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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah KOMPUTASI Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control APTIKOM Journal on Computer Science and Information Technologies (CSIT) Indonesian Journal of Electrical Engineering and Computer Science IAIC Transactions on Sustainable Digital Innovation (ITSDI)
Eri Prasetyo Wibowo, Eri Prasetyo
Universitas Gunadarma
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Library & Information Science Materials Science & Nanotechnology

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Sistem Kontrol dan Monitoring Kadar PH Air pada Sistem Akuaponik Berbasis NodeMCU ESP8266 Menggunakan Telegram Jecika Mailoa; Eri Prasetyo Wibowo; Risdiandri Iskandar
Jurnal Ilmiah Komputasi Vol. 19 No. 4 (2020): Jurnal Ilmiah Komputasi Volume: 19 No. 4, Desember 2020
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32409/jikstik.19.4.336

Abstract

Seiring dengan perkembangan jaman dan meningkatnya jumlah penduduk mempengaruhi kebutuhan akan sandang, pangan dan papan, salah satunya kebutuhan akan lahan kosong yang semakin berkurang, sebagai salah satu upaya pemanfaatan lahan yang terbatas untuk memenuhi kebutuhan makhluk hidup. Untuk mengatasi permasalahan tersebut penulis membuat Sistem Kontrol Dan Monitoring Kadar pH Air Pada Sistem Akuaponik Berbabis Nodemcu Esp8266 Menggunakan Telegram. Akuaponik merupakan penggabungan dan penyempurnaan dari sistem akuakultur dan hidroponik dimana dapat membudidayakan ikan dan tanaman dalam satu lahan yang sama dimana memanfaatkan unsur hara dari kotoran ikan serta ikan memanfaatkan air bersih yang telah tersaring oleh akar tanaman dengan menggunakan metode Deep Flow Technique atau akuaponik pasang surut. menggunakan sensor pH sebagai masukan untuk menentukan derajat keasaman atau basa pada air dan relay sebagai keluaran untuk menyalakan pompa sesuai dengan nilai yang telah ditentukan dimana apabila nilai pH air lebih dari 7,5 maka pompa pada larutan pH Down akan menyala sebaliknya apabila nilai pH air kurang dari 6,5 maka pompa pada larutan pH Up akan menyala. Hasil dari sensor pH akan diteruskan melalui NodeMCU ESP8266 dan akan menampilkan kadar pH air pada ponsel agar dapat dimonitoring dari jarak jauh melalui aplikasi Telegram.
Deteksi Cacat pada Sekrup Berbasis Citra Menggunakan YOLOv5 Nugraha , Yoga Panji Perdana; Wibowo, Eri Prasetyo
Jurnal Ilmiah Komputasi Vol. 23 No. 1 (2024): Jurnal Ilmiah Komputasi : Vol. 23 No 1, Maret 2024
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32409/jikstik.23.1.3516

Abstract

Pada dasarnya perusahaan menginginkan produk yang dihasilkannya berkualitas baik. Kenyataannya kecacatan suatu produk merupakan suatu hal yang sulit dihindari. Maka diperlukan kegiatan pengendalian kualitas. Pengendalian kualitas yang dilakukan manual membutuhkan waktu yang lama dengan tingkat subjektifias serta resiko human error yang tinggi. Diperlukan pemanfaatan teknologi untuk membantu kegiatan pengendalian kualitas. Salah satu teknologi yang dapat dimanfaatkan untuk pengendalian kualitas adalah Artificial Intelligence (AI) dengan metode deep learning menggunakan arsitektur YOLOv5 . Tujuan dari penelitian ini adalah mengembangkan model yang dapat mendeteksi kualitas sekrup berbasis citra yang diunggah. Pengolahan data dilakukan dengan Roboflow untuk proses preprocessing . Proses pembuatan model menggunakan Google Colab dengan bahasa pemrograman python . Hasil penelitian menunjukkan pelatihan mendapatkan waktu pelatihan sebesar 0.404 jam atau 24.24 menit, precision 0.842, recall 0.857, dan mean average precision 0.887. Uji coba yang dilakukan menghasilkan bahwa citra dapat terdeteksi dengan baik. Namun, terdapat beberapa citra yang kurang baik dan maksimal untuk dideteksi.
Classification of Leaves Based on the Shape of Leaves Using Convolutional Neural Network Methods Syahrir, Rizka Zulfani; Eri Prasetyo Wibowo
IAIC Transactions on Sustainable Digital Innovation (ITSDI) Vol 3 No 1 (2021): October
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/itsdi.v3i1.491

Abstract

One part of the tree, namely the leaves, which grow on the branches, has several types of leaves consisting of 4 shapes, ranging from circular shapes, elongated shapes, and some even have a finger shape. Often we mistake the shapes of these leaves. This study discusses the classification of leaves based the shape of the leaf bones using the Convolutional Neural Network, which is used to classify data that has been labeled using one of the methods, namely supervised learning. The purpose of this method is to classify a variable into the variables that have been listed. The goal is to classify leaves based on leaf shape to implement a Convolutional Neural Network algorithm model for leaf classification based on bone shape, which will produce an accuracy value. Accuracy values are obtained from conducting experiments at the training and trial stages. So it can be concluded using the epochs parameter of 30 and a batch size of 128, using ReLU and Softmax activations. The results obtained for the accuracy value for training are 98.52%, while the validation is 89.06%.
Pre-driving fatigue screening from short-term heart rate variability with subject-independent validation Tia Haryanti; Eri Prasetyo Wibowo; Wahyu Kusuma Raharja; Rossi Septy Wahyuni; Imliyati Sari
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2885-2895

Abstract

This study evaluates fatigue screening from 30-second electrocardiogram (ECG) recordings using short-term heart rate variability (HRV) features in a pre-driving context. The dataset comprises 99 participants (one session each) with fatigue labels derived from the Karolinska sleepiness scale (KSS), where the primary label (K1) defines non-fit as KSS ≥ 7. A subject-independent logistic-regression model was trained under a leave-one-subject-out (LOSO) scheme. Probabilities were calibrated using Platt scaling and evaluated through threshold-free metrics (receiver operating characteristic (ROC)-area under the curve (AUC), precision-recall (PR)-AUC) as well as calibration performance using the Brier score. The model achieved ROC-AUC =0.687 (95% confidence interval: 0.591–0.776), PR-AUC =0.621, and a Brier score of 0.200. At the operating threshold t = 0.255, the model achieved sensitivity of 1.000 with no false negatives, while specificity remained 0.091 (95% confidence interval: 0.030–0.140). Reliability analysis indicated reasonable calibration in the operational probability range. These findings support short-term HRV derived from ECG as a screening tool that prioritizes avoiding missed non-fit cases, paired with a triage scheme (fit/review/non-fit) to manage uncertainty near the decision threshold. Future work should incorporate ECG morphology and signal quality cues and aim to improve specificity without sacrificing sensitivity.
SOM-SIS approach to auto summary of clustering results on university academic performance Rahayu Widayanti; Sarifuddin Madenda; Eri Prasetyo Wibowo; Khoerul Anwar
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 21, No 1: February 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v21i1.24238

Abstract

The analysis of the performance of higher education academic quality in terms of student achievement, study period, and drop out rates is still an intensive study among researchers. Several clustering methods are often used to understand student and graduate groups, in influencing college performance. However, the conventional method has only arrived at the results of clustering, so it is difficult to interpret it as a support for academic decisions, especially in mapping the position of universities against other universities nationally. This article introduces a combination of techniques from self organizing map and technique for oorder preference by similarity to an ideal solution (SOM-SIS), an auto-summarizing technique from clustering results as well as mapping university academic performance. First, the academic performance indicators are grouped using the self organizing map (SOM) method and the results are concluded using the technique for order preference by similarity to an ideal solution (TOPSIS) approach. The SOM-SIS technique was tested using data from one of the universities in Indonesia. As a result, the SOM-SIS technique has a 100% compatibility rate with the higher education quality assurance system, through recommendations from three university experts.
A mathematical model for IoT malware propagation with adaptive patching strategy based on R₀: a simple optimal control approach Dwi Ely Kurniawan; Sarifuddin Madenda; Eri Prasetyo Wibowo
Indonesian Journal of Electrical Engineering and Computer Science Vol 43, No 1: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v43.i1.pp219-232

Abstract

This paper presents a mathematical framework for modeling and controlling internet of things (IoT) malware propagation via an adaptive patching strategy governed by the real-time basic reproduction number R₀. We introduce the SEIR-P (susceptible–exposed–infected–recovered–patched) model, where the patching control rate u(t) is a sigmoid feedback function of R₀(t). All epidemiological parameters are calibrated from three empirical malware captures of the IoT-23 dataset (Stratosphere laboratory, Czech Technical University) a publicly available labeled collection of real IoT network traffic comprising 23 captures from infected and benign devices: CTU-IoT-1 (Mirai), CTU-IoT-9 (Torii), and CTU-IoT-17 (IRCBot) yielding the first empirically grounded parameter set for SEIR-type IoT epidemic models with confidence intervals. The optimal control problem is formulated via pontryagin’s maximum principle (PMP), and a closed-form R₀(u) expression is derived via the next-generation matrix (NGM), yielding the critical threshold u_crit = 0.142 day⁻¹. Five comparative simulation scenarios over a 365-day horizon show that the proposed R₀-adaptive strategy achieves a 91.3% reduction in peak infection (360 vs. 4,142 devices), eradicates malware by day 179, and attains the highest cost-effectiveness index (CEI = 1.142). Global asymptotic stability of the disease-free equilibrium under u*(t) is proven via Lyapunov’s method and LaSalle’s Invariance Principle. PRCC sensitivity analysis identifies u_max and β as dominant parameters. This closed-loop framework bridges the gap between abstract epidemic theory and deployable IoT security management.
Co-Authors Antonius Irianto Sukowati Boesono Soerowirdjo Busono Soerowirdjo Clara, Dhea Ajeng Dewi Putrie Lestari Diah Natalisa Dina Amalia Dwi Ely Kurniawan Dyah Nur'ainingsih Ernastuti Ernastuti G. Gunawan Hustinawaty Hustinawaty, Hustinawaty Ikbal Rullah Imliyati Sari Iqbal Baihaqi Jecika Mailoa Johan Harlan Jufriadif Na`am, Jufriadif Khoerul Anwar Musa, Purnawarman Nugraha , Yoga Panji Perdana Qadrisa Mutiara Detila Rahayu Widayanti Risdiandri Iskandar Robby Kurniawan Harahap Rossi Septy Wahyuni Saiful Bahri Musa Sarifuddin Madenda Sri Nawangsari Sukatin, Sukatin Syahrir, Rizka Zulfani Tavipia Rumambi Tia Haryanti Toto Sugiarato Wahyu Kusuma Raharja Wahyuningsih, Erfiana Widyastuti Widyastuti