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JOIV : International Journal on Informatics Visualization
Published by Politeknik Negeri Padang
ISSN : 25499610     EISSN : 25499904     DOI : -
Core Subject : Science,
Computer Science & IT
JOIV : International Journal on Informatics Visualization is an international peer-reviewed journal dedicated to interchange for the results of high quality research in all aspect of Computer Science, Computer Engineering, Information Technology and Visualization. The journal publishes state-of-art papers in fundamental theory, experiments and simulation, as well as applications, with a systematic proposed method, sufficient review on previous works, expanded discussion and concise conclusion. As our commitment to the advancement of science and technology, the JOIV follows the open access policy that allows the published articles freely available online without any subscription.
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Articles 1,172 Documents
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Classification of Predicting Customer Ad Clicks Using Logistic Regression and k-Nearest Neighbors Dani, Yasi; Ginting, Maria Artanta
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1017

Abstract

Nowadays, conventional marketing techniques have changed to online (digital) marketing techniques requiring internet access. Online marketing techniques have many advantages, especially in terms of cost efficiency and fast information delivery to the public. Therefore, many companies are interested in online marketing and advertising on social media platforms and websites. However, one of the challenges for companies in online marketing is determining the right target consumers since if they target consumers who are not interested in buying the product, the advertising costs will be high. One use of online advertising is clicks on ads which is a marketing measurement of how many users click on the online ad. Thus, companies need a click prediction system to know the right target consumers. And different types of advertisers and search engines rely on modeling to predict ad clicks accurately. This paper constructs the customer ad clicks prediction model using the machine learning approach that becomes more sophisticated in effectively predicting the probability of a click. We propose two classification algorithms: the logistic regression (LR) classifier, which produces probabilistic outputs, and the k-nearest neighbors (k-NN) classifier, which produces non-probabilistic outputs. Furthermore, this study compares the two classification algorithms and determines the best algorithm based on their performance. We calculate the confusion matrix and several metrics: precision, recall, accuracy, F1-score, and AUC-ROC. The experiments show that the logistic regression algorithm performs best on a given dataset.
The Design of E-Commerce System to Increase Sales Productivity of Home Industry in Indonesia Nadiyasari Agitha; Ario Yudo Husodo; Royana Afwani; Faishal Mufied Al Anshary
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1589

Abstract

The household industry is the foundation of the existing home industry in Indonesia. It is categorized as a type of Small and Medium Enterprises (MSMEs) that significantly influence the Gross Domestic Product (GDP) ratio by up to approximately 10%. Since the pandemic of Covid-19, the home industry has increasingly stretched, especially in culinary and home craft products. The internet is one of the efforts to increase household industry sales. The household industry needs e-commerce to be a container for marketing its products. In this paper, we design an e-commerce system to support the sales productivity of the household industry in Indonesia. This study's e-commerce system or application is developed through some crucial stages. The stages are analysis through a questionnaire that represents needs in the field, selection of business models, namely B2B models with Virtual Storefront, marketplace concentrators, and lastly, Information Broker. Our infrastructure is determined for e-commerce development, and then strategy analysis is done using portfolio analysis, SWOT analysis, and competitor analysis. Based on the proposed strategy, we made a prototype as a designed e-commerce system that can increase sales for household businesses in cities in Indonesia. Important features in our proposed e-commerce system can accommodate many sellers or the household industry to establish relationships with many buyers based on geographical location, product search features based on closest positions or by city, and product categorization by familiar categories with household products. 
Android-based System Monitoring of Supporting Variables for Nursery-Plant Growth in Plantation Areas Adis Kusyadi Nugraha; Giva Andriana Mutiara; Tedi Gunawan; Gita Indah Hapsari
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1594

Abstract

In cultivating timber trees, farmers must pay attention to the seed selection with superior heredity, hormones, and the condition of the plantation area that supports the growth of nursery plants properly. Several factors that support the growth of nursery plants are nutritional factors, sunlight, temperature, soil pH, water, and soil moisture. In terms of effectiveness and ease of access to information in monitoring the supporting condition factors and facilitating the farmers, an Android-based monitoring system was built to monitor the growth of nursery plants. The system consists of several sensors, such as a soil pH sensor, UV light sensor, and soil moisture sensor embedded with Raspberry pi and firebase. The proposed system was examined on a plantation area of 900 square meters. The testing is conducted by placing a combination of 4 to 8 sensors in the plantation area. Data from each sensor is processed by calculating the average, and the results are rounded to the nearest value. The test stated that to monitor an area of 900 square meters, the area with five sensors implanted can be used as the optimal implementation. Apart from economic reasons, the minor rounding error equals 8.25% compared to the number of other sensors. The results that are informed to the farmers are also within the appropriate range. There are no significant differences, and this approach can be used to implement in a broader area
3D Augmented Reality Marker-based Mobile Apps Design of Face Mask Layer Nur Amirah Kamaluddin; Murizah Kassim; Hafizoah Kassim
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1154

Abstract

The outbreak of COVID-19 has spread rapidly across the globe, affecting how people interact, work and experience their daily lifestyles. The face mask is essential to personal protective equipment (PPE) even though COVID-19 is endemic today. A face mask is required to protect humans, and its description is important. Augmented reality is one new attractive technology for distributing information. This research presents a 3D augmented reality mobile application that visualizes a protective face mask and its material layers called 3DAR-FML. It was designed by utilizing 3D marker-based images on an Android smartphone platform. Ten types of protective face masks and each layer were designed in 3D images using Blender Animation and Unity 3D software. Vuforia Engine applications were used to build a personalized mobile Augmented Reality application. This initiative educates society and markets products in interactive ways that allow visualization of the 3D model of various face masks from android phones. Results present that successful mobile apps were developed. A survey shows that 70% of respondents agreed on the design app based on menu interactivity. This app also has been identified as a helper tool for The National Institute for Occupational Safety and Health (NIOSH) agency Malaysia for safety and health apps for the market and public. This product is significant in societies to gain information on face mask characteristics that help to contribute content based on the standard mask in Malaysia.
The IT Services Management Architecture Design for Large and Medium-sized Companies based on ITIL 4 and TOGAF Framework Santosa, Iqbal; Mulyana, Rahmat
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1590

Abstract

The development of information technology occurs rapidly in almost all areas of life. All companies must immediately carry out a business transformation following the development of information technology to survive amid increasingly fierce competition. One of the keys to this business transformation's success is an enterprise architecture that is used as a reference in planning, developing, operating, and monitoring company information technology. Implementation of service management practices in state-owned enterprises needs to be translated into IT Services Management Architecture Design, that match the IT Governance Principles as mandated in PER-03/MBU/02/2018. This research focuses on preparing an enterprise architecture design in IT service management by referring to ITIL 4 best practices. The resulting solution is a target architecture design in the business domain, data, and applications arranged according to the TOGAF framework. It was carried out in four stages: scope identification, which defines practices; preliminary phase, which resulted in 11 architecture principles; architecture vision that produces a value chain for IT service provider organizations; a business architecture which resulted in a business service/function catalog consisting of 13 business functions and 43 business services; and an information system architecture that produces a conceptual data model on the three main priority processes of IT service management and an application use-case diagram that describes the relationship between the four actors (users, service managers, service desks, and support groups) with their roles in applications. The enterprise architecture has been designed following the scope of IT service management practices commonly used as a reference for all large and medium-sized companies.
K-Means Algorithm Analysis for Election Cluster Prediction Wahyuni, Sri Ngudi; Khanom, Nazmun Nahar; Astuti, Yuli
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1107

Abstract

The general election is a democratic process that is carried out in every country whose system of government is presidential, including Indonesia, which conducts it every five years. In fact, some people abstain, leading to budget wasting and missing target. Thus, it is very important to identify clusters of general election districts and map the number of voters to map the budget for the upcoming election. This process needs prediction to help reduce budgeting risk as an early warning. Based on the latest election data taken from Margokaton, Yogyakarta, Indonesia, many people voted in 2021, but the number of abstainers is high. In this case, cluster prediction is important to identify the election participants in each area. The K-Means algorithm could also predict abstainer areas in election activities to facilitate early mitigation in drafting election budgeting. Therefore, this study aimed to identify the pattern of voters in the election using the K-means algorithm. The data parameters comprised the list of voters, Unused ballot papers, and the sum of abstainers. This study is important because it contributes to reducing the election budget of each area. The data obtained from the Indonesia Ministry of Internal Affairs official website in 2021 were processed using the RapidMiner tool. The results showed more than 11% of the non-voters in cluster 1, 16% in Cluster 2, and 8% in cluster 3. The evaluation of clusters value is 2.04, indicating that the clustering using K-means is suitable, as shown by the DBI value close to 0. The results indicate that testing the cluster optimization of the K-Means algorithm using DBI is highly recommended. Based on this prediction result, the government needs special attention to clusters with many abstainers to decrease the number of abstainers and prevent overbudgeting. These results indicate the need to review the election participant data in 2024. Furthermore, there is a need for continuous socialization and education about election activities to reduce the number of abstainers and prevent overbudgeting.
Chatbot for Diagnosis of Pregnancy Disorders using Artificial Intelligence Markup Language (AIML) Alam Rahmatulloh; Anjar Ginanjar; Irfan Darmawan; Neng Ika Kurniati; Erna Haerani
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1595

Abstract

Artificial Intelligence has evolved in sophistication and widespread use. This study aims to create a chatbot application in the health sector regarding the early diagnosis of pregnancy disorders. Based on basic health research, only 44 percent of pregnant women know the danger signs of pregnancy. The chatbot application developed is expected to facilitate and increase knowledge for pregnant women about the danger signs of pregnancy, especially early diagnosis of pregnancy disorders. The chatbot application was developed with artificial intelligence technology based on Artificial Intelligence Markup Language with the question-answer concept using the Pandorabots framework. The test is carried out in two stages: functional and pattern matching. The functional testing uses the black-box testing method, and the pattern-matching test on the chatbot uses the sentence similarity and bigram methods based on user input and keywords similarity in the bot's knowledge base. The functional testing results show that the chatbot application runs well, with the eligibility criteria reaching 81.4% and the results of the keyword similarity test (pattern matching) are zero to one, in the sense that the value of one has the same similarity between user input and pattern. Meanwhile, the zero value has no similarities, so the bot will respond to it as free input. So it can be concluded that the bot can respond to user questions when the pattern and input have the same level of similarity.
Batik Images Retrieval Using Pre-trained model and K-Nearest Neighbor Minarno, Agus Eko; Hasanuddin, Muhammad Yusril; Azhar, Yufis
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1299

Abstract

Batik is an Indonesian cultural heritage that should be preserved. Over time, many batik motifs have sprung up, which can lead to mutual claims between craftsmen. Therefore, it is necessary to create a system to measure the similarity of a batik motif. This research is focused on making Content-Based Image Retrieval (CBIR) on batik images. The dataset used in this research is big data Batik images. The authors used transfer learning on several pre-trained models and used Convolutional Neural Network (CNN) Autoencoder from previous studies to extract features on all images in the database. The extracted features calculate the Euclidean distance between the query and all images in the database to retrieve images. The image closest to the query will be retrieved according to the number of r, namely 3, 5, 10, or 15. Before the image is retrieved, the retrieval system is used to re-ranked with K-Nearest Neighbor (KNN), which classifies the retrieved image. The results of this study prove that MobileNetV2 + KNN is the best model in terms of Image Retrieval Batik, followed by InceptionV3 and VGG19 as the second and third ranks. Moreover, CNN Autoencoder from previous research and InceptionResNetV2 are ranked fourth and fifth. In this study, it was also found that the use of KNN re-ranking can increase the precision value by 0.00272. For further research, deploying these models, especially for MobileNetV2 is an approach for seeing a major impact on batik craftsmanship for decreasing batik motif plagiarism.
Design of Audio-Based Accident and Crime Detection and Its Optimization Pratama, Afis Asryullah; Sukaridhoto, Sritrusta; Purnomo, Mauridhi Hery; Lystianingrum, Vita; Budiarti, Rizqi Putri Nourma
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1643

Abstract

The development of transportation technology is increasing every day; it impacts the number of transportation and their users. The increase positively impacts the economy's growth but also has a negative impact, such as accidents and crime on the highway. In 2018, the number of accidents in Indonesia reached 109,215 cases, with a death rate of 29,472 people, which was mostly caused by the late treatment of the casualties. On the other hand, in the same year, there were 8,423 mugs, and 90,757 snitches cases in Indonesia, with only 23.99% of cases reported. This low reporting rate is mostly caused by the lack of awareness and knowledge about where to report. Therefore, a quick response surveillance system is needed. In this study, an audio-based accident and crime detection system was built using a neural network. To improve the system's robustness, we enhance our dataset by mixing it with certain noises which likely to occur on the road. The system was tested with several parameters of segment duration, bandpass filter cut-off frequency, feature extraction, architecture, and threshold values to obtain optimal accuracy and performance. Based on the test, the best accuracy was obtained by convolutional neural network architecture using 200ms segment duration, 0.5 overlap ratio, 100Hz and 12000Hz as bandpass cut-off frequency, and a threshold value of 0.9. By using mentioned parameters, our system gives 93.337% accuracy. In the future, we hope to implement this system in a real environment.
Design of Livestream Video System and Classification of Rice Disease Agustin, Maria; Hermawan, Indra; Arnaldy, Defiana; Muharram, Asep Taufik; Warsuta, Bambang
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1336

Abstract

One of the agricultural products which is an important aspect of the life of Indonesian people is rice. Rice disease has a devastating effect on rice production, while detecting rice diseases in real-time is still difficult. Therefore, this study designed a Livestream video system that is equipped with a rice disease Classification system. The Livestream system utilizes 4G network communication and is assisted by the WebSocket protocol to communicate in real-time and for the rice disease Classification system using YOLO algorithm. In addition, Livestream uses the raspberry pi camera V2 to take video stream data. In analyzing the performance of the Livestream system, four tests were carried out, namely: functionality test, connectivity test, classification performance test, and implementation performance test. The test was carried out using the wireshark and conky tools, while the classification training used 5447 images from the Huy Minh do dataset that he provided on the Kaggle website. The results show that all programs run well and get a good QoS value according to the index of the parameter results, it is also found that sending non-base64 can reduce the size of the data to approximately 200,000 bytes/s and the performance of the classification system is good because it has an average accuracy of 80% even though it is quite burdening the raspberry pi. This system can still be optimized and developed further to support research in the field of data transmission and the performance of machine learning in a microcontroller.

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