiyah Metro
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Peran Guru Bimbingan dan Konseling melalui Layanan Bimbingan Klasikal untuk mengurangi Kenakalan Remaja Aziz Aryoso, Ilham; Mudaim, Mudaim; Pranoto, Hadi
Counseling Milenial (CM) Vol. 7 No. 1 (2025): DECEMBER
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/konselor.v7i1.10981

Abstract

The purpose of this study was to determine the profile of juvenile delinquency committed by students and the role of teachers in providing advice to students in reducing juvenile delinquency through classical guidance at SMK Muhammadiyah 2 Metro. This study used a descriptive methodology. The method of collecting information in this exploration is a subjective explanation strategy through observation and interviews. The instruments used in this exam are observation and interview guidelines. The results of the examination can be summarized as follows: (1) The types of juvenile delinquency committed by students are absenteeism, smoking, being late and going to the thermos during teaching and learning activities. (2) The stages of implementing classical guidance services consist of three stages, namely: initial/initial stage, implementation stage, and closing/assessment stage. (3) Through the implementation of classical guidance services, students actually want to reduce juvenile delinquency by improving their way of behaving, the desire to change is accompanied by the ability of students to reduce juvenile delinquency by choosing good associations with this also having a good impact on student behavior and mentality
Pengembangan Media Layanan Bimbingan Klasikal Berbasis TikTok tentang Pentingnya Self-Love dan Cara Menerapkannya dalam Diri Remaja Maylinda, Shelly; Pranoto, Hadi; Susanto, Eko
Counseling Milenial (CM) Vol. 7 No. 1 (2025): DECEMBER
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/konselor.v7i1.10982

Abstract

The purpose of this research is to develop a TikTok-based classical guidance service media on the importance of self-love and how to apply it to adolescents. This research model is based on the development model by Dick and Carry (1996), with a modified development plan consisting of four stages: analysis, design, development, and implementation. Based on the research results, product development in the form of TikTok video content showed a positive response with feasibility results of "Very Feasible" for all indicators. From the data obtained it can be concluded that the delivery of material, design and discussion is clear, the media is designed attractively, the use of media can increase understanding and this media is useful in classical guidance service activities.
Multiclass gas pipeline leak detection using multi-domain signals and genetic algorithm-optimized classification models Suprihatiningsih, Wiwit; Romahadi, Dedik; Pranoto, Hadi; Youlia, Rikko Putra; Anggara, Fajar; Rahmatullah, Rizky
Teknomekanik Vol. 9 No. 1 (2026): Regular Issue
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/teknomekanik.v9i1.38372

Abstract

Pipeline networks are critical infrastructure for oil and gas transport because the occurrence of leaks can rapidly escalate into safety, economic, and environmental crises. Operators are practically required to identify the presence and type of leaks; however, applying multiclass recognition is challenging when labeled data and computing power are limited. Therefore, this study proposes a three-stage pipeline which consists of: (1) adopting the GPLA-12 dataset of acoustic or vibration signals spanning 12 leak types; (2) extracting multi-domain features by combining time-domain descriptors with Power Spectral Density (PSD)-based spectral features; and (3) applying a genetic algorithm (GA) as a wrapper for feature selection to enhance discriminability and reduce dimensionality, which was followed by benchmarking seven conventional classifiers and GA-based refinement of the top model with a focus on the feature subset and hyperparameters. A maximum accuracy of 96.35% was achieved on the GPLA-12 dataset with low computation time and a simple model architecture. The proposed pipeline also attained similar or better accuracy at substantially lower complexity and data requirements compared with prior deep CNN approaches. These results support timely multiclass decision-making in resource-constrained industrial settings. A key observation was that the focus was on supervised leak-type classification from acoustic or vibration signals, while localization, severity estimation, and multi-sensor fusion were beyond the scope of this study.
Pelatihan Penerapan AI untuk Analisis Beban Kerja di PT ASD Atep Afia Hidayat, Atep; Gunardi, Yudi; Pranoto, Hadi; Kholil, Muhammad; Haekal, Jakfat
IRA Jurnal Pengabdian Kepada Masyarakat (IRAJPKM) Vol 3 No 3 (2025): Desember
Publisher : CV. IRA PUBLISHING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56862/irajpkm.v3i3.344

Abstract

Employee productivity is a key determinant of organizational competitiveness; however, manual workload analysis often leads to imbalances between employees’ capacity and assigned tasks, resulting in inefficiency and reduced well-being. This program aims to provide training on the application of Artificial Intelligence (AI), particularly Artificial Neural Networks, for workload analysis to optimize task distribution and enhance productivity at PT Anugerah Sarana Dinamika. The training consisted of a needs assessment, theoretical sessions on AI, hands-on practice in developing a prototype based on company workload data, mentoring, and workload scenario simulations. Evaluation using pre–post tests on a Likert scale showed a significant improvement in participants’ understanding of AI concepts (average score increased from 2.1 to 4.2), with 80% of participants able to operate the workload analysis prototype and an overall satisfaction rate of 88%. The program successfully improved participants’ digital literacy and technical skills and demonstrated the effectiveness of AI in supporting sustainable human resource management transformation.
Lokakarya Sistem Prediksi Pemeliharaan Mesin Menggunakan Algoritma Random Forest pada Perusahaan Layanan Perawatan Kendaraan Almahdy, Indra; Isradi, Muhammad; Pranoto, Hadi; Kholil, Muhammad; Haekal, Jakfat
IRA Jurnal Pengabdian Kepada Masyarakat (IRAJPKM) Vol 3 No 3 (2025): Desember
Publisher : CV. IRA PUBLISHING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56862/irajpkm.v3i3.345

Abstract

This community service program was conducted to address the low efficiency of conventional machine maintenance systems and the high risk of unexpected breakdowns in vehicle service companies. The program aimed to introduce a predictive maintenance system based on the Random Forest algorithm, enabling partners to shift from reactive maintenance practices toward data-driven decision-making. The applied methods included a needs assessment, theoretical training on machine learning fundamentals for 10 participants, hands-on practice using Python and Google Colab, and guided case studies based on the partner’s historical machine data. Evaluation results indicated a 42% improvement in participants’ technical understanding, while the developed system demonstrated the capability to detect potential failures 2–3 days earlier than traditional methods. The implementation successfully reduced unscheduled downtime and operational costs. Overall, this program enhanced the partner’s capacity to implement predictive maintenance, strengthened technicians’ digital literacy, and supported sustainable digital transformation in the vehicle service sector.
Pelatihan Peramalan Permintaan Berbasis Machine Learning untuk Optimalisasi Produksi Mendukung SDG 8 Riyadi, Selamet; Kholil, Muhammad; Rukyat, Nanang; Pranoto, Hadi; Haekal, Jakfat
IRA Jurnal Pengabdian Kepada Masyarakat (IRAJPKM) Vol 3 No 3 (2025): Desember
Publisher : CV. IRA PUBLISHING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56862/irajpkm.v3i3.346

Abstract

This community service program aims to enhance the capabilities of business actors and industry practitioners to apply machine learning-based demand forecasting to optimize production and support the achievement of SDG 8. The training was conducted through interactive workshops consisting of conceptual introduction, hands-on model development, and analysis of forecasting results using production datasets. Participants were guided in variable selection, model accuracy evaluation, and the use of prediction outputs for production decision-making. Evaluation results indicate an 85% improvement in participants' understanding of forecasting concepts and an 80% increase in software-use competence. This program contributes to improving technological literacy, enhancing production planning efficiency, and building digital capacity as part of sustainable economic development.
Co-Authors . Sudarmaji, . Abda Abda Abdi Wahab Abdul Saman Achmad Irfan Muzni Adelia Wulandari Afdal Hanif Pamungkas Afriana, Rafika Agus Wibowo Agus Wibowo Ahimsa, Titos AL UM ANISWATUN KHASANAH Andarwati Andarwati Andi Adriansyah Andi Firdaus Sudarma Anggara, Fajar Aprianto, Edo Ardiansyah Japlani, Ardiansyah Arif Hidayatullah, Muhammad Arifin, Muhammad Alfattah Armaichi, Ella Arsyi Mughni, Mochammad Aryani, Rita Atep Afia Hidayat, Atep Ayu Arwati, I G. Ayu Pratiwi, Ayu Ayuningtyas, Bekti Aziz Aryoso, Ilham Baehaqi Bahar Bahar, Bahar Bambang Supriyanto Basyari, Aziz Buana Paxi Dafit Feriyanto Dea Putri Devita Putri Noviasari Dhofirul Fadhil Dzil Ikrom Al Hazmi Dhonanto, Donny Dian Komala Dewi Dzulfina Almukaromah Eko Susanto Enok Mardiah Ervina Gesti Anggraini Eva Faliyanti Faizal Faizal, Faizal Fajarwati, Retno Felicia Inggit Aspurua Fikri Haikal Firmansyah, Mohamad Ardy Fitri , Muhammad Ginting, Canda Lesmana Ginting, Dianta Gumilang, Donnie Gunardi, Yudi Haekal, Jakfat Hariandi, Yusuf Hasan Basri Hasanudin, Abdul Heri Cahyono Hidayat, Arief Rachmad Himma Firdaus I Made Adianta Idris, Suria Darma Indra Almahdy Indradewa, Rhian Ira Vahlia Irma Yuni istiqomah istiqomah Juhri AM Kamandanu, Ikhsan Karwono Karwono Karwono, Karwono Ketut Tara Agustin Khasanah, Al Um Aniswatun Komang Nitasari Laras Anggraini Lase, Asaeli Tongoni Lestari, Aprilia Luthfi Chusna Yeni Marliza Muchtar Marzuki Noor, Marzuki Maulana, Herlan Maylinda, Shelly Merie Handayani MUDAIM MUDAIM, MUDAIM Muhamad Fitri Muhamad Saidun JN Muhammad Anas Muhammad Arifin Ahmad Muhammad Ilham Bakhtiar Muhammad Isradi Muhammad Kholil Muhammad Saleh Mukaromah, Siti Tri Nafis, Muhammad Luthfi Nanang Ruhyat Norenza, Jessica Novi Pristiningsih Novi Rahmawati Noviyanto, Alvian Nugroho, Akhamad Andriyan Nurahman, Arip Nurato , Nurato Nurato Nurato, Nurato Nurul Atieka Pangestu, Aulia Pani, Pani Pratiwi Kartika Putri Pujowati, Penny Puyo, Christyan Bangkit Pong Rahayu, Reni Rahmadhini, Ega Ayu Rahmatullah, Rizky Ramayana, Abdul Syamad Ramayana, Syamad Refty Aulia Restiana Rido Ardiansyah Rikko Putra Youlia Rio Septora Riski Yuli Kurniawati Rolia, Eva Romahadi, Dedik Rosid, Andiana Rukyat, Nanang Rusmin Nuryadin Sagir Alva Salafuddin, Hafidz Sangidatus Sholiha Sari, Alvita Sari, Anis Widya Satrio Budi Wibowo Selamet Riyadi Sholiha, Sangidatus Sholikhatun Khurniasih Sikumbang, Rama Widjaya Siti Nafiah Sofian, Sofian Sunaryo Sunaryo Surya Darma Susylowati, Susylowati Sutriati, Ani Taufan Purwokusumaning Daru, Taufan Purwokusumaning Taufik Akbar Tika Nur Utami Tri Anjar, Tri Turnip, Guido Narodo U, Akhmad Zamroni Umarwan, Arie Utomo, Wahyu Tri Wahyudi, Ikhsan Wardoyo Wardoyo Wijaya, Yahya Rana Wilbent Daffa, Kelvin Wiwit Suprihatiningsih Yuli Diniawati Yusup Budiman , Baharudin Zakaria, Supaat Zakaria, Supaat