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All Journal Indonesian Journal of Geography Tekno : Jurnal Teknologi Elektro dan Kejuruan ELKHA : Jurnal Teknik Elektro Mechatronics, Electrical Power, and Vehicular Technology TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Edukasi dan Penelitian Informatika (JEPIN) Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal Pendidikan: Teori, Penelitian, dan Pengembangan International Journal of Artificial Intelligence Research INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Knowledge Engineering and Data Science Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control at-tamkin: Jurnal Pengabdian kepada Masyarakat CYCLOTRON Journal of Computer Science and Informatics Engineering (J-Cosine) JFIOnline Infotekmesin Buletin Ilmiah Sarjana Teknik Elektro Jurnal Karinov TRIDARMA: Pengabdian Kepada Masyarakat (PkM) Frontier Energy System and Power Engineering jurnal syntax admiration Jurnal Abdimas Berdaya : Jurnal Pembelajaran, Pemberdayaan dan Pengabdian Masyarakat Unri Conference Series: Community Engagement International Journal of Robotics and Control Systems International Journal of Advanced Science and Computer Applications Bulletin of Pedagogical Research ALINIER: Journal of Artificial Intelligence & Applications Ilmu Komputer untuk Masyarakat Jurnal Fortech SinarFe7 Prosiding Seminar Nasional Pengabdian Kepada Masyarakat Journal of Scientech Research and Development Reflection Journal Jurnal Inovasi Teknologi dan Edukasi Teknik Lentera: Multidisciplinary Studies Bulletin of Social Informatics Theory and Application Jurnal INFOTEL ABDI UNISAP: Jurnal Pengabdian Kepada Masyarakat Jurnal ilmiah teknologi informasi Asia Lentera: Multidisciplinary Studies Jurnal FORTECH Academia Open Energy: Jurnal Ilmiah Ilmu-ilmu Teknik Jurnal JEETech
Siti Sendari
Universitas Negeri Malang
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Optimization of Heavy Point Position Measurement on Vehicles Using Support Vector Machine Melky, Franky; Sendari, Siti; Elbaith, Ilham Ari
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26261

Abstract

During this time, weight point testing is still done manually using a jack until now it has begun to be replaced with hydraulic equipment namely Lift Table Hydraulic (LTH) which is a portable table with a hydraulic system equipped with sensors (Loadcell and LVDT), powerpack control panel, powerpack, relay module and solenoid valve to adjust the table height. This portable table is one component of the heavy point measurement equipment system used for mining and plantation vehicles such as tractors, buses, trucks which are required to have a safe structure in heavy road conditions with rough or uneven surfaces with slopes up to an angle of 15 ° to 20 °. This emphasized research contributes to more accurate testing. Based on these problems, this research was conducted using Support Vector Machine (SVM) for the optimization of heavy point position measurement. The objects used are minibuses with 1 and 19 passengers and buses with 29 and 36 passengers on the proportion of datasets (training: testing) of 80% and 20% using linier kernel. From the experimental results, the accuracy in the condition of 1 passenger is 94.7%; minibus 19 passengers 98%; bus 29 passengers 98.1% and bus 36 passengers 97.4%. The highest accuracy obtains on 29 passengers minibus. 
Performa Metode Klasifikasi Tunggal dan Ensemble Model dalam Identifikasi Baku Mutu Air Prasetya Widiharso; Siti Sendari; Anik Nur Handayani; Nastiti Susetyo Fanani Putri
Infotekmesin Vol 13 No 2 (2022): Infotekmesin: Juli, 2022
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v13i2.1529

Abstract

Water quality classification for the needs of recreational facilities, livestock, fisheries, and plantations is needed to determine utilization based on water quality according to national water quality standards. The methods used in this research are K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Naïve Bayes (NB), and Ensemble Model. The parameters measured consisted of temperature, TDS, TSS, pH, COD, BOD, DO, and rainfall. The main objective of this research is to discover the performance of a single classification method and ensemble model on data types with unbalanced class distributions. Classification objects are divided into two classes. First, is the class for the designation of recreational facilities, fisheries, and livestock. Second, the class for the allotment of crop cultivation. The test results of the application of the KNN obtained 86%, SVM obtained 87%, and NB obtained 90.57%. Meanwhile, through the ensemble model, the results obtained are 94.43% Bagging Classifier, 94.96% Gradient Boosting Classifier, and 95.94% Adaboost Classifier
RoboVR: A Digital Twin Based Framework for Low-Cost 4DoF Robotic Arm Control Wiriasto, Giri Wahyu; Sendari, Siti; Lestari, Dyah; Iqbal, Muhamad Syamsu; Mochtar, Norrima
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 4 (2025): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i4.14703

Abstract

The growing need for remote operation in risky situations has encouraged the use of robots that can be controlled from virtual environments. Integrating digital twin and virtual reality offers a way to monitor and control physical systems in three dimensional space. However, many existing implementations still depend on expensive robotic hardware and closed-loop control, while digital twin and virtual reality in open-loop mode is rarely reported and still faces the lack of feedback and the risk of mismatch between virtual motion and physical motion.This study proposes an experimental digital twin and virtual reality framework built on lightweight hardware to control a 4-DoF robotic arm using Arduino and PCA9685, with Unity acting both as the digital twin environment and as the source of kinematic commands. Method covers integration design, calibration of Unity angle to PWM mapping, execution on servos, and preparation of data to compare virtual joint angles with measured servo angles. Testing and validation were carried out through stepwise rotation of the ‘Base’, ‘Shoulder’, ‘Elbow’, and ‘End-effector’ joints to evaluate how well the physical motion followed the virtual model. Results show that the right and left ‘Base’ joints achieved small mean errors of -1.60 and 1.8 degrees, with variance of 1.35 and 1.62. The ‘Elbow-down’ motion was also accurate with a mean error of 1.43 degrees and a variance of 0.98. The largest deviation occurred in the ‘Shoulder’ joint, at -10.67 and 26.5 degrees. These findings confirm that open-loop digital twin and virtual reality control is feasible for low-cost platforms.
Integrated Kinematic-Dynamic Modeling and Ontology-Based Design of a Two-Link Planar Robotic Manipulator Giri Wahyu Wiriasto; Patmanthara, Syaad; Sendari, Siti; Lestari, Dyah; Iqbal, Muhamad Syamsu
Journal of Computer Science and Informatics Engineering (J-Cosine) Vol 9 No 2 (2025): December 2025
Publisher : Informatics Engineering Dept., Faculty of Engineering, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jcosine.v9i2.695

Abstract

Analisis Kritis terhadap Kebijakan dan Praktik Etika pada ChatGPT Purwanto, Devi Dwi; Wibawa, Aji Prasetya; Elmunsyah, Hakkun; Sendari, Siti
Reflection Journal Vol. 5 No. 2 (2025): Desember
Publisher : Lembaga Penelitian dan Pemberdayaan Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36312/xv4mqp17

Abstract

Perkembangan pesat dalam teknologi Generative AI berbasis teks melalui model seperti ChatGPT membawa dampak yang signifikan di berbagai sektor. Namun, penerapan teknologi ini juga memunculkan tantangan etika yang mendalam, khususnya terkait dengan transparansi, keadilan, akuntabilitas, dan pengelolaan bias algoritmik. Artikel ini mengkaji secara kritis penerapan kebijakan etika dalam desain dan pengembangan ChatGPT, dengan fokus pada prinsip-prinsip etika fundamental seperti keadilan, non-diskriminasi, dan transparansi. Metode analisis yang digunakan dalam artikel ini adala Ex Post Facto, dimana analisis dilakukan setelah peristiwa atau kebijakan diterapkan, guna menilai dampaknya terhadap pengelolaan bias algoritmik dan risiko disinformasi. Pendekatan etika normative juga diterapkan untuk mengevaluasi sejau mana kebijakan diterapkan oleh OpenAI sejalan dengan nilai-nilai keadilan. Penelitian ini juga mengidentifikasi tantangan besar yang dihadapi oleh pengembang dalam memastikan bahwa Generative AI dapat digunakan secara adil dan bertanggung jawab. Selain itu, artikel ini memberikan rekomendasi untuk meningkatkan transparansi dan akuntabilitas dalam penggunaan AI, guna menciptakan ekosistem yang lebih inklusif dan dapat dipertanggungjawabkan. Keunikan artikel ini terletak pada analisis mendalam terhadap kebijakan etika yang diterapkan oleh OpenAI, serta focus pada prinsip-prinsip etika fundamental dalam konteks pengembangan Generative AI, yang belum banyak dibahas dalam penelitian sebelumnya. Kesimpulannya, keberhasilan AI yang etis bergantung pada penerapan kebijakan yang komprehensif dan sistematis, yang tidak hanya efisien secara teknis tetapi juga menghormati nilai-nilai sosial dan hak-hak individu. A critical analysis of Ethical Policies and Practices at ChatGPT The rapid advancement of text-based Generative AI technologies through models such as ChatGPT has had a significant impact across various sectors. However, the adoption of this technology also raises profound ethical challenges, particularly with regard to transparency, fairness, accountability, and the management of algorithmic bias. This article critically examines the implementation of ethical policies in the design and development of ChatGPT, with a focus on fundamental ethical principles such as fairness, non-discrimination, and transparency. The analytical method employed in this study is an ex post facto approach, in which analysis is conducted after the implementation of events or policies to assess their impact on the management of algorithmic bias and the risks of misinformation. A normative ethical approach is also applied to evaluate the extent to which OpenAI’s policies align with principles of justice. This study identifies major challenges faced by developers in ensuring that Generative AI is used fairly and responsibly. In addition, the article offers recommendations to enhance transparency and accountability in AI deployment in order to foster a more inclusive and accountable ecosystem. The novelty of this article lies in its in-depth analysis of the ethical policies implemented by OpenAI and its focus on fundamental ethical principles in the context of Generative AI development, an area that has received limited attention in prior research. In conclusion, the success of ethical AI depends on the implementation of comprehensive and systematic policies that are not only technically efficient but also respectful of social values and individual rights.
Ethical Challenges in Primary vs. Secondary Datasets: A Systematic Review of Manipulation and Transparency Riska, Suastika Yulia; Widiyaningtyas, Triyanna; Elmunsyah, Hakkun; Sendari, Siti
Jurnal Ilmiah Teknologi Informasi Asia Vol 20 No 1 (2026): Volume 20 Issue 1 2026 (8)
Publisher : LP2M Institut Teknologi dan Bisnis ASIA Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32815/jitika.1227

Abstract

The swift advancements in Artificial Intelligence and Machine Learning have rendered datasets essential; nonetheless, their heightened utilization has engendered intricate ethical dilemmas that are frequently neglected. This study seeks to delineate and highlight ethical concerns associated with the collection of primary data and the reutilization of secondary datasets in computer science research. We employed a Systematic Literature Review (SLR) methodology in accordance with the PRISMA 2020 guidelines, examining 72 publications sourced from five esteemed academic databases (Scopus, Web of Science, IEEE Xplore, ACM Digital Library, Google Scholar) published from 2021 to 2025. The study results indicate that ethical difficulties emerge uniformly in both primary and secondary datasets. Primary datasets primarily face challenges related to privacy threats, anonymization, and Informed Consent, whereas secondary datasets are more susceptible to licensing infringements, dataset repurposing, and insufficient preparation transparency. The three domains that predominantly encountered these challenges were Machine Learning, Computer Vision, and Natural Language Processing. Moreover, practices of data manipulation, including cherry-picking and concealed preparation, were identified as detrimental to scientific integrity. This study's findings underscore the need for enhanced ethical standards for datasets and greater transparency in preparation documentation to ensure the repeatability of data-driven research.
Automated PPE Compliance Verification Using YOLOv11l Spatial Logic: Verifikasi Kepatuhan APD Otomatis Menggunakan YOLOv11l dan Logika Spasial Effendi, Muhammad Minhaj; Sendari, Siti
Academia Open Vol. 11 No. 1 (2026): June
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/acopen.11.2026.13886

Abstract

General Background: Monitoring personal protective equipment (PPE) usage is a critical component of occupational health and safety (OHS) in construction, yet manual inspection remains inconsistent and prone to error. Specific Background: Recent advances in computer vision, particularly YOLO-based object detection, have improved PPE detection accuracy in complex environments. Knowledge Gap: However, existing approaches primarily detect PPE presence without verifying its correct usage or associating it with individual workers, leading to inaccurate compliance interpretation. Aims: This study develops an automated PPE compliance verification system using YOLOv11l combined with spatial association logic to assess PPE completeness and anatomical correctness at the individual worker level. Results: The system was trained on 2,788 construction images and achieved high performance with mAP@50 of 0.979, precision of 0.976, recall of 0.954, and peak F1-score of 0.97, while demonstrating accurate classification across PPE categories including helmets, vests, and shoes. Novelty: The integration of zone-based spatial verification enables validation of PPE placement within anatomically defined regions, addressing the limitation of detection-only systems. Implications: This approach supports objective, continuous, and reliable safety auditing in construction environments, offering a scalable alternative to manual OHS monitoring. Highlights• Multi-class detection identifies workers and safety equipment with high accuracy• Region-based validation distinguishes proper gear usage from misplacement• System classifies compliance status through structured decision logic KeywordsAutomated PPE Verification; Construction Safety; Deep Learning; Spatial Association Logic; YOLOv11l
Analysis of System Reporting and Validation in DC Microgrid DSC Research: An Ex Post Facto Reproducibility Study Hidayat, Khusnul; Afandi, Arif Nur; Elmunsyah, Hakku; Sendari, Siti
ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK 2025: ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK (Special Issue on Engineering Paradigm 2025 Edition)
Publisher : Universitas Panca Marga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51747/energy.si2025.256

Abstract

Distributed secondary control (DSC) is essential for voltage regulation and current sharing in DC microgrids with high renewable penetration. However, the diversity of system configurations, control strategies, and validation approaches challenges the reproducibility of published results. This study evaluates reporting and validation practices in 74 DSC-related articles from a reproducibility and research ethics perspective. Using a document-based ex post facto design, we construct a Reproducibility Readiness Index (RRI) based on system configuration reporting, validation completeness, and performance metric clarity. Results show that most studies exhibit low to medium reproducibility readiness, with only about one third achieving high levels. While system descriptions are generally adequate, validation setups and performance metrics are often incomplete or qualitative. Studies including hardware-in-the-loop or prototype experiments tend to score higher, though weak documentation remains a major limitation. These findings emphasize reproducibility as both a technical and ethical concern and support the need for stronger transparency and open science practices in DSC research on DC microgrids.
Optimalisasi Energi Pada Lift Berdasarkan Gerak Vertikal pada Lift Menggunakan Hybrid Naive Bayes Adika Prana Ihsanuddin; Siti Sendari; Ilham Ari Elbaith Zaeni; M. Afnan Habibi; Danang Arengga Wibowo
Jurnal JEETech Vol. 6 No. 2 (2025): Nomor 2 November
Publisher : Universitas Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32492/jeetech.v6i2.6203

Abstract

Penelitian ini bertujuan untuk mengoptimalkan penggunaan energi pada sistem lift berdasarkan gerak vertikal menggunakan algoritma Hybrid Naive Bayes. Proses optimalisasi didasarkan pada pengumpulan data dilakukan di Gedung B11 Fakultas Teknik Universitas Negeri Malang selama periode waktu tertentu, dalam upaya mengurangi konsumsi energi pada gedung bertingkat, efisiensi energi lift menjadi salah satu fokus utama. Dengan memanfaatkan data penggunaan lift yang meliputi pola pergerakan vertikal, waktu operasional, serta beban muatan, penelitian ini melakukan klasifikasi dan prediksi efisiensi energi. Algoritma Hybrid Naive Bayes dipilih karena kemampuannya dalam menangani ketidakpastian data serta keandalannya dalam klasifikasi, terutama saat dikombinasikan dengan metode optimisasi lainnya. Hasil prediksi efisiensi energi yang akurat juga memungkinkan manajemen gedung untuk menerapkan strategi operasional yang lebih hemat energi dan ramah lingkungan. Dengan demikian, penelitian ini diharapkan memberikan kontribusi signifikan dalam pengelolaan energi yang lebih efisien pada sistem lift di gedunggedung tinggi.
Federated Ensemble Learning with SHAP–LIME Interpretability for Smart Home Energy Prediction Rahma Puspitasari; Siti Sendari; Muhammad Arif Hermawan; Joshua Andrian; Ira Kumala Sari
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 3, August 2026 (Article in Progress)
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i3.2665

Abstract

The increased adoption of IoT-based Smart Home systems in Indonesia has resulted in a growing volume of device-level energy data, opening up opportunities for the development of predictive models to support efficient household electricity consumption. However, challenges related to accuracy, interpretability, and data privacy remain a major concern, especially when data is distributed across multiple devices. This study evaluates the performance of four tree-based ensemble models, namely Random Forest, Gradient Boosting, XGBoost, and LightGBM, in centralized learning and federated learning scenarios using the Indonesia Smart Home Dataset. After undergoing feature preprocessing and refinement, including the removal of Sofa Pressure and Bed Pressure due to high noise, each model was trained and evaluated using MAE, MSE, and RMSE metrics. Federated learning was implemented through the Federated Averaging (FedAvg) algorithm to maintain data privacy without the need to transfer raw data between devices. The results show that LightGBM consistently provides the best performance in both scenarios and demonstrates resilience to data fragmentation and heterogeneity. Although there was a slight increase in error in federated learning, the error values remained within an acceptable range. SHAP and LIME analyses revealed that high-power devices such as air conditioners, water pumps, rice cookers, lights, and refrigerators had the greatest contribution.
Co-Authors A.N. Afandi Abdul Wafi Abdur Rohman Achmad Jefri Achmad Jefri Achmad Jefri Adika Prana Ihsanuddin Afnan Habibi, M. Agung Bella Putra Utama Agung Endro Nugroho Agus Rahma Dani, Ayunda Aji Prasetya Wibawa Alief Fajar Syahputra Amalia Nurutami Amalia Prameswari Alvina Andi Khoirudin Andis Wijaya Anik Nur Handayani Anisatul Qomariyah Arengga Wibowo, Danang Arengga, Danang Argeshwara, Dityo Kreshna Arifin, Samsul Aripriharta - Arnista Vindriyanti Ashar, Muhammad Ashrofil Muzaki Ayunda Agus Rahma Dani Bagaskoro, Muhammad Cahyo Baliyah Ahmad Fathoni Benny Agung Prasetyo Billah, Egi Nursari Burhanudin Yusuf Abdullah Ar Ramadhan Cahyaning Wulandari Cahyaning Danang Arengga Wibowo Dava Desti Yanti Dendi Mukti Putranto Desti Yanti, Dava Devi Dwi Purwanto Dian Candra Lestari Didik Dwi Prasetya Dwi Mukti Asmoro Sari Dwi Puri Fatmawati Dyah Lestari Effendi, Muhammad Minhaj Eko Noerhayati Elbaith, Ilham ari Eli Hendrik Sanjaya Elista Kartika Sari Elmunsyah, Hakku Faiz Syaikhoni Aziz Fajar Syahputra, Alief Fakhruddin, Dhiyaurrahman Fatma Cahyaningrum Fitri, Shofiana Franky Melky Giri Wahyu Wiriasto Guyub Raharjo Hakiki, A.Riyan Rahman Hakkun Elmunsyah Hanny Prasetya Hariyadi Haq, Sigit Prasetyo Harits Ar Rosyid Hary Suswanto Heru Wahyu Herwanto Hidiyah, Tabita May Hsien-I Lin I Made Wirawan Ilham Ari Elbaith Ilham Ari Elbaith Zaini Ira Kumala Sari Ira Kumalasari Irham Fadlika Irvan, Mhd James Aditama Januar Arief Muhammad Joshua Andrian Joumil Aidil Saifuddin Kamil Faqih Kartika Sari, Elista Khoiruddin Asfanie Khusnul Hidayat Kotaro Hirasawa Kumalasari, Ira Langlang Gumilar Listyo Yudha Irawan M. Afnan Habibi M. Bagus Arifin Made Radikia Prasanta Mahfud Jiiono Mahfud Jiono Mario Leo Nardo Melky, Franky Melta Dhemahestri Misik Rahayu Oktaningsih Moch. Burhanuddin Alfarobbi Mochamad Farhan Ali Irfani Mochammad Haidar Ridho Mochtar, Norrima Moh. Zainul Falah Mohammad Yussril Asri Mokh Sholihul Hadi Mokhammad Nasrulloh Mokhtar , Norrima Binti Muhamad Syamsu Iqbal Muhammad Aditya Firnanda Muhammad Arif Hermawan Muhammad Fajar Saifuddin Muhammad Hanif Abdur Razaq Muhammad Tahfidlul Azmi Muhammad Yoga Pranata Mukti Putranto, Dendi Muladi Mustika, Soraya N. Muzayana Muzayana Nastiti Susetyo Fanani Putri Nastiti Susetyo Fanani Putri Nastiti Susetyo Fanany Putri Ni’am, Faj’run Nobri Wicaksono Nur Halim Nurutami, Amalia Nuzul Zaeni Eki Ramadhanu Prana Ihsanuddin, Adika Prasetya Widiharso Prasetya Widiharso Prasetya Widiharso Prastiwi, Mellinia Regina Putri, Nanda Regita Rahma Puspitasari Ria Rahmawati Rina Dewi Indahsari Rindi Santika Agustin Ristanto Aji Prakoso Rizki Jumadil Putra Rizky Asilia Puspita Sari Rosmin, Norzanah Samsul Arifin Setumin , Samsul Setumin, Samsul Shofiana Fitri Shrestha, Rajendra Prasad Soraya Norma Mustika Soraya, Fenthy Soraya, Fenthy Suastika Yulia Riska Supardjan A. Margono Susilo, Suhiro Wongso Syaad Patmanthara Syabani, Muhiban Syafaat, Mokhammad Syafiq Ubaidillah Syamsul Arifin Syamsul Bachri Triyanna Widiyaningtyas Utomo, Imam Tree Wahyu Sakti Gunawan Irianto Wahyu Tri Handoko Waridno, Aji J. T. Wibowo, Danang Arengga Wibowo, Dian S. Wibowo, Fauzy Satrio Wildan Iswahyudi Yogi Dwi Mahandi Yudhi Christianto Yuni Rahmawati Zaeni, Ilham Ari Elbaith Zulkarnain, Aldo Z. A.