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Mochammad Anshori
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INDONESIA
The Journal of Enhanced Studies in Informatics and Computer Applications
Published by Institut Teknologi, Sains, dan Kesehatan RS.DR. Soepraoen Kesdam V/BRW
ISSN : 30466997     EISSN : 30466938     DOI : https://doi.org/10.47794/jesica.v1i1
Core Subject : Science,
Computer Science & IT
Journal of Enhanced Studies in Informatics and Computer Applications (JESICA) is an international peer-reviewed journal that aims to provide the best analysis and discussion to its readers in the development scope of Data Science, Software Engineering, Computer Applications, Health Informatics, and Internet of Things. JESICA publishes original research findings and quality scientific articles that present cutting-edge approaches including methods, techniques, tools, implementation, and applications.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 25 Documents
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Comparing Discriminant Analysis Function for Early Prediction of Smartphone Addiction Mufid Musthofa; Mochammad Anshori
Journal of Enhanced Studies in Informatics and Computer Applications Vol. 2 No. 1 (2025): JESICA Vol. 2 No. 1 2025
Publisher : Institut Teknologi, Sains, dan Kesehatan RS.DR. Soepraoen Kesdam V/BRW

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47794/jesica.v2i1.12

Abstract

The pervasive use of smartphones in daily life has led to significant benefits, but excessive use has caused alarming behavioral and health issues, particularly among adolescents. Addressing smartphone addiction requires early detection to enable timely interventions. This study investigates the application of machine learning, specifically Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA), for the early prediction of smartphone addiction. The research used a dataset containing 394 instances categorized into "addicted" and "non-addicted" classes. Dataset is derived from questionnaire responses. After preprocessing steps, including feature selection and ordinal encoding, the data was split using 10-fold cross-validation to ensure robust evaluation. The models were assessed using metrics such as accuracy, precision, recall, and F-measure. Results indicate that LDA significantly outperforms QDA across all metrics, achieving an accuracy of 94.16%, a precision of 94.2%, a recall of 94.2%, and an F-measure of 94.2%. Additionally, the Receiver Operating Characteristic (ROC) curve analysis showed an Area Under the Curve (AUC) of 0.9875 for LDA, indicating its high reliability and stability in classifying smartphone addiction. QDA, while effective, has a slightly lower performance due to the linear separability of the dataset. This study concludes that LDA is a robust and effective method for early prediction of smartphone addiction, offering valuable insights for health monitoring systems. The findings provide a foundation for future applications of discriminant analysis in addressing behavioral health issues.
Prediction of Unemployment Rates in East Java Region Using Multiple Linear Regression Method Muhammad Farhan Alauddin; Sentot Achmadi; Joseph Dedy Irawan
Journal of Enhanced Studies in Informatics and Computer Applications Vol. 2 No. 1 (2025): JESICA Vol. 2 No. 1 2025
Publisher : Institut Teknologi, Sains, dan Kesehatan RS.DR. Soepraoen Kesdam V/BRW

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47794/jesica.v2i1.20

Abstract

Unemployment is a major problem for developing countries, including Indonesia. The average unemployment rate in East Java is 5.202 percent from 2004 to 2023, according to data from Badan Pusat Statistik (BPS). These figures show that unemployment is still a major problem for local governments, despite changes. The purpose of this study is to create an application that can predict the number of unemployed in the future. The method used is multiple linear regression where this method was chosen because it can show how the year and labor force participation rate, which are two independent variables, correlate with the unemployment rate as the dependent variable. The data used comes from BPS and covers 19 years. This prediction model is integrated into a web-based platform to make the research results easier to access and use. This platform will display data and analysis results interactively, so that it can be used by local governments, academics, and other individuals looking for information about unemployment. The results of the study show that the regression model created is quite accurate. Based on the results of testing that has been carried out on the existing features, the system has displayed the appropriate output.
Implementation of Extreme Programming (XP) in the Development of Dental Clinic Information Systems Bintang Pramudya; Dinda Chesar Putri Ramadhani; Hilda Nuzulul Mujaddidah; Risqy Siwi Pradini
Journal of Enhanced Studies in Informatics and Computer Applications Vol. 2 No. 1 (2025): JESICA Vol. 2 No. 1 2025
Publisher : Institut Teknologi, Sains, dan Kesehatan RS.DR. Soepraoen Kesdam V/BRW

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47794/jesica.v2i1.22

Abstract

Along with the development of information technology, dental clinics are required to improve efficiency and quality of service through an integrated information system. This study aims to develop a web-based Dental Clinic Information System using Laravel with the Extreme Programming (XP) method as its development approach. This system is designed to manage electronic medical records, examination schedules, drug stock, and patient bills efficiently, according to the Regulation of the Minister of Health of Indonesia Number 24 of 2022 concerning electronic medical records. This research method involves four stages in XP: planning, design, coding, and testing. At the design stage, Use Case and Activity Diagram modeling are created to ensure the system meets user needs. The system implementation uses Laravel Filament to build an integrated admin dashboard. Testing is carried out using Black-Box Testing to ensure all features function properly. The results of the study show that the developed system can improve the operational efficiency of dental clinics, reduce manual recording errors, and speed up the administration process. With the XP approach, system development becomes more flexible and adaptive to changes in user needs. It is hoped that this system can be applied in small to medium-scale dental clinics to improve the quality of health services.
MediStock: Medical Stock Website Development Using Design Thinking Novelia Mega Puspita; Dita Kurnia Rachmasari; Naufal Alif Vivaldi; Mochammad Anshori
Journal of Enhanced Studies in Informatics and Computer Applications Vol. 2 No. 1 (2025): JESICA Vol. 2 No. 1 2025
Publisher : Institut Teknologi, Sains, dan Kesehatan RS.DR. Soepraoen Kesdam V/BRW

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47794/jesica.v2i1.25

Abstract

Pharmacies play a vital role in public health by ensuring the availability of essential medications. However, inefficient inventory management systems, particularly in Malang, lead to operational inefficiencies, stock discrepancies, and regulatory compliance challenges. This study aims to develop a web-based inventory management system, MediStock, using the Design Thinking methodology to address these issues effectively. The research employs a human-centered approach, focusing on user needs and experiences through the stages of empathize, define, ideate, prototype, and testing. The system integrates real-time stock monitoring, predictive analytics, and compliance with electronic medical records, enhancing operational efficiency and regulatory adherence. Results indicate that MediStock significantly improves inventory management by minimizing stock discrepancies, optimizing procurement processes, and ensuring real-time visibility of medicine stocks. The heuristic evaluation revealed high usability and adaptability among different user groups, confirming the system's effectiveness and the user-centered design. These findings highlight the potential of Design Thinking to bridge the gap between complex technological solutions and user needs in healthcare inventory management. This study contributes to the field by providing an innovative, user-friendly inventory management solution that enhances operational efficiency and regulatory compliance. Future research should explore the scalability of the system and its integration with broader healthcare management systems to maximize its impact on the healthcare sector.
Design and Implementation of a Construction Budgeting Application for Residential Projects on Android Platform Using the Waterfall Method Deddy Rudhistiar; Muhammad Hasan Wahyudi; Hadi Surya Wibawanto Sunarwadi; Amar Rizqi Afdholy; Thesa Adi Saputra Yusri
Journal of Enhanced Studies in Informatics and Computer Applications Vol. 2 No. 1 (2025): JESICA Vol. 2 No. 1 2025
Publisher : Institut Teknologi, Sains, dan Kesehatan RS.DR. Soepraoen Kesdam V/BRW

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47794/jesica.v2i1.26

Abstract

Building a home is one of the most important and basic human needs. However, uncertainty around construction schedules and cost details often pose significant obstacles for individuals and families. The process can be overwhelming, especially for those with less experience in building homes. This is where information technology can play a vital role in streamlining the home-building process. By harnessing the power of technology, the building process can be simplified, providing greater transparency and convenience to users. To address these challenges, the Halo Rumah app was developed. The app helps users choose the right design and materials for their home construction. The app has an intuitive interface that allows users to explore a variety of home designs tailored to different preferences and choose materials that suit their needs and budget. Additionally, the app offers a range of advanced tools that help users estimate and calculate the costs of building a home, giving them a clear picture of their financial needs before starting the building process. By integrating these features, the Halo Rumah app ensures that users can make informed decisions, reduce uncertainty, and plan their dream home more effectively. Based on black box testing of 5 features tested on the Halo Rumah application, the research results show that the 5 features in the application can function according to the expected results.
Systematic Literature Review on Data Security and Privacy for e-Government Mela Firdini Azzahra; Ardhan Aghsal Dwi Putra; Jingga Mustika Putri; Maulana Aditya; Risqy Siwi Pradini
Journal of Enhanced Studies in Informatics and Computer Applications Vol. 3 No. 1 (2026): JESICA Vol. 3 No. 1 2026
Publisher : Institut Teknologi, Sains, dan Kesehatan RS.DR. Soepraoen Kesdam V/BRW

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47794/jesica.v3i1.35

Abstract

The use of e-Government is increasing along with efforts to improve the efficiency, transparency, and quality of public services. However, advances in digitalization are also accompanied by cybersecurity risks and threats to data privacy. This study aims to examine the implementation of data security and privacy in e-Government, as well as evaluate the technologies used to mitigate data leaks and misuse. The method used is Systematic Literature Review of articles published between 2021 and 2025 through Scopus, ScienceDirect, and Google Scholar databases. The research selection followed the PRISMA 2020 guidelines, resulting in 16 articles meeting the eligibility criteria. The study findings indicate that information security implementation in government institutions remains inconsistent, with key challenges related to weak security management, technical system vulnerabilities, and low public trust in personal data protection. Several technologies considered to have potential to improve security include blockchain, advanced cryptography, and automation for vulnerability detection, although their implementation remains hampered by cost, scalability, and human resource readiness. Overall, this study emphasizes that a comprehensive approach that combines technology, management, and increased security awareness is needed to strengthen data protection in e-Government.
Stock Forecasting using Daily Sales Transaction at Hundred Smoke Outlets with the Trend Moment Method Aditya Prakasa; Sentot Achmadi; Joseph Dedy Irawan
Journal of Enhanced Studies in Informatics and Computer Applications Vol. 3 No. 1 (2026): JESICA Vol. 3 No. 1 2026
Publisher : Institut Teknologi, Sains, dan Kesehatan RS.DR. Soepraoen Kesdam V/BRW

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47794/jesica.v3i1.36

Abstract

Hundred Smoke Outlet is a culinary business experiencing fluctuating customer demand that changes weekly, creating a risk of imbalance between raw material inventory and actual needs. This research aims to develop a web-based forecasting system using the Trend Moment method that processes daily sales data and converts it into a Bill of Materials (BOM) structure to more accurately predict raw material requirements. The system is designed with two user types: admin and staff, who can manage sales data, inventory, and run the forecasting process. Based on black-box testing results for 11 scenarios across various system features, all functions performed as expected with a 100% success rate. Forecast accuracy was evaluated using the Mean Absolute Percentage Error (MAPE), which showed a maximum error of 0.50, a minimum error of 0, and an average error of 22.62%. These results indicate that the system can provide a fairly good level of accuracy in supporting raw material requirement planning.
The 2D Android Game ‘Bung Tomo Adventure’ uses the Finite State Machine Method Doan Oggie Adriansz; Agung Panji Sasmito; Deddy Rudhistiar
Journal of Enhanced Studies in Informatics and Computer Applications Vol. 3 No. 1 (2026): JESICA Vol. 3 No. 1 2026
Publisher : Institut Teknologi, Sains, dan Kesehatan RS.DR. Soepraoen Kesdam V/BRW

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47794/jesica.v3i1.37

Abstract

Educational games are an effective media to improve the youth’s understanding on national history, especially when the game is presented in an interactive and engaging manner. The adoption of Finite State Machines (FSM) in 2D games makes the game to be more realistic and more dynamic. This research aims to develop an Android-based ‘Bung Tomo Adventure’ game by adopting the FSM method to control character’s behavior and to enhance the user's playing experience. The research methodologies include literature review, system’s design, coding using Godot Engine, Black Box Testing, device testing, and user evaluation. Besides that, the Black Box testing outcome shows that the game features work as expected. The user experience testing shows that 87% respondents gave positive response (categories Good and Average), which means this game is quite well received especially in terms of storyline and ease of play, although some aspects such as visual appearance and game distribution need more improvement.
Hierarchical Clustering Analysis of Biopharmaceuticals Crop Production Across Indonesian Provinces Mayang Anglingsari Putri; Ismail Hasvi; Deby Ananda Difah; Miratul Alifah; Fifin Ayu Mufarroha; Irawati Nurmalasari; Irpan Kusyadi
Journal of Enhanced Studies in Informatics and Computer Applications Vol. 3 No. 1 (2026): JESICA Vol. 3 No. 1 2026
Publisher : Institut Teknologi, Sains, dan Kesehatan RS.DR. Soepraoen Kesdam V/BRW

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47794/jesica.v3i1.38

Abstract

This study analyzes biopharmaceuticals (medicinal crop) production across Indonesian provinces using 2023 data from eight major commodities: ginger, galangal, kencur, turmeric, lempuyang, temulawak, temu ireng, and keji beling. Data from 38 provinces were normalized and analyzed using agglomerative hierarchical clustering with Ward’s linkage and Euclidean distance. The results identify three distinct clusters representing high, medium, and low production levels, with Java provinces dominating the high-production cluster, while provinces outside Java fall into moderate and low clusters. These findings highlight regional disparities and potential specialization in biopharmaceuticals cultivation. This study contributes a comprehensive national-scale multivariate clustering framework for medicinal crop production and demonstrates the applicability of hierarchical clustering for spatial agricultural analysis. The findings provide practical implications for policymakers in designing targeted agricultural development strategies, regional specialization planning, and supply chain optimization in Indonesia’s biopharmaceuticals sector.
A Systematic Literature Review of Artificial Intelligence Algorithms for Deepfake Detection Aulia Roessati Putri; Bintang Aulia Novala; Deva Muhammad Syaiful Arifin; Zulhilmi Luthfiah; Risqy Siwi Pradini
Journal of Enhanced Studies in Informatics and Computer Applications Vol. 3 No. 1 (2026): JESICA Vol. 3 No. 1 2026
Publisher : Institut Teknologi, Sains, dan Kesehatan RS.DR. Soepraoen Kesdam V/BRW

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47794/jesica.v3i1.39

Abstract

The evolution of information technology has positioned multimedia content as a pillar of digital communication, but at the same time, it has opened a gap for serious threats in the form of deepfakes. This highly realistic media manipulation challenges information authenticity, privacy, and cybersecurity, which, for Information Technology professionals, presents both technical and ethical challenges. This Systematic Literature Review (SLR) aims to map the development of Artificial Intelligence based algorithms in deepfake detection. Using the PRISMA methodology on 20 selected primary articles (2021-2025), this study aims to identify trends in the use of AI algorithms for deepfake detection, determine the most effective approaches, and analyze the factors contributing to their effectiveness. The analysis results show a paradigm shift from single models (such as CNN) to hybrid architectures (CNN-LSTM-Transformer) and complex multimodal fusion systems. It was found that hybrid algorithms are the closest approach to best practice due to their ability to handle spatial and temporal dimensions simultaneously. Key contributing factors include hierarchical feature extraction, generative data augmentation, and the integration of Explainable AI (XAI).

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