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Muhammad Nur Faiz
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Department of Informatics Engineering Politeknik Negeri Cilacap Jln. Dr.Soetomo No.01 Sidakaya, Cilacap, Indonesia
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Journal of Innovation Information Technology and Application (JINITA)
Published by Politeknik Negeri Cilacap
ISSN : 27160858     EISSN : 27159248     DOI : https://doi.org/10.35970/jinita.v2i01.119
Core Subject : Science, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Engineering
Software Engineering, Mobile Technology and Applications, Robotics, Database System, Information Engineering, Interactive Multimedia, Computer Networking, Information System, Computer Architecture, Embedded System, Computer Security, Digital Forensic Human-Computer Interaction, Virtual/Augmented Reality, Intelligent System, IT Governance, Computer Vision, Distributed Computing System, Mobile Processing, Next Network Generation, Natural Language Processing, Business Process, Cognitive Systems, Networking Technology, and Pattern Recognition
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 160 Documents
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A Local Government Application Capability Level Information System Audit using COBIT 5 Framework Bagus Dwi Andika; Sucipto Sucipto; Arie Nugroho
Journal of Innovation Information Technology and Application (JINITA) Vol 5 No 2 (2023): JINITA, December 2023
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v5i2.1971

Abstract

The ASN application stores State Civil Apparatus and Employee Work Target master data. ASN application has never been audited. This study aimed to measure the capability level of the ASN application using the COBIT 5 framework. The audit results contain current findings and expectations for the future, then analyze the gaps and make recommendations for improvement. Audit results based on domains DSS01, DSS02, DSS03, DSS04, DSS05, and DSS06 achieve capability level 1 performance process. The ASN application manager has successfully implemented a process that has achieved its goals by finding evidence of work product output. To achieve the expected level, namely level 2 managed process, it is recommended that you complete incomplete output documents and carry out activities that have not been carried out per COBIT 5.
Clustering Productive Palm Land using the K- Means Clustering Algorithm Geofanny Widianto Sihite; Eka Prasetyaningrum
Journal of Innovation Information Technology and Application (JINITA) Vol 5 No 2 (2023): JINITA, December 2023
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v5i2.2051

Abstract

Indonesia is a country with a tropical climate that has many oil palm plantations. CV. Alkema Deo is one of the companies that manage oil palm plantations in Sampit City, East Kotawaringin Regency, Central Kalimantan. CV. Alkema Deo was founded in 2016 and has two plantation locations located on Jl. General Sudirman Km. 18, East Kotawaringin and Seibabi Village, Telawang District, East Kotawaringin. In this study, a qualitative approach was applied using a descriptive research pattern. In qualitative research, data is obtained from sources using various data collection techniques. Research using qualitative methods emphasizes the analysis of thought processes related to the dynamics of the relationship between observed phenomena, and always uses scientific logic. Based on the results of research for authors on a CV. Alkema Deo, the use of Excel in companies is quite good at processing data, but on a CV. Alkema Deo does not yet have land groupings based on productivity levels, so it is difficult to see the level achieved in 6 months based on the set target, and daily production control in terms of area and block area. Data obtained from CV. Alkema Deo is grouped based on area, block, and productivity. Application of data mining for grouping productive oil palm land on a CV. Alkema Deo with 4 variables, namely: land area, length, average production yield, percentage of achievement using the K-Means Algorithm to produce three clusters, namely 8 blocks or 50% including the high productive group (C2), 1 block or 6% blocks including the medium productive plantation group (C1), and 7 blocks or 44% including the small productive plantation group (C0).
From Text to Insights: NLP-Driven Classification of Infectious Diseases Based on Ecological Risk Factors Saviour Inyang; Imeh Umoren
Journal of Innovation Information Technology and Application (JINITA) Vol 5 No 2 (2023): JINITA, December 2023
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v5i2.2084

Abstract

Numerous factors can affect the development of infectious diseases that emerge. While many are the result of natural procedures, such as the gradual emergence of viruses over time, certain ones are the result of human activity. Human activities form an integral part of our ecosystem, and especially the ecological aspect of human activities can encourage disease transmission. Additionally, Health ecologists examine changes in the biological, physical, social, and economic settings to understand how these alterations impact the mental and physical well-being of individuals. Hence, this research adopts a Framework-Based Method (FBM) in carrying out the task of classification of infectious diseases. The Framework-Based Method outlines all phases that this research follows to carry out the infectious disease classification process, providing a structured and reproducible approach. Results show that: XGB: Confusion matrix accuracy: 76%, Kappa: 73%, RF: Confusion matrix accuracy: 65%, Kappa: 60%, SVM: Confusion matrix accuracy: 63%, Kappa: 58%, ANN: Confusion matrix accuracy: 71%, Kappa: 67%, LDA: Confusion matrix accuracy: 76%, Kappa: 73%, GBM: Confusion matrix accuracy: 60%, Kappa: 53%, KNN: Confusion matrix accuracy: 43%, Kappa: 34%, and DT: Confusion matrix accuracy: 37%, Kappa: 29%. Furthermore, a Deep Learning model BERT was integrated with the best classification model XGBoots to create an interactive interface for users to carry out infectious disease classification. This integration enhances user experience and accessibility, contributing to the practical application of machine learning and Natural language processing in ecological disease classification
Expert System for Diagnosing Inflammatory Bowel Disease Using Certainty Factor and Forward Chaining Methods Linda Perdana Wanti; Nur Wachid Adi Prasetya; Oman Somantri
Journal of Innovation Information Technology and Application (JINITA) Vol 5 No 2 (2023): JINITA, December 2023
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v5i2.2096

Abstract

Identification of inflammatory bowel disease quickly and accurately is motivated by the large number of patients who come with pain in the abdomen and receive minimal treatment because they are considered to be just ordinary abdominal pain. This study aims to identify inflammatory bowel disease which is still considered by some people as a common stomach ache quickly, and precisely and to recommend therapy that can be done as an initial treatment before getting medical action by medical personnel. The method used in this expert system research is a combination of forward chaining and certainty factors. The forward chaining method traces the disease forward starting from a set of facts adjusted to a hypothesis that leads to conclusions, while the certainty factor method is used to confirm a hypothesis by measuring the amount of trust in concluding the process of detecting inflammatory bowel disease. The results of this study are a conclusion from the process of identifying inflammatory bowel disease which begins with selecting the symptoms experienced by the patient so that the diagnosis results appear using forward chaining and certainty factor in the form of a percentage along with therapy that can be given to the patient to reduce pain in the abdomen. A comparison of the diagnosis results using the system and diagnosis by experts, in this case, specialist doctors, shows an accuracy rate of 82,18%, which means that the expert system diagnosis results can be accounted for and follow the expert diagnosis.
Decision Making for The Most Outstanding Students Award using TOPSIS: a Case Study at Institut Teknologi Sumatera Borneo Satria Pratama; Nike Dwi Grevika Drantantiyas; Ilham Marvie; Noveliska Br Sembiring; Muhammad Abi Berkah Nadi
Journal of Innovation Information Technology and Application (JINITA) Vol 5 No 2 (2023): JINITA, December 2023
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v5i2.2114

Abstract

The internal selection of Pemilihan Mahasiswa Berprestasi, or known as Pilmapres, is an annual competition held by Institut Teknologi Sumatera (ITERA) to award the most outstanding student of the year which will be further sent to compete in regional and national event of Pilmapres held by Balai Pengembangan Talenta Indonesia. This study aimed to implement TOPSIS as a decision-making tool to determine the winner of Pilmapres ITERA in 2023. The criteria used in this study were general achievements, English competencies, and creative ideas, with weight of 50, 20, and 30, respectively. The scores for the criteria for each of the students are obtained from nine members of the board of jury in the final stage of Pilmapres ITERA in 2023. The calculation result using TOPSIS concluded that the 1st, 2nd, and 3rd winners of the internal selection of Pilmapres ITERA in 2023 were Alpha, Beta, and Omega, with the final preference scores of 0.995, 0.799, and 0.795, respectively.
Traffic Image Analysis Based on Stacked Denoising Autoencoder Neural Network Daehyon Kim
Journal of Innovation Information Technology and Application (JINITA) Vol 5 No 2 (2023): JINITA, December 2023
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v5i2.2133

Abstract

This study aims to explore major neural network models - Stacked Denoising Autoencoder (SDAE), Deep Belief Network (DBN), Backpropagation - that have recently garnered attention and propose the most suitable and reliable artificial neural network model for real-time road traffic information collection. In this study, to enhance the reliability of experimental results, numerous experiments were conducted under identical conditions (such as parameter values and network configuration) by setting different initial values for the weight vector. The results of the experiments were statistically validated to draw conclusions. The research results showed that the SDAE model exhibited the most superior performance, while the accuracy of the DBN was somewhat lower compared to the SDAE model. On the other hand, the Backpropagation model demonstrated a relatively low predictive accuracy compared to both models, particularly showing a significant influence of the initial values
Application of the NIST 800-86 Framework to Forensic Digital Evidence for Signal and Litmatch Nurul Puspa Hapsari; Bita Parga Zen
Journal of Innovation Information Technology and Application (JINITA) Vol 6 No 1 (2024): JINITA, June 2024
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v6i1.2025

Abstract

Exchanging messages is a routine that cannot be avoided at this time. With the development of technology, exchanging messages has become easier to do. The thing that makes exchanging messages easier is the instant messaging application. Examples of instant messaging applications are the Signal and Litmatch applications. In addition to the ease of exchanging messages, there are also negative impacts, such as threats and bullying. Forensic analysis is carried out to find and obtain evidence of digital crimes. This research was conducted to find and obtain evidence of Signal and Litmatch applications by conducting case scenarios and using the National Institute of Standards and Technology (NIST) 800-86 method. This study uses the MobilEdit Forensic and Autopsy tools to obtain evidence from the Signal and Litmatch applications.
An AIoT-Based Automated Farming Irrigation System for Farmers in Limpopo Province Relebogile Langa; Michael Nthabiseng Moeti; Thabiso Maubane
Journal of Innovation Information Technology and Application (JINITA) Vol 6 No 1 (2024): JINITA, June 2024
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v6i1.2187

Abstract

Limpopo, one of South Africa's nine provinces, is mostly rural, where agriculture serves as the primary occupation for around 89 percent of the total population. Agriculture relies on water, making it its most valuable asset. Through irrigation, water is supplied to crops for growth, frost control, and crop cooling. Irrigation can occur naturally, as with precipitation, or artificially, as with sprinklers. However, artificial irrigation is wasteful as it is regulated and monitored through human intervention, leading to water scarcity which is one of the obstacles that threatens the agricultural sector in the province of Limpopo. A machine learning precipitation prediction algorithm optimizes water usage. The paper also describes a system with multiple sensors that detect soil parameters, and automatically irrigate land based on soil moisture by switching the motor on/off. The objective of this paper is to develop an automated farming irrigation system that is both efficient and effective, with the intention of contributing to the resolution of the water crisis in the province of Limpopo. The proposed solution ought to be capable of decreasing labour hours, generating cost savings, ensuring consistent and efficient water usage, and gathering informed data to inform future research. Thus, farmers will have greater access to information regarding when to irrigate, how much water to use, weather alerts, and recommendations. In acquiring these, the ARIMA model was applied alongside DSRM for implementing the mobile application. The results obtained indicate that the use of AI and IoT (AIoT) in agriculture can improve operational efficiency with reduced human intervention as there is real-time data acquisition with real-time processing and predictions.
A Classification Data Packets Using the Threshold Method for Detection of DDoS Sukma Aji; Davito Rasendriya Rizqullah Putra; Imam Riadi; Abdul Fadlil; Muhammad Nur Faiz; Arif Wirawan Muhammad; Santi Purwaningrum; Laura Sari
Journal of Innovation Information Technology and Application (JINITA) Vol 6 No 1 (2024): JINITA, June 2024
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v6i1.2224

Abstract

Computer communication is done by first synchronizing one computer with another computer. This synchronization contains Data Packages which can be detrimental if done continuously, it will be categorized as an attack. This type of attack, when performed against a target by many computers, is called a distributed denial of service (DDoS) attack. Technology and the Internet are growing rapidly, so many DDoS attack applications result in these attacks still being a serious threat. This research aims to apply the Threshold method in detecting DDoS attacks. The Threshold method is used to process numeric attributes so obtained from the logfile in a computer network so that data packages can be classified into 2, namely normal access and attack access. Classification results using the Threshold method after going through the fitting process, namely detecting 8 IP Addresses as computer network users and 6 IP addresses as perpetrators of DDoS attacks with optimal accuracy.
Website Penetration Analysis Against XSS Attacks using Payload Method Luthfi Arian Nugraha
Journal of Innovation Information Technology and Application (JINITA) Vol 6 No 1 (2024): JINITA, June 2024
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v6i1.2225

Abstract

This research aims to analyze the effectiveness of various penetration testing methods in identifying and mitigating XSS (Cross-Site Scripting) vulnerabilities in web applications. XSS is a type of web security attack that takes advantage of weaknesses in web applications to insert malicious code into web pages displayed to users. This attack can steal user data, take over user sessions, or spread malware. This research uses a penetration testing method with a black-box approach, where the researcher does not know the construction of the system being tested. Tests were conducted on 10 random websites, including 5 open-source websites and 5 commercial websites. The test results show that the payload method used is effective in exploiting XSS vulnerabilities on some websites. Of the 10 websites tested, 6 of them were successfully exploited using different payload methods. This research highlights the importance of using open-source penetration testing tools in detecting and addressing security vulnerabilities in web applications. These tools are easy to implement, supported by extensive documentation, and have a strong community. This research also emphasizes the importance of a deep understanding of how penetration testing tools work to identify and address security vulnerabilities. To address XSS vulnerabilities, this research recommends good programming techniques such as programming language updates, use of OOP (Object-Oriented Programming), MVC (Model-View-Controller) concepts, and use of frameworks. Further research can be done to develop and test new payload methods, explore the use of other penetration testing tools, and test security vulnerabilities in other types of web applications.

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