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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) TEKNIK Transmisi: Jurnal Ilmiah Teknik Elektro GEMA TEKNOLOGI TRANSIENT: Jurnal Ilmiah Teknik Elektro Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Seminar Nasional Informatika (SEMNASIF) Jurnal Sisfokom (Sistem Informasi dan Komputer) TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol EkBis: Jurnal Ekonomi dan Bisnis Journal of Information Systems and Informatics Indonesian Journal of electronics, electromedical engineering, and medical informatics Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Iwan Setiawan
Departemen Teknik Elektro, Fakultas Teknik, Universitas Diponegoro
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering

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Predictive Models Talented Researcher Using Modern Approach Quantum Machine Learning (QML) Lukman Anas; Aghus Sofwan; Iwan Setiawan
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 2 (2026): MAY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i02.2593

Abstract

Scientific advancement is profoundly shaped by the ability of exceptional individuals to investigate and produce novel insights. Nonetheless, conventional assessment techniques that depend on bibliometric metrics such as the Scopus Score, Sinta, Google Scholar (GS) , and the H-Index—frequently neglect to encapsulate the intricate dynamics associated with research quality. In order to rectify these inadequacies, this study introduces a model aimed at the identification of gifted researchers through a Quantum Machine Learning (QML) methodology. The proposed framework seeks to surmount the constraints of ranking systems that rely exclusively on scientometric indicators by incorporating a reconstructed kernel Hilbert space (RKHS). The research methodology is delineated into four principal phases: (1) data gathering and preprocessing, (2) QSVM model training, (3) researcher score identification and visualization, and (4) performance assessment by comparing actual and anticipated scores. QSVM was tested using a dataset of researchers from various fields. Results show that QSVM accurately predicts researcher performance, with variances between Scores for the whole thing range from -0.25 to 0.05. The plan that was offered congruence with actual performance data supports its robustness. The ranking analysis shows a low mistake rate, proving QSVM's academic performance evaluation accuracy. QML-based categorization models can be scalable and data-driven alternatives to standard research assessment methods, according to this study.
Sistem Kontrol Injeksi Arus pada Inverter Berbasis Metode Proportional Integral Eko Wirahadi Saputra; Hermawan Hermawan; Iwan Setiawan
TEKNIK Vol 41, No 2 (2020)
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/teknik.v0i0.23932

Abstract

Saat ini energi terbarukan yang bersumber dari energi hijau terus dikembangkan dan dioptimalkan. Khusus pemanfaatan energi terbarukan dengan keluaran daya DC, maka secara praktis diperlukan sistem  inverter agar bisa digunakan sebagai pengganti sumber energi daya AC. Agar bekerja secara optimal, secara internal dalam sebuah sistem inverter sumber tegangan, perlu dilengkapi sistem umpan balik arus. Salah satu metode pengontrolan arus inverter yang relative popular adalah metode kontrol Proporsional-Integral. Dalam penelitan ini, dirancang inverter satu fasa tipe Full Bridge dengan metode kontrol proportional-integral menggunakan mikrokontroler 16-bit dsPIC30F4011. Dengan sensor arus ACS712-05B arus keluaran inverter diumpan-balikkan. Pengujian dilakukan dengan variasi beban 8 Ω dan 10 Ω, dengan memvariasikan sejumlah nilai parameter kp dan ki. Berdasarkan hasil pengujian metode proportional integral untuk pengontrollan arus pada inverter satu fasa memiliki respon sistem cepat dan relative stabil.
Extending the Technology Acceptance Model with Perceived Immersive Learning for VR Usage in Higher Education Ajub Ajulian Zahra Macrina; Iwan Setiawan; Darjat
Journal of Information System and Informatics Vol 8 No 4 (2026): August
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i4.1696

Abstract

This study examines the adoption of Virtual Reality (VR)-based learning in Indonesian higher education by extending the Technology Acceptance Model (TAM) through Perceived Immersive Learning (PIL). The study investigates how perceived usefulness, perceived ease of use, PIL, behavioral intention, and self-reported usage behavior are related. A quantitative approach using Partial Least Squares Structural Equation Modeling (PLS-SEM) was applied to data collected from 180 university students with experience using VR-based immersive learning technologies. The measurement and structural models were analyzed using SmartPLS 4. Results show that perceived ease of use significantly influences perceived usefulness and PIL, while perceived usefulness also has a significant positive effect on PIL. In addition, PIL significantly affects behavioral intention, which subsequently influences self-reported VR usage behavior. The model explains 58.6% of the variance in behavioral intention (R² = 0.586), indicating substantial explanatory power. These findings demonstrate that immersive learning experiences play a central role in translating technology perceptions into behavioral intention and subsequent usage behavior. The study contributes to TAM literature by empirically positioning PIL as a mediating construct and offers practical insights for universities seeking to design more engaging, usable, and meaningful VR-based learning environments that can strengthen students' acceptance and sustained engagement with VR.
Comparative Machine Learning Bias Correction of Satellite-Derived Solar Irradiance at Tropical Stations in Central Java, Indonesia Eko Riyanto; Iwan Setiawan; Uky Yudatama
Journal of Information System and Informatics Vol 8 No 5 (2026): October
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i5.1778

Abstract

Satellite-derived Global Horizontal Irradiance products carry systematic bias in tropical regions, so site-level correction is a prerequisite for their use where ground networks are sparse. This study compares bias-correction methods for NASA POWER daily irradiance in Central Java, Indonesia, and asks how much reported accuracy reflects algebraic dependence between predictors and the ground reference. Ten PUSDATARU stations were analysed over 2020–2024 (17,072 station-days; 8,459 from five exactly co-located stations). Random Forest, XGBoost, LightGBM, Elastic Net, and Quantile Mapping were trained chronologically (2020–2022/2023/2024) against persistence and climatology baselines, with preprocessing fitted on training data only, paired significance tests, leave-one-station-out validation, and SHAP attribution. Random Forest reduced RMSE from 3.398 to 2.674 MJ m⁻² day⁻¹ (21.3%), lowered mean bias from 0.988 to 0.221, and raised R² from 0.266 to 0.546; across ten stations XGBoost reduced error from 5.921 to 2.831. Differences among tree ensembles were not statistically robust, and the weighted ensemble gave no gain at the highest inference cost. Adding ground-observed sunshine as a predictor raised the coefficient of determination to 0.9994, identified as recovery of the Ångström–Prescott expression defining the reference (70.9% attribution). Near-perfect accuracies against sunshine-derived references therefore measure leakage, not skill; correction also failed to transfer spatially, motivating sub-degree geostationary products as the next step.
Pengaturan Suhu Dan Kelembaban Serta Penyiraman Pestisida Secara Otomatis Untuk Peningkatan Produksi Jamur Tiram Yonathan Dwi Nugroho; Iwan Setiawan; Yuniarto Yuniarto
Gema Teknologi Vol 21, No 4 (2022): October 2021 - April 2022
Publisher : Vocational School Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/gt.v21i4.90288

Abstract

Oyster mushroom production is often hindered by suboptimal temperature and humidity, as well as manual pest control, leading to inefficiency. This project proposes a prototype system for automatic temperature and humidity control, along with scheduled pesticide spraying, to improve the efficiency and productivity. This system uses an ESP-32 microcontroller, a DHT22 sensor for temperature and humidity monitoring, and an RTC DS1307 for scheduling. Actuators include heaters, Peltier coolers, exhaust fans, humidifiers, and a pesticide pump. Users can monitor and configure the system locally via a 16x2 LCD and keypad or remotely through a web dashboard. Calibration of the DHT22 sensor reduced temperature reading errors from 4.32% to 0.31% and humidity errors from 1.4% to 0.22%. The system maintained temperature within 26–28°C (setpoint 27°C, differential 2) and humidity within 88.5–91.5% (setpoint 90%, differential 3). The automated pesticide spraying functioned according to schedule (every 3 days for a duration of 5 seconds at 17:00). The web dashboard interface showed good performance with a Google Lighthouse score of 100 and a speed index of 0.9 seconds. Oyster mushrooms grow optimally at approximately 4 cm per day and are ready for harvest by the fourth day.
Co-Authors Aditya Mahendra Afrisal, Hadha Aghus Sofwan Aghus Sofwan Agung Nugroho Ajub Ajulian Zahra Macrina Ammar Akbar Ghifari Basyar Andromeda, Trias Annisa Gallela Anjani Ardyono Priyadi Arief Marwanto Arif Mulyanto Aris Triwiyatno Berkah Fajar Tamtomo kiono Darjat Denis Denis Didik Setiyadi Eduward Tigor Eko Riyanto Eko Wirahadi Saputra Enda Wista Sinuraya Evan Hafidzudin Elinda Faizly van Ghozlizar gema romadhona Ghanis Kauchya Nugraha Hermawan Hermawan Jumrianto Jumrianto Kurniawan Kurniawan Kusnanto Mukti Wibowo Lukman Anas Mauridhi Hery Purnomo Mochammad Facta Mochammad Facta Muhammad Iqbal Muhammad Izzy Ramadhan Munawar Agus Riyadi N. Prasath Presillia Grisviani Grisviani Riva Amrulloh Rizki Nurilyas Ahmad Royan Royan Rum Sapundani Santoso, Imam Septy Setia Nugraha Sonny Hady Winoto Sorikhi Sorikhi Sukarno Budi Utomo Susatyo Handoko Tunjung Dwi Madyanto Uky Yudatama Umair Al-Anshory Wahyudi Wahyudi Wahyudi Wahyudi Winda Pratiwi Yonathan Dwi Nugroho Yuniarto Yuniarto, Yuniarto Zahirah Ahlami