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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Robotics and Automation (IJRA) TELKOMNIKA (Telecommunication Computing Electronics and Control) Sinergi Jurnal Teknologi Elektro ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika InComTech: Jurnal Telekomunikasi dan Komputer Techne : Jurnal Ilmiah Elektroteknika Petir TESLA: Jurnal Teknik Elektro Minda Baharu TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol CCIT (Creative Communication and Innovative Technology) Journal Jurnal Abdi Masyarakat (JAM) Indonesian Journal of Electrical Engineering and Computer Science Journal of Innovation and Community Engagement Jurnal Abdimas ADPI Sains dan Teknologi Jurnal Profesi Insinyur Universitas Lampung Journal of Integrated and Advanced Engineering (JIAE) Formosa Journal of Science and Technology (FJST) Internet of Things and Artificial Intelligence Journal International Journal of Engineering Continuity Jurnal INFOTEL Metrik Serial Humaniora dan Sains SNTE
Setiyo Budiyanto
Universitas Mercu Buana
Arts Humanities Astronomy Automotive Engineering 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 Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Social Sciences Other

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Predictive Analysis of The National Defense Index (IBN) on National Resilience Using Classification and Regression Trees (CART) Method Qotrunnada, Farah Mufida; Budiyanto, Setiyo; Wadjdi, Achmad Farid
Formosa Journal of Science and Technology Vol. 4 No. 1 (2025): January 2025
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/fjst.v4i1.13271

Abstract

IBN (National Defense Index) is an important indicator in measuring public awareness and participation in maintaining the sovereignty and stability of the country. With the increasing complexity of challenges to national resilience, a predictive approach is needed to understand the relationship between IBN and other variables that influence national resilience. The CART method is used to identify patterns and determine significant variables that play a role in strengthening national resilience. Through this model, the study aims to provide deeper insight for the formulation of more effective policies in building strong national resilience. The results of the study were obtained by identifying factors that influence the National Defense Index (IBN). The results of this study are expected to be a consideration for the basis for strategic policies in achieving the great goal of Indonesia Emas 2045 as a strong, sovereign, and sustainable country.
Machine Learning Approach to Analyze the Relationship Between State Defense Index and Human Development to Strengthen National Defense Qotrunada, Farah; Budiyanto, Setiyo; Wajdi , Achmad Farid
Internet of Things and Artificial Intelligence Journal Vol. 5 No. 1 (2025): Volume 5 Issue 1, 2025 [February]
Publisher : Association for Scientific Computing, Electronics, and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/iota.v5i1.869

Abstract

In efforts to strengthen national defense, it is important to understand how factors in human development, such as education, health, and economic welfare, can influence public awareness of national defense. This study aims to analyze the relationship between the National Defense Index (IBN) and the Human Development Index (IPM) in Indonesia using a Machine learning approach. To strengthen national defense, it is essential to understand how factors in human development, such as education, health, and economic welfare, can affect public awareness of national defense. Machine learning methods are applied to analyze the significant relationship between IBN and IPM, which is expected to provide insights for the development of more data-driven national defense policies. The results show that the Machine learning model can predict IBN values with high accuracy, supported by a Mean Squared Error (MSE) of 0.000638 and an R-squared value of 0.9026. This indicates that 90.26% of the variability in IBN values can be explained by the model, suggesting accurate predictions that are relevant for data-driven policies. Collaboration with various stakeholders is expected to enhance the application of these findings in further studies and the formulation of national defense policies. 
Optimasi Disaster Recovery Planning (DRP) pada Site Recovery Manager (SRM) Menggunakan Algoritma Genetika Yoga Putra Setiawan; Budiyanto, Setiyo
Techné : Jurnal Ilmiah Elektroteknika Vol. 24 No. 1 (2025)
Publisher : Fakultas Teknik Elektronika dan Komputer Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31358/techne.v24i1.580

Abstract

Bencana IT yang tak terduga dapat mengganggu operasional bisnis, terutama jika tidak ada kesiapan yang memadai. Dampaknya mencakup kehilangan data dan informasi penting yang berpengaruh pada kontinuitas operasional. Salah satu langkah strategis dalam menghadapi gangguan ini adalah dengan menganalisis potensi bencana serta merancang Disaster Recovery Planning (DRP). Tantangan utama dalam DRP adalah menentukan prioritas pemulihan mesin virtual secara efisien dan mengelola Recovery Point Objective (RPO) secara optimal, mengingat keterbatasan waktu dalam proses pemulihan. Penelitian ini mengusulkan metode optimasi DRP berbasis algoritma genetika yang diterapkan pada VMware Site Recovery Manager (SRM). Algoritma genetika digunakan untuk menentukan prioritas pemulihan VM berdasarkan SLA dengan tahapan pembentukan kromosom, inisialisasi populasi, evaluasi fitness, seleksi, crossover, dan mutasi guna memperoleh solusi optimal. Evaluasi fitness mempertimbangkan pengurangan downtime dan minimisasi gangguan operasional. Hasil penelitian menunjukkan algoritma genetika mampu mengurangi downtime IT secara signifikan, dengan peningkatan efisiensi pemulihan hingga 50%. Model ini terbukti efektif dalam mengoptimalkan SRM dan dapat menjadi referensi bagi strategi pemulihan bencana IT di masa depan.
Development of an IoT-Based Prototype for Optimizing Hazardous Materials and Equipment Storage to Enhance HSE in Laboratories Windasari, Silviana; Abdurohman, Abdurohman; Rochmad, Imbuh; Budiyanto, Setiyo
International Journal of Engineering Continuity Vol. 4 No. 1 (2025): ijec
Publisher : Sultan Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/ijec.v4i1.414

Abstract

Laboratory incidents are often precipitated by misplacement of hazardous materials and delayed recognition of unsafe conditions. Earlier laboratory safety solutions typically centered on sensors and dashboards, including IoT monitoring, improve situational awareness but remain largely reactive, operate at room/building scale, seldom enforce item-level storage rules, and rarely report alert selectivity (false-alarm behaviour). This work presents a compact prototype that combines RFID-based storage-zone verification with environmental sensing to support Health, Safety, Security, and Environment (HSSE) compliance at the storage-unit level. An ESP32-based controller integrates three RFID readers (low/medium/high-risk compartments) with temperature humidity and gas sensors; data are streamed to an IoT interface for real-time visualization and notification (e.g., implemented via Blynk), while rule-based logic triggers local (buzzer) and remote alerts when a tagged item is placed in the wrong zone or thresholds are exceeded. A scenario-driven evaluation across 18 cases (correct/mismatched placements for representative items) yielded 100% RFID tag detection and placement validation, an average response time of 2.37 s, and no false alarms under correct placements. These results indicate that joining placement verification with multi-sensor monitoring provides selective, low-latency warnings while avoiding nuisance alerts, establishing quantitative baselines for scalable smart-laboratory HSSE enforcement.
Design of a conductive material detection system Silaban, Freddy Artadima; Budiyanto, Setiyo; Silalahi, Lukman Medriavin
IAES International Journal of Robotics and Automation (IJRA) Vol 9, No 4: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v9i4.pp292-299

Abstract

The development of technology and industry development in the 4.0 era is very fast along with these developments in the control of production results such as medicine, food, and safety must be faster and more accurate. To face free trade and global economic competition, every company is required to produce products that have good quality by the standards. By using an experimental method which is the development of this study aims to make a conductive material detector (metal detector) for the pharmaceutical industry, the food industry, and security as compared to using conductive material sensors that are integrated with the Arduino microcontroller. Application testing is carried out to find out whether the Blynk application on an android smartphone with Blynk on a Debian server that has been made previously runs well or not and the alarm system testing uses a buzzer and LED to detect conductive material passing through. Conductive sensor test results showed that the instrument can detect 6 conductivity materials such as stainless steel, aluminum, steel, zinc, copper, and tin. The average response time to detect conductive material is 3 seconds, the average ADC value of the conductive material is 0.55. The test results also successfully send information and data to the Blynk application so that it can be monitored online.
Implementasi Etika Profesi Pelaksanaan Pemetaan Cerdas Lokasi Industri Rumahan Berbasis WEB-SIG di Dinas P3ACSKB Provinsi Kepulauan Bangka Belitung Santoso, Hadi; Rochmad, Imbuh; Budiyanto, Setiyo
Jurnal Profesi Insinyur Universitas Lampung Vol. 6 No. 1 (2025)
Publisher : Fakultas Teknik Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jpi.v6n1.149

Abstract

Pemerintah Daerah Provinsi Kepulauan Bangka Belitung menghadapi kendala dalam mengklasifikasikan data industri rumahan berdasarkan Peraturan Menteri PPPA No. 2 Tahun 2016. Untuk mengatasi hal tersebut, diusulkan penerapan algoritma K-means guna mengelompokkan data industri rumahan secara lebih optimal. Proses ini diimplementasikan melalui aplikasi cerdas berbasis web dengan pendekatan Cross-Industry Standard Process for Data Mining (CRISP-DM), yang meliputi enam tahapan: pemahaman bisnis, pemahaman data, persiapan data, pemodelan, evaluasi, dan penerapan. Sebanyak 4560 rumah tangga dijadikan sampel dalam kegiatan ini. Evaluasi kinerja algoritma menggunakan Davies Bouldin Index (DBI) menunjukkan hasil optimal pada iterasi kelima, menghasilkan tiga klaster: pemula (C1) sebanyak 4308, berkembang (C2) sebanyak 167, dan maju (C3) sebanyak 85. Nilai DBI sebesar 0,184 mengindikasikan validitas klaster yang baik. Implementasi algoritma ini dalam sistem informasi geografis berbasis web di Dinas P3ACSKB juga memperhatikan prinsip etika profesi, termasuk tanggung jawab, transparansi, dan akurasi data, sebagai dasar pemanfaatan teknologi informasi secara profesional dan berkelanjutan untuk mendukung pengembangan ekonomi lokal.
Strategi Optimasi Antrian SPKLU dengan Algoritma Genetika untuk Mengurangi Waktu Tunggu dan Ketidakseimbangan Slot Saragih, Yuliarman; Resi Sujiwo Bijokangko; Setiyo Budiyanto; Saragih, Carolan Ignatius
Seminar Nasional Teknik Elektro Vol. 4 No. 1 (2025): SNTE III
Publisher : Forum Pendidikan Tinggi Teknik Elektro Indonesia Pusat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46962/snte.25.067

Abstract

Pertumbuhan penggunaan kendaraan listrik (EV) memicu kebutuhan akan sistem manajemen Stasiun Pengisian Kendaraan Listrik Umum (SPKLU) yang efisien, khususnya dalam mengoptimalkan penjadwalan pengisian dan pemerataan beban antar stasiun. Penelitian ini mengusulkan penerapan Genetic Algorithm (GA) untuk mengoptimalkan penugasan EV ke SPKLU dengan mempertimbangkan jarak, perkiraan waktu tiba (Estimated Time of Arrival), kapasitas slot, dan durasi pengisian. Metode ini dibandingkan dengan pendekatan konvensional tanpa AI yang hanya mengandalkan jarak terdekat. Dataset yang digunakan bersifat sintetis, mencakup 30 EV dan 6 SPKLU dengan kapasitas slot bervariasi (5–7 slot). Simulasi dijalankan menggunakan pestimasi waktu ketibaan OpenRouteService untuk perhitungan jarak dan waktu tempuh aktual. Hasil menunjukkan bahwa GA mampu menurunkan rata-rata waktu tunggu EV sebesar 64,4% (dari 11,8 menit menjadi 4,2 menit), mempercepat total waktu penyelesaian dari 410 menit menjadi 320 menit, serta mengurangi variansi utilisasi slot antar SPKLU sebesar 67,86%. Temuan ini membuktikan bahwa GA tidak hanya meningkatkan efisiensi operasional, testimasi waktu ketibaanpi juga memeratakan beban infrastruktur pengisian. Penelitian ini memberikan kontribusi terhadap pengembangan strategi optimasi SPKLU berbasis AI dan membuka peluang untuk penerapan realtime dengan integrasi data lalu lintas dinamis.
Co-Authors Abdul Hamid Abdurohman, Abdurohman Adi Kurnia Agus Dendi Rochendi Agus Dendi Rochendi Agus Dendi Rochendi Agus Dendi Rochendi Ahmad Firdausi Alvin Sepbrian Andi Adriansyah Apipi Saputra Aprilia Dian Oftari Arga Gilang Rolanda Arif Rahman Hakim Arissetyanto Nugroho Badaruddin Badaruddin Beny Nugraha Dadang Gunawan David Martin Antoyo Dian Widi Astuti, Budi Irawan Prima Putra, Dian Widi Astuti, Dimas Jatikusumo Erman Al Hakim Erry Yulian Triblas Adesta Fahraini Bacharuddin Fajar Banardi, Deswandi Fajar R Febryyanti Nawang Wulan Fina Supegina Freddy A Silaban Freddy Artadima Silaban Galang Persada Nurani Hakim Galih Bangun Santosa Gao Hongmin Gunawan Osman Hadi Santoso Hadi Wuryanto Hanifah Diana Harry Candra Sihombing Haryono, Muhammad Budi Imbuh Rochmad Imelda Uli Vistalina Simanjuntak Imelda Uli Vistalina Simanjuntak Julpri Andika Kristiani N Nahampun Lukman M Silalahi Lukman Medriavin Silalahi Lusianna EP Siagian M. Hafiz Ibnu Hajar Mochamad Furqon Ismail Mudrik Alaydrus Muhammad Hafizd Ibnu Hajar Muhammad Ikhsan Muhammad Jamil Muhammad JAMIL Putri Wulandari Qotrunada, Farah Qotrunnada, Farah Mufida Rachmat Muwardi Raden Sutiadi RAHAYU, FAJAR Rahmad, Khozaeni Bin Resi Sujiwo Bijokangko Rini Kusumawardani, Rini Rio Mubarak Rochendi, Agus Dendi Said Attamimi Saragih, Carolan Ignatius Selamet Kurniawan Septi Andryana Silviana Windasari Simanjuntak, Imelda Uli Vistalina Triyanto Pangaribowo Ucuk Darusalam Wadjdi, Achmad Farid Wahyu Kusuma Raharja Wajdi , Achmad Farid Wijaksana, Wibi Wijaksana Yoga Putra Setiawan Yudhi Gunardi Yudistiro Yudistiro Yudistiro Yudistiro, Yudistiro Yuliarman Saragih