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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Transmisi: Jurnal Ilmiah Teknik Elektro Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Mahasiswa TEUB Jurnal EECCIS e-Jurnal Arus Elektro Indonesia Journal of Information Systems Engineering and Business Intelligence ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Jurnal Teknologi dan Sistem Komputer Network Engineering Research Operation [NERO] Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Indonesian Journal of Electrical Engineering and Computer Science JAREE (Journal on Advanced Research in Electrical Engineering) International Journal of Engineering, Science and Information Technology Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Rahmadwati Rahmadwati
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Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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SISTEM PENDETEKSIAN TANDA PENGENAL DI SEBUAH GEDUNG UNTUK MENENTUKAN SASARAN TEMBAK MUSUH BERBASIS TEMPLATE MATCHING Riza Hasbi Ash Shiddieqy; Rahmadwati Rahmadwati; Panca Mudjirahardjo
Transmisi: Jurnal Ilmiah Teknik Elektro Vol 25, No 1 Januari (2023): TRANSMISI: Jurnal Ilmiah Teknik Elektro
Publisher : Departemen Teknik Elektro, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/transmisi.25.1.32-40

Abstract

Pengenalan dari berbagai sudut pandang atau point of view untuk mendeteksi keberadaan seseorang dengan memanfaatkan pengolahan citra digital dapat dijadikan pembeda dari berbagai karakteristik dalam suatu objek gambar yang ada. Pencarian dan pembuatan database citra adalah salah satu komponen yang berperan sebagai informasi visual. Pengenalan citra dari bentuk dan posisi objek yang akan dideteksi merupakan hasil uji yang akan merepresentasikan seberapa cocok dengan input gambar terdeteksi dengan benar. Objek akan deberikan noise serta halangan atau obstacle contoh ada beberapa orang yang menutupi objek atau gedung dan pohon. Dalam penelitian ini menerapkan template matching merupakan sebuah teknik pengolahan citra digital untuk menemukan bagian kecil dari gambar yang sesuai dengan tempalate gambar. Aplikasi ini dirancang menggunakan matlab 2019b sebagai software pembantu dan membuat database pengolahan citra.
IMPLEMENTASI AYUNAN MEKANIS MENGGUNAKAN INDIKATOR FREKUENSI DAN INTENSISTAS SUARA TANGISAN BAYI PADA PLANT SMART INKUBATOR BAYI MENGGUNAKAN LOGIKA FUZZY DENGAN METODE MAMDANI Robintang Sotardodo Situmorang; n/a Rahmadwati; Moch. Rusli
Jurnal Mahasiswa TEUB Vol. 11 No. 6 (2023)
Publisher : Jurnal Mahasiswa TEUB

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

Abstract

An incubator functions to provide an environment that resembles the womb for premature babies or infants in need of care. To achieve optimal results, it is crucial for the baby to remain in an incubator with stable conditions. In certain situations, babies often feel uncomfortable and express their discomfort by crying. This discomfort can be addressed by using a swinging motion. By swinging, the baby will feel familiar with the movement since they frequently experienced it while in the womb. This research utilize a fuzzy logic control system with the frequency and intensity/level of the baby's cries as input and a PWM signal that drives the mechanical swing and an LCD indicator as output. The fuzzy logic control employed is the Mamdani method, consisting of fuzzification, rule base, and defuzzification stages. The fuzzification method used is the min-max method, while the defuzzification method employs the Mean of Maximum method. Based on the research results, the device is capable of identifying the baby's crying sound based on frequency (The range is around 250-1000 for normal and 100-2000 for hyperphonation) and intensity/level of cries in an environment with an ideal noise level for babies (in this case, a healthcare facility with a noise level of 55-65 dB). It then controls the motor to initiate the swing at various speeds and provides information on the LCD display. Keywords--- Craddle, Fuzzy, Cry, Infant, Frequecy, desiBell, Automatic.
Skema Digital Watermarking Citra dengan Metode TLDCT dan Chinese Remainder Theorem Danang Aditya Nugraha; Rahmadwati; Muhammad Aziz Muslim
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 6 No 2: Mei 2017
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1719.404 KB)

Abstract

Currently, it is easy to access the wide variety of digital content on the internet, and therefore protection effort for digital content is an important problem that needs to solve. One kind of digital content that urgently needs to protect is digital image. Protection is needed not only for the copyright but also for the authenticity of digital image because there are so many advanced image editing software, that are easy to use. This paper presents a scheme for digital image protection through Digital Watermarking process using Two Level Discrete Cosine Transform, and Chinese Remainder Theorem for color digital image. This scheme has an ability to detect and to recover some kind of changes that occur in the watermarked image. This ability can be achieved because this scheme is categorized into fragile digital watermarking, thus, has low robustness. However, based on experiment results, this scheme shows a good invisibility. It is higher than the previous research and has a minimum distortion, proven by Peak Signal to Noise Ratio (PSNR) value of around 45.
Identifikasi Penyakit Katarak berdasarkan Citra Fundus menggunakan Siamese Convolutional Neural Network RAHMADWATI, RAHMADWATI; IMRAN, AZZAM ZAHFRAN; ASWIN, MUHAMMAD; FERDIANA, KHAIRUNISA
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 12, No 4: Published October 2024
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v12i4.838

Abstract

ABSTRAKKatarak merupakan penyakit yang dipengaruhi oleh faktor-faktor tertentu seperti usia, aktivitas dan penderita penyakit genetik seperti diabetes, hipertensi, asam urat serta riwayat keluarga katarak. Diagnosis penyakit katarak ini dapat dipengaruhi oleh faktor subyektif seperti pengalaman dan keahlian dokter. Untuk mengatasi hal tersebut dan menurunkan tingkat subyektivitas diperlukan pendekatan yang akurat dan konsisten yaitu sistem identifikasi penyakit katarak terbantukan komputer. Penelitian ini bertujuan sebagai deteksi dini katarak. Metode SCNN digunakan untuk mengidentifikasi citra fundus mata katarak. Fine tuning parameter SCNN memberikan performa yang baik pada proses pelatihan dan pengujian yaitu 100 epoch, optimizer : RMS Prop dan loss function Binary Crossentropy. Performansi yang diberikan yaitu akurasi 91,25%, kepresisian 91%.Kata kunci: penyakit katarak, siamese convolutional neural network, citra fundus. ABSTRACTThe cataract is a disease that influenced by certain factors such as age, activity and people with genetic disease such as diabetes, hypertension, uric acid and family history of cataract. The diagnosis of cataracts based on opthamologist experience and expertise which signifies a level of a diagnostic subjectivities. In order to overcome that problem and reduce the level of subjectivity, the need for an accurate and consistent computer aided identification for cataract disease is inevitable. This research aims to as an early detection of cataracts. The SCNN is applied for identify the cataract disease based on eye fundus image. Fine tuning SCNN parameters which provide good performances in the training and testing process with 100 epochs, RMSProp optimizer, Binary Crossentropy Loss function.This system gives promising result with the accuracy 91,25% , precision level is 91%.Keywords: cataract disease, siamese convolutional neural network, fundus images
Perbandingan Metode Cost Sensitive pada Decision Tree dan Naïve Bayes untuk Klasifikasi Data Multiclass Febriantono, M Aldiki; Pramono, Sholeh Hadi; Rahmadwati, Rahmadwati
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 14 No. 1 (2020)
Publisher : Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jeeccis.v14i1.625

Abstract

Abstrak– Knowledge discovery is the method of extracting information from data in making informed decisions. Seeing as classifiers do have a lot of learning patterns in the data, testing an imbalanced dataset becomes a major classification issue. The cost-sensitive approach on the decision tree C4.5 and nave Bayes is used to solve the rule of misclassification. The glass, lympografi, vehicle, thyroid, and wine datasets were collected from the UCI Repository and included in this analysis. Preprocessing attribute selection with particle swarm optimization was used to process the data collection. Besides, the cost-sensitive decision tree C4.5  and the cost-sensitive naive Bayes method were used in the research. On the glass, lympografi, vehicle, thyroid, and wine datasets, the accuracy of the test results was 72.34 %, 68.22 %, 75.68 %, 93.82 %, and 93.95 %, respectively, using the cost-sensitive decision tree C4.5. While the cost-sensitive naive Bayes method outperforms the others by 32.24 %, 82.61 %, 25.53 %, 97.67 %, and 94.94 % on the dataset, respectively.
Sistem Monitoring Inkubator Bayi Multifungsi dengan Fototerapi dan Ayunan Mekanis Berbasis ESP32 Idhil, Andi Nurul Isri Indriany; Fadilla, Rafa Raihan; Anggraini, Monika Ayu Puji; Dewi, Ajeng Kusuma; Sanjaya, Mochamad Rofi; Nurrohman, Muhammad Yogi; Rahmadwati, Rahmadwati
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 14 No. 3 (2020)
Publisher : Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jeeccis.v14i3.668

Abstract

Abstract— Many infant mortality rates are due to premature events. Premature babies are at high risk for hypothermia and hyperbilirubinemia. To overcome this, an incubator can be used as a warmer and light therapy as blue light therapy for yellow babies. However, both medical devices have still been found using manual control. If the health worker is tired of working and manually controlling both devices, it can put the baby at risk. Multifunctional infant incubator based on ESP32, which is an infant incubator equipped with phototherapy and a mechanical swing. This multifunctional baby incubator has the ability to warm the baby's body, the baby yellow light therapy, and can calm the baby when crying. This tool can be monitored remotely using the Internet of Things (IoT). The sensors used are the DHT22 sensor and the sound sensor. Multifunctional baby incubator can make it easier for hospital or basic health care facility level to monitor baby's health in real time without being at the device location and the resulting data can be stored neatly. Keywords— Internet of Things, Monitoring, Incubator, Phototherapy. Abstrak–- Angka kematian bayi banyak disebabkan oleh kejadian prematur. Bayi prematur berisiko tinggi terhadap hipotermia dan hiperbilirubinemia. Untuk mengatasinya dapat digunakan inkubator sebagai penghangat dan fototerapi sebagai terapi sinar biru bayi kuning. Akan tetapi, masih ditemukan kedua alat kesehatan tersebut menggunakan pengontrolan secara manual. Apabila petugas kesehatan kelelahan bekerja dan melakukan pengontrolan kedua alat secara manual dapat menempatkan bayi dalam bahaya. Inkubator bayi multifungsi berbasis ESP32 yaitu inkubator bayi yang dilengkapi dengan fototerapi dan ayunan mekanis. Inkubator bayi multifungsi ini memiliki kemampuan untuk menghangatkan tubuh bayi, terapi sinar bayi kuning, dan dapat menenangkan bayi ketika menangis. Alat ini dapat dipantau dari jarak jauh menggunakan Internet of Things (IoT). Adapun sensor yang digunakan yaitu sensor DHT22 dan sensor suara. Inkubator bayi multifungsi dapat mempermudah pihak rumah sakit ataupun tingkat fasilitas pelayanan kesehatan dasar untuk mengontrol kesehatan bayi secara real time tanpa ada di lokasi alat dan data yang dihasilkan dapat tersimpan dengan rapi.Kata Kunci— Internet of Things, Monitoring, Inkubator, Fototerapi.
Rancang Bangun Pengendali Suhu pada Fermentasi Kefir Berbasis Kontroler PI Rahmadwati, Rahmadwati; Habibi, Boby Yusuf
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 15 No. 1 (2021)
Publisher : Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jeeccis.v15i1.1539

Abstract

Kefir merupakan susu fermentasi yang memiliki rasa, warna, dan konsistensi yang menyerupai yogurt dan memiliki aroma khas yeasty. Kefir memiliki kandungan gula susu (laktosa) yang relatif rendah dibandingkan susu murni dan cocok bagi penderita lactose intolerant atau tidak tahan terhadap laktosa. Fermentasi bahan pangan adalah hasil kegiatan dari beberapa spesies mikroba seperti bakteri, khamir dan kapang. Mikroba fermentasi mendatangkan hasil akhir yang dikehendaki. Proses fermentasi kefir berlangsung pada suhu 25-37°C. Pada umumnya fermentasi kefir masih dibuat dengan menggunakan proses manual dengan hanya meletakkannya di suatu tempat tertutup tanpa tahu berapa suhu yang ada pada proses tersebut.  Sehingga tidak jarang sebagian masyarakat mengalami kegagalan dalam proses pembuatannya. Pada penelitian ini dilakukan pengontrolan suhu berbasis Arduino Uno dengan kontroler PI pada box fermentasi kefir. Kontroler PI dipilih karena karakteristik respon yang diinginkan adalah respon yang cepat dan memiliki nilai error yang kecil.  Aktuator berupa elemen pemanas (heater) dan sensor suhu DS18B20 sebagai feedback system.  Proses perancangan kontroler PI menggunakan metode Ziegler-Nichols yang pertama dan didapatkan parameter kontroler PI dengan gain yaitu Kp = 26,45 dan Ki = 0,61. Nilai setpoint 32℃. Pada pengujian keseluruhan sistem tanpa gangguan didapatkan performansi respon settling time (ts) sebesar 954 s atau 15,9 menit dan error sebesar 0,593%. Pada pengujian keseluruhan sistem dengan gangguan didapatkan performansi respon settling time (ts) sebesar 960 s atau 16 menit, error sebesar 0,593% dan recovery time sebesar 165 s atau 2,75 menit.
Enhancing CNN Performance for Alzheimer’s Disease Classification through Genetic Algorithm Optimization Wildan Arif Maulana; Zainul Abidin; Rahmadwati Rahmadwati
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 2, May 2026
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i2.2543

Abstract

The rise in global life expectancy has contributed to a rapidly expanding elderly population and a corresponding increase in Alzheimer’s disease cases, highlighting the need for more accurate and objective diagnostic methods. Although MRI is widely used for brain assessment, early-stage Alzheimer’s detection remains challenging because structural differences between disease stages are often subtle and prone to subjective interpretation by clinicians. To address this limitation, this study proposes a custom Convolutional Neural Network (CNN) developed from scratch for classifying Alzheimer’s disease using brain MRI images. Data diversity was enhanced through augmentation comparison strategies, including Albumentations, which achieved 84.8% accuracy; CutMix, which achieved 88.3% accuracy, and a combined Albumentations-CutMix approach, which enabled the base model to achieve 92.1% classification accuracy. Subsequently, a Genetic Algorithm (GA) was applied to optimize key hyperparameters, enabling efficient exploration of the solution space compared to manual tuning and improving model performance to 96.4% accuracy. The optimized model demonstrated improved stability and generalization across all classes, highlighting the capability of the proposed computational framework to function as a reliable tool for supporting the early detection of Alzheimer-related cognitive decline.
Adaptive Traffic Light Signal Control Using Fuzzy Logic Based on Real-Time Vehicle Detection from Video Surveillance Zulfa Fahrunnisa; Rahmadwati Rahmadwati; Raden Arief Setyawan
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 2 (2024): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i2.28712

Abstract

Intersections often become the focal points of congestion due to poor traffic signal management, reduced productivity, increased travel duration, gas emissions, and fuel consumption. Existing traffic light systems maintained constant signal duration regardless of traffic situations, resulting in green signals for lanes with no vehicle queues that increased waiting times in other lanes. Therefore, a real-time traffic signal optimization system using Fuzzy Logic control, utilizing vehicle queue and flow rate real-time data from video surveillance, is needed. This research used recorded video from surveillance cameras in Banten Province, Indonesia, during daylight conditions. Vehicle queues and flow rate data were used as parameters to determine traffic light signals. The YOLO algorithm obtained these parameter values, then served them as inputs for the Fuzzy Logic system to determine signal duration. The accuracy of the traffic situation estimation system fluctuated within a range of 40% to 100%. Simulation results showed an improvement of approximately 18% by evaluating the total number of vehicles that exited the queue and reduced vehicle waiting time by about 21% compared to the existing system on intersection efficiency. Consequently, the proposed system can reduce pollution and fuel consumption, contributing to urban sustainability and public well-being enhancement. Despite the improvements over the previous systems, the accuracy of the vehicle detection system may vary with traffic density based on the extent of occlusions present, which is an area that needs further refinement. This research's contributions include utilizing real-time video footage from surveillance cameras above traffic lights to obtain real traffic conditions and identify potential errors such as occlusion of overlapping vehicle due to very congested roads. Another contribution is the adjustment of the Fuzzy membership function based on the vehicle detection system's ability to ensure precise determination of green signal duration, even when the input data contains errors.
Lightweight Skeleton–Based Hand Gesture Recognition Using Machine Learning for Human-Robot Interaction Panca Mudjirahardjo; Rahmadwati; Angger Abdul Razak; Raden Arief Setyawan; Tanjo Yui
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 2 (2026): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.12.2.223-235

Abstract

Background: Gesture-based control has emerged as a natural and intuitive approach for human–robot interaction (HRI). Advances in computer vision, particularly skeletal pose estimation, provide robust solutions to hand gesture recognition that are independent of lighting conditions and other sensor dependencies. However, most previous studies have relied heavily on deep neural network approaches, such as graph convolutional networks (GCN) and transformer-based architectures, which require substantial computational resources, making them less suitable for real-time robotic system applications. Objective: This study aims to develop a lightweight skeleton pose-based gesture recognition framework for human – robot interaction, focusing on accuracy, robustness, and computational efficiency. Methods: In this experiment, hand movement reference points were extracted using skeleton pose estimation. From these reference points, referred to as joint coordinates, feature vectors are constructed. These feature vectors are then used as input to the ML model, including SVMs, RF, GBM, and LGBM. The machine learning models were comparatively evaluated in terms of F1-score recognition, latency, and robustness in dynamic environments. Results: The SVM classifier consistently outperformed RF, GBM, and LGBM, achieving an F1-score of 91.3%. Real-time processing with an average latency of 44 ms per frame (≈22.7 FPS) and demonstrated stable performance under dynamic operating conditions. These results indicate that the selected skeleton keypoints and feature representation are effective in capturing discriminative hand-gesture patterns while maintaining low computational overhead. Conclusion: When combined with conventional machine learning classifiers, this study demonstrates that efficient skeleton pose-based feature construction offers a viable alternative for lightweight computation in gesture-based human – robot interaction. This study’s findings indicate that reliable and responsive gesture-based control supports practical deployment on embedded humanoid robot platforms.   Keywords: Skeleton-based gesture recognition, human – robot interaction, machine learning, computer vision, embedded robotics
Co-Authors Akhmad Sabarudin Akio Kitagawa Alva Kosasih Angger Abdul Razak Angger Abdul Razak Anggraini, Monika Ayu Puji Anindita, Ruth Astari Anisari Mei Prihatini Aziz Muslim Azizurrahman Rafli Boby Yusuf Habibi Dachlan, Hary Soekotjo Danang Aditya Nugraha Deron Liang Dewi, Ajeng Kusuma Dhaffin Rayhzan Ferdian Dharmawan - Eka Maulana Erni Yudaningtyas Erni Yudhaningtyas Fadilla, Rafa Raihan Faiza Alif Fakhrina Farrel Rafif Ferdian Febriantono, M Aldiki FERDIANA, KHAIRUNISA Gigih Gumilar Golshah Naghdy Habibi, Boby Yusuf Hadi Suyono Huda, Masykur Idhil, Andi Nurul Isri Indriany Ika Kusumaning Putri IMRAN, AZZAM ZAHFRAN Luthfiyah Rachmawati M. Aldiki Febriantono M. Aziz Muslim Mahdin Rohmatillah Masykur Huda Moch. Rusli Mudjirahardjo, Panca Muh Wahid Anshori Riza Muhammad Aswin Muhammad Aziz Muslim Muhammad Dzikrullah Suratin, Muhammad Dzikrullah Muhammad Fauzan Edy Purnomo Muhammad Zulfikri Muhammad Zulfikri Nugraha, Danang Aditya Nurrohman, Muhammad Yogi Onny Setyawati Panca Mudjirahardjo Panca Mudjirahardjo Raden Arief Setyawan Raden Arief Setyawan Rakhmad Romadhoni Riza Hasbi Ash Shiddieqy Robintang Sotardodo Situmorang Rudy Yuwono Ruth Astari Anindita Sanjaya, Mochamad Rofi Sari, Sapriesty Nainy Shaskia Vilardl Ri Cahya Sholeh Hadi P. Sholeh Hadi Pramono Sultoni, Sultoni Suyono, Hadi Tanjo Yui Vita Kusumasari Wijono Wijono Wildan Arif Maulana Yeni Sumantri Yuyu Wahyu Zainul Abidin Zulfa Fahrunnisa