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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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Discovering Prescription Patterns in Type 2 Diabetes Based on Demographic Attributes Using Association Rules Yani, Putri; Hikmah, Maulida; Mahdiana, Deni
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 | DOI: 10.24014/ijaidm.v8i3.38082

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.
Prediction of Student Academic Stress Levels Using the Decision Tree Algorithm and Particle Swarm Optimization Dzakiyyah, Syifa Ghina; Mahdiana, Deni
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 | DOI: 10.24014/ijaidm.v8i3.38081

Abstract

Academic stress was recognized as a major challenge for university students because it negatively affected learning outcomes, mental health, and overall well-being. The purpose of this research was to develop and validate a predictive model of student academic stress levels and to evaluate whether optimization techniques improved the performance of a baseline classifier. Data were collected from 413 active students of Universitas Sapta Mandiri from the 2022 and 2023 cohorts using the Perception of Academic Stress (PAS) scale, which consisted of 18 indicators, together with demographic, academic, and psychosocial attributes. The Decision Tree (DT) algorithm was selected for its interpretability and transparency in multi-class classification. To improve generalization, its parameters were optimized using Particle Swarm Optimization (PSO) with 10 particles and 20 iterations. The baseline model achieved an accuracy of 93 percent, with the highest recall observed in the low-stress group. After optimization, the accuracy increased to 95 percent, and the recall for the high-stress group reached 0.96, indicating greater sensitivity to students at risk. These results confirmed that the research objectives were achieved, as the integration of DT with PSO enhanced both accuracy and class balance. The proposed model was consistent with the intended purpose of supporting early detection and timely academic and psychological interventions in higher education institutions.
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.
Prediksi Tingkat Kecanduan Media Sosial Generasi Z Menggunakan Algoritme PSO Dan Decision tree Anisah Masyuuroh; Deni Mahdiana; Nidya Kusumawardhany
Jurnal Ticom: Technology of Information and Communication Vol 14 No 2 (2026): Jurnal Ticom-Januari 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.v14i2.198

Abstract

Tingginya penggunaan media sosial di kalangan Generasi Z dapat menyebabkan kecanduan yang sering dianggap wajar, namun berdampak negatif terhadap kesehatan mental dan produktivitas. Dampak tersebut meliputi gangguan tidur, penurunan fokus belajar maupun bekerja, serta meningkatnya kecemasan akibat perbandingan sosial di dunia maya. Beberapa individu juga mengalami ketergantungan emosional terhadap validasi digital seperti jumlah like, komentar, atau interaksi lainnya. Oleh karena itu, deteksi dini terhadap tingkat kecanduan media sosial sangat diperlukan untuk mencegah dampak jangka panjang yang merugikan, terutama bagi Generasi Z yang sangat terhubung secara digital. Penelitian ini bertujuan untuk mengestimasi tingkat kecanduan media sosial pada Generasi Z menggunakan metode Decision tree, yang dipilih karena mampu menghasilkan klasifikasi yang akurat serta model yang mudah dipahami. Metode ini bekerja dengan membagi data berdasarkan atribut-atribut relevan untuk mengenali pola perilaku digital secara sistematis. Algoritma Decision tree dioptimalkan menggunakan Particle Swarm Optimization (PSO) untuk memilih fitur paling berpengaruh dan meningkatkan performa model. Hasil penelitian menunjukkan akurasi sebesar 93,75%, recall 94,20%, dan precision 95,16%. Setelah optimasi PSO, akurasi meningkat menjadi 96,42%, recall 97,03%, dan precision 97,00%, yang menunjukkan peningkatan performa prediksi secara signifikan.
Systematic Literature Review: Implementasi Dan Manfaat Big Data Iskandar, Daniel; Mahdiana, Deni
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 11, No 3 (2022): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v11i3.4024

Abstract

Teknologi dan sistem informasi telah mengubah hampir semua sektor bisnis dan sosial.  Implementasi sistem informasi dalam skala luas menghasilkan jumlah data yang sangat besar, dalam bentuk yang bervariasi, dan tersebar.  Istilah ‘data adalah minyak’ menggambarkan betapa bernilainya data bila kita dapat mengelola dan memanfaatkannya dengan tepat.  Penelitian ini adalah sebuah tinjauan literasi yang bertujuan untuk mengetahui topik yang paling banyak dibahas tentang implementasi dan manfaat Big Data yang dipublikasikan Elsevier mulai tahun 2021 hingga Maret 2022, serta untuk mengetahui permasalahan, metode dan hasil atau kesimpulan yang dipaparkan oleh para peneliti.  Berdasarkan hasil penggunaan fitur pencarian di situs www.sciencedirect.com, kami menemukan 309 literasi mengenai Big Data, dan setelah dilakukan analisa dan penyaringan kami mendapatkan 10 jurnal internasional yang memenuhi kriteria objek penelitian ini.  Terdapat beragam topik yang diangkat oleh para peneliti sebagai permasalahan dalam jurnal-jurnal tersebut, di antaranya mengenai masalah perkotaan, kesehatan, Covid-19, ilmu pengetahuan, Industri 4.0, Internet, dan keuangan.  Hasil penelitian kami menunjukkan implementasi Big Data dalam dunia kesehatan paling banyak dijadikan objek penelitian, sedangkan implementasi dalam masalah perkotaan berada pada urutan kedua.  Implementasi dan pengelolaan Big Data yang baik akan memberikan kita akses kepada informasi yang sangat bermanfaat dan bisa memberikan dampak yang signifikan di semua sektor, hal ini sejalan dengan terus meningkatnya jumlah penelitian mengenai Big Data dalam sepuluh tahun terakhir.Kata kunci: Big data, Elsevier, sciencedirect, systematic literature review, studi literatur
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