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akbar iskandar
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Ceddi Journal of Information System and Technology (JST)
Published by Yayasan Cendekiawan Inovasi Digital Indonesia
ISSN : 2829808X     EISSN : 28296575     DOI : https://doi.org/10.56134/jst.v1i1.1
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
Ceddi Journal of Information System and Technology (JST) is a peer-reviewed journal that publishes articles through fair and transparent quality control. We understand that authors need facilities for their papers, whereas readers expect reliable information from these journals. Therefore, our editorial team and reviewers strive to maintain quality and ethics in the authorship and publishing of all articles. In principle, we strive to provide the best service for the research community around the world. We hope this journal can be a new source of insight and inspiration for future research. Ceddi Journal of Information System and Technology (JST) publish the best articles the results of research on issues of concern, the latest and the trend internationally. Submitted papers must be written in English at title and abstract of paper for the initial review stage by editors and further review process by minimum of two reviewers. The scope of the journal includes: - Information Systems - Web Application - Computer Network - Mobile Application - Game Development - Decision Support System - Big Data - E-Commerce - Cloud Computing - Data Mining
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 51 Documents
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Web-Based Rapid Application Development (RAD) for Marketing of Ende Lio Traditional Bond Motif Woven Fabric Neneng Awaliah; Akbar Hendra; Amran Amiruddin; Daud, Daud; Akbar Iskandar
Ceddi Journal of Information System and Technology (JST) Vol. 2 No. 1 (2023): April
Publisher : Yayasan Cendekiawan Digital Indonesia (CEDDI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56134/jst.v2i1.36

Abstract

The marketing of Ende Lio traditional bond motif ikat woven fabric predominantly relies on conventional market stalls. However, with the growing demand for diverse products derived from ikat weaving, such as garments, bags, tunics, and accessories, there is a need for a more efficient marketing strategy. This study aims to implement Rapid Application Development (RAD) and design a web-based marketing system for the Marketing of Ende Lio Traditional Bond Motif Woven Fabric to enhance accessibility and increase public awareness of the products. The objectives of this research include developing an intuitive and interactive web-based platform for marketing Ende Lio's woven ikat fabrics, improving the efficiency of the marketing process, ensuring a seamless user experience, and evaluating the system's impact on sales and customer satisfaction. The RAD methodology, renowned for its incremental and time-constrained software development approach, was employed. The design process involved the use of Use Case Diagrams, and unit testing was conducted to validate the functionality and performance of the application.
Python-Powered Precision: Unraveling Consumer Price Index Trends in Makassar City through a Duel of Long Short-Term Memory and Gated Recurrent Unit Models Abd. Rahman; First Wanita; Rose Arisha; Aditya Halim Perdana Kusuma; Azhary, Zulmy
Ceddi Journal of Information System and Technology (JST) Vol. 2 No. 2 (2023): December
Publisher : Yayasan Cendekiawan Digital Indonesia (CEDDI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56134/jst.v2i2.44

Abstract

This research aims to carry out a predictive analysis of the Consumer Price Index in the city of Makassar to anticipate possible impacts on inflation and deflation in the future. The Consumer Price Index is an indicator that can be used as a basis for measuring changes in the prices of goods and services purchased by consumers which have an impact on inflation in a region. The CPI is very useful for knowing the level of increase in prices, services, and income, as well as measuring the amount of production costs. This data was obtained through the official website of the Central Statistics Agency (BPS) for the Makassar city area. The methods used in this research are Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). The results of this research show that based on analysis and testing, the LSTM model has an MAE of 1.0849 and the GRU model has an MAE of 0.9915, which shows that there is no significant difference between the two methods and both methods can work very well, however, The lowest error value was obtained in the GRU model using a 70:30 dataset ratio, 9 number of sequences, 16 neurons in hidden layer 1 and 32 neurons in hidden layer 2, and 1000 number of epochs.
Digital Forensic Evidence Analysis In Revealing Defamation On Social Media (Twitter) Using The Static Forensics Method Reski Badillah; Andi Yulia Muniar; Abd. Rahman; Febri Hidayat Saputra; Mansyur; Supriadi Sahibu
Ceddi Journal of Information System and Technology (JST) Vol. 2 No. 2 (2023): December
Publisher : Yayasan Cendekiawan Digital Indonesia (CEDDI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56134/jst.v2i2.45

Abstract

This research addresses the persistent challenge of defamation, notably prevalent on the Twitter platform, where the discovery of digital evidence is hampered by robust privacy protections. The study aims to investigate and identify digital evidence in defamation cases on Twitter, focusing on optimizing the evidence discovery process. Employing static forensics to prevent data alterations during acquisition from devices associated with defamation, the research successfully uncovered various digital evidence, including text from deleted comments, usernames, emails, and deleted image files linked to defamation. Out of the initial 28 reported data instances, 22 pieces of evidence were identified, resulting in an impressive 79% accuracy rate. The investigative procedures align with the chain of custody, ensuring the reliability of the collected evidence. This study not only contributes valuable insights into digital evidence discovery in online defamation cases but also highlights the efficacy of static forensics as a method. These findings provide a foundation for robust digital forensic practices, crucial for addressing challenges posed by online defamation on social media platforms.
Application of the Adaptive Boosting Method to Increase the Accuracy of Classification of Type Two Diabetes Mellitus Patients Using the Decision Tree Algorithm Hao Chieh Chiua; Robbi Rahim; Mahmud Mustapa; Kamaruddin; Akbar Hendra; Asnimar; Abigail, Omita
Ceddi Journal of Information System and Technology (JST) Vol. 2 No. 2 (2023): December
Publisher : Yayasan Cendekiawan Digital Indonesia (CEDDI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56134/jst.v2i2.47

Abstract

One of the data mining processes that is often used in machine learning is the data classification process. A decision tree is a classification algorithm that has the advantage of being easy to visualize because of its simple structure. However, the decision tree algorithm is quite susceptible to incorrect classification calculations due to the presence of noise in the data or imbalance in the data, which can reduce the overall level of accuracy. Therefore, the decision tree algorithm should be combined with other methods that can increase the accuracy of classification performance. Machine Learning is used through an artificial intelligence approach to solve problems or carry out optimization. Adaptive Boosting is used to optimize classification calculations. This study aims to examine the performance of Adaptive Boosting in the process of classifying second-degree diabetes mellitus patients using the Decision Tree algorithm. Diabetes mellitus is known as a chronic condition of the human body, the cause of which is an increase in the body's blood sugar levels because the body is unable to produce insulin or is unable to utilize insulin effectively, which is usually referred to as hyperglycemia.. By using a 60:40 data split, the Decision Tree algorithm produces an accuracy value of 95.71%, while the Adaptive Boosting-based Decision Tree results reach a value of 98.99%.
Information System for Personnel at the South Sorong District Regional Personnel Agency Office Ummiati Rahmah; Mahmud Mustapa; Wulansari, Lusiana; Mansyur; Harpa Pali
Ceddi Journal of Information System and Technology (JST) Vol. 3 No. 1 (2024): April
Publisher : Yayasan Cendekiawan Digital Indonesia (CEDDI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56134/jst.v3i1.48

Abstract

Due to the increasing complexity of globalization and technological advancements, the adoption of HRIS (Human Resource Information System) has become a key priority for government bodies and similar organizations. HRIS plays a vital role in effectively managing the workforce, particularly in the public sector. This research aims to enhance the efficiency of personnel data management processes in South Sorong Regency. The primary focus is on ensuring the security of employee data storage. Extreme Programming is utilized as the system development method, encompassing design, development, coding, and testing stages. PHP is chosen as the programming language, with a MySQL database for data storage. The black box testing technique is employed to guarantee system functionality and reliability. The implementation of this application demonstrates smooth operation, with testing yielding a high satisfaction rate among respondents at 93%. Consequently, the system is deemed functionally sound in delivering the intended outcomes.
Implementation Of Fuzzy Multi-Criteria Decision Making To Design An Expert System For Prediction Of Digestive Diseases At Dogs Tisna Kusuma, Irene; Kusnadi, Adhi; Adline Twince Tobing, Fenina
Ceddi Journal of Information System and Technology (JST) Vol. 2 No. 2 (2023): December
Publisher : Yayasan Cendekiawan Digital Indonesia (CEDDI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56134/jst.v2i2.50

Abstract

A dog is a kind of animal that is often looked after at home. Except for being known as a human's friend, a dog is also able to be trained to do many things. The age of the dog depends on the type of race. If it's fed well, exercises, and visits the doctor regularly, a dog can live longer. Dogs are very sensitive to certain diseases; one of them is intestinal disease. An expert system of a dog's digestion is built for the dog's lover so that they know exactly what's disease, which relies on the appearance of the symptoms. The symptoms of the different intestinal diseases look almost similar. For this reason, we need an appropriate method to get a precise result. Fuzzy multiple-criteria decision-making can help make the right decision that has some consideration points. This method is also equipped with optimal ultimate selections, which leads to a very precise result. The goal of this application is to know the right kind of disease and its therapy. This study has been validated by matching the results of the application with the doctor's diagnosis. The compatibility level is 80%.
Design and Implementation of Computerized Restaurant Table Booking System Egigogo, Raji Abdullahi; Tijjani Naniya, Muhammad; Ahmad Abubakar, Abubakar; Almansir Mansir
Ceddi Journal of Information System and Technology (JST) Vol. 3 No. 1 (2024): April
Publisher : Yayasan Cendekiawan Digital Indonesia (CEDDI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56134/jst.v3i1.64

Abstract

The dining landscape has transformed significantly with technological advancements, fundamentally altering how restaurants handle customer reservations. Traditionally, reservations were managed via phone calls or in-person visits, leading to issues like lengthy wait times, uncertain table availability, and limited communication channels. To address these challenges, this paper concentrates on crafting and implementing a Restaurant Table Booking System, aimed at streamlining reservations and enhancing customer experiences. Employing an agile software development methodology ensures flexibility, encourages customer input, and facilitates swift value delivery. Frontend development utilizes HTML, CSS, and Bootstrap for an intuitive user interface, while PHP and MySQL handle backend logic and database operations. Implementation emphasizes reliability and security through the Apache web server.
Forecasting of Flood in the Non-Tidal River of Northern Regions of Bangladesh Using Machine Learning-Based Approach Hasan, Md Khalid; Islam, Md Mofizul; Fahmida, Maisha
Ceddi Journal of Information System and Technology (JST) Vol. 3 No. 1 (2024): April
Publisher : Yayasan Cendekiawan Digital Indonesia (CEDDI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56134/jst.v3i1.69

Abstract

Floods are among the most devastating natural disasters, causing extensive damage to property and posing a threat to human lives. However, significant progress has been made in mitigating their impact through the development of effective early warning systems. Over the past two decades, advances in machine learning (ML) technology have played a crucial role in enhancing the predictive capabilities of these systems. A recent study focused on predicting floods in non-tidal rivers by proposing various machine-learning models. The research findings indicate that the Random Forest algorithm emerges as the most effective, offering an accuracy of 87% with high precision, recall, and F1 scores, using an 80:20 training and testing data ratio. These findings provide valuable insights for hydrologists and make a significant contribution to flood forecasting and mitigation efforts. The study has significant implications for flood understanding and management, offering a better understanding of machine learning model performance in predicting floods in non-tidal rivers. This research provides a solid foundation for the development of more efficient early warning systems. The information gleaned from this study can be utilized by hydrologists, climate scientists, and other related practitioners to develop more accurate and reliable forecasting strategies in the face of flood threats. Thus, this research is not only a valuable scientific contribution but also a practical tool for future flood disaster risk mitigation efforts.
Waste Service Retribution Management in Selayar Islands Regency through Website-Based Solutions Wisda; Nuraida Latif; Apriana, Rezki; Neneng Awaliah; Kumar Vivek; Kamaruddin
Ceddi Journal of Information System and Technology (JST) Vol. 3 No. 1 (2024): April
Publisher : Yayasan Cendekiawan Digital Indonesia (CEDDI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56134/jst.v3i1.78

Abstract

The waste retribution management system at the Environmental Service office in Selayar Islands Regency is currently suboptimal. Customers are required to register for waste collection in person at the office, and the payment process must be done through a collection officer who visits the customer's location. Additionally, there is no formal channel for customers to provide feedback on the waste services they receive. To address these issues, an application is proposed to streamline waste service usage for the community and facilitate reporting for Environmental Service employees. The study aims to design and implement an Application for Data Collection and Reporting of Waste Service Retribution at the Environmental Service of Selayar Islands Regency. The research utilized field research, library research, interviews, and documentation to gather relevant data. The research methodology employed was Rapid Application Development (RAD), which involves stages such as requirements planning, user design, development, and implementation. The findings of the study suggest that the proposed application is highly feasible, based on the results of the respondents' assessments. This application is envisioned to significantly improve waste service management and customer experience in Selayar Islands Regency.
Customer Segmentation Through RFM Analysis and K-Means Clustering: Leveraging Data-Driven Insights for Effective Marketing Strategy Akande, Oluwatobi; Asani, Emmanuel Oluwatobi; Dautare, Bestman Tobechukwu
Ceddi Journal of Information System and Technology (JST) Vol. 3 No. 1 (2024): April
Publisher : Yayasan Cendekiawan Digital Indonesia (CEDDI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56134/jst.v3i1.81

Abstract

Consumer segmentation is a very effective methodology that could enable organizations to gain a deeper comprehension of their consumer base and customize their tactics accordingly in order to cater to their unique requirements. Through the process of categorizing clients according to common attributes, organizations can acquire valuable knowledge regarding their requirements, inclinations, and purchasing behaviors. This comprehension empowers firms to develop focused marketing strategies and provide customized experiences that foster customer loyalty and enhance revenue generation. The prevalent categories of criteria in consumer segmentation encompass demographic, psychographic, geographic, and behavioral factors, whilst the prevailing methodologies for constructing customer segments involve rule-based and cluster-based techniques. Rule-based segmentation involves the utilization of pre-established rules to allocate clients into distinct segments. Conversely, cluster-based segmentation employs statistical techniques to identify inherent clusters or groups within the customer population. This research investigates the application of the K-Means clustering technique for the purpose of segmenting customer behavioral data into several categories, namely Platinum, Gold, Silver, Bronze, or Bad. The clustering approach employed demonstrated a notable degree of accuracy and precision. Through the implementation of an appropriate strategy for customer segmentation, organizations have the potential to strengthen their product offers, concentrate their marketing communications, and augment client loyalty.

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