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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Abdimas Talenta : Jurnal Pengabdian Kepada Masyarakat JITK (Jurnal Ilmu Pengetahuan dan Komputer) Journal of Applied Engineering and Technological Science (JAETS) Indonesian Journal of Electrical Engineering and Computer Science Indonesian Journal of Education, Social Sciences and Research (IJESSR) Wawasan : Jurnal Ilmu Manajemen, Ekonomi dan Kewirausahaan Komposit : Jurnal Pengabdian Dan Pemberdayaan Masyarakat
Romi Fadillah Rahmat
Universitas Sumatera Utara
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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Applications For Detecting The Rate Of Fruit In Mangrove Plants Sharfina Faza; Meyatul Husna; Ajulio Padly Sembiring; Rina Anugrahwaty; Silmi; Romi Fadillah Rahmat; Rhama Permadi Ahmad
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 6 No. 2 (2023): Issues January 2023
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v6i2.8379

Abstract

Mangrove plants are one of the plants that really help aquatic ecosystems between the sea, coast, and land. Mangrove plants provide many ecological, social, and economic benefits. In Indonesia, mangrove plants have 202 species with the same anatomy as other plants in general, consisting of roots, fruits, stems and leaves. Nowadays, the location of mangrove plants in Indonesia has experienced the fastest damage in the world due to conversion to ponds, settlements, industry and plantations. One of the efforts to restore aesthetic value and restore the ecological function of mangrove forest areas is rehabilitation using mangrove fruit. In the rehabilitation process, farmers generally use the manual method with the naked eye to determine fruit ripeness on mangrove plants, so the resulting level of accuracy is not optimal. To overcome this problem, an application is needed that can facilitate farmers in determining fruit maturity in mangrove plants so that it can help determine the maturity level of mangrove fruit. The development of this application utilizes the Deep Learning method as well as the utilization of digital image processing techniques with Grayscaling, Adaptive Threshold, Sharpening and Smoothing techniques. The results of this study are an application that can detect the level of fruit maturity in mangrove plants with an accuracy of 99.11%. With this application, determining the maturity level of fruit on mangrove plants can be easily done.
Human blood group type detection prototype focusing on agglutinin using microcontroller based photodiode Lubis, Arif Ridho; Harefa, Hafid Rahman; Al-Khowarizmi, Al-Khowarizmi; Julham, Julham; Lubis, Muharman; Rahmat, Romi Fadillah
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7007

Abstract

Blood is a fluid in the body that mainly serves as a medium for transporting various substances in the body. Detection of human blood group types with this microcontroller utilizes dark and light properties. The dark character appears due to agglomeration, while the light nature arises because of no agglomeration, for this to happen, a liquid reagent is needed. Administration of this liquid uses the aviator's breathing oxygen (ABO) system, which consists of reagent a, reagent b, and reagent c and mixing it with blood on the test paper. The number of blood samples in each reagent is based on blood lancet. Furthermore, the sensors used to detect these properties are photodiode and light emitting diode (LED) each of 3 pieces. The Arduino Uno is used to process sensor input while at the same time producing displayed human blood group type on the display screen. The test is carried out involving 12 blood samples and a medical officer. Medical officer are tasked reading directly the results of mixing between reagents and blood samples, after that are compared with the system. The results show that the deviation of the system reading is 0.167 for the sensor reading distance with the sample as far as 0.5 cm.
Braille letter recognition in deep convolutional neural network with horizontal and vertical projection Rahmat, Romi Fadillah; Purnamawati, Sarah; Mardianto, Willy; Faza, Sharfina; Sulaiman, Riza; Nadi, Farhad; Lubis, Arif Ridho
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7222

Abstract

Brail is a written mode of communication utilized by individuals with visual impairments to engage in interpersonal exchanges. The braille writing system consists of patterns printed on specialized paper that feature embossed dots. Braille documents enable the visually impaired to acquire knowledge and information exclusively through the application of their sense of contact. Comprehending braille is not a simple undertaking, particularly for the general populace. Because braille is not a required subject in Indonesian education, the majority of the population lacks proficiency in the language. This may therefore result in a minor communication barrier between visually impaired individuals and non-impaired individuals. In order to address this challenge, the present study employs digital image processing via the deep convolutional neural network (DCNN) technique to facilitate comprehension of braille document contents by non-braille speakers. This study employs a deep learning technique that is highly accurate, effective at image processing, and capable of recognizing complex patterns. This study employed the following image processing methods: grayscaling, filtering, contrast enhancement, thresholding, morphological operation, and resizing. Following testing in this investigation, it was determined that the proposed method accurately identifies embossed braille images with a precision of 99.63%.
Modification of Multilayer Perceptron Using Detection Rate Model for Prediction of Nominal Exchange Rate Al-Khowarizmi Al-Khowarizmi; Romi Fadillah Rahmat; Michael J Watts; Akrim Akrim; Arif Ridho Lubis; Muhammad Basri
Journal of Applied Engineering and Technological Science (JAETS) Vol. 6 No. 2 (2025): 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.v6i2.6117

Abstract

An artificial neural network (ANN) is a network of a group of units to be processed which is modeled based on the behavior of human neural networks. ANN has one of its tasks, namely prediction. Multilayer perceptron (MLP) is one of the ANN methods that can be prediction all of data. Where the prediction needs to be reviewed because the prediction process does not always run normally. So, it takes a good measurement accuracy in order to get an accuracy sensitivity. The accuracy technique in this paper is carried out using Mean Absolute Percentage Error (MAPE) based on absolute error and detection rate. The results obtained with absolute error achieve an accuracy of 99.73% while the accuracy based on the detection rate achieves an accuracy of 99.49%. this can be seen in the case of the prediction of (Indonesian Rupiah) IDR exchange rate against United State Dollar (USD) with the MLP algorithm by testing using MAPE to achieve sensitivity with absolute error.
Multi-Detection System Using Faster R-CNN for Fish Species Classification and Quality Assessment on Android Sharfina Faza; Arif Ridho Lubis; Meryatul Husna; Rina Anugrahwaty; Muhammad Rafif Rasyidi; Romi Fadillah Rahmat
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 2 (2026): Issues January 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i2.16374

Abstract

Species identification and quality assessment of fish in trade still rely on manual visual observation, which is subjective and requires specialized expertise. This method's limitations make it difficult for consumers to distinguish species with similar morphology and accurately assess fish quality, which can lead to inappropriate purchasing decisions. This research develops a multi-detection system based on Faster R-CNN with VGG16 backbone for fish species classification and quality assessment simultaneously on Android platform. The system uses convolutional layers to extract visual features from input images, Region Proposal Network for fish object detection and localization, and fully connected layers for simultaneous classification of species and quality levels. The research dataset consists of 3,000 images of five fish species (gourami, tilapia, nile tilapia, snapper, and pomfret) with four quality levels, divided into 2,400 training images and 600 testing images. The trained model is converted to TensorFlow Lite format for implementation on Android devices. Test results show the multi-detection system achieves 92% accuracy in fish species classification and quality assessment, demonstrating the effectiveness of the Faster R-CNN approach for multi-detection applications in the Android-based fisheries sector.
Digitalisasi Layanan Pemesanan pada Kedai Kopi melalui Implementasi Aplikasi Berbasis Mobile Insidini Fawwaz; Romi Fadillah Rahmat; Desilia Selvida; Nurrahmadayeni; Dewi Sartika Br Ginting
Jurnal Pengabdian dan Pemberdayaan Masyarakat Vol. 4 No. 1 (2026): Edisi Juni
Publisher : Jurusan Teknik Sipil, Politeknik Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51510/komposit.v4i1.3306

Abstract

Perkembangan teknologi digital telah mendorong pelaku usaha mikro dan kecil untuk mengadopsi sistem layanan berbasis teknologi guna meningkatkan efisiensi operasional dan kualitas pelayanan pelanggan. Salah satu sektor yang membutuhkan transformasi digital adalah usaha kedai kopi yang masih banyak menggunakan sistem pemesanan secara manual. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk membantu mitra, yaitu sebuah kedai kopi lokal, dalam mengimplementasikan aplikasi pemesanan berbasis mobile sebagai solusi digitalisasi layanan. Metode yang digunakan meliputi observasi kebutuhan, pengembangan aplikasi, pelatihan penggunaan sistem, serta evaluasi penerapan aplikasi. Hasil kegiatan menunjukkan bahwa implementasi aplikasi mobile mampu mempercepat proses pemesanan, mengurangi kesalahan pencatatan pesanan, serta meningkatkan kepuasan pelanggan dan efisiensi operasional kedai kopi. Selain itu, mitra menunjukkan kemampuan yang baik dalam mengoperasikan aplikasi setelah pelatihan dilakukan. Program ini membuktikan bahwa digitalisasi layanan berbasis mobile dapat menjadi solusi efektif dalam meningkatkan daya saing UMKM di bidang kuliner. Temuan ini sejalan dengan berbagai penelitian yang menunjukkan bahwa aplikasi mobile dapat meningkatkan efisiensi pemesanan dan pengelolaan transaksi pada usaha kedai kopi.
METAHEURISTIC HYPERPARAMETER OPTIMIZATION FOR DEEP LEARNING: A COMPARATIVE STUDY Zulfahmi Syahputra; Romi Fadillah Rahmat
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 12 No. 1 (2026): JITK Issue August 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v12i1.8269

Abstract

One of the primary challenges in developing effective deep learning models lies in identifying optimal hyperparameter configurations, particularly within high-dimensional and complex search spaces. Traditional tuning strategies, including grid search and random search, are widely known to be computationally inefficient and frequently yield suboptimal outcomes. To address this limitation, this study presents a comparative analysis of two metaheuristic algorithms — Particle Swarm Optimization (PSO) and Dwarf Mongoose Optimization (DMO) — as advanced alternatives for hyperparameter tuning in deep learning models trained on the CIFAR-10 dataset. Both algorithms were rigorously assessed using a comprehensive set of performance metrics, namely accuracy, precision, recall, and F1-score, supplemented by confusion matrix analysis to capture class-level behavior. Experimental findings confirm that both approaches yield substantial improvements in model performance. Notably, PSO demonstrated superior results, achieving a validation accuracy of 89.15% and a test accuracy of 87.82%, while DMO reached a final accuracy of 86.38%. In terms of optimization behavior, PSO exhibited greater convergence stability and more uniform class-level performance, whereas DMO proved more effective in broadly exploring the search space. Overall, this study reinforces the potential of metaheuristic-based optimization as a robust framework for hyperparameter tuning and underscores the critical role of optimization stability in achieving reliable model generalization.
Deep reinforcement learning-based credit risk prediction model for multi-attribute data Al-Khowarizmi Al-Khowarizmi; Ferry Fachrizal; Romi Fadillah Rahmat; Arif Ridho Lubis; Hariyadi Hariyadi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4624-4634

Abstract

This paper aims to develop a multi-attribute data-based credit risk prediction model using the deep reinforcement learning (DRL) approach. In the context of creditworthiness evaluation, a model is needed that is not only accurate but also adaptive to complex data dynamics and risk patterns. This study uses a dataset of 5,000 entries that have gone through a preprocessing process, with features such as credit score, income, age, loan amount, and employment history. Initial correlation analysis through heatmap visualization shows that credit score has a strong negative relationship with credit risk, while other features show weak to moderate correlations. The evaluation of model performance is shown through the area under the curve (AUC) value on the receiver operating characteristic (ROC) curve of 0.88, which indicates excellent discrimination ability. The final evaluation results show that the DRL model has an accuracy of 91%, precision of 89%, recall of 90%, and F1-score of 89.5%, indicating that the model not only has high accuracy but is also balanced in its sensitivity and specificity. Thus, the application of DRL has proven effective in predicting credit risk adaptively and accurately, and can be implemented as an intelligent solution in an automated credit decision-making system based on multi-attribute data.
Temporal convolutional network for Indonesian word by word sign language translator on MediaPipe framework Sarah Purnamawati; Albert Albert; Anggi Yohanes Pardede; Iqbal Fakhriza; Farhad Nadi; Mohammad Fadly Syahputra; Romi Fadillah Rahmat
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4081-4095

Abstract

Sign language is a language created to help the deaf-mute communicate with others. In Indonesia, there are two sign languages: Indonesian sign language system or sistem isyarat bahasa Indonesia (SIBI) and Indonesian sign language or bahasa isyarat Indonesia (BISINDO), but the deaf community prefers to use BISINDO. Deaf-mute people use sign language to communicate, but normal people usually avoid learning it because they feel it's unnecessary for daily communication. This will certainly be a problem for the deaf-mute because people who do not learn sign language will certainly have difficulty understanding what they are trying to convey. Therefore, an application that can translate sign language is needed to help overcome communication problems between the deaf-mute and normal people. This research focuses on BISINDO translation using a temporal convolutional network (TCN) with MediaPipe methods. The amount of data used consists of 2,700 videos representing 27 word-level signs in BISINDO; this number increases to 32,400 after undergoing the data augmentation process. The model built was tested in translating BISINDO at the word level in simulation, and a thorough comparison between TCN and bidirectional long short-term memory (BiLSTM); both of them were running on the MediaPipe platform separately. From the experiment, it is shown that the performance of TCN exceeds BiLSTM with an accuracy rate as high as 88.64%.
Evaluasi Pengelolaan Master Data Material dan Harga pada Modul Material Management dalam Mendukung Kualitas Penyusunan Harga Perkiraan Sendiri (HPS) dan Procurement: Studi Kasus Pada PT Perkebunan Nusantara IV Regional 1 Cantika Chairunnisa; Romi Fadillah Rahmat; Muhammad Anggia Muchtar
Wawasan : Jurnal Ilmu Manajemen, Ekonomi dan Kewirausahaan Vol. 4 No. 4 (2026): Oktober: Wawasan : Jurnal Ilmu Manajemen, Ekonomi dan Kewirausahan
Publisher : Fakultas Teknik Universitas Maritim AMNI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58192/wawasan.v4i4.4854

Abstract

PT Perkebunan Nusantara IV Regional 1 has implemented the Integrated Procurement System (IPS) to support Material management through Material and Price Master Data. However, the utilization of this data in supporting the preparation of Owner’s Cost Estimate (HPS) and procurement activities has not been fully optimized due to differences in users’ understanding of the information available within the system. This study aims to evaluate the contribution of Material management to the quality of HPS preparation and procurement effectiveness, as well as to develop an improvement framework that can be utilized by the company. The study employed a qualitative approach using an evaluative case study method. Data were collected through interviews, observations, document reviews, and expert judgment. The analysis was conducted using Contribution Analysis, CMMI Assessment based on SCAMPI Class C, Gap Analysis, and Fishbone Analysis. The results indicate that material management contributes to providing more standardized, consistent, and accessible material and price information, which supports the quality of HPS preparation and procurement effectiveness. The maturity level of Material management was assessed at Level 2 (Managed). This study proposes the Material management Performance Improvement Framework (MMPIF) as a guideline for continuous performance improvement.
Co-Authors Ajulio Padly Sembiring Al Khowarizmi Albert Albert Andrew Handoko Anggi Yohanes Pardede Cantika Chairunnisa Dani Gunawan Dedy Arisandi Desilia Selvida Dewi Sartika Br Ginting Erna Budhiarti Nababan Farhad Nadi Fawwaz, Insidini Faza, Sharfina Ferry Fachrizal - Handra Saito Harefa, Hafid Rahman Hariyadi, Hariyadi Husna, Meryatul Iqbal Fakhriza Janferson Panggabean Joko Kurnianto Jonatan Sianturi Julham Julham Lubis, Arif Ridho Lubis, Tri Murti M.Pd, Akrim Mardianto, Willy Meyatul Husna Mhd Faris Pratama Michael J Watts Mohammad Fadly Syahputra Muhammad Anggia Muchtar Muhammad Basri Muhammad Fariz Ichwan Muhammad Fermi Pasha Muhammad Rafif Rasyidi Muharman Lubis Nadi, Farhad Nurrahmadayeni, Nurrahmadayeni Opim Salim Sitompul Purnamawati, Sarah Rhama Permadi Ahmad Rina Anugrahwaty Riza Sulaiman Sarah Purnamawati Sawaluddin Seniman Sianturi, Jonatan Silmi Yundari, Yundari Zulfahmi Syahputra