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Published by IAIC Publishing
ISSN : 27745880     EISSN : 27745899     DOI : https://doi.org/10.34306/conferenceseries
Core Subject : Economy, Science, Education, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education
IAIC International Conference Series (IICS) managed by Indonesian Association on Informatics and Computing (IAIC) and supported by Alphabet Incubator . All URL of published articles will have a digital object identifier (DOI). The open-access IAIC International Conference Series provides a fast, versatile and cost-effective proceedings publication service for your conference. Proceedings are an important part of the scientific record, documenting and preserving work presented at conferences worldwide. Key publishing subject areas include: Computer Science, Informatics, Electronics Engineering, Communication Network and Information Technologies.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 48 Documents
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The Semosemo: Vehicle Rental Application in Manado City Green Ferry Mandias; Christiady Somba Sirappa; Pierre Jericho Effendy; Timothy Matthew Jeremi Dirk
IAIC International Conference Series Vol. 4 No. 1 (2023): SEMNASTIK 2023
Publisher : IAIC Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/conferenceseries.v4i1.619

Abstract

In everyday human life, there are many aspects that cause a decision to be made. Agreements can be made in writing or unwritten, reciprocal agreements and unilateral agreements, obligatory agreements and one of them is a lease agreement. The lease agreement can help the parties, both from the leaser and the lessee. Car Rental is one of the businesses providing transportation services that involves the use of mobile devices to find out information about the services provided by the company. Car Rental is closely related to transportation services to help people who need car rental for various purposes. To use rental services in Manado City, usually the tenant must go to the rental place, and that is less efficient to do. Therefore, the problem found is how to make the Semosemo Vehicle Rental Application in Manado City. With the aim of making the Semosemo Vehicle Rental Application in the City of Manado. This research uses the prototype method and is also assisted by software such as React Native, Figma, and Visual Studio Code. The result is that the Semosemo application can be made to help rent vehicles in the city of Manado and the application can run accordingly.  
Agile Method in Developing Electronic Local Government Food Reserve Distribution Services (E-CPPD) in Sukabumi City Asril Adi Sunarto; Euis Kania Kurniawati
IAIC International Conference Series Vol. 4 No. 1 (2023): SEMNASTIK 2023
Publisher : IAIC Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/conferenceseries.v4i1.621

Abstract

Indonesia is a country with a region that has disasters here. As a Regional Apparatus Organization which must distribute regional government food reserves to the community when natural disasters strike, Dinas Ketahanan Pangan, Peternakan dan Perikanan Kota Sukabumi took the initiative to develop an application that can speed up the distribution of aid to the community. This national food reserve policy can support national defense in emergency conditions. The hope is that the development of this application can speed up the administration of official correspondence, where the administration of this correspondence is an element that slows down actions in almost every department, resulting in the length of time that citizens receive assistance. There are many discussions and interviews with various users who need to adapt an environment that requires flexibility in changes to system development, so this system development uses the spiral method. As a result, based on the user requirement list, 100% of user needs can be completed on time. The result, almost nine (9) tons of rice have been distributed to residents spread across 22 of the 33 sub-districts in Sukabumi City.
A Survey of Blockchain in Governance: Framework Selection and Future Implementation in Indonesian Government Eltyasar Putrajati Noman; Djoko Budiyanto Setyohadi
IAIC International Conference Series Vol. 4 No. 1 (2023): SEMNASTIK 2023
Publisher : IAIC Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/conferenceseries.v4i1.623

Abstract

Blockchain technology enables users to connect without the need for a third party or central server. This is achieved through the use of a decentralized system, ensuring that all data and information transacted are encrypted, verified, validated, and stored using mathematical consensus algorithms. This leads to blockchain being recognized as a technology characterized by decentralization, security, anonymity, transparency, immutable data, and trust. Blockchain is frequently associated with digital currency, although digital currency is just one of the outcomes of applying blockchain technology, resulting in cryptocurrencies. Currently, blockchain technology is a trend among academics and practitioners who are researching and developing blockchain technology for application in various domains, including government. Government systems and public servants often encounter issues related to data security. Hence, the research has the purpose to offer comprehension and perspectives on implementing blockchain technology within the government sector to enhance public service information security. The research was carried out by reviewing Scopus-indexed international articles published between 2019 and 2023, which are relevant to frameworks, consensus algorithms, and applications employed in the governmental domain. The research outcomes revealed that the Hyperledger Fabric framework, coupled with the Practical Byzantine Fault Tolerance (PBFT) algorithm, is the most suitable option for potentially developing blockchain-based government or public service applications for future implementation. Regarding this research, there are future challenges in the form of constructing prototypes and evaluating their effectiveness and efficiency. Therefore, further research and development efforts are essential to ensure that the application of blockchain technology in the government sector can be realized as required in the future.
Comparative SVM and Decision Tree Algorithm in Identifying the Eligibility of KIP Scholarship Awardee Asriyanik; Agung Pambudi
IAIC International Conference Series Vol. 4 No. 1 (2023): SEMNASTIK 2023
Publisher : IAIC Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/conferenceseries.v4i1.625

Abstract

Scholarship selection process has specific rules, but if the number of applicants exceeds the quota, a selection process is needed. Based on the observation of a university in Sukabumi, the selection for KIP scholarship has not yet had a standard method. Several methods can be used to assist the selection process, such as classification based on historical data of applicants. The algorithms used for classification include Decision Tree (DT) and Support Vector Machine (SVM). The research process uses SEMMA (Sample, Explore, Modify, Model, Assess) method. Dataset for KIP scholarship awardee from 2021-2022 consist of 519 samples with 16 attributes. From the exploration results, the most important features for model modeling are Status DTKS, Status P3KE, Father's income, mother's income, combined income, and performance. These attributes are converted into numerical data to facilitate model fitting. The K-Fold Cross-Validation results for the Decision Tree model in the case of KIP Scholarship classification yield an accuracy of 78.44% for the entire test dataset, a precision of 0.73107, indicating that 73.11% of the predictions are true, a recall (sensitivity) of 78.45%, and an F1 score of 73.20%. The results for the SVM model are an accuracy of 80.17%, a precision of 84.44%, and a recall of 80.17%.
Classification of Coffee Leaf Diseases using the Convolutional Neural Network (CNN) EfficientNet Model Muhammad Imron Rosadi; Lukman Hakim; M. Faishol A.
IAIC International Conference Series Vol. 4 No. 1 (2023): SEMNASTIK 2023
Publisher : IAIC Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/conferenceseries.v4i1.627

Abstract

Coffee leaf disease is a problem that needs attention because it affects the quality and productivity of the coffee harvest and is detrimental to farmers. Therefore, a system is needed to identify types of coffee leaf diseases using artificial intelligence. There are four types of coffee leaf diseases, namely Miner leaf, Phoma leaf, Rust leaf, and Nodisease leaf. The research used the EfficientNet Architecture Convolutional Neural Network (CNN) method to detect types of disease on coffee leaves. This method was chosen because it is capable and reliable in processing digital images for pattern recognition. The dataset used is 1,464 images with dimensions of 2048 x 1024 pixels with RGB type which are divided into 1,264 training data and 400 testing data. Several architectures used in EfficientNet are EfficientNet B0, EfficientNet B1, EfficientNet B2, EfficientNet B3, EfficientNet B4. Parameters used are Lanczos resampling, Epoch 25, Learning Rate 0.0001, Loss Function Sparse Categorical Cross Entropy, Optimizer Adam. The results of training data testing, namely the CNN EfficientNet B1 Architecture Model method, got the best accuracy of 97% and a loss of 0.1328 and testing data testing got an accuracy of 0.97% and a loss of 0.1328. The architecture of the EfficientNet B1 model is better than other architectural models, namely VGG16, ResNet50, MobileNetv2, EfficientNet B0, EfficientNet B2, EfficientNet B3, EfficientNet B4, EfficientNet B5, EfficientNet B6, EfficientNet B7.
Application of Agile Development Methods in the Development of Integrated Systems for Vehicle Body Repair Didik Indrayana; Prajoko; Asril Adi Sunarto
IAIC International Conference Series Vol. 4 No. 1 (2023): SEMNASTIK 2023
Publisher : IAIC Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/conferenceseries.v4i1.629

Abstract

PT XYZ Auto Body Repair is a company that focuses on repairing and servicing vehicles, especially cars that have been involved in accidents or disasters. Currently, data processing still uses physical forms, which has proven to be inefficient because it takes significant time, labor, and resources. Collecting and inputting data from various forms requires a large effort, while systems that are not integrated cause delays in providing the required information. These challenges impact the company's ability to make decisions quickly and on time, especially in the face of increasingly tight and complex business competition. Therefore, an efficient and integrated solution is needed. Seeing this problem, it was decided to develop an integrated vehicle repair system by applying agile development methods, especially the Extreme Programming model. This approach allows development in an iterative, fast, adaptive manner, and actively involves users at every stage of development. Experience has shown that applying the Extreme Programming model can produce an integrated system that meets user needs in a short time. With this system, companies can produce reports quickly without reduplication or repetitive data processing. All parts involved in the vehicle repair process will be connected to one company server, creating the efficiency and accuracy needed to support business growth in a dynamic business environment.
Breast Cancer Screening Application Based on Android with the Certainty Factor Method Suprapto; Kenty Wantri Anita
IAIC International Conference Series Vol. 4 No. 1 (2023): SEMNASTIK 2023
Publisher : IAIC Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/conferenceseries.v4i1.633

Abstract

According to Globocan records, in Indonesia in 2020 there were 396.314 new cancer cases. And 234.511 people were declared dead. Women are a group with a high risk of developing cancer. If cancer is detected at an early stage, this can increase the chance of cure to 80-90%. Early detection of cancer can be done using several methods, for example, for breast cancer, the method of checking can be using the SADANIS (Clinical Breast Examination) and SADARI (Self Breast Examination) methods. In this research, a mobile application will be developed that can be used as a guide in carrying out early cancer detection independently. The early detection system uses an Android-based expert system and certainty factor method. The case study in this research is on breast cancer. Based on the results of accuracy testing with expert diagnosis as a reference, an accuracy value of 90% was obtained. The inaccuracy of this expert system is 10% which can be caused by several possibilities, namely the expert's subjectivity in providing confidence values for disease symptoms or the small number of symptoms entered.
Redesigning the User Interface in the Mobile-Based Ngaji.AI Application Using the Design Thinking Method Aminudin; Aldiensyah; Gita Indah Marthasari; Ilyas Nuryasin; Saiful Amien; Galih Wasis Wicaksono; Didih Rizki Chandranegara; I'anatut Thoifah
IAIC International Conference Series Vol. 4 No. 1 (2023): SEMNASTIK 2023
Publisher : IAIC Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/conferenceseries.v4i1.635

Abstract

Ngaji.AI is a mobile-based application that makes it possible to learn the recite very flexibly, wherever and whenever we can use it to learn the recite. This application is supported by artificial intelligence (AI) which provides direct and accurate assessments of how to recite Al-Quran verses properly and correctly and this application has been released on the Google Playstore platform and has been downloaded by more than 5 thousand. The Ngaji.AI application is faced with a crucial challenge, after direct observation of children and through the results of previous user input on Playstore, most of the input from users states that it needs to improve the User Interface (UI) design to make it easier to operate for children. The application of the Design Thinking method is an approach that prioritizes creativity and deep understanding of users and the problems they face and is indeed suitable for developing UI/UX of an application. Testing using the System Usability Scale (SUS) in the first test before the redesign got an average score of 50.25 and after the redesign got a significant score of 83.75. This reflects a significant increase in the level of satisfaction and ease for children in learning to recite the recite on the Ngaji.AI application.
Risk Management for New Student Admission Information Systems at Higher Education using the Octave Allegro Approach Titus Kristanto; Riza Akhsani Setyo Prayoga; Muhammad Nasrullah; Mustafa Kamal; Wahyuddin S
IAIC International Conference Series Vol. 4 No. 1 (2023): SEMNASTIK 2023
Publisher : IAIC Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/conferenceseries.v4i1.637

Abstract

In the current digital era, especially in the world of education, the use of information and communication technology (ICT) is growing rapidly to meet needs. Universities rely on information systems, especially in managing new student admissions. The new student admission selection information system contains sensitive and dangerous prospective student data, as well as the risks that arise in the information system, limited to data processing during the new student admission process and the administration process, thus causing problems. The New Student Registration Information System is one of the services provided by the university as part of the new student registration process. Therefore, risk management is needed to minimize the impact of risks on maintaining data integrity, confidentiality, and availability. The aim of the research is to identify, analyze, and evaluate risks when using information systems for new student admission procedures. The approach used in risk management is Octave Allegro, and Octave Allegro is used to help evaluate information assets. The method used is data collection by conducting interviews with related sources. Based on the findings on the New Student Admissions site, there are 5 risk areas; 9 IT risks were identified as a result of potential risk analysis; and 4 IT risks were mitigated based on recommendations.
Predictions using Support Vector Machine with Particle Swarm Optimization in Candidates Recipient of Program Keluarga Harapan Arie Satia Dharma; Evi Rosalina Silaban; Hana Maria Siahaan
IAIC International Conference Series Vol. 4 No. 1 (2023): SEMNASTIK 2023
Publisher : IAIC Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/conferenceseries.v4i1.639

Abstract

Program Keluarga Harapan (PKH) is a conditional social assistance program as an effort to alleviate poverty which is allocated to poor vulnerable households. The determination of candidates for the Program Keluarga Harapan assistance recipients is still carried out in village meetings, so it takes quite a long time and there is potential for subjectivity in the assessment carried out by Village Government officials which can lead to differences of opinion between deliberation participants in assessing the eligibility of residents as PKH recipients. For this reason, this research will use an optimization method, namely Particle Swarm Optimization (PSO) to select the most optimal attribute out of 39 attributes. After that, a classification algorithm, namely the Support Vector Machine (SVM), was chosen to form a classification model for Candidates for Social Assistance for the Program Keluarga Harapan (PKH). The classification of Candidates for Social Assistance Recipients of the Program Keluarga Harapan (PKH) was carried out in 2 experiments, namely before and after optimization. Experiments before optimization give an accuracy value of 92.44%. While the Support Vector Machine accuracy value after optimization gives an accuracy value of 92.51%. Based on the experimental results, it can be concluded that the Particle Swarm Optimization method can increase the accuracy of the Support Vector Machine algorithm by 0.07%. And the best model is the Support Vector Machine after optimizing Particle Swarm Optimization by using the 17 most optimized attributes in determining class targets.

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