Article Per Year (5 Year)
p-Index From 2021 - 2026
7.075
P-Index
This Author published in this journals
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 Formosa Journal of Computer and Information Science 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

Published : 68 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 68 Documents
Search

 3   4   5   6   7 
Discovering Prescription Patterns in Type 2 Diabetes Based on Demographic Attributes Using Association Rules Putri Yani; Maulida Hikmah; Deni Mahdiana
Indonesian Journal of Artificial Intelligence and Data Mining Vol. 8 No. 3 (2025): November 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

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

Abstract

Type 2 diabetes mellitus (T2DM) is a chronic disease that requires effective long-term therapeutic management. Appropriate and continuous treatment is crucial to prevent complications and improve patients’ quality of life. In clinical practice, prescription patterns vary significantly and are influenced by demographic and clinical characteristics. This study aimed to analyze prescription patterns of T2DM patients based on demographic and clinical attributes, and to identify frequently co-prescribed drug combinations using the Apriori algorithm. A total of 3,500 prescription records were obtained from RSUD H. Damanhuri Barabai. The analysis was conducted in two stages: (1) association between demographic factors (age, gender, blood pressure) and prescribed drugs, and (2) association among drugs regardless of patient demographics. With minimum support of 3%, confidence thresholds of 60% and 35%, and lift greater than 1.5, fifteen valid rules were identified in the demographic-to-drug analysis, and nine rules in the drug combination analysis. Strong patterns were observed, such as the prescription of Empagliflozin and Insulin Degludec for hypertensive patients aged 40–49, and the co-prescription of Acarbose and Glimepiride. These findings demonstrated that the Apriori algorithm was effective in identifying meaningful prescription patterns. Beyond methodological contributions, the results provide practical value for hospitals by supporting pharmacy managers in drug procurement planning, optimizing stock management, and designing distribution strategies that anticipate patient needs based on prescription trends.
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.
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.
Model Diagnosis Penyakit Diabetes Mellitus Menggunakan SMOTE dan Algoritme Random Forest Fathimah Azzahra; Deni Mahdiana; Nidya Kusumawardhany
Jurnal Ticom: Technology of Information and Communication Vol 14 No 3 (2026): Jurnal Ticom-Mei 2026
Publisher : Asosiasi Pendidikan Tinggi Informatika dan Komputer Provinsi DKI Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70309/ticom.v14i3.224

Abstract

Diabetes Mellitus merupakan penyakit metabolik kronis yang prevalensinya terus meningkat secara global. Deteksi dini terhadap risiko diabetes sangat penting untuk mencegah munculnya komplikasi yang lebih serius di kemudian hari. Namun, proses ini menghadapi tantangan tersendiri, terutama dalam menentukan variabel risiko yang paling relevan dan memperoleh hasil klasifikasi yang akurat dalam mengidentifikasi individu yang berpotensi mengidap diabetes. Untuk menjawab tantangan tersebut, penelitian ini memanfaatkan algoritma Random Forest, yang dikenal memiliki performa baik dalam menangani masalah klasifikasi, termasuk dalam konteks medis seperti deteksi penyakit. Random Forest juga unggul dalam mengolah data berukuran besar serta mampu memberikan informasi mengenai tingkat kepentingan setiap fitur pada dataset. Penelitian ini juga menerapkan metode SMOTE (Synthetic Minority Oversampling Technique) guna mengatasi ketidakseimbangan distribusi data antar kelas, yang seringkali memengaruhi performa model secara keseluruhan. Dengan kombinasi algoritma Random Forest dan teknik SMOTE, diperoleh hasil evaluasi model dengan AUC sebesar 0.979, akurasi 91,36%, precision 98,46%, recall 72,32%, dan specificity 99,51%. Nilai-nilai ini menunjukkan bahwa model mampu memberikan performa yang tinggi dan andal. Pendekatan yang digunakan dalam penelitian ini tidak hanya meningkatkan akurasi dalam proses diagnosis dini diabetes, tetapi juga membantu mengungkap faktor-faktor risiko utama yang berperan, sehingga dapat menjadi dasar dalam strategi pencegahan dan pengendalian yang lebih tepat sasaran.
Penerapan Algoritma K-Nearest Neighbor pada Twitter untuk Analisis Sentimen Masyarakat Terhadap Larangan Mudik 2021 Diah Ayu Lestari; Deni Mahdiana
Informatik : Jurnal Ilmu Komputer Vol 17 No 2 (2021): Agustus 2021
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52958/iftk.v17i2.3629

Abstract

Media sosial Twitter merupakan media yang banyak digunakan oleh masyarakat dalam menyampaikan sebuah opini yang sedang hangat dibahas. Kebijakan larangan mudik yang diterapkan oleh pemerintah saat ini belum diketahui opini atau pendapat masyarakat terhadap pelaksanaan larangan mudik 2021 ini, sehingga pemerintah kesulitan dalam mengevaluasi kebijakan larangan mudik tersebut. Penelitian ini akan melakukan analisis sentimen menggunakan algoritma K-Nearest Neighbor (K-NN) dan menerapkan metodologi Cross-Industry Standard Process for Data Mining (CRIPS-DM). Pada penelitian ini data bersih yang digunakan berjumlah 4.799 tweet, yang diambil dari media sosial twitter pada 04 April 2021 – 17 Mei 2021 dengan sentimen positif berjumlah 834 tweet dan 3.965 tweet sentimen negatif. Penelitian ini menghasilkan bahwa K-NN dapat diimplementasikan dengan baik dikarenakan mencapai nilai akurasi sebesar 86.67 % dengan nilai recall 39.52 %, precision 70.97 % dan spencificity sebesar 96.60%  menggunakan split data perbandingan 80 untuk data training dan 20 untuk data testing dengan nilai k=3. Sehingga dapat dikatakan bahwa algoritma K-NN dapat mengklasifikasikan data secara benar dan baik.
Analisis dan Perancangan User Interface dan User Experience BNI Life Mobile dengan Metode User Centered Design Jasmin Maula Putri; Erly Krisnanik; Helena Nurramdhani; Tjahjanto Tjahjanto; Deni Mahdiana
Informatik : Jurnal Ilmu Komputer Vol 18 No 1 (2022): April 2022
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52958/iftk.v17i4.4319

Abstract

PT. BNI Life Insurance atau yang biasa dikenal dengan BNI Life merupakan perusahaan asuransi yang menyediakan berbagai produk seperti asuransi kehidupan (jiwa), kesehatan, pendidikan, investasi, pensiun, dan syariah. Dalam menjalankan transaksinya, BNI Life memiliki aplikasi bernama BNI Life Mobile untuk melakukan klaim asuransi yang dapat diunduh melalui Play Store dan App Store. Dengan adanya aplikasi BNI Life Mobile ini, diharapkan dapat memudahkan nasabah untuk mengklaim produknya. Tetapi, hal tersebut harus didukung dengan tampilan antar muka yang baik, menarik, dan mudah dipahami oleh pengguna. Penelitian ini bertujuan untuk melakukan analisis terhadap UI/UX pada aplikasi BNI Life Mobile untuk mengetahui nilai kegunaannya (usability) dengan menerapkan metode User Centered Design (UCD). Penerapan metode UCD dilakukan dengan melaksanakan kuesioner dan prototyping dengan teknik System Usability Scale (SUS). Penelitian ini menghasilkan suatu tampilan antar muka baru dalam bentuk prototype yang dapat digunakan sebagai saran untuk BNI Life dengan peningkatan nilai usability sebesar 20 agar pengguna aplikasi BNI Life Mobile dapat merasakan kegunaan dan experience yang baik saat menggunakannya.
Implementation of a Decision Support System Based on the Simple Additive Weighting (SAW) Method and Apriori Algorithm for Student Achievement Evaluation Mochammad Bagus Priyantono; Deni Mahdiana
Formosa Journal of Computer and Information Science Vol. 5 No. 2 (2026): August 2026
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/fjcis.v5i2.16835

Abstract

This study aims to develop and implement a Decision Support System (DSS) to assess student achievement using the Simple Additive Weighting (SAW) method, complemented by the Apriori algorithm as a validation tool. The system evaluates students based on five criteria: academic performance, attendance, championships, discipline, and activeness. The SAW method is used to calculate preference scores and rank students objectively and systematically, while Apriori identifies frequently occurring transaction patterns and validates the consistency of new student data against the training dataset. Test results show that both SAW and Apriori achieved an accuracy of 87% compared to manual calculations, indicating that the combination of these two methods can provide consistent and reliable decisions.
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 Diana Putri djuan narita Ebine, Masato Eko Aprianto Erly Krisnanik Fahlevi, Noval Fathimah Azzahra Febriansyah Ramadhan Gita Cahyani, Annisa Putri Haderiansyah Haderiansyah Hasibuan, Tuhfatul Habibah Helena Nurramdhani Ikhwan Ikhwan Irgi Arifal Nulhakim Iskandar, Daniel janah purwanti Jasmin Maula Putri 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 Mahendrasyah, Ihjal Manarul Haikal Casandy Manda, Seftifin Ratna Maulida Hikmah Mirza Sutrisno Mochammad Bagus Priyantono Mohammad Aldinugroho Abdullah Muhammad Abduh Khairullah Muhammad Arifin Mutia Hasanah Nidya Kusumawardhany Purwo Setyo Aji Putri Yani 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 Syifa Ghina Dzakiyyah Tjahjanto, Tjahjanto Wiguna, Kevin Zahran, Aziz