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The Telematika, with registered number ISSN 2442-4528 (online) ISSN 1979-925X (print) is a scientific journal published by Universitas Amikom Purwokerto. The journal registered in the CrossRef system with Digital Object Identifier (DOI) prefix 10.35671/telematika. The aim of this journal publication is to disseminate the conceptual thoughts or ideas and research results that have been achieved in the area of Information Technology and Computer Science. Every article that goes to the editorial staff will be selected through Initial Review processes by the Editorial Board. Then, the articles will be sent to the Mitra Bebestari/ peer reviewer and will go to the next selection by Double-Blind Preview Process. After that, the articles will be returned to the authors to revise. These processes take a month for a minimum time. In each manuscript, Mitra Bebestari/ peer reviewer will be rated from the substantial and technical aspects. The final decision of articles acceptance will be made by Editors according to Reviewers comments. Mitra Bebestari/ peer reviewer that collaboration with The Telematika is the experts in the Information Technology and Computer Science area and issues around it.
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Telematika
Published by Universitas Amikom Purwokerto
ISSN : 1979925X     EISSN : 24424528     DOI : 10.35671/telematika
Core Subject : Education,
Education
Jl. Letjend Pol. Soemarto No.126, Watumas, Purwanegara, Kec. Purwokerto Utara, Kabupaten Banyumas, Jawa Tengah 53127
Arjuna Subject : -
Articles 210 Documents
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Performance Evaluation of Naive Bayes Algorithm for Classification of Fertilizer Types Rastri Prathivi; April Firman Daru; Sara Sharifzadeh
Telematika Vol 15, No 1: February (2022)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v15i1.1410

Abstract

Determining the right fertilizer is very important to get optimal plant growth results. Each plant requires different nutrient requirements. Different soil types cause the soil's nutrient content and PH value to differ from one type to another. Regional conditions in a place will also cause the need for plant absorption of nutrient content to be more varied. By using the classification of the problems that have been mentioned, it can be solved by studying patterns from existing fertilizer use data into knowledge that can be used to determine decisions. In this study, modeling with the Naïve Bayes algorithm has been applied to the existing fertilizer use data where the probability value of each class has been calculated to get the highest probability value of a class. The measurement of the accuracy value of the modeling used is measured using the Split Validation method, where the training data will be divided into training data and testing data so that the accuracy value of the model is obtained. From the applied modeling, an accuracy value of 60% is obtained, which shows the level of accuracy of the model obtained from the classification results in the form of the name of the fertilizer, which is expected to help in determining the name of the fertilizer that needs to be used.
Study of The Effect Stuart and Prandtl Numbers on Diamond Nano Fluid Flowing Through Cylindrical Surface Yolanda Norasia; Mohamad Tafrikan; Mohammad Ghani; Asmianto Asmianto; Indira Anggriani
Telematika Vol 16, No 1: February (2023)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v16i1.2103

Abstract

Fluid flow problems can be constructed using applied mathematical modeling and solved numerically using computational fluid dynamics (CFD). Nondimensional variables, stream functions, and similarity variables are used to simplify the governing equations from Newton's law, and thermodynamics law. These equations consist of continuity equations, momentum equations, and energy. Backward Euler method numerically solves the equations. The results show that the smaller the influence of the given Stuart number and Prandtl number, the fluid velocity and temperature will increase. Diamond nano fluid with water base fluid moves faster and experiences an increase in temperature faster than engine oil base fluid. this is due to the thermo-physical heat capacity of the water base fluid being greater than that of the engine oil.
Classification of COVID-19 Cough Sounds using Mel Frequency Cepstral Coefficient (MFCC) Feature Extraction and Support Vector Machine Muhammad Meftah Mafazy; Mohammad Reza Faisal; Dwi Kartini; Fatma Indriani; Triando Hamonangan Saragih
Telematika Vol 16, No 2: August (2023)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v16i2.2569

Abstract

A lot of research has been carried out to detect COVID-19, such as swabs, rapid antigens, and using x-ray images. However, this method has the disadvantage that it requires taking samples through physical contact with the patient. One way to avoid physical contact is to use audio through coughing with the aim of reducing the transmission of COVID-19. Audio feature extraction such as the Mel Frequency Cepstral Coefficient (MFCC) has often been used in audio classification research, such as the classification of musical genres and so on. This study aims to compare more or less the features of audio classification performance through coughing sounds for early detection of COVID-19 using a Support Vector Machine based on the Linear and Radial Basis Function (RBF). The dataset used is the COVID-19 Cough audio dataset, before classifying, the audio data is processed into a spectrogram and then feature extraction is carried out. Classification is divided into 2 schemes, using default parameters, then using the specified configuration parameters. From the research results, the highest AUC is 0.572266 in the linear kernel-based SVM classification. Meanwhile, when using the RBF kernel, the highest AUC is 0.560181.
Smart Farming System for Monitoring and Optimizing Paddy Field with Internet of Things Technology Bagus Adhi Kusuma; Sarmini Sarmini; Wiga Maulana Baihaqi; Sitaresmi Wahyu Handani
Telematika Vol 16, No 1: February (2023)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v16i1.2183

Abstract

Rice is a type of plant that is very easy to find, especially those who live in rural areas. Most people make rice as a source of staple food. One of them is in Bugel Sampang Hamlet, Central Java, where the agricultural sector of the village often experiences rice harvest failure due to dry weather. The hilly geographical conditions that do not allow irrigation systems are the main problem, while the soil fertility of the hamlet is relatively good, as a result farmers find it difficult to optimize the treatment dose for agricultural land due to dry weather which often makes harvest conditions less than optimal. From the problems described above, the researcher aims to create an internet of things-based prototype by integrating a realtime firebase cloud service database, so that farmers can monitor the condition of their rice fields in real time, as well as monitor the weather that can be accessed using a website-based system. The method used is to integrate a microcontroller with sensors, namely DHT11, soil moisture sensor, barometer or air pressure sensor, anemometer or wind speed sensor, rainfall sensor, raindrop sensor using Arduino Mega 2560 and NodeMCU. Then the sensor acquisition data on the Arduino Mega 2560 is sent to the NodeMCU Lua Wifi V3 ESP8266 ESP12 using a JSON variable to be sent to firebase with an internet connection. The prototype has gone through thirty days running tests, while testing the information system using blackbox testing with data from the firebase realtime database. The results of the study concluded that the prototype was able to monitor the condition of the land properly and the system worked well and could support the optimization of farmers in the treatment of rice fields, so that the use of fertilizers, water, and other treatment efforts became more efficient.
A Comparative Study on the Combination of Classification Algorithm and Language Model Implementation for Smart Accounting System Bagas Adi Makayasa; Agung Fatwanto
Telematika Vol 16, No 2: August (2023)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v16i2.2258

Abstract

Micro, Small, and Medium Enterprises (MSMEs) normally dealing with financial documentation and reporting problems due to in sufficient budget for hiring professional accounting services. Although some of them might have utilized off the shelf accounting softwares, they still face many obstacles in compiling a proper financial documentation because the employed software do not have an automatic transaction classification capability to assist users in recording any transactions. This study was aimed to investigate the opportunity of implementing automatic transaction classification for accounting system by using a Natural Language Processing (NLP) approach to automatically interpret the suitable account for any financial transactions based on the text written on the transaction forms. An experiment was conducted to compare the performance of eight combinations comprising of four classification algorithms (i.e. SVM, KNN3, KNN5, and NB) with two language models (i.e. TF-IDF and BoW). The result showed that KNN5 and TF-IDF pair gave highest performance with accuracy 82,5%, precision 82,54%, recall/sensitivity 83,7%, specificity 92,06%, and F1 Score 81,5%.
Use of Hybrid Methods in Making E-commerce Product Recommendation Systems to Overcome Cold Start Problems Budi Santosa; Muhamad Azam Fuadi; Mangaras Yanu Florestiyanto; Vynska Amalia Permadi; Wilis Kaswidjanti
Telematika Vol 16, No 1: February (2023)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v16i1.2080

Abstract

The large number of users and the items offered in e-commerce make it difficult for buyers to choose the right items and sellers to offer their items to the right buyers. To overcome this problem, a system that can offer and recommend goods automatically, namely a recommendation system is needed. One of the most popular methods used to create a recommendation system is collaborative filtering, the recommendations are created based on similarities in user behavior. Unfortunately, this method has a weakness, namely cold start, where the recommendations will be inaccurate on data that has a lot of new users and items due to minimal historical data regarding user behavior. This problem will be tried to be solved in this study using a hybrid method, where this method combines more than 1 method to create a list of recommendations so that it will cover the shortcomings of each method. This study uses Amazon's e-commerce product and transaction data. The use of the hybrid method in this study can overcome the cold start problem by using switching and mixed methods, by not using the collaborative filtering model on new user recommendations or users who have little interaction. New users will receive recommendations based on the combination of popularity-based and content-based filtering models. This can be seen from the Mean Absolute Error (MAE) value of the model, where the MAE value for the data with a minimum user has at least 3 times rating is 0.566883, for the minimum 7 times, the MAE value is smaller, 0.487553.
Convolutional Neural Networks for Classification of Lung Cancer Based on Histopathological Images Sarifah Agustiani; Denny Pribadi; Agus Junaidi; Siti Khotimatul Wildah; Ali Mustopa; Yoseph Tajul Arifin
Telematika Vol 16, No 2: August (2023)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v16i2.2356

Abstract

Lung cancer is one of the deadliest types of cancer characterized by the uncontrolled growth of cancer cells in the lung tissue due to the accumulation of carcinogens. Lung cancer ranks second in the most cases with 2.206 million new cases and ranks first in deaths. This lung cancer often does not cause symptoms in the early stages, because it only appears after the tumor is large enough or the cancer has spread to surrounding tissues or organs, so it is necessary to have early detests to prevent severity and determine follow-up treatment. This study aims to classify lung cancers using digital pathology images with data of 15000 images obtained from the LC25000 dataset containing 5,000 images for each class. The method used in this classification process uses convolutional neural networks (CNN) which is one of the implementations of Deep Learning used for digital image processing. Using this method, the doctor can diagnose and find out the type of lung cancer quickly without spending much time. Thus, the faster the prediction results received by the doctor / health expert, the faster the next action or handler will be, this study produces a fairly accurate accuracy value even though it uses a shallow CNN architecture because it only consists of 5 layers with 3 convolution layers and 2 fully connected layers, with the resulting accuracy value of 98.53%.
Smart Solar Tracker and Energy Control Based on Internet of Things (IoT) Budi Artono; Kunto Aji Yudhoyono; Raden Jasa Kusumo Haryo; Tri Lestariningsih
Telematika Vol 16, No 1: February (2023)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v16i1.2576

Abstract

ndonesia's electricity consumption per capita in 2022 will reach 1,173 kWh/capita sourced from the Ministry of Energy and Mineral Resources. This consumption rate increased by around 4% compared to 2021, as well as a new record high in the last five decades. This must be accompanied by the availability of energy from power plants, especially renewable energy, namely solar energy because this solar power plant is considered safer for the environment and has a minimal maintenance schedule. In addition, it requires maximum utilization of solar panels and a monitoring system in real time so that the reliability of the power plant is maintained, the Smart Solar Tracker and Energy Control Based on Internet Of Things (IoT) are the answer to this problem. This research uses PV (Photovoltaic) as a power source in the system accompanied by a tracker drive in the form of actuators and servo motors that move in the direction of the sun. This IoT is integrated with a database server so officers can monitor and control if the device is damaged. The IoT module in this research uses the ESP8266 which functions for device control and relay. In addition to reading the voltage and current, both incoming and outgoing, use the ACS 712 voltage sensor and current sensor, not only that, there is also an LDR sensor to read the position of the sun.
Comparison of Industrial Business Grouping Using Fuzzy C-Means and Fuzzy Possibilistic C-Means Methods Mega Lestari; Dwi Kartini; Irwan Budiman; Mohammad Reza Faisal; Muliadi Muliadi
Telematika Vol 16, No 2: August (2023)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v16i2.2548

Abstract

The industrial business sector plays a role in the development of the economic sector in developing countries such as Indonesia. In this case, many industrial businesses are growing, but the data has not been processed or analyzed to produce important information that can be processed into knowledge using data mining. One of the data mining techniques used in this research is data grouping, or clustering. This research was conducted to determine the comparison results of the Cluster Validity Index on Fuzzy C-Means and Fuzzy Possibilistic C-Means methods for clustering industrial businesses in Tanah Bumbu Regency. In each process, 5 trials were conducted with the number of clusters, namely 3, 4, 5, 6, and 7, and for the attributes used: Male Labor, Female Labor, Investment Value, Production Value, and BW/BP Value. Furthermore, this study will evaluate the Cluster Validity Index, namely the Partition Entropy Index, Partition Coefficient index, and Modified Partition Coefficient Index. This research provides the best performance results in the Fuzzy C-Means method with the results of the Cluster Validity Index on the Partition Entropy Index of 0.21566, Partition Coefficient Index of 0.88078, and Modified Partition Coefficient Index of 0.82117, and the best number of clusters is 3 with the labels of low competitive industry clusters, medium competitive industry clusters, and highly competitive industry clusters.
Dynamical Analysis of the Spread of COVID-19 model and its Simulation with Vaccination and Social Distancing Ummu Habibah; Angelina Renny Christin Octavia Sianturi
Telematika Vol 16, No 1: February (2023)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v16i1.2373

Abstract

The model's creation and dynamical analysis were covered in this paper, SEIRS on the effects of vaccination and social isolation on the transmission of COVID-19. The susceptible individual subpopulation (S), the exposed individual subpopulation (E), the infected individual subpopulation (I), and the recovered individual subpopulation (R) are the four subpopulations that make up the human population in this model. This concept is founded on the notion that someone who has recovered from the illness is nonetheless vulnerable to reinfection. The carried out dynamical analysis includes the determination of the equilibrium point, the fundamental reproduction number (R_0), and evaluation of the local stability of the equilibrium point. The outcomes of the dynamical analysis show that there are two equilibrium points in the model: the endemic equilibrium point and the disease-free equilibrium point. Mathematical R_0>1 indicates the presence of an endemic equilibrium point, whereas a disease-free equilibrium point is always present. If the Routh-Hurwitz conditions are met, the endemic equilibrium point is locally asymptotically stable, but the disease-free equilibrium point is locally asymptotically stable if R_0

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