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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Reconfigurable and Embedded Systems (IJRES) Semantik Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) International Journal of Advances in Intelligent Informatics Seminar Nasional Informatika (SEMNASIF) ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika JURNAL NASIONAL TEKNIK ELEKTRO JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Robotics and Control (JRC) Journal of Applied Engineering and Technological Science (JAETS) Jurnal Abdi Insani Madani : Indonesian Journal of Civil Society Journal of Applied Data Sciences Prosiding Konferensi Nasional PKM-CSR eProceedings of Applied Science eProceedings of Engineering Jurnal Rekayasa elektrika Jurnal INFOTEL
Indrarini Dyah Irawati
Telkom University
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Nursing Public Health Social Sciences Other

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DCT and SVD Sparsity-Based Compressive Learning on Lettuces Classification Lutvi Murdiansyah Murdiansyah; Gelar Budiman; Indrarini Dyah Irawati; Sugondo Hadiyoso; A. V. Senthil Kumar
Journal of Applied Engineering and Technological Science (JAETS) Vol. 6 No. 1 (2024): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v6i1.4506

Abstract

Compressive Sensing (CS) technique in image compression represents efficient signal which offering solutions in image classification where the resources are constrained especially on a large image processing, storage resource, and computing performance. Compressive learning (CL) is a framework that integrates signal acquisition via compressed sensing (CS) and machine/deep learning for inference tasks directly on a small number of measurements, On the other hand, in real-world high-resolution (HR) data, where the image dataset is very limited CL, has the drawback of reduced accuracy under conditions of aggressive compression ratio. Here, a reconstruction method is necessary to maintain high levels of accuracy. To address this, we proposed a framework Deep Learning (DL) and Compressive Sensing that processing a small dataset of 92 images maintaining high accuracy. The framework developed in this paper employs processing sensing matrix A in compressive sensing with two transformation methods: DCT CL with Multi Neural Networks and the SVD method with GoogleNet framework. To maintain the same computation efficiency as DCT Compressive learning, SVD with GoogleNet framework provides a solution for object recognition, achieving accuracy values ranging from 89.47% to 63.15% for compression ratios of 3.97 to 31.75. This performance shows a linear tendency concerning the PSNR level, an index of signal reconstruction quality, and demonstrates an efficient process in the S matrix.
Advanced Smart Bracelet for Elderly: Combining Temperature Monitoring and GPS Tracking Sugondo Hadiyoso; Indrarini Dyah Irawati; Akhmad Alfaruq; Tasya Chairunnisa; Muhamad Roihan; Suyatno Suyatno
Journal of Applied Engineering and Technological Science (JAETS) Vol. 6 No. 1 (2024): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v6i1.6182

Abstract

Indonesia is entering an aging population period, marked by an increase in the number of elderly individuals, accompanied by a rise in dementia cases. This situation leads to higher dependency among the elderly on others for assistance or long-term care. Dementia can cause elderly people to lose their sense of direction, often wandering aimlessly, making them difficult to track. To address this issue, a wearable smart bracelet is proposed to monitor the location and a vital body parameter such as body temperature. The system is equipped with a tracking application that can send an alert if the user is outside a designated area. It automatically sends a warning message to the caregiver's or family member's smartphone when abnormal signs are detected. The bracelet is designed like a wristwatch, to be worn on the wrist. It is small, lightweight, and battery-operated. Temperature and location data can be transmitted in real-time using an internet network to mobile devices. The device can notify when the user is outside the specified area. Test results indicate that the device has high accuracy and reliability in monitoring location and body temperature with accuracy around 98.5%, as well as sending notifications through a Telegram bot when certain thresholds are exceeded. This device can work properly for up to 5 hours on a single battery charge. With this device, it is expected to help monitor and support the care of the elderly so that they can improve their quality of life. This device can also provide an emergency alarm if the elderly are outside the area.
Feature Selection Using Pearson Correlation for Ultra-Wideband Ranging Classification Indah Hapsari, Gita; Munadi, Rendy; Erfianto, Bayu; Dyah Irawati, Indrarini
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 2 (2025): April 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i2.6281

Abstract

Indoor positioning plays a crucial role in various applications, including smart homes, healthcare, robotics, and asset tracking. However, achieving high positioning accuracy in indoor environments remains a significant challenge due to obstacles that introduce NLOS conditions and multipath effects. These conditions cause signal attenuation, reflection, and interference, leading to decreased localization precision. This research addresses these challenges by optimizing feature selection LOS, NLOS, and multipath classification within Ultra-Wideband (UWB) ranging systems. A systematic feature selection approach based on Pearson correlation is employed to identify the most relevant features from an open-source dataset, ensuring efficient classification while minimizing computational complexity. The selected features are used to train multiple machine-learning classifiers, including Random Forest, Ridge Classifier, Gradient Boosting, K-Nearest Neighbor, and Logistic Regression. Experimental results demonstrate that the proposed feature selection method significantly reduces model training and testing times without compromising accuracy. The Random Forest and Gradient Boosting models exhibit superior performance, maintaining classification accuracy above 90%. The reduction in computational overhead makes the proposed approach highly suitable for real-time applications, particularly in edge-computing environments where processing efficiency is critical. These findings highlight the effectiveness of Pearson correlation-based feature selection in improving UWB-based indoor positioning systems. The optimized feature set facilitates robust LOS, NLOS, and multipath classification while reducing resource consumption, making it a promising solution for scalable and real-time indoor localization applications.
Perancangan Dan Realisasi Smart Door Lock Menggunakan Rfid Berbasis Iot Pradana, Gde Agus Wira Satria; Irawati, Indrarini Dyah; Alfaruq, Akhmad
eProceedings of Applied Science Vol. 11 No. 2 (2025): April 2025
Publisher : eProceedings of Applied Science

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Abstract

Abstrak— Dalam era digital, keamanan dan kenyamananakses menjadi kebutuhan penting dalam pengelolaanpintu dan ruangan. Sistem smart door lock berbasis RFIDdan Internet of Things (IoT) dirancang untuk memenuhikebutuhan ini dengan menggunakan electromagnetic locksebagai mekanisme penguncian. Sistem ini tidak hanyamempermudah akses ke laboratorium menggunakankartu RFID milik mahasiswa dan dosen, tetapi jugasecara otomatis mencatat kehadiran mereka. Penelitianmenunjukkan keberhasilan sistem dalam membaca kartuRFID hingga 98% pada jarak 2-5 cm dan penguncianpintu yang responsif melalui relay. Data absensi berhasildikirim secara real- time ke Firebase menggunakanESP8266, menjadikan sistem ini efisien dan andal untukmanajemen akses dan presensi. Dengan otentikasiberbasis RFID, peningkatan keamanan melaluielectromagnetic lock, dan kontrol akses yang lebih baik,sistem ini menawarkan solusi sederhana dan dapatditerapkan di berbagai lingkungan seperti rumah, kantor,atau ruang dengan kebutuhan keamanan tertentu. Kata Kunci : RFID, scanning, IOT, EM Lock, Smart doorlock
Optimizing Machine Learning-Based Network Intrusion Detection System with Oversampling, Feature Selection and Extraction Shiddiq, Rama Wijaya; Karna, Nyoman; Irawati, Indrarini Dyah
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 2 (2025): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i2.30675

Abstract

Network security is a global challenge that requires intelligent and efficient solutions. Machine Learning (ML)-based Network Intrusion Detection Systems (NIDS) have been proven to enhance accuracy in detecting cyberattacks. However, the main challenges in implementing ML-based IDS are dataset imbalance and large dataset size. This research addresses these challenges by applying oversampling techniques to balance the dataset, feature selection using random forest to identify the most relevant features, and feature extraction using Principal Component Analysis (PCA) to further reduce the selected important features. Additionally, K-fold cross-validation is used to test the features to minimize bias and ensure the model does not suffer from overfitting, while Optuna is implemented to automatically optimize model parameters for maximum accuracy. Since IDS performance deteriorates with high-dimensional features, the combination of methods used is evaluated based on feature selection applied to the model using datasets wtih 45 features selected from UNSW-NB15, 78 features from CIC-IDS-2017, and 80 features from CIC-IDS-2018 using various ML algorithms. The results demonstrate that the combination technique with feature selection, along with maximum optimization for each model significantly improves performance on large and imbalanced datasets reaching 99% accuracy compared to conventional methods in network traffic analysis.
Perancangan Website Form Data Defa untuk Monitoring Pemeliharaan dan Pendataan di Wilayah Telekomunikasi Regional III PT. Telkom Indonesia Musyaffa, Nadhif Athallah; Irawati, Indrarini Dyah
eProceedings of Applied Science Vol. 10 No. 3 (2024): Juni 2024
Publisher : eProceedings of Applied Science

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Abstract

Defa merupakan singkatan dari Digital Energy and Facility yang dipergunakan untuk bidang CME (Civil,Mechanical and Engineering) di PT. Telekomunikasi Indonesia yang sedang melakukan proses digitalisasi. Pada wilayah JawaBarat yang dikelola oleh Divisi Regional III, pengelolaan DEFA Wilayah Telekomunikasi III diberikan kepada tim AccessNetwork Element OM yang tercakup kedalam divisi Regional Network Operation. DEFA juga mensupport listrik catuan sertainternet of things pada Telkom. Pemeriksaan yang dilakukan secara manual dan pendataan dengan format file yang berbedabeda menyulitkan tim dalam monitoring perangkat. Hasil dari perancangan website Form Data Defa menggunakan kuesioner survei yang diisi oleh 12 users telah didapatkan hasil rata-rata52.5% respon sangat setuju, 40% respon setuju, dan 7.5% respon netral. Kata Kunci- Website, Laravel, Form, Agile
IMPLEMENTASI SISTEM MONITORING INDOOR HYDROPONIC FARMING BERBASIS WEBSITE Gabriel Sabadtino Siahaan; Indrarini Dyah Irawati; Dadan Nur Ramadan
eProceedings of Applied Science Vol. 10 No. 6 (2024): Desember 2024
Publisher : eProceedings of Applied Science

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Abstract

Hidroponik merupakan metode pertanian modern yang memungkinkan tanaman tumbuh tanpa menggunakan tanah, melainkan dengan larutan nutrisi yang kaya akan unsur hara penting untuk pertumbuhan. Dalam proyek ini, dirancang sistem monitoring berbasis website untuk memvisualisasikan data dari sensor hidroponik dan panel surya secara real-time. Sistem ini menggunakan ESP32 sebagai perangkat penerima data dan database cloud untuk menyimpan dan mengidentifikasi data, yang kemudian ditampilkan melalui dashboard pada website. Dengan sistem ini, pengguna dapat dengan mudah memantau kondisi tanaman hidroponik secara jarak jauh melalui internet. Hasil perancangan dan pengujian menunjukkan bahwa sistem monitoring yang dikembangkan berhasil terintegrasi dengan sensor dan database, serta mampu menampilkan data dari dua dashboard terpisah: satu untuk rak hidroponik yang memantau suhu air, TDS, pH, DO, suhu, dan kelembapan ruangan, dan satu lagi untuk panel surya yang memantau suhu air, tegangan, daya, dan suhu panel. Meskipun ada beberapa kendala teknis terkait sensor, secara keseluruhan sistem ini berfungsi dengan baik tanpa gangguan signifikan dalam penyambungan sensor, penghubungan database, serta penampilan data di website. Website monitoring ini terbukti efektif dan bermanfaat dalam pengelolaan sistem hidroponik berbasis teknologi. Kata kunci — Hidroponik, Sistem Monitoring, Software Monitoring, Website Monitoring
Lora Communication System for Early Detection and Monitoring of Water Toxicity in Floating Net Cages Rahmafadilla, Rahmafadilla; Irawati, Indrarini Dyah; Rizal, Mochammad Fahru; Maidin, Siti Sarah
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.787

Abstract

Floating Net Cages/ Keramba Jaring Apung (KJA) are at risk of polluting the air, which can affect fish farming. Therefore, an early monitoring system is needed that can measure air quality such as temperature, pH, and dissolved oxygen (DO) in real-time. This system utilizes the LoRa RFM95W module to wirelessly transmit environmental data from sensors installed on the cages, which continuously monitor water quality parameters such as temperature, pH, and DO in real-time. The data obtained is then processed to monitor changes in water toxicity in real-time, allowing early detection of potential threats to the ecosystem. Tests were conducted at distances of 50m, 180m, 300m, 340m, and 440m. The results showed that the system worked well up to a distance of 300m with RSSI values between -85 dBm to -120 dBm and SNR more than 2 dB. However, at distances of 340m and 440m, the signal decreased and the delay increased. At a depth of 340m, only one experiment was successful with RSSI -134 dBm and SNR -6 dB, while at a depth of 440m, only a few experiments were successful with RSSI between -122 dBm to -132 dBm and SNR between 1 dB to -6 dB. The prototype system successfully transmitted real-time air quality data to a web-based monitoring center. Data from the sensors were sent via the LoRa network to a central server for further monitoring.
Sistem Komunikasi Lora Untuk Pemantauan Dini Toksisitas Air Danau Pada Keramba Jaring Apung Fadilla , Rahma; Irawati, Indrarini Dyah; Rizal, Mochammad Fahru
eProceedings of Applied Science Vol. 11 No. 4 (2025): Agustus 2025
Publisher : eProceedings of Applied Science

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Abstract

Keramba Jaring Apung (KJA) di danau berpotensi mencemari air dan mengganggu budidaya ikan, sehingga diperlukan sistem pemantauan dini. Sistem ini menggunakan sensor untuk mengukur suhu, pH, dan oksigen terlarut (DO) secara real-time, serta modul LoRa RFM95W untuk mengirimkan data secara nirkabel. Pengujian dilakukan pada jarak 50 hingga 440 meter. Hasil menunjukkan sistem bekerja optimal hingga 300 meter dengan nilai RSSI -85 dBm hingga -120 dBm dan SNR di atas 2 dB (LOS). Pada jarak 330 dan 440 meter, kualitas komunikasi menurun (NLOS), delay meningkat hingga 11,3 sampai 15,2 detik, dan terjadi kehilangan paket hingga 70%. Meskipun begitu, prototipe berhasil mengirimkan data ke pusat pemantauan berbasis web, memungkinkan deteksi dini perubahan kualitas air dan potensi toksisitas. Sistem ini efektif untuk mendukung pemantauan kualitas air secara real-time di lingkungan perairan budidaya. Kata kunci— Keramba Jaring Apung (KJA), LoRa RFM95W, RSSI, Delay, SNR
Analysis of Pneumonia from Chest X-Ray Images Using an Optimized Ensemble Machine Learning Models with Voting Classifier Monita, Vivi; Hanan Lutfianto, Naufal; Dyah Irawati, Indrarini
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3689

Abstract

Pneumonia is a pulmonary disease resulting from infections caused by bacteria, viruses, or fungi that invade the lungs. This condition leads to inflammation due to the accumulation of fluids, blood cells, and other substances in the alveoli. Common symptoms experienced by patients include fever, coughing, and production of phlegm. Although pneumonia can affect individuals of any age, those with weakened immune systems are particularly vulnerable. Children and elderly individuals are especially prone to contracting this illness. The present research employs an ensemble learning approach for pneumonia detection using chest x-ray images to address this issue, specifically integrating support vector machines (SVMs) and random forests (RFs). The primary aim is to evaluate the effectiveness of ensemble learning through a voting classifier in improving pneumonia detection accuracy compared to individual machine learning models. The methodology includes preprocessing the data with contrast-limited adaptive histogram equalization (CLAHE), which minimizes noise by defining a kernel matrix and substituting each pixel's intensity with the weighted average of its neighboring pixels and itself. The research also involves training models using SVM and RF algorithms with hyperparameter optimization. These individual models are then assessed and compared using performance metrics such as accuracy, area under the curve (AUC), specificity, sensitivity, confusion matrix, and computational efficiency. By harnessing the strengths of ensemble learning, this research aims to contribute to the development of reliable pneumonia detection systems, with potential applications in clinical environments where timely and accurate diagnosis is essential for patient management. This research achieved 99.40% and 96.32% accuracy, 99.97% and 96.52% AUC, and 0.0436% and 0.0451% loss. This method tackles others that use deep learning and single machine learning with all balanced datasets.
Co-Authors ., Ridwan A. V. Senthil Kumar Abi Hakim Amanullah Adi Arief Wicaksono ADIANGGIALI, ANYELIA Afandi, Mas Aly Akhmad Alfaruq Akhmad Hambali Alfaruq, Akhmad Andri Juli Setiawan Anggun Fitrian Isnawati Anwar Muqorobin Aprilia, Rizky Arfianto Fahmi Arif Indra Irawan ARIS HARTAMAN Ary Nugroho, Bambang Asep Mulyana Ayu Irmawati Bagus Budi Wibowo Bayu Erfianto Dadan Nur Ramadan Didi Supriyadi Dzikri Fajduani, Fazrian Ezi Rohmat Fadilla , Rahma Fairuz Azmi Fajrul Falaah, Alif Fandi Fachrulrozi, Muhammad Farhan Alghifari Chaniago Saputro, Muhammad Gabriel Sabadtino Siahaan Gelar Budiman Gita Indah Hapsari Hadjwan, Razel Hafidudin . Hanan Lutfianto, Naufal Ibnu Syahban M, Novaldi Inung Wijayanto Istikmal Ivosierra Andrea Larasaty Jaya, M. Izham Justisia Satiti Larasaty, Ivosierra Andrea LATIP, ROHAYA Leanna Vidya Yovita Lenna Vidya Yovita Lestari Lestari Lionel Saonard, Aldo Lutvi Murdiansyah Murdiansyah Maidin, Siti Sarah Miftahul Khairat Sukma Muh. Kurniawan, A. Muhamad Roihan Muhammad Dimas Arfianto Muhammad Dimas Arfianto, Muhammad Dimas Muhammad Iqbal Musyaffa, Nadhif Athallah Nadhya Gita Anggana Natia Pradnyaswari, Luh Gede Nita Laananila, Grace Nur Ramadhan, Dadan Nurwan Reza Fachrurrozi Nyoman Karna, Nyoman Paundra Aldila Pradana, Gde Agus Wira Satria Pradika Caesarizky Kurniahadi Prayoga, Andry Priawan, Agi Rahmafadilla, Rahmafadilla Ramadhan, adan Nur Ramdani, Ahmad Zaky Rassem, Taha H. Rasyidah, - Rendy Munadi Reni Dyah Wahyuningrum Ridha Muldina Negara Ridwan . Rita Purnamasari Rizal, Mochammad Fahru Rizky Aulia Rahman ROHMAT TULLOH Roykhan Sukma, Hanif Sandova, Fisal Oktafian Penta Sandy Purniawan Santosa, Harjono Priyo Sasmi Hidayatul Yulianing Tyas Shahreen Kasim, Shahreen Shiddiq, Rama Wijaya Silvia, Helen Siti Sarah Maidin Siti Zahrotul Fajriyah Sofia Naning Hertiana Sri Huning Anwariningsih Suci Alfi Syahri Tune, Andi Suci Aulia Suci Aulia Sugeng Santoso Sugondo Hadiyoso Susi Susanti Suyatno Suyatno Syifa Nurgaida Yutia Tasya Chairunnisa Tita Haryanti Triasari, Biyantika Emili Uwais Razaqtana, Muhammad Vivi Monita Wartingrum, Nadia Wijanarko, Sulistyo Yudha Purwanto Yudiansyah Yudiansyah YULI SUN HARIYANI Zamri, Nurul Aqilah Zero Fomandes, Muhammad Zhao, Zhong