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Anggi Zafia
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Journal of Informatics, Information System, Software Engineering and Applications (INISTA)
Published by Universitas Telkom
ISSN : -     EISSN : 26228106     DOI : https://doi.org/10.20895/inista
Core Subject : Science,
Computer Science & IT
Journal of Informatics, Information System, Software Engineering and Applications (INISTA) is a scientific journal published by Lembaga Penelitian dan Pengabdian Masyarakat (LPPM) of Institut Teknologi Telkom Purwokerto with ISSN 2622-8106 , Indonesia. Journal of INISTA covers the field of Informatics, Information System, Software Engineering and Applications. First published will be in September 2018 for an electronic version. The aims of Journal of INISTA are to disseminate research results and to improve the productivity of scientific publications. Journal of INISTA is published twice in Mei and November. Publication will be published "Volume 2 number 2" in May 2020.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 126 Documents
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Implementation of Random Forest Classification and Support Vector Machine Algorithms for Phishing Link Detection Tampinongkol, Felliks Feiters; Kamila, Ahya Radiatul; Wardhana, Ariq Cahya; Kusuma, Adi Wahyu Candra; Revaldo, Danny
Journal of INISTA Vol 7 No 1 (2024): November 2024
Publisher : LPPM Institut Teknologi Telkom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/inista.v7i1.1588

Abstract

This research compares two machine learning methods, Support Vector Machine (SVM) and Random Forest Classification (RFC), in detecting phishing links. Phishing is an attempt to obtain sensitive information by masquerading as a trustworthy entity in electronic communications. Detecting phishing links is crucial in protecting users from this cyber threat. In this study, we used a dataset consisting of features extracted from URLs, such as URL length, the use of special characters, and domain information. The dataset was then split into training and testing data with an 80:20 ratio. We trained the SVM and RFC models using the training data and evaluated their performance based on the testing data. The results show that both methods have their respective advantages. SVM, known for handling high-dimensional data well and providing optimal solutions for classification problems, demonstrated a high accuracy rate in detecting phishing links. However, SVM requires a longer training time compared to RFC. On the other hand, RFC, an ensemble method known for its resilience to overfitting, showed performance nearly comparable to SVM in terms of accuracy but with faster training time and better interpretability. This comparison indicates that RFC is more suitable for scenarios requiring quick results and easy interpretation, while SVM is more appropriate for situations where accuracy is critical, and computational resources are sufficient. In conclusion, the choice of phishing link detection method should be tailored to specific needs and available resource constraints. This research provides valuable insights for developing more effective, efficient, and relevant phishing detection systems.
Implementation of The Weighted K-Nearest Neighbors Algorithm in The Classification of Beef and Pork Images Afrianto, Nurdi; Meiditra, Irzon
Journal of INISTA Vol 7 No 1 (2024): November 2024
Publisher : LPPM Institut Teknologi Telkom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The demand for meat in Indonesia is still high, especially for the consumption of beef and pork, which are important commodities in the market. Although meat provides essential nutrients, pork has health risks because it contains more than 40 dangerous pathogens and various bacteria. In traditional markets in Indonesia, the fraudulent practice of mixing pork and beef to gain greater profits is a serious problem. This is very detrimental to consumers, especially Muslims who do not consume pork. The study used machine learning, the Weighted K-nearest neighbor (WKNN) algorithm, to classify meat based on color features. The stages used began with collecting a dataset of 400 images and divided into 200 images of pork and beef for each. Images were taken using a Canon EOS Kiss X50 DSLR camera at ISO 100-200 for good image quality. Feature extraction uses HSV and RGB algorithms that focus on color. Furthermore, the data is divided into 70% for training and 30% for testing. The model was evaluated with a confusion matrix, namely accuracy, precision, and F1 score, which each produced an accuracy of 85%, 86%, and 80%. The research is updated on the application of WKNN for meat classification in traditional markets.
Deep Learning Model Based on Particle Swarm Optimization for Buzzer Detection Kurniawati, Ika
Journal of INISTA Vol 7 No 1 (2024): November 2024
Publisher : LPPM Institut Teknologi Telkom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/inista.v7i1.1622

Abstract

Along with the development of the internet, the presence of buzzers is increasingly widespread on social platforms, especially on Twitter. Buzzers have played an important role in influencing and spreading misinformation, manipulating public opinion, and harassing and intimidating online social media users. Therefore, an effective detection algorithm is needed to detect buzzer accounts that endanger social networks because they affect neutrality. In this research, we propose a Deep Neural Network model to detect buzzer accounts on Twitter. We conducted experiments on 1000 datasets using PSO-based Deep Neural Network models and Ada Boost-based Deep Neural Networks to obtain the best model in detecting buzzer accounts. The results show that the performance of the PSO-based Deep Neural Network is the best with 98.90% accuracy compared to Ada Boost-based Deep Neural Network 95.30% or without feature weight and boosting algorithms with 46.60% accuracy. This clearly shows the superiority of our proposed method. These results are expected to help maintain neutral information on social media and minimize noise in the data that will be used for sentiment analysis research.
The Application of LSTM in the AI-Based Enhancement of Classical Compositions Fudholi, Dzikri Rahadian; Putri, Delfia Nur Anrianti; Wijaya, Raden Bagus Muhammad AdryanPutra Adhy; Kusnadi, Jonathan Edmund; Amarissa, Jovinca Claudia
Journal of INISTA Vol 7 No 1 (2024): November 2024
Publisher : LPPM Institut Teknologi Telkom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/inista.v7i1.1628

Abstract

Music enhancement through deep learning methodologies presents an innovative approach to refining and augmenting classical compositions. Leveraging a comprehensive dataset of classical piano MIDI files, this study employs LSTM networks with attention mechanisms for music refinement. The model, trained on diverse compositions, demonstrates proficiency in capturing tempo nuances but faces challenges in replicating varied pitch patterns. Assessments by 28 individuals reveal positive reception, particularly in melody integration, scoring notably high at 8 out of 10. However, while praised for cohesion, bass lines received slightly lower scores, suggesting opportunities for enhancing originality and impact. These findings underscore the LSTM model's capability to generate harmonious melodies and highlight refinement areas, particularly in innovating bass lines within classical compositions. This study contributes to advancing automated music refinement, guiding further developments in LSTM-based music generation techniques.
Data Mining Analysis of K-means Algorithm and Decision Tree for Early Detection of Students at Risk of Dropping Out Akbar, Imam; Samad, Ita Sarmita; Rahmat, Rahmat; Rosmiana, Sri
Journal of INISTA Vol 7 No 2 (2025): May 2025
Publisher : LPPM Institut Teknologi Telkom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/inista.v7i2.1630

Abstract

Dropout occurs in higher education, where students are unable to complete their studies within a specified timeframe. It has become a significant concern in education due to its substantial impact on individuals, institutions, and society. This study aims to develop a model for predicting the early potential for students' dropout using the K-Means Algorithm and decision trees. The research method consists of a Dataset, Data Preprocessing, K-means implementation, labeling student data, and Decision Tree implementation. This study resulted in 4 clusters. The students in Cluster 1 have an excellent average GPA, a substantial number of credits, and are very active. The students in Cluster 2 have a lower average GPA and are less active than in Cluster 1. The students in Cluster 3 show a relatively good average GPA, which is lower than in Clusters 1 and 2. The number of active students indicates that students in this cluster are much less active or at risk of D.O. than those in clusters 1 and 2. Cluster 4 indicates that the average GPA of students is very low, often close to zero, and they are generally inactive in academic activities. Thus, they are significantly at risk of D.O. at Universitas Muhammadiyah Enrekang. This research provides significant results, both in terms of accuracy and data interpretation. The resulting insights enable universities to make more strategic and targeted decisions, thereby reducing the risk of university dropout rates, increasing resource efficiency, and supporting the overall educational success of students. The accuracy of the resulting model is 98.52% which indicates that the model has excellent performance in classifying students at risk of D.O.
Expert System for Diagnosing Diseases in Coffee Plants Using Forward Chaining and Classic Probability Algorithms Novita, Eka Revi; Gunawan, Indra; Gunawan, Wawan
Journal of INISTA Vol 7 No 1 (2024): November 2024
Publisher : LPPM Institut Teknologi Telkom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/inista.v7i1.1633

Abstract

Coffee is a highly demanded product and a key commodity in Indonesia due to its significant market value and contribution to the country's foreign exchange. The majority of coffee plantations are found in highland areas, particularly in West Lampung Regency, which produced approximately 56,054 tons of coffee in 2022. However, coffee production has been declining due to low productivity and quality. One major issue is the limited knowledge of coffee farmers in West Lampung about diseases affecting coffee plants, leading to improper treatment. Another challenge is the lack of experts to guide farmers in managing and treating coffee diseases effectively. To address these problems, an expert system is needed to provide information and solutions for dealing with coffee diseases. This system incorporates expert knowledge into a computerized platform to assist in diagnosing coffee plant diseases. The system is developed using the waterfall method and employs forward chaining and classic probability algorithms to diagnose diseases and calculate the accuracy of results. Users can diagnose based on symptoms and receive treatment recommendations. This web-based expert system aims to assist farmers in early disease diagnosis and provide appropriate solutions for managing coffee diseases
MSME Digitalization Using Point of Sale Calculator to Improve Efficiency and Productivity Rony, Muhammad Ainur; Anggraeni, Motika Dian
Journal of INISTA Vol 7 No 1 (2024): November 2024
Publisher : LPPM Institut Teknologi Telkom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/inista.v7i1.1635

Abstract

Digitalization of Micro, Small, and Medium Enterprises (MSMEs) in Indonesia is crucial to increase competitiveness in the era of globalization. Due to cost considerations, many MSMEs continue to rely on conventional methods to manage their businesses, leading to inefficiencies in transaction handling and sales data analysis. This traditional approach often results in errors, reporting delays, and suboptimal decision-making, which can hinder growth and productivity. This study focuses on the implementation of a free cloud-based Point of Sale (POS) calculator application as a digital solution to address these challenges. The POS application streamlines the transaction process and generates real-time sales reports, ultimately improving operational efficiency and strategic decision-making. The development method used in this study is the System Development Life Cycle method. The POS application development process includes comprehensive Requirements Analysis, System and Software Design, Implementation, Testing, and Maintenance. User feedback was collected through structured interviews and surveys to evaluate the application's ease of use and its benefits in improving MSME operations. The findings showed significant improvements in operational efficiency, as the POS application speeds up transaction processing and provides timely, data-driven insights for decision-making.
Design and Implementation KP-SPAMS Transaction Information System utilizing Laravel Framework and Extreme Programming Methodology Abdullah, Moch Zawaruddin; Hani'ah, Mamluatul; Yunhasnawa, Yoppy; Wakhidah, Rokhimatul
Journal of INISTA Vol 7 No 1 (2024): November 2024
Publisher : LPPM Institut Teknologi Telkom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/inista.v7i1.1645

Abstract

The Community-Based Drinking Water and Sanitation Management Group (KP-SPAMS) oversees the Community-Based Drinking Water and Sanitation Provision Program (PAMSIMAS), which is essential for providing clean water services to rural areas. Nevertheless, KP-SPAMS continues to face challenges related to operational transaction management, such as the documentation of customer data, water usage, invoicing, and financial reporting. This research aims to develop a web-based transaction information system, utilizing the Laravel framework and the Extreme Programming methodology, to meet the specific requirements of KP-SPAMS Sumber Waras located in Ngenep Village, Malang Regency. The Extreme Programming methodology facilitates adaptable and cooperative software development, enabling quick responses to evolving customer requirements. The system's primary functionalities are customer registration, water usage recording, automatic billing, and payment reporting. The implementation results indicate that this system may enhance operational efficiency, accountability, and traceability of all transaction processes in KP-SPAMS, facilitating improved decision-making and superior service quality for the community. User Acceptance Testing results show that 80% of users rated the system positively, with 53.33% agreeing and 26.67% strongly agreeing that the system meets their needs and provides a satisfactory experience. Only 6.67% of responses indicated dissatisfaction, and no respondents strongly disagreed, demonstrating that the system aligns well with user expectations and offers a solid foundation for future improvements.
Application Decision support System for superior and high Achieving using the Analytical Hierarchy Process Method Indarso, Taruna Pratama; Taurusta, Cindy; suprianto, Suprianto
Journal of INISTA Vol 7 No 2 (2025): May 2025
Publisher : LPPM Institut Teknologi Telkom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/inista.v7i2.1655

Abstract

Traditional student selection methods often rely on subjective judgment, resulting in inconsistencies and potential biases. Therefore, a more systematic, objective, and efficient method is needed. The proposed system evaluates students based on four key criteria: religious competence, academic performance, extracurricular activities, and ethics. The Analytical Hierarchy Process (AHP) is used to assign priority weights to each criterion through a series of pairwise comparisons, facilitating a structured evaluation process. The Decision Support System (DSS) was developed using the Waterfall model, which includes stages of requirement analysis, system design, implementation, testing, and maintenance. Real student data from grades 7, 8, and 9 were used during system testing at Junior High School TPI Porong. This study aims to develop a mobile-based DSS to identify high-achieving students at Junior High School Taman Pendidikan Islam (TPI) Porong using the AHP method. The ranking results generated by the system were compared to manual evaluations conducted by teachers and showed over 90% consistency. Furthermore, a feasibility test involving 15 teachers indicated a 98.7% satisfaction rate, highlighting the system’s effectiveness and ease of use. The application presents rankings in a user-friendly interface, enabling teachers and school administrators to make informed decisions about student achievement. By implementing this system, schools can ensure a more transparent and data-driven process for selecting high-achieving students. The DSS not only improves the evaluation process but also supports the development of a fairer and more accountable education system. This research contributes to the advancement of technology-based educational tools that assist in decision-making within school environments.
House Sales Promotion Application Using Android-Based Augmented Reality Technology Saputra, Cahnur; Taurusta, Cindy; Azinar, Azmuri Wahyu
Journal of INISTA Vol 7 No 2 (2025): May 2025
Publisher : LPPM Institut Teknologi Telkom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/inista.v7i2.1657

Abstract

Augmented Reality (AR) is a technology in the field of multimedia that integrates 3D objects into real-world environments using a camera as the medium and can also be applied to mobile Android devices to enhance interactivity and visualization. This research was conducted due to shortcomings or issues in house marketing, namely the lack of detailed information about the rooms in the houses being promoted. This occurs because brochures only display the exterior of the house and are still in a 2D format. Additionally, prospective buyers who live far from the promoted housing area are unable to visit in person and cannot view the interior details of the houses being promoted or sold. Therefore, an application will be developed to visualize both the exterior and interior designs in 3D by implementing Augmented Reality technology. This is expected to make the house sales promotion for the housing area more realistic and interactive. Additionally, prospective buyers can view the exterior and interior designs of the house realistically, even without visiting the housing location directly. The house sales promotion application using Augmented Reality technology requires a camera as an input device. The application tracks and detects flat objects as markers, and after pressing the "Start" button, a 3D object that appears realistic will be automatically displayed. When the "stop" menu is pressed, the 3D image will automatically disappear. The home sales promotion application, which utilizes Augmented Reality (AR) technology, has received positive responses from respondents, achieving a high success rate. Based on the Likert scale, the application obtained an average score of 94.5%, demonstrating its effectiveness in enhancing housing promotion for potential buyers. Testing was conducted to assess the impact of AR technology in enhancing the marketing appeal and facilitating potential buyers' understanding of both the exterior and interior designs of the house more interactively and realistically.

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