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All Journal Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika SemanTIK : Teknik Informasi JURNAL MEDIA INFORMATIKA BUDIDARMA Indonesian Journal of Artificial Intelligence and Data Mining INOVTEK Polbeng - Seri Informatika EKONOMIS : Journal of Economics and Business IKRA-ITH Informatika : Jurnal Komputer dan Informatika Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Teknologi Sistem Informasi dan Aplikasi Informatik : Jurnal Ilmu Komputer SEINASI-KESI Jurnal Riset Informatika JURNAL MANAJEMEN BISNIS JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Teknologi Informasi dan Pendidikan Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics Jurnal Kajian Manajemen dan Wirausaha Idealis : Indonesia Journal Information System SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia Kresna: Jurnal Riset dan Pengabdian Masyarakat Bit (Fakultas Teknologi Informasi Universitas Budi Luhur) Jurnal Sisfotek Global Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Jurnal Ticom: Technology of Information and Communication Telematika MKOM SmartComp Jurnal Ticom: Technology of Information and Communication Teknologi : Jurnal Ilmiah Sistem Informasi
Deni Mahdiana
Program Studi Sistem Informasi, Fakultas Teknologi Informasi, Universitas Budi Luhur
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Social Sciences

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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
DEVELOPMENT OF DATA WAREHOUSE TO PREDICATE THE REGARDING OF UMROH CONGREGATIONS USING THE MANEAREST NEIGHBOUR ALGORITHM (CASE STUDY PT. BAHANA SUKSES SEJAHTERA) Akhmad Wijaya Kusuma; Deni Mahdiana
Jurnal Teknik Informatika (Jutif) Vol. 3 No. 4 (2022): JUTIF Volume 3, Number 4, August 2022
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jutif.2022.3.4.265

Abstract

Bahana Sukses Sejahtera is a travel company whose one goal is to serve Umrah activities to the holy land of Mecca, Saudi Arabia. The company has been carrying out travel activities for 15 years. Interest from travel activities, especially with the aim of Umrah to the holy land of Mecca from year to year is increasing because the economic conditions of prospective pilgrims are getting better. More and more Umrah travel enthusiasts certainly create abundant data but of course if not processed properly the data will not produce good information. How to get good information with good data processing so that information can be used as knowledge for company decision making. The activity of utilizing big data into information is to use information technology and databases and then design a data warehouse. Data warehouse design and data mining is the most effective technique to produce information that has knowledge for corporate decision making. The knowledge generated from the data warehouse is that one of them can produce information to predict prospective Umrah travel pilgrims who may be canceled due to something that makes it impossible to leave. One of the algorithms used for the predicting process can use the nearest neighbor algorithm. The results of the algorithm can produce predictive information about factors that are very influential on the cancellation of the departure of prospective Umrah pilgrims, for example due to family reasons. Based on this explanation, this research was developed by utilizing information technology and software to make predictive analysis based on existing data regarding the cancellation of the departure of Umrah pilgrims at the travel company PT. Bahana Sukses Sejahtera.
USABILITY TESTING OF BUDI LUHUR UNIVERSITY E-LEARNING SYSTEM USING SYSTEM USABILITY SCALE Yuwan Jumaryadi; Deni Mahdiana
Jurnal Teknik Informatika (Jutif) Vol. 3 No. 4 (2022): JUTIF Volume 3, Number 4, August 2022
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jutif.2022.3.4.275

Abstract

The usability level of an application is very influential on user satisfaction in using the system. In addition, the usability level is also useful as a starting point for the improvement process of the system being developed. System Usability Scale (SUS) is one of the most widely used testing methods to measure the usability of a system. Users of the Budi Luhur University e-learning system will be given 10 questions and answered on a Likert scale. The results of the calculation of 101 respondents obtained a SUS score of 65.52. This value is in the OK category, but still needs improvement from several aspects such as in terms of user interface and performance. This can be seen from the assessment of the e-learning system that is accessed from the mobile application which gets a score of 41, in contrast to the e-learning system accessed from the website which gets a score of 86. In addition, from the performance of the e-learning system, the score is 77.
SYSTEMATIC LITERATURE REVIEW OF THE CLASS IMBALANCE CHALLENGES IN MACHINE LEARNING Rifqi Fitriadi; Deni Mahdiana
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 5 (2023): JUTIF Volume 4, Number 5, October 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.5.970

Abstract

The significant growth of data poses its own challenges, both in terms of storing, managing, and analyzing the available data. Untreated and unanalyzed data can only provide limited benefits to its owner. In many cases, the data we analyze is imbalanced. An example of natural data imbalance is in detecting financial fraud, where the number of non-fraudulent transactions is usually much higher than fraudulent ones. This imbalance issue can affect the accuracy and performance of machine learning classification models. Many machine learning classification models tend to learn more general patterns in the majority class. As a result, the model may overlook patterns that exist in the minority class. Various research has been conducted to address the problem of imbalanced data. The objective of this systematic literature review is to provide the latest developments regarding the cases, methods used, and evaluation techniques in handling imbalanced data. This research successfully identifies new methods and is expected to provide more choices for researchers so that imbalanced data can be properly handled, and classification models can produce unbiased, accurate, and consistent results.
SYSTEMATIC LITERATURE REVIEW APPLICATION OF METHODS IN INFORMATION SYSTEMS DEVELOPMENT Gita Cahyani, Annisa Putri; Mahdiana, Deni
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.1954

Abstract

In today's rapidly changing digital age, information system development is critical to the growth and success of organizations and businesses. It is critical to select the appropriate system development method because it can impact many aspects of the system, including efficiency, dependability, and alignment with organizational requirements. Businesses or organizations may struggle to determine the best approach based on their project environment and specific requirements. The goal of this study is to gain a better understanding of the various approaches to systems development and how they work. In information systems, each system development technique's complexity and effectiveness will be investigated using a qualitative approach in conjunction with descriptive analysis. A better understanding of the features and benefits of each method, such as agile, waterfall, Rapid Application Development (RAD) and others, will be enable organizations to make more precise and goal-driven decisions. Furthermore, this study will look at previous research on a variety of topics and discussions, such as information system design and the practical application of cutting-edge software. Determine trends, best practices, and issues that have emerged during the development of current information systems. One of the motivations for this research is the growing complexity of project environments, as well as the need for dependable and efficient systems. Learning more about systems development techniques can assist organizations and businesses in lowering project risks, increasing efficiency, and identifying solutions that better meet their business goals. The waterfall method is most popular because it provides more control over the system development process.
IMPLEMENTATION OF DEEP LEARNING MODELS IN HATE SPEECH DETECTION ON TWITTER USING AN NATURAL LANGUAGE PROCESSING APPROACH Arifin, Muhammad; Mahdiana, Deni
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.5.2043

Abstract

In the digital era, the misuse of the freedom to communicate on the internet often leads to problems such as the spread of hate speech, which can harm individuals based on race, religion, and other characteristics. This issue requires effective solutions for content moderation, particularly on social media platforms like Twitter. This research develops a deep learning model utilizing Natural Language Processing (NLP) to detect hate speech and aims to improve existing content moderation mechanisms. The methods used include data collection, preprocessing through techniques such as case folding, tokenization, lemmatization, and model creation using TensorFlow Extended (TFX) involving embedding, dense, and global pooling layers. The model is trained to optimize accuracy by minimizing the loss function and closely monitoring evaluation metrics. The results show that this model achieves a prediction accuracy of 84%, an AUC value of 0.796, and a binary accuracy of 76%. The conclusion of this research is that the use of deep learning and NLP in detecting hate speech offers a highly potential approach to enhancing digital content moderation, providing a solution that is not only efficient but also accurate.
SYSTEMATIC LITERATURE REVIEW ON THE APPLICATION OF UI/UX DESIGN METHODS IN SYSTEM DEVELOPMENT Ramadani, Romi; Mahdiana, Deni
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 - SENIKO
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.2073

Abstract

In the current modern digital era, system development is undoubtedly rapid and massive, especially across various sectors such as healthcare, business, and public services. In system development, many aspects are considered, one of which is the appearance of the user interface. Interface design becomes an intriguing aspect and has an influence on system or application development. System development surely involves user interface and user experience aspects as part of the human-computer interaction (HCI) discipline. This research aims to identify research opportunities in UI/UX aspects in system development, with data obtained from relevant journals spanning from 2019 to 2024 as a representation of the latest study on UI/UX design research. This study utilizes the Systematic Literature Review (SLR) method. The results of this research provide a systematic literature review of existing studies on UI/UX design. This research can benefit the HCI community by applying methods in UI/UX design in system development to shape the direction of future research.
Random Forest and Artificial Neural Network Data Mining for Environmental and Public Health Risk Modeling in Flood-Prone Urban Areas of Indonesia Mahdiana, Deni; Ebine, Masato; Wibowo, Arief
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.5387

Abstract

Floods in urban Indonesia pose severe environmental and public health challenges, exacerbating water contamination, vector proliferation, and disease outbreaks. Rapid urbanization, inadequate drainage systems, and climate change have intensified these impacts, emphasizing the need for integrated predictive frameworks. This study aims to develop a Data Mining (DM)-based modeling approach that combines environmental and health indicators to predict flood-related disease risks. Random Forest (RF) and Artificial Neural Network (ANN) algorithms were applied to multi-domain datasets from 30 flood-prone urban sub-districts between 2018 and 2023, encompassing rainfall, drainage density, land use, and water quality variables, integrated with disease incidence data such as diarrhea, dengue, and leptospirosis. The ANN model achieved superior predictive performance (93% accuracy, AUC 0.93) compared to RF (90% accuracy, AUC 0.90), identifying rainfall intensity, drainage density, and coliform contamination as the most influential predictors. These results demonstrate the capability of AI-driven DM techniques to capture complex interdependencies between environmental and health systems. The developed framework contributes to the field of informatics by providing a scalable, data-driven early warning tool for flood-related health risks, supporting evidence-based decision-making in disaster risk management and enhancing public health resilience in rapidly urbanizing regions.
Artificial Intelligence in Green and Sustainable Investment: a Bibliometric and Systematic Literature Review Kamalia, Antika Zahrotul; Wibowo, Arief; Mahdiana, Deni
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.5287

Abstract

Green and sustainable investment has gained increasing global attention due to the urgency of the climate crisis, social demands, and the adoption of Environmental, Social, and Governance (ESG) principles. However, research on the application of artificial intelligence (AI) in this domain remains fragmented and lacks a comprehensive mapping. This study aims to map the trends, research directions, and key findings related to AI in green and sustainable investment using a bibliometric and systematic literature review (SLR) approach. Data were retrieved from the Scopus database and screened with the PRISMA framework, resulting in 24 articles analyzed through VOSviewer and thematic synthesis. The results indicate significant developments in energy efficiency, green buildings, machine learning, and sustainability, alongside an expanding pattern of international collaboration. Nonetheless, limitations remain, including insufficient cross-sectoral integration, limited empirical studies in developing countries, and the lack of AI models that holistically incorporate risk, ESG, and SDGs indicators. The main contribution of this study lies in providing a structured literature mapping that can serve as a foundation for developing more integrative AI frameworks and expanding research contexts to optimize sustainable green investment. These findings are expected to be valuable for researchers and practitioners in advancing innovation and strengthening the AI-driven sustainable finance ecosystem.
Optimizing Bag of Words and Word2Vec with Vocabulary Pruning and TF-IDF Weighted Embeddings for Accurate Chatbot Responses in Indonesian Treasury Services Aprianto, Eko; Mahdiana, Deni; Wibowo, Arief
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.5370

Abstract

The high volume of support tickets submitted to the HAI DJPb Service Desk has caused delays and inconsistent response quality in payroll-related inquiries across Indonesian treasury work units (Satker). To improve the accuracy and efficiency of public service responses, this research proposes an optimized text-vectorization framework for chatbot development using a hybrid combination of Bag of Words (BoW), Word2Vec, vocabulary pruning, and TF-IDF weighted embeddings. The dataset consists of 2024 ticket logs, curated FAQs, and questionnaire data related to the Satker Web Payroll Application. The method includes preprocessing (snippet removal, normalization, tokenization, stopword removal, stemming), vocabulary pruning based on empirical frequency thresholds (<5 and >80) while preserving domain-specific technical terms, and semantic weighting through TF-IDF. Four vectorization models—BoW, BoW with pruning, Word2Vec, and Word2Vec + TF-IDF—were evaluated using cosine similarity, response time, and accuracy. Results show that BoW achieved the highest accuracy of 88.32%, while Word2Vec produced the most stable response time with an average of 47.32 ms and a cosine similarity of 0.99. The findings demonstrate that frequency-based representations remain highly effective for structured administrative datasets, while weighted embeddings improve semantic relevance. This study contributes to the field of Informatics by providing an efficient hybrid vectorization framework tailored for Indonesian administrative language, enabling more accurate and scalable chatbot solutions for e-government services.
Co-Authors A Djafar, Muhammad Agung Abdurrahman, Faris Nur Achmad Fauzi adang badru jaman,anggun fergina, adang badru jaman,anggun fergina Ade Davy Wiranata Ade Setiadi Adi Saputra, Yulian Adiputra, Januar Ahadti Puspa Sari Airlambang, Dwiki Akhmad Wijaya Kusuma Amalia Khairunisa Andhika Arethuza Ari Anisah Masyuuroh Anita Diana Antika Zahrotul Kamalia Arief Wibowo Arif Rahman Arifin Istighfari Zahro Atik Ariesta auddie mahlyda Bagas Wahyu Putratama Bayu Aji Susilo Brury Trya Sartana Chairul Kahfi Dahlia Mariyam Ohorella Dedy Mirwansyah Devit Setiono Diah Ayu Lestari, Diah Ayu Diana Putri djuan narita Dzakiyyah, Syifa Ghina Ebine, Masato Eko Aprianto Erly Krisnanik Fahlevi, Noval Febriansyah Ramadhan Gita Cahyani, Annisa Putri Haderiansyah Haderiansyah Hasibuan, Tuhfatul Habibah Hikmah, Maulida Irgi Arifal Nulhakim Iskandar, Daniel janah purwanti Jejen Jaenudin Jumaryadi, Yuwan Ken Putri, Lulasnov Viola Prameswari Khafistia Hayyu Kharmytan, Yan Baktra Kraugusteeliana Kraugusteeliana Kusumawardhany, Nidya Kusumo Adi Lauw Li Hin Leonardus Adityo Toto Pratomo Maemunah Maemunah Mahendrasyah, Ihjal Manarul Haikal Casandy Manda, Seftifin Ratna Maulana, Hanif Mirza Sutrisno Mohammad Aldinugroho Abdullah Muhammad Abduh Khairullah Muhammad Arifin Mutia Hasanah Nidya Kusumawardhany Nurramdhani, Helena Purwo Setyo Aji putri yani, putri Putri, Jasmin Maula Rahmat Hidayat Ramadani, Romi Ratna Kusumawardani Ratna Kusumawardani Renaldi Setiawan Putra Rifqi Fitriadi Riskiyono, Fajar Rusdah Rusdah Rusdah Sarastuti, Elina Seftifin Ratna Manda Solehan Solehan Sri Devi Yulita Sugiarto S Supardi Supardi Syahid, Achyar Jhonathan Syifa Aryanti Tjahjanto, Tjahjanto Wiguna, Kevin Zahran, Aziz