Article Per Year (5 Year)
p-Index From 2021 - 2026
1.817
P-Index
This Author published in this journals
All Journal Jurnal Edukasi Elektro TRANSIENT: Jurnal Ilmiah Teknik Elektro Jurnal Teknologi Informasi dan Ilmu Komputer Sistemasi: Jurnal Sistem Informasi Science in Information Technology Letters Jurnal Riset Rumpun Matematika dan Ilmu Pengetahuan Alam (JURRIMIPA) Equivalent: Jurnal Ilmiah Sosial Teknik Journal of Global Engineering Research & Science (J-GERS) Jurnal Multidisiplin Pengabdian Masyarakat (JMPM)
Agung Pangestu
Jurusan Teknik Elektro, Universitas Diponegoro Semarang Jl. Prof. Sudharto, SH, Kampus UNDIP Tembalang, Semarang 50275, Indonesia
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences Other

Published : 12 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 12 Documents
Search

 1   2 
Strategi Efektif Penjaringan dan Penyaringan Perangkat Desa (P3D) di Kecamatan Lumbir: Kolaborasi Akademisi dan Masyarakat Al-Hakim, Rosyid; Ardianto, Rian; Suryani, Riska; Jayusman, Hadi; Nisa, Khoirun; Samodra, Galih; Melani, Reina; Sebayang, Septian; Pangestu, Agung
Jurnal Arba - Multidisiplin Pengabdian Masyarakat Vol. 2 No. 1 (2025): Februari
Publisher : Jurnal Arba - Multidisiplin Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Program Penjaringan dan Penyaringan Perangkat Desa (P3D) di dua desa yang berada di Kec. Lumbir bertujuan untuk meningkatkan transparansi dan kualitas seleksi perangkat desa melalui inovasi berbasis teknologi. Program ini melibatkan 46 pendaftar, 21 anggota tim panitia, serta dukungan dari pemerintah desa, kecamatan, dan unsur keamanan. Inovasi utama dalam program ini adalah penerapan sistem Computer Based Test (CBT) dengan fitur live scoring, yang mencakup tiga aspek penilaian: ujian tulis (40%), praktik pengoperasian komputer (30%), dan wawancara kompetensi (30%). Hasil program menunjukkan tingkat keberhasilan sebesar 90%, yang mencerminkan efektivitas metode seleksi yang diterapkan. Kolaborasi multi-stakeholder dan penggunaan fasilitas laboratorium komputer universitas menjadi faktor kunci keberhasilan program ini. Namun, beberapa tantangan seperti adaptasi peserta terhadap sistem CBT dan koordinasi antar pemangku kepentingan berhasil diatasi melalui pelatihan dan penguatan komunikasi digital. Program ini menghasilkan template sistem seleksi digital yang dapat di replikasi di desa lain, peningkatan kapasitas panitia lokal, dan dokumentasi SOP (Standard Operational Procedure) yang terstandar. Rekomendasi untuk pengembangan program meliputi peningkatan kapasitas SDM (Sumber Daya Manusia), pengembangan sistem evaluasi, dan replikasi program di desa lain. Program ini diharapkan menjadi model inovatif dalam rekrutmen perangkat desa yang lebih transparan dan profesional.
Using Regression Model Analysis for Forecasting the Likelihood of Particular Symptoms of COVID-19 Pangestu, Agung; Sumirat, Ucu; Al-Hakim, Rosyid Ridlo; Yusro, Muhammad; Ekawati, Risma; Alrahman, Mahmmoud H. A.; Arif, Machnun; Muchsin, Achmad; Wahyudiana, Nadhilla H
Sistemasi: Jurnal Sistem Informasi Vol 13, No 1 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i1.3463

Abstract

A certainty factor (CF) rule-based technique is frequently used by traditional expert systems (TES) in the medical industry to compute several symptoms and identify the inference solutions. The primary concern for this TES was predicting the likelihood of a particular ailment in the circumstances of new patients. Based on symptoms connected to clinical indicators in patients' diagnosis, CF is estimated. This TES probably won't be able to forecast unknown things, like the possibility of a particular ailment. Therefore, supervised learning techniques like linear regression can address this issue. We attempted to analyze the current COVID-19 TES by modeling the regression equation to forecast the chance of a particular disease that is COVID-like based on the CF value and the confidence level of the symptoms. To examine the most effective regression model to address the issue, we employed multi-linear regression (MLR) and multi-polynomial regression (MPR). The findings demonstrate that the MLR and MPR models are the most accurate regression models for estimating the chance of a disease associated with COVID-like symptoms. Our work built a basis for the creation of expert systems by concentrating more on MLES (machine learning expert systems) analytical techniques than TES.
Co-Authors Achmad Muchsin Aidulghani, Bachtiar Al Hakim, Rosyid Al Hakim, Rosyid R Al-Hakim, Rosyid Al-Hakim, Rosyid Ridlo Alrahman, Mahmmoud H. A. Antono, Zakia Maulida Ardianto, Rian Arief, Yanuar Z. Ariep Jaenul Arif, Machnun Dilla, Bartsa Djatmiko, Wisnu Efri Sandi Faizah, Safira Gani, Moh Hari Febriana Hexa Apriliana Hidayah Hidayah, Hexa Islami, Ichsani Nurul Jayusman, Hadi Khoirun Nisa Melani, Reina Muhammad Haikal Satria Muhammad Ikhsan Muhammad Yusro Olivia, Arisa Prihantini Prihantini Putri, Esa Putri, Esa Rinjani Cantika Risma Ekawati, Risma Rohana, Assa Kesthy Ropiudin . Rosyid Ridlo Al-Hakim Samodra, Galih Sebayang, Septian Setiawan, Antonius Darma Setyowisnu, Glagah E Setyowisnu, Glagah E. Sri Riani, Sri Sudjadi Sudjadi Sumardi . Suryani, Riska Tira Roesdiana Ucu Sumirat Wahyudiana, Nadhilla H Widi, Brainvendra Wilyanti, Sinka Yanuar Zulardiansyah Arief