Article Per Year (5 Year)
p-Index From 2021 - 2026
3.512
P-Index
This Author published in this journals
All Journal Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Jurnal Informatika Agrointek JUSIFO : Jurnal Sistem Informasi Annual Research Seminar JURNAL MEDIA INFORMATIKA BUDIDARMA Faktor Exacta Jurnal SOLMA INOVTEK Polbeng - Seri Informatika Techno Nusa Mandiri : Journal of Computing and Information Technology IKRA-ITH Informatika : Jurnal Komputer dan Informatika ILKOM Jurnal Ilmiah KOMPUTA : Jurnal Ilmiah Komputer dan Informatika Jurnal Mantik Jurnal Teknik Informatika C.I.T. Medicom Jurnal Teknik Informatika (JUTIF) Transformasi Teknokris
Ali Khumaidi
Universitas Krisnadwipayana
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Other

Published : 33 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 33 Documents
Search

 1   2   3   4 
Rancang Bangun Prototipe Sistem Keamanan Akses Pintu menggunakan Radio Frequency Identification(RFID) Berbasis Internet of Things (Studi Kasus : PT Nok Indonesia) Khumaidi, Ali; Pranoto, Setiadi Wiro
TRANSFORMASI Vol 21, No 1 (2025): TRANSFORMASI
Publisher : STMIK BINA PATRIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56357/jt.v21i1.426

Abstract

Security is a crucial aspect in industrial environments, including at PT. NOK Indonesia, a company engaged in manufacturing. To ensure the safety of data, systems, and workers, an efficient and modern security system is essential. As technology advances, solutions such as Radio Frequency Identification (RFID) and the Internet of Things (IoT) have become increasingly adopted. RFID enables fast, reliable, and contactless identification, making it highly suitable for automatic door access systems. Currently, PT. NOK Indonesia still relies on a manual key system, which presents risks such as lost keys and inefficient access control. This study aims to design and develop a prototype of an automatic door access system using RFID sensors to enhance security. The research methods include observation, interviews, prototype development, and a series of tests, (such as speed response, solenoid function, buzzer response to registered and unregistered cards, testing with card protectors, and response distance measurements). The system was built using Arduino Uno, an RFID Reader, and programmed with Arduino IDE. Testing results show the system can read RFID cards in under 0.5 seconds and accurately log all access attempts. Integrating RFID and IoT improves both security and operational efficiency, replacing manual systems with a safer, more convenient, and reliable solution.
Hybrid Fuzzy-AHP and Machine Learning with Sensitivity Analysis for Urban Flood Risk Assessment Agus Riyanto; Dwi Ismiyana Putri; Gita Puspa Artiani; Ali Khumaidi
JUSIFO : Jurnal Sistem Informasi Vol 11 No 2 (2025): JUSIFO (Jurnal Sistem Informasi) | December 2025
Publisher : Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jusifo.v11i2.32227

Abstract

Urban flooding poses a growing challenge in rapidly urbanizing regions due to the combined effects of climate variability, land-use change, and infrastructure limitations. This study proposes a hybrid framework integrating the Fuzzy Analytical Hierarchy Process (Fuzzy-AHP), ensemble machine learning, and sensitivity analysis to support urban flood risk assessment. Fuzzy-AHP is employed to incorporate expert judgment and address uncertainty through triangular fuzzy numbers, while Random Forest and XGBoost are used to capture non-linear relationships and temporal patterns in heterogeneous flood-related data. The framework is applied to 1,008 observations from 12 districts in Bekasi City, Indonesia, covering the period 2018–2024. Model performance indicates strong discriminatory capability in distinguishing flood and non-flood conditions. Sensitivity analysis is explicitly positioned as a policy-oriented diagnostic and prioritization tool, enabling the identification of influential variables relevant for seasonal planning and early warning strategies. The results highlight the dominant role of climate-related factors, particularly rainfall and temporal variables, in shaping urban flood risk. Overall, the proposed framework demonstrates the complementary integration of expert knowledge and data-driven learning, offering a transferable methodological reference for flood risk assessment in complex urban environments.
A Hybrid Decision Support Framework for Food and Nutrition Security Assessment Using Multi-Criteria Decision Making and Machine Learning Solikin, Solikin; Wicaksono, Harjunadi; Setyarini, Tri Ana; Khumaidi, Ali; Darmawan, Risanto
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.5474

Abstract

Food and nutrition security assessment requires an adaptive analytical approach due to the multidimensional and temporal complexity of food systems. This study proposes a hybrid decision support system integrating Multi-Criteria Decision Making (MCDM) methods, namely Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), with machine learning to evaluate and predict food security indicators dynamically. Panel data from West Java and East Nusa Tenggara for the period 2018–2024 were analyzed to capture structural and temporal characteristics. AHP was used to determine expert-based indicator weights, which were applied in TOPSIS to generate regional food security scores. These scores were subsequently modeled using machine learning with temporal feature engineering, including lag variables and rolling statistics, and evaluated using time-series cross-validation. The results reveal a strong negative correlation (−0.7398) between AHP weights and machine learning feature importance, indicating complementary expert-based and data-driven perspectives. Ridge Regression achieved the best predictive performance with an R² of 0.9983 on training data and 0.8186 under cross-validation. East Nusa Tenggara outperformed West Java in TOPSIS scores (0.4829 vs. 0.4626), highlighting the importance of food stability and utilization. This study advances Informatics by enabling dynamic and adaptive food security decision support.
Co-Authors Achmad Pahrul Rodji Agus Riyanto Ajat Zatmika Alif Utomo Andika Saputra Andrian Muljadi Angga Mulya Lelana Aris Purwanto Avip Kurniawan Chusna, Nuke L Darmawan, Risanto Darmawan, Risanto Darmiyanti, Lydia Dhistianti Mei Rahmawantari Dwi Ismiyana Putri Faisal Ruswanto Getah Ester Hayatulah Gita Puspa Artiani Harjono Padmono Putro Harjunadi Wicaksono, Harjunadi Harry Dwiyana Kartika Harry Dwiyana Kartika Herry Wahyono Heru Sukoco Herwanto Herwanto Herwanto Herwanto Hudzaifah Al jihad Indra Permana Solihin Kusmanto, Panji Lukman Aditya Lydia Darmiyanti Moch Fachri Mochammad Fachri Nuke L. Chusna Nuke L. Chusna Nur Hikmah Nur Hikmah Nurpadilah, Aini Pranoto, Setiadi Wiro Rahayu, Tuntun Rahmat Hidayat Reztrianti, Diajeng Ridwan Raafi'udin Solikin Solikin Sony Hartono Wijaya Tri Ana Setyarini Tuntun Rahayu Wargijono Utomo Wiga Ariani Yonas Prima Arga Rumbyarso Yusuf Aras Ronisah