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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.
Arjuna Subject : -
Articles 542 Documents
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Managing Information Technology Risks to Achieve Business Goals: A Case of Pharmaceutical Company Luthfi Ramadani; Berlian Maulidya Izzati; Yosephine Mayagita Tarigan; Rosanicha Rosanicha
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1816

Abstract

Extant literature has shown that sectoral characteristics play a critical role in business value creation through information technology (IT). Therefore, managing IT and its associated risks needs to consider specific industrial traits to understand the distinct business nature and regulations that shape IT-enabled business value creation. This study presents an in-depth analysis of business goals, IT processes, and IT risks in the case of a pharmaceutical company through which appropriate controls are designed to ensure business value creation through IT. Drawing on a case study of a pharmaceutical company in Indonesia, we found that managing IT risks in the pharmaceutical industry entails two main objectives: 1) ensuring compliance with external laws and regulations as well as internal policies, 2) supporting the optimization of business functions, processes, and costs. Throughout one year of engagement during the project, this study identified ten risks associated with the operation of business processes. Risks are dominated by moderate levels given the current state of controls and appetite, most of which emerge from the company’s existing internal processes. Internal actors are involved in all risks, with most events occurring due to laws and regulations. Further, the study designs and elaborates IT risk controls by drawing from COBIT 5 Seven Enablers. Overall, IT risk management through cascading processes of analysis ensures the alignment of IT risk controls with achieving business goals in the pharmaceutical industry.
The Gamification of E-learning Environments for Learning Programming Christian Garcia Villegas; Nilson Augusto Lemos Aguero
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1602

Abstract

Gamification is the most active methodology utilized in the E-learning environment for teaching-learning in computing; however, this does not restrict its use in other areas of knowledge. Gamification combines elements of play and its design techniques in a non-ludic context, achieving a motivation factor for the students. This systematic study aimed to collect and synthesize scientific evidence from the gamification field for learning programming through the E-learning environment. In order to do this, a systematic literature review was done, following the guidelines proposed by Petersen, which propose the definition of questions, search strategies, inclusion/exclusion criteria, and characterization. As a result of this process, eighty-one works were completely reviewed, analyzed, and categorized. The results revealed favorable learning among the students, the most used platforms and gamification elements, the most used languages and focuses of programming, and the education level, where gamification is most used to learn to program in an E-learning environment. These findings evidenced that gamification is a good active strategy for introducing beginning students to programming through an E-learning environment. Within this context, Learning programming through the use of gamification is a topic that is growing and taking force, and after what occurred during the pandemic, it is projected that there will continue to be more students who are focused on understanding its implementation and the impact it has on the different levels of education and the areas of knowledge.
Vehicles Speed Estimation Model from Video Streams for Automatic Traffic Flow Analysis Systems Maizatul Najihah Arriffin; Salama A. Mostafa; Umar Farooq Khattak; Mustafa Musa Jaber; Zirawani Baharum; - Defni; Taufik Gusman
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1820

Abstract

Image and video processing have been widely used to provide traffic parameters, which will be used to improve certain areas of traffic operations. This research aims to develop a model for estimating vehicle speed from video streams to support traffic flow analysis (TFA) systems. Subsequently, this paper proposes a vehicle speed estimation model with three main stages of achieving speed estimation: (1) pre-processing, (2) segmentation, and (3) speed detection. The model uses a bilateral filter in the pre-processing strategy to provide free-shadow image quality and sharpen the image. Gaussian filter and active contour are used to detect and track objects of interest in the image. The Pinhole model is used to assess the real distance of the item within the image sequence for speed estimation. Kalman filter and optical flow are used to flatten vehicle speed and acceleration uncertainties. This model is evaluated with a dataset that consists of video recordings of moving vehicles at traffic light junctions on the urban roadway. The average percentage for speed estimation error is 20.86%. The average percentage for accuracy obtained is 79.14%, and the overall average precision of 0.08.
Automatic Summarization of Court Decision Documents over Narcotic Cases Using BERT Galih Wasis Wicaksono; Sheila Fitria Al asqalani; Yufis Azhar; Nur Putri Hidayah; Andreawana Andreawana
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1811

Abstract

Reviewing court decision documents for references in handling similar cases can be time-consuming. From this perspective, we need a system that can allow the summarization of court decision documents to enable adequate information extraction. This study used 50 court decision documents taken from the official website of the Supreme Court of the Republic of Indonesia, with the cases raised being Narcotics and Psychotropics. The court decision document dataset was divided into two types, court decision documents with the identity of the defendant and court decision documents without the defendant's identity. We used BERT specific to the IndoBERT model to summarize the court decision documents. This study uses four types of IndoBert models: IndoBERT-Base-Phase 1, IndoBERT-Lite-Bas-Phase 1, IndoBERT-Large-Phase 1, and IndoBERT-Lite-Large-Phase 1. This study also uses three types of ratios and ROUGE-N in summarizing court decision documents consisting of ratios of 20%, 30%, and 40% ratios, as well as ROUGE1, ROUGE2, and ROUGE3. The results have found that IndoBERT pre-trained model had a better performance in summarizing court decision documents with or without the defendant's identity with a 40% summarizing ratio. The highest ROUGE score produced by IndoBERT was found in the INDOBERT-LITE-BASE PHASE 1 model with a ROUGE value of 1.00 for documents with the defendant's identity and 0.970 for documents without the defendant's identity at a ratio of 40% in R-1. For future research, it is expected to be able to use other types of Bert models such as IndoBERT Phase-2, LegalBert, etc.
Sentiment Analysis of Neobank Digital Banking using Support Vector Machine Algorithm in Indonesia Kusnawi Kusnawi; Majid Rahardi; Van Daarten Pandiangan
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1652

Abstract

Currently, in the industrial era 4.0, information and communication technology is very developed, whereas, in this era, there is an increase in complex activities, one of which is in the banking sector. With the ease and efficiency of online finance, people want to switch to using digital banks. Neobank is an online savings and deposit application from Bank Neo Commerce (BCN) that the public can use by using the Internet. One of the online services is mobile banking which can be used by both Android and iOS versions of customers. Users can review Neobank's performance and services through the Google Play Store to improve and evaluate Neobank's performance. Neobank application reviews on the Google Play Store are increasing. Therefore, a review analysis is needed by conducting a sentiment analysis on Neobank's review. The data amounted to 3159 user reviews collected from reviews of the Neobank application on the Google Play Store. This study aims to classify Neobank user review data, including positive or negative sentiments. The method used in this study is an experimental method using the Support Vector Machine algorithm. The accuracy results obtained using the Support Vector Machine algorithm are 82.33%, which is owned by the scenario of 90% training data and 10% test data. The precision results are 82%, and recall is 81%. Future studies can add datasets from various sources so that there are even more datasets so as to increase the accuracy of model classification.
Adopting the eGameFlow Model in an Educational Game to Increase Knowledge about Vaccination Nur Farahin Mohd Johari; Muhammad Amirul Naim Muhammad Ridzuan L; Norshahidatul Hasana Ishak; Hazrati Zaini
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1809

Abstract

Vaccination is one preventive measure to prevent oneself from getting any diseases. Vaccinations work by training our immune system to produce antibodies and weaken the targeted disease. Unfortunately, the number of unvaccinated children has increased because some parents reject or doubt the effectiveness of vaccines. This skepticism could result in a resurgence of vaccine-preventable diseases. Additionally, some contribute to vaccine refusal due to other reasons like vaccine misinformation, religious convictions, and insufficient knowledge. The game aims to develop knowledge and awareness to enhance vaccine behavior and acceptance among individuals. eGameFlow model were used as the methodology for game development. This model was chosen as it focuses on the educational game environment, which addresses learning components in the game. There are eight criteria to be considered to evaluate enjoyment using this model. To measure the users’ enjoyment, a set of questionnaires adopted from the eGameFlow model has been used for the evaluation. It is created explicitly to measure learners’ enjoyment of e-Learning games. This game gathers positive feedback from 30 respondents and shows promising results to achieve the objective. All eGameFlow criteria were positive towards enjoyment, with knowledge improvement being the highest contributor. The overall average of the evaluation was at an agreeable level, with a score of 81%, considered as achieving the goal. For future enhancement in increasing player’s enjoyment and game effectiveness, the game can be created in 3D environment to provide deep immersion and autonomy to the player.
Evaluation of the Compatibility of TRMM Satellite Data with Precipitation Observation Data Nurhamidah Nurhamidah; Rafika Andari; Ahmad Junaidi; Darwizal Daoed
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1578

Abstract

The availability of hydrological data is one of the challenges associated with developing water infrastructure in different areas. This led to the TRMM (Tropical Precipitation Measurement Mission) design by NASA, which involves using satellite weather monitoring technology to monitor and analyze tropical precipitation in different parts of the world. Therefore, this validation study was conducted to compare TRMM precipitation data with observed precipitation to determine its application as an alternate source of hydrological data. The Kuranji watershed was selected as the study site due to the availability of suitable data. Moreover, the validation analyses applied include the Root Mean Squared Error (RMSE), Nash-Sutcliffe Efficiency (NSE), Coefficient Correlation (R), and Relative Error (RE). These used two calculation forms: one for the uncorrected data and another for the corrected data. The results showed that the best-adjusted data validation from the Gunung Nago station in 2016 was recorded to be RMSE = 62,298, NSE = 0.044, R = 0.902, and RE = 11,328. The closeness of the R-value to one implies that the corrected TRMM data outperforms the uncorrected ones. Therefore, it was generally concluded that the TRMM data matches the observed precipitation data and can be used for hydrological study in the Kuranji watershed
Optimization of the Preprocessing Method for Edge Detection on Overlapping Cells at PAP Smear Images Nita Merlina; Edi Noersasongko; Pulung Nurtantio Andono; M Arief Soeleman; Dwiza Riana
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1329

Abstract

The complexity of the cell structure and high overlap cause poor image contrast. Complex imaging factors can make automatic visual interpretation more difficult. Segmentation separates a digital image into different parts with homogeneous attributes so that different areas have different features. The challenges faced in performing nucleus segmentation on Pap Smear (PS) images are poor contrast, the presence of neutrophils, and uneven staining of overlapping cells. This research was conducted to improve image quality in identifying the nucleus accurately. The method used is the Polynomial Contrast Enhancement (PCE) model as an approach to preprocessing. This method functions to change the contrast of the Pap smear image against the overlapping cells so that it becomes a significant contrast in detecting the edge of the nucleus object. The detection process uses the Robert and Prewitt edge detection method to test the identification of the nucleus object on 797 PS Repository images of the University of Nusa Mandiri (RepomedUNM). The accuracy result obtained is 86.8%. Comparing Robert's edge detection and Prewitt's edge detection shows that the PCE approach as a filter method can overcome color contrast problems and detect more accurately. The difficulty in detecting the nucleus from the PS image against the overlapping cells can be solved. This method can distinguish overlapping cells from their core during testing, thus becoming a reference in identifying cells with improved accuracy and testing larger data sets.
Early Detection of Asymptomatic Covid-19 Infection with Artificial Neural Network Model Through Voice Recording of Forced Cough Aisyah Khairun Nisa; I Gede Pasek Suta Wijaya; Arik Aranta
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1812

Abstract

SARS-CoV-2 is a virus that spreads the infection known as COVID-19, or Coronavirus 2019. According to data from the World Health Organization as of March 15, 2021, Indonesia has 1,419,455 cumulative cases and 38,426 cumulative deaths, ranking third among countries in terms of fatalities, behind Iran and India. Because COVID-19 was disseminated through direct contact with respiratory droplets from an infected individual, it spread swiftly and widely. According to the American Centers for Disease Control and Prevention, more than 50% of transmission rates are anticipated from asymptomatic individuals. The antigen tests have an accuracy of results ranging from 80–90% and are utilized for early detection of COVID-19. The cost of the antigen test is set to increase as of September 3, 2021, with prices ranging from IDR 99.000 to IDR 109.000; however, researchers are steadfastly searching for the best alternate methods for the early diagnosis of COVID-19. According to MIT News Office, a forced cough recording can identify an asymptomatic COVID-19 infection. Through the vocal recording of a forced cough, this study uses an artificial neural network (ANN) deep learning model to identify asymptomatic COVID-19 patients. The Artificial Neural Network (ANN) can distinguish asymptomatic people from forced cough recordings with an accuracy of up to 98% and a loss value of less than 3% by employing oversampling data. This model can be applied as a free, universal method for the early identification of COVID-19 infection.
Students Demography Clustering Based on The ICFL Program Using K-Means Algorithm Rachmadita Andreswari; Rokhman Fauzi; Berlian Maulidya Izzati; Vandha Pradwiyasma Widartha; Dita Pramesti
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1916

Abstract

Independent Campus, Freedom to Learn (ICFL) Program is one of the manifestations of student-centered learning. This program can help students reach their full potential by allowing them to pursue their passions and talents. This study aims to see how the segmentation of students participating in the ICFL program is based on demographic data. This research is based on survey responses from students participating in the ICFL program. The method used in this study is input data preparation, pre-processing, data cleansing, and data analysis. The information will be pre-processed before being utilized and evaluated. To help produce better outcomes in data clustering, the K-Means clustering approach is used, which is processed using the Python computer language. The data is clustered using the K-Means clustering approach based on gender characteristics, Grade Point Average (GPA), university entrance selection, ICFL category, and year or semester when participating in ICFL. This study resulted in three clusters with each of its criteria. The dominant gender is found in clusters 2 (100% female) and 3 (100% male). Software Development was the most popular ICFL category among students in cluster 1, accounting for 67%, while Design and Analysis Information Systems was the most popular in clusters 2 and 3. The most dominant ICFL program is found in three clusters. ICFL - Internship program in which at least 40% of participants come from each cluster. The research results are expected to assist stakeholders in evaluating the implementation of the ICFL program.  

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