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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 49 Documents
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Search results for , issue "Vol 7, No 3 (2023)" : 49 Documents clear
Visualization Mapping of the Socio-Technical Architecture based on Tongkonan Traditional House Taufiq Natsir; Bakhrani Rauf; Faisal Syafar; Ahmad Wahidiyat Haedar; Faisal Najamuddin
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1788

Abstract

The socio-technical architecture of constructing a community's traditional house is a zine-qua-non at the locus of developing tourism destinations in several areas worldwide. A socio-technical system is an old approach that is realigned with developing integrated tourism components, especially various tourist attractions based on local cultural treasures. The results of this qualitative research with a phenomenological approach analyze and explain the noumena (meaning) behind the phenomena (facts) regarding socio-technical architecture based on Tongkonan traditional houses in Tana Toraja, Indonesia. The study results found that architectural works are full of symbolic meaning in constructing Tongkonan traditional houses. The crystallization of basic values and value orientation as the noumena (meaning) behind the socio-technical architectural phenomenon of the Tongkonan traditional house that stands upright is because five pillars support it as a representation of 5A (Attractions, Accessibility, Accommodation, Amenity, Ansilarity) as a component of tourism development. The Tongkonan roof model, which at first glance looks like a person praying by raising their hands up or to God, the Creator of the universe, is proof of the basic values and orientation of the socio-cultural and spiritual values of the Toraja people. The image of a rooster, sun, and arrangement of horns mounted on the Tongkonan wall proves the rich treasures of local socio-cultural life (local wisdom, local genius) of the local community as a result of creativity and innovation that sustainably has value.
A Novel Approach of Animal Skin Classification Using CNN Model with CLAHE and SUCK Method Support Abdul Haris Rangkuti; Varyl Athala Hasbi
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1153

Abstract

This study describes the process of classifying animal skin images which are rather difficult to obtain optimal image characteristics. For this reason, in the pre-processing stage, we propose two methods to support feature extraction: sharpening using a convolutional kernel (SUCK-Sharpening) and adaptive histogram equalization with limited contrast (CLAHE-Equalized). SUCK works by operating on these pixel values using direct math to build a new image; this final value is the new value of the current pixel. CLAHE overcomes the limitations of the global approach by performing local contrast enhancement. Because of the advantages of the two methods, it becomes a solution to get features processed at the feature extraction and classification stage. The process of animal skin imagery has characteristics in terms of shape and texture, including the characteristics of animal skin color. In this study, some experiments have been carried out on several CNN models, with an average classification accuracy of more than 70% using the sharpened and equalized methods on six animal skins. More detail, the average classification accuracy using 3 CNN models supported by two methods, namely Sharpening and Equalize on the CNN Resnet 50V2 model is 67.73% and 73.78%, InceptionV3 model at 82.13%, and 74.76% and Densenet121 models were 87.64% and 87.46 %. This research can be continued to improve the accuracy of other animal skin images, including determining fake or genuine skin images.
Ranjana Script Handwritten Character Recognition using CNN Jen Bati; Pankaj Raj Dawadi
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1725

Abstract

This paper proposes a public image database for Ranjana script Handwritten Character Datasets (RHCD), publicly available for Ranjana script researchers or anyone interested in the subject. To the best of our knowledge, the Ranjana script Handwritten Character Dataset (RHCD) is the first publicly available database for Ranjana script researchers. Ranjana script descended from the Brahmi script, consists of 36 consonant letters, 16 vowel letters, and 10 numerical letters. The focus of this research is three-fold: the first is to create a new database for Ranjana script Handwritten Character Recognition; the second is to test the character recognition accuracy of the created RHCD using existing CNN algorithms like LeNET-5, AlexNET, and ZFNET algorithm; the third is to propose a model by investigating different hyper-tuning parameters to improve the recognition accuracy of the created RHCD. The research method applied in this study is dataset collection, digitization & cropping, pre-processing, dataset splitting, data augmentation, and finally, implementing the CNN model (existing and proposed). Performance evaluation is based on the test accuracy, precision, recall, and F1-score. The experiment result shows that our model ranks first, with a testing accuracy of 99.73% for 64x64 pixels resolution with precision, recall, and F1-score value 1. Creation and recognition of Ranjana script characters, vowel modifiers, and compound characters can be the next milestone to be achieved. Segmentation of words and sentences into characters and recognizing each character individually can be the next research domain.
Identification of Coffee Types Using an Electronic Nose with the Backpropagation Artificial Neural Network Roza Susanti; Zaini Zaini; Anton Hidayat; Nadia Alfitri; Muhammad Ilhamdi Rusydi
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1375

Abstract

Coffee is one of the famous plants’ commodities in the world. There are some coffee powders such as Arabica dan Robusta. This study aimed to identify two various coffee powders, Arabica and Robusta based on the blended aroma profiles, employing the backpropagation Artificial Neural Network (ANN). Four taste sensors were employed, namely TGS 2602, 2610, 2611, and 2620, to capture the diverse coffee aroma. These detectors were combined with the aroma sensors having transducers integrated with signal amplifiers or processors, which featured a load of 10 KΩ resistance. Three aroma types were investigated, namely Arabica coffee, Robusta coffee, and without coffee beans. The neural network architecture consisted of four inputs from all sensors, with one hidden layer housing eight neurons. Two neuron outputs were employed for classification, with 70 samples used for training ANN for each type. During the training phase, the developed neural network showed an impressive accuracy rate of 91.90%. TGS 2602 and 2611 sensors showed the most significant differences among the three aroma types. When analyzing ground Robusta coffee, TGS 2602 and 2611 sensors recorded 2.967 volts and 1.263 volts, with a gas concentration of 17.92 ppm and 2441.8 ppm. Similarly, the sensors for ground Arabica coffee displayed 3.384 volts and 1.582 volts with a gas concentration of 20.445 ppm and 3058.5 ppm in both TGS 2602 and 2611, respectively. The implemented ANN with aroma sensor as input successfully identify the coffee powders.
K-Means Clustering Algorithm for Partitioning the Openness Levels of Open Government Data Portals Emigawaty Emigawaty; Kusworo Adi; Adian Fatchur Rochim; Budi Warsito; Adi Wibowo
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1761

Abstract

More and more local governments in Indonesia are making their data available to the public. This benefits data scientists, researchers, business owners, and other potential users seeking datasets for empirical research and business innovation. However, just because Open Government Data (OGD) portals are accessible does not mean that they necessarily adhere to the established rules and principles of data openness. To evaluate the level of openness of 24 OGD portals in Indonesia, this study used the K-means Clustering algorithm to partition them into three levels: Leaders, Followers, and Beginners. A group of 30 participants, including researchers, data scientists, business enablers, and graduate students, rated the portals on 32 sub-questions related to the eight main principles of data disclosure, focusing on health, population, and education datasets. The study found that eight portals were categorized as Leaders, ten as Followers, and seven as Beginners regarding their level of openness. The study demonstrated that the K-means Clustering algorithm can be effectively used to assess the degree of openness of OGD portals in Indonesia based on eight main principles of data openness. The study recommends increasing the number of OGD portals in eastern territories to supplement the existing case studies in the western and central regions.
Economic Impact due Covid-19 Pandemic: Sentiment Analysis on Twitter Using Naïve Bayes Classifier and Support Vector Machine Qurrotul Aini; Raffie Rizky Fauzi; Eva Khudzaeva
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1474

Abstract

Covid-19 is an outbreak caused by severe acute respiratory syndrome. Covid-19 first appeared in Indonesia on March 2, 2020, with two confirmed cases and increased to 1285 cases in 30 provinces. One of the impacts of the Covid-19 pandemic is on the economic aspect, which has experienced a drastic decline in income. This study aims to classify public opinion to determine the level of public sentiment on the economic impact of the Covid-19 pandemic and to identify parameters that influence the accuracy of the sentiment analysis classification model. The methods used in this current research are Lexicon, Support Vector Machine (SVM), and Naive Bayes Classifier (NBC). First, Lexicon is used for scoring and labeling the preprocessed data. Second, SVM is used to classify the sentiment, then find the best accuracy using linear, radial, polynomial, and sigmoid kernels. Third, NBC is used to classify sentiment as a comparison method. The results indicated that 255 tweet data consisted of 44 positive tweets (17.25%), 46 neutral tweets (18.04%), and 165 negative tweets (64.71%). Therefore, it can be inferred that the economic impact on the Indonesian people due to the Covid-19 pandemic has a high negative sentiment value. In the performance, SVM yielded a better accuracy of 100%, precision, recall, and F-measure are 1. This study proves that selecting the kernel type and applying underfitting can improve the accuracy of SVM. Also, SVM can perform well on a small amount of training data.
Comparison of K-Means & K-Means++ Clustering Models using Singular Value Decomposition (SVD) in Menu Engineering Nina Setiyawati; Dwi Hosanna Bangkalang; Hindriyanto Dwi Purnomo
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1053

Abstract

The menu is one of the most fundamental aspects of business continuity in the culinary industry. One of the tools that can be used for menu analysis is menu engineering. Menu engineering is an analytical tool that assists restaurants, companies, and small and medium-sized enterprises (SMEs) in assessing and making decisions on marketing strategies, menu design, and sales so that it can produce maximum profit. In this study, several menu engineering models were proposed, and the performance of these models was analyzed. This study used a dataset from the Point of Sales (POS) application in an SME engaged in the culinary field. This research consists of three stages. First, pre-processing the data, comparing the models, and evaluating the models using the Davies Bouldin index. At the model comparison stage, four models are being compared: K-Means, K-Means++, K-Means using Singular Value Decomposition (SVD), and K-Means++ using SVD. SVD is used in the dataset transformation process. K-Means and K-Means++ algorithms are used for grouping menu items. The experiments show that the K-Means++ model with SVD produced the most optimal cluster in this research. The model produced an average cluster distance value of 0.002; the smallest Davies-Bouldin Index (DBI) value is 0.141. Therefore, using the K-Means++ model with SVD in menu engineering analysis produces clusters containing menu items with high similarity and significant distance between groups. The results obtained from the proposed model can be used as a basis for strategic decision-making of managing price, marketing strategy, etc., for SMEs, especially in the culinary business.
The study on Malaysia Agricultural E-Commerce (AE): Customer Purchase Intention Kai Wah Hen; Choon Sen Seah; Deden Witarsyah; Shazlyn Milleana Shaharudin; Yin Xia Loh
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1372

Abstract

Electronic commerce (E-Commerce) became an essential trading platform after the Covid-19 pandemic. From essential products to luxury brands, consumers can find almost everything on the normal E-Commerce platforms with the exception of fresh agricultural products. Agricultural E-Commerce (AE) is introduced to overcome the market needs. Technology Acceptance Model (TAM) is studied and integrated with additional variables to determine the needs of AE in Malaysia. In this study, five variables (product quality, logistic service quality, perceived price & value, platform design quality, and platform security) were studied to determine the Malaysian consumers’ purchase intention towards the AE. Five hypotheses were developed to identify the relationship between the variables. A total of 300 AE users have contributed their perception as respondents in this study through a survey questionnaire. The collected data were processed before the data analysis via Statistical Package for The Social Science (SPSS) version 25.0. Descriptive analysis, and inferential analysis were conducted. The result shows that all five variables are significantly related to the purchase intention towards AE. The product quality has the highest significant value (0.805) towards the purchase intention on AE, followed by logistic service quality, platform security, platform design quality and perceived price and value. Implication, limitation, and recommendation were also being discussed to assist the AE stakeholders in improving their AE.
Factors Influencing Readiness towards Halal Logistics among Food and Beverages Industry in the Era of E-Commerce in Indonesia Prafajar Suksessanno Muttaqin; Erlangga Bayu Setyawan; Nia Novitasari
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.2055

Abstract

Based on Global Islamic Economy Indicator 2019/2020 report, Indonesia is in the fourth position globally as a country that uses a Sharia economic system. Seeing Indonesia's opportunities, it should be able to act as a regional and global halal hub. Efforts to encourage the halal industry through strengthening the halal value chain are one of the strategies to encourage Indonesia to become a global halal hub player. This study utilizes the structural equation modeling to examine relationships among key factors affecting readiness towards halal logistics in the food and beverages industry in Indonesia. 13 key factors are confirmed with measurement-model results, including (1) Cleanliness, (2) Safety, (3) Islamic Dietary Law, (4) Physical Segregation, (5) Material Handlings, (6) Storage and Transport, (7) Packaging and Labelling, (8) Ethical Practices, (9) Training and Personnel, (10) Resource Availability, (11) Innovative Capability, (12) Marketing Performance, (13) Financial Performance. The population in this study is in the food and beverage industries, especially in Semarang, Yogyakarta, Malang, and Surabaya. Cluster random sampling was used in this research with as many as 150 sample respondents. A survey with an online questionnaire was conducted in this research. The structural-model results reveal directions of relationships among key factors. Resource availability, training and personnel, and innovative capability are the most important factor in halal supply chain readiness. Further research can focus on other industrial sectors, such as fashion and tourism, as stated in the 2019-2024 Indonesian Sharia Economic Masterplan
Preliminary study: Readiness of WLAN Infrastructure at Malaysian Higher Education Institutes to support Smart Campus Initiative Roziyani Rawia; Mohd Rizal Mohd Isa; M. N. Ismaila; Aznida Abu Bakar Sajak; Azmi Mustafa
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1242

Abstract

Smart campus initiative enables higher education to enhance services, decision-making, and campus sustainability. The initiatives are being actively implemented globally by higher education, including in Malaysia. The recent COVID-19 pandemic has underscored the need for the education sector to explore a digital revolution. The adaptation of digital technologies has improved many aspects, including the teaching and learning experiences and administration tasks, which results in more efficient task handling. This study investigates the readiness of the WLAN infrastructure at Malaysian Public Higher Education Institutes (HEIs) in implementing smart campus initiatives and measures readiness based on the availability of WLAN Infrastructure, WLAN logical architecture and WLAN populated coverage area. This study administered a questionnaire to 19 respondents, all of whom are IT personnel from Malaysian public HEIs to gather preliminary data on the readiness of WLAN infrastructure at Malaysian Public HEI to support the adaptation of smart campus initiatives in their teaching and learning activities. This study is a preliminary study concerning the readiness of WLAN infrastructure at Malaysian Public HEI in adapting smart campus initiatives. The findings show that, even though WLAN service is available at all Malaysian Public HEI, it is essential to enhance the adopted logical architecture and WLAN coverage to prepare HEI to become smart campuses. The findings of this study can provide the fundamental guidelines for the Ministry of Higher Education in determining the baseline of WLAN infrastructure required by Malaysian HEI to support smart campus initiatives.

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