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Ansari Saleh Ahmar
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JINAV: Journal of Information and Visualization
Published by PT. Mattawang Mediatama Solution
ISSN : -     EISSN : 27461440     DOI : https://doi.org/10.35877/jinav
Core Subject : Science, Education, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Library & Information Science Mathematics
JINAV: Journal of Information and Visualization is an international peer-reviewed open-access journal dedicated to interchange for the results of high-quality research in all aspects of information science and technology, data, knowledge, communication, and their 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 JINAV follows the open access policy that allows the published articles freely available online without any subscription.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 139 Documents
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Implementation of Augmented Reality in the Development of Bioentrepreneurship-Based LKPD for Visualization of Fungi Material in High School Sahil, Jailan; Nur, Taslim D.; Ahmad, Hasnah; Madan, Arifin; Rasyid, Rusman
JINAV: Journal of Information and Visualization Vol. 5 No. 2 (2024)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav3123

Abstract

The digital era demands educational transformation with relevant learning methods to prepare students to face the world of work. This study developed a Student Activity Sheet (LKPD) based on bioentrepreneurship assisted by Augmented Reality (AR) on fungi material. The aim is to encourage students' creativity and innovation and improve the quality of learning. The research method uses the R&D model with the define, design and develop (3D) stages. This research was conducted in one of the high schools in Ternate City. The research subjects consisted of three expert validators and 15 class X IPA students. The results of the study showed that the LKPD developed was very valid and feasible to use, and effectively improved the quality of student learning on fungi material in high schools.
Utilization of Virtual Reality as a Sustainable Tourism Promotion Strategy Based on Information Technology in Banten Province Permana, Basuki Rakhim Setya; Kenedi, Kenedi; Huda, Miftahul
JINAV: Journal of Information and Visualization Vol. 5 No. 2 (2024)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav3055

Abstract

The rapid development of technology has introduced Virtual Reality (VR) as a new approach to promoting sustainable tourism. This research focuses on the utilization of VR as an innovative strategy for tourism promotion in Banten Province, Indonesia, particularly in key tourist destinations such as Anyer Beach, Tanjung Lesung Beach, Umang Island, and the Great Mosque of Banten. By leveraging VR, tourists can experience a more immersive and interactive preview of these locations, fostering greater interest in visiting and supporting sustainable tourism practices. The research was conducted using surveys, gathering responses from 1,000 participants. The findings indicate that VR significantly enhances tourists' experience by offering detailed visualizations, increasing awareness of environmental sustainability, and encouraging eco-friendly travel behavior. This study demonstrates that VR can be a powerful tool in promoting sustainable tourism, offering both educational and entertainment value, while contributing to the preservation of local culture and natural resources. The results suggest that the integration of VR in tourism promotion strategies can play a key role in advancing eco-friendly tourism development.
Utilization of Virtual Assistance (Chatbot) for an Integrated Information Portal as Part of a Marine Tourism Promotion Strategy in Banten. Hamdan, Hamdan; Romli, Ombi; Auliana, Sigit; Permana, Basuki Rakhim Setya
JINAV: Journal of Information and Visualization Vol. 5 No. 2 (2024)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav3056

Abstract

This study aims to develop and analyze the implementation of Virtual Assistance (Chatbot) as an integrated information medium for marine tourism promotion in Banten Province. The chatbot technology is designed to provide easy access to real-time information about tourist destinations for prospective visitors. The research was conducted at several popular beaches in Banten, such as Carita, Anyer, Sawarna, and Tanjung Lesung. Through surveys of visitors and tourism operators, data on the effectiveness of chatbots were collected and analyzed. The results show that the implementation of chatbots helps improve tourist satisfaction in information-seeking and strengthens the promotion of marine tourism in Banten.
Evaluating ARIMA Models for Short-Term Rainfall Forecasting in Polewali Mandar Regency Ahmar, Ansari Saleh; Mokhtar, Ali
JINAV: Journal of Information and Visualization Vol. 5 No. 2 (2024)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav3266

Abstract

This study aims to forecast rainfall in Polewali Mandar Regency using the ARIMA model. This is a quantitative study that uses secondary data, specifically monthly rainfall data (in mm) from January 2008 to December 2020, obtained from the NERC EDS Centre for Environmental Data Analysis. Two ARIMA models were tested: ARIMA(0,1,1)(0,1,1)[12] and ARIMA(1,1,1)(0,1,1)[12], with model selection based on the Akaike Information Criterion (AIC), which balances model fit and complexity. The AIC calculation revealed that the ARIMA(1,1,1)(0,1,1)[12] model had a lower AIC value (1677.33) compared to the ARIMA(0,1,1)(0,1,1)[12] model (1678.16), making ARIMA(1,1,1)(0,1,1)[12] the preferred model. Using this model, the forecasted rainfall for the next five months is as follows: January 2021: 279.8745 mm, February 2021: 238.2206 mm, March 2021: 237.1745 mm, April 2021: 349.3206 mm, and May 2021: 336.0976 mm. These forecasts provide valuable information for water resource management, agricultural irrigation planning, and disaster mitigation related to rainfall. The study emphasizes the importance of selecting the appropriate model to improve forecasting accuracy.
The Influence of Intellectual Capital, Learning Capability, and Technological Orientation on the Innovation Capability and Sustainable Competitive Advantage of Batik SMEs in Indonesia Utomo, Bangun Prajadi Cipto; Sentosa, Ilham; Osman, Sharina; Santosa, Tri Djoko
JINAV: Journal of Information and Visualization Vol. 5 No. 2 (2024)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav3277

Abstract

In industries that produce environmental waste, concern for environmental sustainability is the main requirement to achieve business sustainability. Apart from environmental problems, in an era of increasingly fierce industrial competition, innovation capability is a factor that influences a company's competitive advantage in winning market competition. This study investigates the antecedents of innovation capabilities and sustainable competitive advantage in Batik SMEs in Indonesia. The population in this research is the small and medium-scale batik industry in the Solo Raya area. The number of samples taken was 113 respondents and determined using the purposive sampling method. The results of this study found that intellectual capital, learning ability, and technological orientation directly affect innovation ability and indirectly affect the sustainable competitive advantage of Batik SMEs in Indonesia. The ability to innovate directly influences the sustainable competitive advantage of Batik SMEs in Indonesia.
Application of Cluster Analysis of Self Organizing Map (SOM) Method in the Community Literacy Development Index in Indonesia Sanra Ariani; Muhammad Nusrang; Muhammad Kasim Aidid
JINAV: Journal of Information and Visualization Vol. 6 No. 1 (2025)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.asci1571

Abstract

Self Organizing Map (SOM) is a method with a form of unsupervised learning, with Artificial Neural Network (ANN) training techniques that use a winner takes all basis, where only the neuron that is the winner will be updated. This study applies the cluster analysis of the SOM method in grouping provinces in Indonesia based on the characteristics of the Community Literacy Development Index (IPLM). The selection of the best cluster is based on internal validation i.e. connectivity, index Dunn and Silhouette. Based on the cluster validation results, 3 clusters were obtained that group provinces based on IPLM characteristics. of the 7 (seven) elements that make up the IPLM, 2 of them, namely energy and community visits, are shown in cluster 1. 5 other elements such as libraries, collections, SNP libraries, community involvement and library members are shown in cluster 3. Meanwhile, cluster 2 does not show significant IPLM-forming elements.
Performance Analysis of API Protocol Models as Recommendations for Developers in Application Development Fhonna, Rizky Putra; Afrillia, Yesy; Ilhadi, Veri; Arif, Abdul Halim; Selian, Riko Ardiansyah
JINAV: Journal of Information and Visualization Vol. 5 No. 2 (2024)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav3041

Abstract

The evaluation of various API types reveals distinct strengths and weaknesses. REST APIs exhibit inefficient performance with high average response times and an error rate of approximately 14%, indicating potential delays and instability under load. SOAP APIs, with an average response time of 167 ms, perform better than REST in terms of speed but still lag behind GraphQL and have a slightly higher error rate of 14.80%. GraphQL demonstrates the fastest average response time at around 171 ms, offering high efficiency in data delivery, although its error rate is notably high at 15%, signaling a need for improved stability. RPC APIs, with an average response time of 238 ms, are less speedy compared to GraphQL and SOAP but excel in stability with a very low or zero error rate, making them highly reliable under high loads. Overall, GraphQL is optimal for applications requiring rapid data interaction, RPC is best suited for scenarios demanding high consistency and reliability, SOAP offers a middle ground, and REST may be appropriate for simpler, less demanding applications.
Fuzzy Geographically Weighted Clustering Analysis of Poverty Indicators in South Sulawesi, Indonesia Annisa, Nurawalia; Aidid, Muhammad Kasim; Meliyana, Sitti Masyitah
JINAV: Journal of Information and Visualization Vol. 6 No. 1 (2025)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav3949

Abstract

Cluster analysis is a method used to group data into several clusters, where the data within a single cluster exhibit high similarity, while the data between clusters show low similarity. This study aims to classify the regencies and cities in South Sulawesi based on poverty indicators using the Fuzzy Geographically Weighted Clustering (FGWC) method. FGWC is an integration of the classical fuzzy clustering approach with geo-demographic components, incorporating geographical aspects into the analysis. As a result, the clusters formed are sensitive to environmental effects, which influence the values of cluster centers. In this study, the optimal number of clusters was determined using the IFV (Index of Fuzzy Validity) validity index, which indicated an optimal solution of three clusters. Cluster 1 consists of 9 regencies/cities characterized by a high level of poverty. Cluster 2 comprises 7 regencies/cities with a moderate level of poverty. Cluster 3 includes 8 regencies/cities with a low level of poverty.
Enhancing Computational Learning through Visual Programming Media: An Empirical Study of Academic Achievement Sidiq, Muhammad Robby; Napitupulu, Efendi; Farihah, Farihah
JINAV: Journal of Information and Visualization Vol. 6 No. 1 (2025)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav4019

Abstract

The integration of computational thinking into formal education curricula has become essential as information and communication technology continues to transform educational paradigms. This study addresses a significant challenge in computer science education, where 58% of junior high school students fail to achieve minimum learning standards in programming subjects. Objective this study to develop and evaluate the feasibility and effectiveness of visual programming learning media using Scratch 3.0 to enhance academic achievement in computer science education among seventh-grade students. This research employed a research and development approach using a 4-D model (Define, Design, Develop, Dissemination) conducted at Dr. Wahidin Sudirohusodo Junior High School over two months. The study involved comprehensive validation by three expert validators and implementation across three phases: a small group (n=5), a medium group (n=15), and field testing (n=30). The data collected included expert validation questionnaires, pre- and posttest assessments, and perception surveys, which were analyzed via descriptive statistics and t tests. Expert validation demonstrated high feasibility, with material validation scoring of 87%, media validation scoring of 83.5%, and instructional design validation scoring of 84.5%, all of which were categorized as "Very Good." The effectiveness evaluation revealed an overall effectiveness of 92.73% in the "very good" category. Statistical analysis revealed significant differences between the experimental and control groups (t-stat = 5.06, p < 0.001), with the experimental group achieving a mean score of 77.00 compared with 70.60 for the control group. Scratch 3.0-based visual programming learning media is feasible and effective for enhancing computer science learning outcomes, demonstrating superior performance compared with conventional teaching methods in junior high school education.
Classification of Stunting Status Using the Naive Bayes Classifier Algorithm with Backward Elimination Feature Selection Pasaribu, Hafni Maya Sari; Abdullah, Dahlan; Rosnita, Lidya
JINAV: Journal of Information and Visualization Vol. 6 No. 1 (2025)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav4100

Abstract

Stunting is one of the major health issues affecting toddlers that can influence their physical growth and developmental progress, ultimately impacting their quality of life. It is characterized by a child’s height being below the standard for their age. To address this issue, a method is needed to classify the stunting status in toddlers. This study aims to classify stunting status in toddlers using the Naive Bayes Classifier algorithm, with feature selection performed using the Backward Elimination method to improve classification accuracy.The dataset used in this research was collected in 2023 from the Lueng Daneun Public Health Center, located in Peusangan Simblah Krueng Subdistrict, Bireun District. The dataset includes several features such as age, gender, family income, height, weight, sanitation, clean water access, and formula milk consumption. The application of the backward elimination feature selection method is intended to identify the most significant and relevant features for the target variable. The Naive Bayes Classifier was implemented using the Python programming language. The analysis results indicated that the remaining feature, namely the sanitation condition, had a significant contribution to the classification process. The dataset consisted of 244 entries, divided into 195 training data and 49 testing data with an 80:20 ratio. The initial classification results showed an accuracy of 77.55%, a precision of 60.00%, a recall of 64.29%, and an F1-score of 62.07%. After feature selection, the accuracy increased to 81.63%, precision to 63.16%, recall to 85.71%, and the F1-score slightly improved to 72.73%. These results indicate that feature selection in the Naive Bayes model demonstrates good performance.

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