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JTIM : Jurnal Teknologi Informasi dan Multimedia
Published by Puslitbang Sekawan Institute Nusa Tenggara
ISSN : 27152529     EISSN : 26849151     DOI : https://doi.org/10.35746/jtim.v2i1
Core Subject : Science,
Computer Science & IT
Cakupan dan ruang lingkup JTIM terdiri dari Databases System, Data Mining/Web Mining, Datawarehouse, Artificial Integelence, Business Integelence, Cloud & Grid Computing, Decision Support System, Human Computer & Interaction, Mobile Computing & Application, E-System, Machine Learning, Deep Learning, Information Retrievel (IR), Computer Network & Security, Multimedia System, Sistem Informasi, Sistem Informasi Geografis (GIS), Sistem Informasi Akuntansi, Database Security, Network Security, Fuzzy Logic, Expert System, Image Processing, Computer Graphic, Computer Vision, Semantic Web, Animation dan lainnya yang serumpun dengan Teknologi Informasi dan Multimedia.
Arjuna Subject : -
Articles 7 Documents
 1 
Search results for , issue "Vol 3 No 4 (2022): February" : 7 Documents clear
Sistem Pendeteksi Kerusakan Buah Mangga Menggunakan Sensor Gas Dengan Metode DCS - LCA Murad Murad; Sukmawaty Sukmawaty; Ansar Ansar; Rahmat Sabani; Syahroni Hidayat
JTIM : Jurnal Teknologi Informasi dan Multimedia Vol 3 No 4 (2022): February
Publisher : Puslitbang Sekawan Institute Nusa Tenggara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v3i4.169

Abstract

Fruits, including mangoes, produce a wide variety of volatile organic compounds that give them their distinct aroma. Characteristics of fruit aroma is one of the important keys in determining consumer acceptance in the commercial fruit market based on individual preferences. So a possible way to determine the level of ripeness/damage is to feel the distinctive aroma presented by the fruit using a gas sensor. This study aims to build a system that can detect mango damage based on its aroma. The sensors used are TGS 2600, MQ3, MQ4, MQ2, and MQ8 which are connected to the Arduino Mega 2560. The learning model used is an ensemble learning model of Dynamic Classifier Selection (DCS) with Local Class Accuracy (LCA)/DCS-LCA. This algorithm combines Logistic Regression, Selection Tree, Support Vector Machine (SVM), Naïve Bayes, Random Forest, and Neural Networks. The model was then tested with a comparison of the amount of test data and training data of 70%:30%. The test results showed that the overall system Accuracy was 75% and the ability to detect mango fruit damage was 71%. The DCS-LCA classifier model outperforms each of its constituent base classifiers.
Klasifikasi Kebakaran Hutan Menggunakan Metode K-Nearest Neighbor : Studi Kasus Hutan Provinsi Kalimantan Barat Ari Rudiyan; Akhmad Erik Dzulkifli; Khabib Munazar
JTIM : Jurnal Teknologi Informasi dan Multimedia Vol 3 No 4 (2022): February
Publisher : Puslitbang Sekawan Institute Nusa Tenggara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v3i4.177

Abstract

A very wide forest could cause natural disaster such as forest fire that resulting losses to inhabitant, one of which effecting health and safety. West Kalimantan province is one of the province in Indonesia that has wide area of forest with 8,200,000 ha of forest and 1,600,000 ha of peatland all over the Kalimantan Island. Therefor this study is focusing on the data of west Kalimantan province forest. The aim of the study is to classify forest fire in West Kalimantan Province and followed by designing a REST API application of forest fire detector. In hope that in future, the application will be useful to prevent forest fire in the area of west Kalimantan Province. K-Nearest Neighbor method and balltree algorithm are used in this study to collect and process the data. The sample that are collected about 30% of 14,201 data with accuracy up to 92% with K = 18.
Analisis Sentimen Pada Agen Perjalanan Online Menggunakan Naïve Bayes dan K-Nearest Neighbor Eka Wahyu Sholeha; Selviana Yunita; Rifqi Hammad; Veny Cahya Hardita; Kaharuddin Kaharuddin
JTIM : Jurnal Teknologi Informasi dan Multimedia Vol 3 No 4 (2022): February
Publisher : Puslitbang Sekawan Institute Nusa Tenggara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v3i4.178

Abstract

Social media has impact for decision maker to get more insights broadly. Including for online travel agent company, where costumer’s interest to use online travel agent for their chosen agent will grows along with the high number of customer’s satisfaction. As a one of the most important point in distribution, company provides a platform that reliable and effective to purchase a trip and share information of their experience through Online travel agent. It is important to know how consumer considerate which one the online travel agent they choose. One of their method is looking at the reviews. Facebook is one of social media that provide numerous reviews through comments sections. The research purposes are twofold, algorithm comparison and reveal the effect of uppercase as well as punctuation mark. The accuracy comparison between Naïve Bayes and K-Nearest Neighbor is provided against the datasets. This research collects the data from user comments on Facebook about the biggest three online travel agents in Indonesia. We classify the comments into three categories which are positive, negative, and neutral. The result of this research is found that K-Nearest Neighbor have slightly higher accuracy than the Naïve Bayes. Moreover, lowercase text without punctuation achieves better accuracy for both of algorithm.
Model Logika Fuzzy Tsukamoto Dalam Perancangan Sistem Informasi Sebaran Industri Kecil dan Menengah Kabupaten Bondowoso Nugroho Setyo Wibowo; Erna Selviyanti; Wenny Dhamayanti
JTIM : Jurnal Teknologi Informasi dan Multimedia Vol 3 No 4 (2022): February
Publisher : Puslitbang Sekawan Institute Nusa Tenggara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v3i4.185

Abstract

The existence of the COVID-19 pandemic and the global economic crisis did not affect the existence of Small and Medium Industries and even experienced relatively stable growth. Small and Medium Industries in the manufacturing sector were able to realize an investment value of up to IDR 72.3 trillion in the third quarter of 2020. In the context of creating competitive Small and Medium Industries, strengthening the national industrial structure, alleviating poverty, expanding job opportunities, and producing industrial goods and/or services for export purposes, the strategic role of Small and Medium Industries needs to be increased through the digitization of Small and Medium Industries. This study uses Fuzzy Tsukamoto's logic calculations in making the membership function of the distribution area of ​​Small and Medium Industries in Bondowoso Regency. From the Fuzzy Logic model obtained, an information system design for the distribution of Small and Medium Industries can be made in the form of context diagrams and interface designs. From this design, it will be easier for the Bondowoso Regency Cooperatives and Industry Office to develop a web and mobile-based application in the context of the process of digitizing Small and Medium Industries. The existence of this information system will provide benefits for the Office of Cooperatives, Industry and Trade as well as Small and Medium Industry players in Bondowoso Regency, not only being able to visualize the distribution of Small and Medium Industries but also being able to provide an overview of Small and Medium Industry parameter data that can be used as decision makers. decisions for existing stakeholders.
ESVISIGN: Tanda Tangan Digital Sekolah Vokasi IPB Walidatush Sholihah; Sofiyanti Indriasari; Inna Noviyanti; Anggi Mardiyono; Nur Aziezah
JTIM : Jurnal Teknologi Informasi dan Multimedia Vol 3 No 4 (2022): February
Publisher : Puslitbang Sekawan Institute Nusa Tenggara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v3i4.188

Abstract

All activities have been online since the Covid-19 pandemic broke out in Indonesia. IPB also supports government policy by partially locking down the campus area. Administrative activities are also in online mode. Online correspondence activities still require a signature as a form of validity and authentication of the letter. The scanned signature is different from the digital signature. Thus an application was made to create digital signatures. This application is named eSVi sign. This web-based application was made by using Prototype method. Prototype method consists of communication, planning and modeling quickly, making prototypes, submitting the system to users and feedback. This app uses a hash function with SHA256. Each signed document is assigned a hash value which is stored in storage. When the document is verified, the system will look for a hash value that matches the document in the database. The method to test this application was black box testing. This application can be accessed on the https://ipb.link/esvisign. This digital signature is only used within the College of Vocational Studies IPB University.
Implementasi Convolutional Neural Network Untuk Deteksi Emosi Melalui Wajah Rizki Rafiif Amaanullah; Gracia Rizka Pasfica; Satria Adi Nugraha; Mohammad Rifqi Zein; Faisal Dharma Adhinata
JTIM : Jurnal Teknologi Informasi dan Multimedia Vol 3 No 4 (2022): February
Publisher : Puslitbang Sekawan Institute Nusa Tenggara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v3i4.189

Abstract

The human emotional condition can be reflected in speech, gestures, and especially facial expressions. The problem that is often faced is that humans tend to be subjective in assessing people's emotions. Humans can easily guess someone's emotions through the expressions shown, as well as computers. Computers can think like humans if they are given an algorithm for human thinking or artificial intelligence. This research will be an interaction between humans and computers in analyzing human expressions. This research was conducted to prove whether the implementation of CNN (Convolutional Neural Network) can be used in detecting human emotions or not. The material needed to conduct facial recognition research is a dataset in images of various kinds of human expressions. Based on the dataset that has been obtained, the images that have been collected are divided into two parts, namely training data and test data, where each training data and test data has seven different emotion subfolders. Each category of images is 35 thousand data which will later be trimmed to around a few thousand data to balance the dataset. According to their class, these various expressions will be classified into several emotions: angry emotions, happy emotions, fearful emotions, disgusting emotions, surprising emotions, neutral emotions, and sad emotions. The results showed that from the calculation of 40 epochs, 81.92% was obtained for training and 81.69% for testing.
Optimasi Neural Network Dengan Menggunakan Algoritma Genetika Untuk Prediksi Jumlah Kunjungan Wisatawan Fatimatuzzahra Fatimatuzzahra; Rifqi Hammad; Ahmad Zuli Amrullah; Pahrul Irfan
JTIM : Jurnal Teknologi Informasi dan Multimedia Vol 3 No 4 (2022): February
Publisher : Puslitbang Sekawan Institute Nusa Tenggara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v3i4.190

Abstract

West Nusa Tenggara is one of the tourist attractions in Indonesia which has a certain attraction for tourists. With the increase in tourism in NTB, it is necessary to make adequate efforts to maintain tourist objects and attractions. In an effort to maintain a tourist attraction, the NTB provincial tourism office needs to analyze and predict the arrival of local and international tourists. The current analysis and prediction process is still being carried out by collecting data from each tourist attraction entrance. The processed data produces predictions of tourist arrivals, both local and international, where the data processing process takes a long time and requires high human resources. To overcome these problems, it is done by applying computational predictions. Computational predictions can minimize the prediction time and human resources required. The method used is a neural network algorithm with optimized parameters using a genetic algorithm. The optimized parameters are the hidden layer, the number of neurons in the input layer, momentum and others. The data used is time series data from 1997 to 2018. From the neural network experiment, the parameters of the number of neurons in the input layer xt-7 are determined, the number of neurons in the hidden layer 10, the training cycle value is 400, the learning rate value is 0.3 and the momentum value is 0.2. From the experiment, the RMSE value of 0.050 was obtained. While the RMSE value for the neural network algorithm parameters optimized using the genetic algorithm is 0.044. Because of this, it can be stated genetic algorithm with neural network can be used to determine the hidden layer and the number of hidden nodes, the right features, momentum, initialize, and optimize the weight of the neural network. So that the application of the genetic algorithm to optimize the parameter values of the neural network algorithm is better than the application of the neural network algorithm without optimization.

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