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Lembaga Penelitian dan Pengabdian Masyarakat (LPPM) Institut Teknologi Telkom Purwokerto Jl. D. I. Panjaitan, No. 128, Purwokerto 53147, Indonesia
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Jurnal INFOTEL
Published by Universitas Telkom
ISSN : 20853688     EISSN : 24600997     DOI : https://doi.org/10.20895/infotel.v15i2
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Jurnal INFOTEL is a scientific journal published by Lembaga Penelitian dan Pengabdian Masyarakat (LPPM) of Institut Teknologi Telkom Purwokerto, Indonesia. Jurnal INFOTEL covers the field of informatics, telecommunication, and electronics. First published in 2009 for a printed version and published online in 2012. The aims of Jurnal INFOTEL are to disseminate research results and to improve the productivity of scientific publications. Jurnal INFOTEL is published quarterly in February, May, August, and November. Starting in 2018, Jurnal INFOTEL uses English as the primary language.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 473 Documents
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Rupiah Banknotes Detection Comparison of The Faster R-CNN Algorithm and YOLOv5 Hanif, Muhammad Zuhdi; Saputra, Wahyu Andi; Choo, Yit Hong; Yunus, Andi Prademon
JURNAL INFOTEL Vol 16 No 3 (2024): August 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i3.1189

Abstract

Money is an essential part of human life. Humans are never separated from activities related to money. As time goes by, money is not only a means of transactions between humans but also between humans and machines. Machines can recognize money in various ways, including object detection. Object detection is one of the most popular branches of computer vision. There are many methods for carrying out object detection, such as Faster R-CNN and YOLO. Faster R-CNN has been widely used in various fields to perform object detection tasks. Faster R-CNN has advantages over its predecessor because it uses a Region Proposal Network (RPN) as a substitute for selective search, which requires less compilation time. YOLO (You Only Look Once) is the most frequently used object detection method. This method divides the image into grids; each part of the grid predicts objects and their probabilities. The main advantages of YOLO are its high speed and ability to recognize objects in various conditions and positions with reasonably high accuracy. This research compares the Faster R-CNN algorithm model using the ResNet-50 architecture with YOLOv5 to recognize rupiah banknotes. The dataset used is 1120 images consisting of 8 classes. The YOLOv5 model trained on RGB data had the best results, with calculation accuracy reaching 1. Test results on three images also showed suitable results. The hope is that this research can be applied in other research to build a system for recognizing rupiah banknotes.
Development of Smart Hydroponics System using AI-based Sensing Putra, Septafiansyah Dwi; Heriansyah, Heriansyah; Cahyadi, Eko Fajar; Anggriani, Kurnia; Imron S Jaya, Moh Haris
JURNAL INFOTEL Vol 16 No 3 (2024): August 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i3.1190

Abstract

This paper proposes a smart hydroponic system that operates automatically using a fuzzy logic algorithm, integrating IoT functionalities to support smart agriculture. The system allows for remote monitoring and control via the internet, providing real-time data on water levels, pH levels, temperature, and nutrient solution temperature. Precise dosing and temperature control are critical for optimal plant growth, and the system schedules temperature measurements to ensure stability. Unstable temperature can affect pH levels, thereby impacting nutrient absorption. The proposed system employs sensors to continuously monitor the electrical conductivity (EC) and pH levels of the nutrient solution. Fuzzy control is utilized to regulate the nutrient solution pump, automatically adjusting EC and pH levels to promote optimal plant growth. This approach reduces the time burden on producers and provides more precise control over the nutrient solution, resulting in improved growth outcomes. The main contributions of this work are as follows: the development and implementation of an AI-based system integrating a controller, IoT environment, fuzzy logic algorithm, and NFT (nutrient film technology) hydroponics; the creation of a user-friendly interface for farmers through the Smart-Hydroponic application, enabling hybrid monitoring and control of hydroponic farms; the establishment of an IoT-based cloud environment for sensor data monitoring; the implementation of a smart hydroponic system for nutrient sensing, monitoring, and control; and a comparative analysis between smart and conventional hydroponics based on morphological results.
Analisis dan Desain Model UI/UX untuk Pengguna Manusia Lanjut Usia dalam Transformasi Bisnis Pertanian Digital Madawara, Herdin Yohnes; Sembiring, Irwan; Kristianto, Budhi
JURNAL INFOTEL Vol 16 No 3 (2024): August 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i3.1199

Abstract

Digital agriculture faces challenges, namely in integrating Elderly People (SENIORS) in digital transformation. The purpose of the study is to analyze and design UI/UX models according to user needs, expectations, and challenges. The method used is the Design Thinking approach which begins with literature study, then interviews are conducted to identify problems faced by users, and analyze the need to determine problem priorities. UI/UX prototypes are carried out to design solutions that suit user needs, and evaluations are carried out to collect user feedback. The data is assessed through the System Usability Scale (SUS) approach to appraise the designed user interface (UI). The evaluation results showed a "good" rating based on the SUS category, with a final average score of 74,391, so that the solutions designed meet usability standards for SENIORS users in the context of digital agriculture. Thus, the research carried out contributes to increasing the effectiveness and adoption of SENIORS in digital agriculture, as well as by designing user-friendly UI/UX models, making an important step towards inclusive and sustainable digital agriculture.
Design and Building of a Breeding House for IoT-based Goat Farming Jumi, Jumi
JURNAL INFOTEL Vol 16 No 3 (2024): August 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i3.1223

Abstract

Abstract — Goat farming is an industry that supplies goat meat for food purposes. This makes goats have a high potential to boost the economy of the community as their meat products are needed by everyone. These conditions motivate farmers to improve the quality of livestock maintenance so that the livestocks produced are of the highest quality. The development of information technology has facilitated a wide range of human activities, including monitoring activities in goat cages. The use of Information Technology helps farmers to improve the quality of livestock maintenance. The large cage area and the distance between cages and communal settlements are the main reasons for the planned IoT-based cage. This research has developed an IoT-based goat cage design that can be used to monitor livestock maintenance in real time via a website or Android. The methods used in this study were cattle identity information, cattle health through body temperature, cage.
The Comparative Analysis Of Multi-Criteria Decision-Making Methods (MCDM) In Priorities Of Industrial Location Development Pinem, Agusta Praba Ristadi; Hendrawan, Aria; Wakhidah, Nur
JURNAL INFOTEL Vol 16 No 4 (2024): November 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i4.1099

Abstract

The process of prioritizing the development of an industrial area's site is a matter that necessitates a mature approach. The establishment of an industrial region has significant social implications for the surrounding locality. However, it is also necessary to take into account the availability of variables that facilitate the functioning of such an industrial zone. The goal of the study "A Comparative Analysis of Multi-Criteria Decision Making Methods (MCDM) for Determining the Priority of Industrial Area Location Development" is to compare and contrast different MCDM methods in the context of deciding which industrial area locations should be developed first. A case study was undertaken, examining various possible industrial sites for future development. Multiple approaches, namely MOORA, WASPAS, ARAS, COPRAS, and AHP, are employed to ascertain the prioritization of industrial area development locations. This study presents a comparative analysis of each approach by using the Spearman Rank correlation and utilizing the factual data obtained from the Department of Capital Plantation and Integrated One Door Services (DPMPTSP). The external research is anticipated to involve a comprehensive review of the literature on the efficacy of Multiple Criteria Decision Making (MCDM) methods. This research has the potential to assist both governmental bodies and private entities in establishing priorities for the development of industrial areas, taking into account prevailing circumstances and conditions while also considering various significant factors and criteria.
Optimizing Autism Spectrum Disorder Identification with Dimensionality Reduction Technique and K-Medoid Martono, Galih Hendro; Sulistianingsih, Neny
JURNAL INFOTEL Vol 16 No 4 (2024): November 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i4.1142

Abstract

This research addresses the challenges of diagnosing and treating Autism Spectrum Disorder (ASD) using dimensionality reduction techniques and machine learning approaches. Challenges in social interaction, communication, and repetitive behaviours characterize ASD. The dimension reduction used in this research aims to identify what features influence autism cases. Several dimension data reduction techniques used in this research include PCA, Isomap, t-SNE, LLE, and factor analysis, using metrics such as Purity, silhouette score, and the Fowlkes-Mallows index. The machine learning approach applied in this study is k-medoid. By employing this method, our goal is to pinpoint the unique characteristics of autism that may facilitate the detection and diagnosis process. The data used in this research is a dataset collected for autism screening in adults. This dataset contains 20 features: ten behavioural features (AQ-10-Adult) and ten individual characteristics. The results indicate that Factor Analysis outperforms other methods based on purity metrics. However, due to data structure issues, the t-SNE method cannot be evaluated using purity metrics. PCA and LLE consistently provide stable silhouette scores across different values. The Fowlkes-Mallows index results closely align, but t-SNE tends to yield lower values. The choice of algorithm requires careful consideration of preferred metrics and data characteristics. Factor analysis is adequate for Purity, while PCA and LLE consistently perform well. This research aims to improve the accuracy of ASD identification, thereby enhancing diagnostic and treatment precision.
Solusi Rantai Pasokan Berbasis Blockchain Menggunakan Teknologi IPFS dan QR untuk Tenun Tradisional di Nusa Tenggara Barat Wijayanto, Heri; Andara, Melki Jonathan; Wiraguna, Diky; Agitha, Nadiyasari; Rahaman, Mosiur
JURNAL INFOTEL Vol 16 No 4 (2024): November 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i4.1195

Abstract

Traditional weaving is a significant cultural heritage in West Nusa Tenggara, Indonesia, renowned for its unique and intricate woven fabrics. However, the industry faces challenges such as product counterfeiting and the need for more transparency in the supply chain, hindering its growth and economic potential. This paper proposes a solution by leveraging blockchain technology to enhance traceability, security, and efficiency throughout the supply chain of traditional woven products. Integrating the InterPlanetary File System (IPFS) and Quick Response (QR) codes further fortifies data integrity and provides consumers with comprehensive product information. A prototype web application is developed, demonstrating the practical implementation of this framework. Rigorous testing of the prototype validates the correct functionality of the proposed solution. This innovative framework aims to safeguard the authenticity and sustainability of the traditional woven products from West Nusa Tenggara, fostering a more competitive and secure industry while preserving cultural heritage.
A Systematic Literature Review of BERT-based Models for Natural Language Processing Tasks Fatwanto, Agung; Zamakhsyari, Fardan; Ndungi, Rebbecah
JURNAL INFOTEL Vol 16 No 4 (2024): November 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i4.1206

Abstract

Research area in natural language processing (NLP) domain has made major advances in recent years. The Bidirectional Encoder Representations from Transformers (BERT) and its derivative models have been at the vanguard, gaining notice for their exceptional performance across a variety of NLP applications. As a response to this context, hence, this study aims to conduct a systematic literature review on current research in BERT-based models in order to describe their characteristic variations on three frequently demanded natural language processing (NLP) tasks, i.e. text classification, question answering, and text summarization. This study employed a systematic literature review method as prescribed by Kitchenham. We collected 4,120 papers from publications indexed by Scopus and Google Scholar from which 42 complied to our defined review criteria and finally chosen for further analysis. Our review came up with three conclusions. First, in order to select appropriate models for particular NLP tasks, three primary concerns should be considered: i) the type of NLP problem to be resolved (i.e. NLP task to be served), ii) the specific domain to be handled (such as financial, medical, law/legal or others), and iii) the intended language to be applied (such as English or others). Second, learning rate, batch size, and the type of optimizer were the three most considered hyperparameters to be properly arranged in model training. Third, the most widely used metrics for text classification tasks were F1-score, accuracy, precision, and sensitivity (recall), while question answering, and text summarization tasks were mostly used the Exact Match and ROUGE respectively.
Two Omnidirectional Antenna Models for Ship Hull Corrosion Detection Radar System Ludiyati, Hepi; Zharfani Ashlah, Aqiila Putri; Jayanti, Erika Dwi; Madiawati, Hanny; Sulaeman, Enceng
JURNAL INFOTEL Vol 16 No 4 (2024): November 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i4.1213

Abstract

This paper discusses two modified monopole antenna models. These antenna models are designed to detect hull corrosion of ships, which is predicted to be the cause of hull leaks. The modification was carried out by adding a conical parasitic element aimed at widening the bandwidth and increasing the antenna gain. The conical parasitic on the first antenna is directed upwards, while the second is the opposite. The first model uses a cone with a larger diameter than the second model. The calculation of the cone diameter is based on half the wavelength of the frequencies generated by the antenna. With a smaller diameter, PTFE is added between the monopole and the parasitic cone to avoid short circuits. This configuration is intended to see the effective antenna model widen the bandwidth and increase the gain. Antenna performance testing was carried out using a vector network analyzer and software-defined radio. The test results show that both antenna models are omnidirectional in radiation. The first model operates at a frequency of 3904.7 - 7793.1 MHz with a bandwidth of 3888.4 MHz and a highest gain of 10 dBi. The second model operates at a frequency of 2282.4 - 3324.1 MHz with a bandwidth of 1041.7 MHz and a highest gain of 8dBi. Thus, the first antenna model has a higher bandwidth and gain than the first model, but both have met the requirements for ship hull corrosion detection antennas.
A Random Oversampling and BERT-based Model Approach for Handling Imbalanced Data in Essay Answer Correction Sani, Dian Ahkam
JURNAL INFOTEL Vol 16 No 4 (2024): November 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i4.1224

Abstract

The task of automated essay scoring has long been plagued by the challenge of imbalanced datasets, where the distribution of scores or labels is skewed towards certain categories. This imbalance can lead to poor performance of machine learning models, as they tend to be biased towards the majority class. One potential solution to this problem is the use of oversampling techniques, which aim to balance the dataset by increasing the representation of the minority class. In this paper, we propose a novel approach that combines random oversampling with a BERT-base uncased model for essay answer correction. This research explores various scenario of text pre-processing techniques to optimize model accuracy. Using a dataset of essay answers obtained from eighth-grade middle school students in Indonesian language, our approach demonstrates good performance in terms of precision, recall, F1-score and accuracy compared to traditional methods such as Backpropagation Neural Network, Naïve Bayes and Random Forest Classifier using FastText word embedding with Wikipedia 300 vector size pretrained model. The best performance was obtained using the BERT-base uncased model with 2e-5 learning rate and a simplified pre-processing approach. By retaining punctuation, numbers, and stop words, the model achieved a precision of 0.9463, recall of 0.9377, F1-score of 0.9346, and an accuracy of 94%.

Page 43 of 48 | Total Record : 473

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