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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Media Infotama JSI: Jurnal Sistem Informasi (E-Journal) Proceeding of the Electrical Engineering Computer Science and Informatics Indonesian Journal of Artificial Intelligence and Data Mining Seminar Nasional Teknologi Informasi Komunikasi dan Industri EKONOMIS : Journal of Economics and Business PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer International Journal of New Media Technology Jurnal ULTIMATICS The IJICS (International Journal of Informatics and Computer Science) JOURNAL V-TECH (VISION TECHNOLOGY) Indonesian Journal of Electrical Engineering and Computer Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) TIN: TERAPAN INFORMATIKA NUSANTARA Bulletin of Computer Science Research Journal of Informatics Management and Information Technology Journal of Social Responsibility Projects by Higher Education Forum Bulletin of Data Science ABDINE Jurnal Pengabdian Masyarakat Journal of Computing and Informatics Research Jurnal Komunikasi, Sains dan Teknologi (JKST) Jurnal (FORSINTA) Informatika, Sistem Informasi dan Kehutanan Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS West Science Interdisciplinary Studies Journal of Informatics, Electrical and Electronics Engineering West Science Interdisciplinary Studies Jurnal Rekayasa Sistem Informasi dan Teknologi Bulletin of Informatics and Data Science Karunia: Jurnal Hasil Pengabdian Masyarakat Indonesia Jurnal Ilmu Komputer, Teknologi Dan Informasi Journal of Computer Science and Information Technology Polygon: Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi Akademika Masyarakat Berkarya: Jurnal Pengabdian dan Perubahan Sosial Journal of Applied Research In Computer Science and Information Systems Proletarian : Community Service Development Journal International Journal of Innovation Research in Education, Technology and Management (IJIRETM) Journal of Decision Support System Research
Hetty Rohayani
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Artificial Intelligence Recommendation System for Optimizing Steam Power Plant Heat Rate: A Conceptual Design Ardiansyah, Lulu; Rohayani, Hetty
Journal of Information System Research (JOSH) Vol 7 No 1 (2025): October 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i1.7858

Abstract

Steam power plants are one of the major electricity generation units in many countries around the world. The thermal efficiency of power plants is primarily dependent on decision making by the operator on real time process parameters. This decision-making process currently utilizes human expertise, in conjunction with static setpoints and operating procedures. However, variability in human operator performance and plant operating conditions often leads to non-optimal heat rate values. The purpose of this paper is to develop a conceptual framework for an artificial intelligence-based operator decision-support system for real-time heat rate optimization, integrating Model-Based Design (MBD) and Design Science Research (DSR) principles. The framework presented in this paper is informed by past high efficiency operational experience and machine learning methodology to describe the necessary steps in generating actionable, explainable recommendations for process parameter adjustments. The conceptual framework presented, which incorporates both predictive capabilities as well as domain expertise, is intended to bridge the gap between the development of predictive models and their eventual deployment as prescriptive operational support systems by providing a high-level blueprint of a system design that is expected to lead to more robust and consistent decision making. The key functional components of the framework include data capture, preprocessing, inference modeling and, ultimately, presentation of recommendations on a human-machine interface. An initial, theoretical appraisal of the proposed framework suggests promising potential for improving operational efficiency, reducing fuel consumption, and lowering emissions, and it is expected to serve as a useful reference for ongoing and future development efforts.
Application of the ANFIS Model in Predicting Diabetes Mellitus Disease Nurfazila, Aprilia; Rohayani, Hetty
ULTIMATICS Vol 17 No 2 (2025): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v17i2.4479

Abstract

This study presents the application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) model for predicting Diabetes Mellitus using two primary input features, namely glucose level and body mass index (BMI). The research employs a quantitative experimental approach using the public diabetes dataset obtained from Kaggle. The data underwent preprocessing steps, including cleaning, normalization, and splitting into training and testing subsets. The ANFIS model was designed with fuzzification, rule-based inference, and a hybrid learning algorithm to optimize membership function parameters. Model evaluation was conducted using accuracy, precision, recall, and F1-score. The results show that the ANFIS model achieved an accuracy of 69.70% on the test dataset, demonstrating strong sensitivity in detecting diabetic cases but generating a notable number of false positives. These findings indicate that ANFIS has potential as an early-screening decision support tool, although further optimization and additional features are required to enhance predictive performance.
Expert System for Diagnosing Human Psychological Disorders Using the Forward Chaining Method Hesti; Hetty Rohayani
IJNMT (International Journal of New Media Technology) Vol 12 No 2 (2025): Vol 12 No 2 (2025): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v12i2.4512

Abstract

This study aims to design an expert system capable of providing early diagnosis of human psychological disorders using the Forward Chaining method. The research was conducted through a literature review, the development of a knowledge base, and the formulation of rule-based diagnostic structures derived from psychological references, including the DSM-5. The system is designed to identify eight common psychological disorders using twenty primary symptoms that are mapped into IF–THEN rules. The inference process operates by matching user-selected symptoms with the rules contained in the knowledge base. The results indicate that the Forward Chaining method can systematically and logically generate early diagnostic indications for disorders such as depression, anxiety, bipolar disorder, PTSD, and others. A case simulation demonstrates that the reasoning mechanism is able to produce accurate conclusions based on the combination of symptoms entered. Although the system has not yet been implemented as a software application, this study confirms that the conceptual design of an expert system using Forward Chaining can serve as an effective tool for early mental-health detection and has strong potential for further development.
Perancangan Sistem Terdistribusi Data Pembayaran UKT Mahasiswa Berbasis Web (Studi Kasus: Universitas Muhammadiyah Jambi) Hetty Rohayani; Rico; Wahyu Hidayat
JOURNAL VISION TECHNOLOGY (V-TECH) Vol. 6 No. 2 (2023): JOURNAL V-TECH (VISION TECHNOLOGY)
Publisher : LPPM Universitas Adiwangsa Jambi

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

Abstract

Sistem pembayaran UKT di Universitas Muhammadiyah Jambi yang ada saat ini masih menggunakan sistem manual dan kurang efisien, sehingga membutuhkan solusi untuk mengatasi masalah tersebut. Oleh karena itu dikembangkan sebuah sistem terdistribusi data pembayaran UKT untuk mahasiswa berbasis web yang mana dapat diakses yang ingin membayarkan UKT tersebut. Sistem ini memanfaatkan web sebagai sarana untuk melakukan transaksi pembayaran UKT tersebut.Dengan menggunakan sistem pembayaran UKT berbasis web, mahasiswa dapat melakukan pembayaran UKT secara online dan dapat melacak status pembayaran mereka secara real-time. Selain itu, sistem ini juga memungkinkan administrator untuk mengelola data pembayaran UKT secara efisien dan dapat memastikan bahwa setiap transaksi pembayaran UKT dilakukan secara benar dan akurat.Hasil uji coba sistem ini menunjukkan bahwa sistem terdistribusi data pembayaran UKT ini memiliki tingkat keamanan yang tinggi dan mempermudah proses pembayaran UKT bagi mahasiswa dan administrator. Oleh karena itu, sistem ini dapat menjadi solusi untuk memperbaiki sistem pembayaran UKT di Universitas Muhammadiyah Jambi saat ini.
Literatur Review: Penerapan Naive Bayes Proses Data Mining Untuk Analisis Media Sosial Dian Aprilia Dewi; Hetty Rohayani
JOURNAL VISION TECHNOLOGY (V-TECH) Vol. 8 No. 2 (2025): JOURNAL V-TECH (VISION TECHNOLOGY)
Publisher : LPPM Universitas Adiwangsa Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35141/ndt0c242

Abstract

In data processing, the naïve bayes method is used as the main algorithm in the data mining process. The selection of this method is based on the advantages of naïve bayes in processing text data efficiently. With this approach, public opinion recorded in uploads on social media can be grouped into positive, negative, or neutral sentiment categories. The research process begins with data collection from social media, then text pre-processing is carried out such as data cleaning. The naïve Bayes model is then trained to recognize opinions based on the available dataset. The results of this study indicate that social media can be a source of data for public opinion analysis. In addition, the naïve Bayes method has proven effective in grouping public opinion. This analysis can be an input for policy makers, business actors, and academics to find out the public's voice objectively. This study contributes to the development of social media-based data mining studies in Indonesia. Thus, this approach is expected to support more responsive decision making towards public opinion in the digital space.
Penerapan Data Mining Menggunakan Algoritma K-Means Untuk Menganalisa Penjualan Rumah Parfume Saputri, Yolanda; Rohayani, Hetty; Rico, Rico; Rico
JOURNAL VISION TECHNOLOGY (V-TECH) Vol. 8 No. 2 (2025): JOURNAL V-TECH (VISION TECHNOLOGY)
Publisher : LPPM Universitas Adiwangsa Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35141/we6g7d42

Abstract

store is a shop that offers various types of perfume scents under the IM brand. Even though it provides a wide range of choices, not all types of perfume sell quickly, some are in demand and some are less desirable. Data on sales, purchases and expenses at the store is irregular, so that the data only functions as an archive without being used for developing marketing strategies. The data that has been collected should be used as a decision-making system to solve business problems. To achieve this, the authors designed a data mining application in this study with the hope of providing maximum and effective results in analyzing perfume sales at the IM Parfume Rantau prapat store. The application of Data Mining with the K-Means Algorithm is proven to provide the best analysis and be a solution in developing the perfume business. Through clustering modeling with the K-Means algorithm and by dividing the number of clusters into 3, rapid miner succeeded in forming three clusters, where cluster 1 consisted of 9 products, cluster 2 had 3 products, and cluster 3 had 13 products out of a total of 25 product items observed.
EXPERT SYSTEM FOR DIAGNOSING DISEASES IN CUCUMBER PLANTS USING FORWARD CHAINING METHOD assidiq, ilham; Hetty Rohayani; Hendra Kurniawan
International Journal of Innovation Research in Education, Technology and Management Vol. 3 No. 1 (2026): February 2026
Publisher : PT. BERBAGI TEKNOLOGI SEMESTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61098/ijiretm.v3i1.248

Abstract

The purpose of this research is to develop an expert system to diagnose plant diseases in cucumber using the forward chaining method. The agricultural sector, particularly vegetable cultivation, faces great challenges due to the spread of diseases that reduce productivity and economic value. Expert systems mimic human expertise to accurately diagnose diseases and provide practical solutions by providing effective recommendations. The forward chain, rule-based reasoning approach, ensures systematic analysis to derive conclusions from known facts, thereby improving diagnostic accuracy. The focus of this research is to identify common diseases in cucumber plants and encode the expertise into a functional system. The development of the system involved gathering knowledge from agricultural experts, creating rules, and implementing them in a user-friendly interface. Preliminary results show the high accuracy and potential of the system to help farmers quickly diagnose diseases and take preventive measures. This paper contributes to sustainable agriculture by integrating an expert system to effectively address plant health issues. Future enhancements may include real-time monitoring and integration with IoT devices.
Literature Review: Implementation of the Naive Bayes Algorithm for Classification in Various Fields of Data Mining Nurfazila, Aprilia; rohayani, hetty
International Journal of Innovation Research in Education, Technology and Management Vol. 3 No. 1 (2026): February 2026
Publisher : PT. BERBAGI TEKNOLOGI SEMESTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61098/ijiretm.v3i1.269

Abstract

The significant increase in data volume across various sectors demands efficient, accurate, and adaptive classification methods. The Naive Bayes algorithm is one of the probabilistic classification techniques widely used in data mining due to its model simplicity and its capability to handle high-dimensional data. This study aims to systematically review the application of the Naive Bayes algorithm for data classification in various sectors in Indonesia through a Systematic Literature Review (SLR) approach. Data were obtained from scientific journals published in the last five years (2019–2024) relevant to the topic and analyzed using qualitative descriptive methods. The review results show that Naive Bayes is widely applied in the fields of health, education, social sciences, economics, and technology. Most studies report high accuracy rates, particularly in text classification and imbalanced dataset cases. However, the limitation of this algorithm lies in the assumption of attribute independence, which is often not met in real-world cases. Therefore, several studies combine Naive Bayes with other methods to improve performance. This study provides a comprehensive overview of the strengths and weaknesses of Naive Bayes and serves as a reference for selecting appropriate classification methods in future data mining applications.
K-NN Based Prediction of AI Tool Utilization by Non-Technical University Students Akhsay, Tengku; Rohayani, Hetty
International Journal of Innovation Research in Education, Technology and Management Vol. 3 No. 1 (2026): February 2026
Publisher : PT. BERBAGI TEKNOLOGI SEMESTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61098/ijiretm.v3i1.270

Abstract

The increasing integration of Artificial Intelligence (AI) tools, particularly ChatGPT, into higher education necessitates a deeper understanding of their adoption patterns among non-technical students. While AI offers significant benefits for learning and academic tasks, its utilization varies across disciplines, with non-technical fields often exhibiting lower adoption rates. This study addresses the critical need to predict AI adoption among students in non-technical majors such as Business, Education, Humanities, and Social Sciences. We employ the K-Nearest Neighbor (K-NN) algorithm to classify and forecast the likelihood of these students using ChatGPT for academic purposes. The dataset, comprising survey responses from 48 non-technical students, includes attributes like AI knowledge level, frequency of personal and academic AI use, and interest in AI careers. After rigorous data preprocessing, including encoding and normalization, the dataset was split into training (70%) and testing (30%) sets. The K-NN model, with an optimized K-value determined through cross-validation, utilized Euclidean distance for classification. Our findings indicate that approximately 39.6% of non-technical students are predicted to utilize AI tools like ChatGPT for their academic activities, closely aligning with actual survey responses. This research provides valuable insights for educational institutions to tailor teaching methods, offer targeted support, and develop relevant digital literacy programs, ensuring AI becomes an inclusive and empowering educational tool for all students.
Penulisan Jurnal Ilmiah Berbasis Teknologi Digital untuk Meningkatkan Kompetensi Publikasi Mahasiswa Marthiawati, Noneng; Rohayani, Hetty; Kurniawansyah, Kevin; Jasmir, Jasmir; Gustinar, Gustinar
Journal of Social Responsibility Projects by Higher Education Forum Vol 6 No 2 (2025): November 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jrespro.v6i2.9656

Abstract

This community service activity was conducted at Universitas Muhammadiyah Jambi, involving students from the Information Systems study program as the primary participants. The main problems identified among participants included a limited understanding of the structure of scientific articles, inadequate paraphrasing skills, and a lack of competence in using reference management applications. In addition, the utilization of digital technology to support scientific writing was not yet optimal, despite the availability of adequate technological facilities. This activity aimed to improve students’ competencies in writing scientific articles in accordance with journal standards and to enhance their ability to utilize digital technology as a supporting tool for writing and publication. The method applied was a training-based approach combined with intensive mentoring. The training materials covered the structure of scientific articles, proper academic writing techniques, paraphrasing strategies to avoid plagiarism, the use of reference management tools, and the application of digital technology in scientific writing. The contribution of this activity lies in strengthening students’ practical academic skills while fostering a productive and technology-oriented academic culture. The results indicated a significant improvement in students’ understanding and skills. The average score increased from 54.25 in the pre-test to 79.63 in the post-test, representing a 46.78 percent improvement. Furthermore, most participants were able to independently produce draft scientific articles with a more systematic structure and effectively use reference management tools. The activity also contributed to increased motivation among students to write and publish scientific work.
Co-Authors -, Irsyadunas Abd Halim Ade Irma Agustina Lubis Ade Pratama Adi Supriyatna Afrizal J, S.Kom Afrizal. J Agi Nanjar Agustina, Safira Ahmad Heriansyah Akbar, Zulfikri Akhsay, Tengku Alysa Najwa Amalia, Dira Amandha, Shandy Amin Amin Ananda Sri Mardiana Andrico, Ricky Anisa Rizki Septia Anisa Rizki Septia Anugrah, Septriyan Ardian, Iqbal Ardiansyah, Lulu Arif Mursidan Arini, Zaza Mutiara Armandito Armiwita, Armiwita Arniwita, Arniwita Arpan Saputra Harahap Assidiq, Ilham Azzamy, Muhammad Nabil Barkat Harefa Bella Putri Cahyani Beni Irawan Bintang, Muhammad Rizki Olihta Bister Purba Boangmanalu, Mei Mariana Dani, Rian Derist Touriano Desyanti - Desyanti Dewi Lestari Dewi Lestari Dian Aprilia Dewi Dina Fitria Murad Dwi Nopriyani Ebenezer Bangun Ediansa, Oka Eka Gustina Bancin Endah Tri Kurniasih Endah Tri Kurniasih Erick Fernando Erick Fernando Erick Fernando Erick Fernando Erick Fernando Erick Fernando B311087192 Erlin Windia Ambarsari Ermaini Ermaini Fachruddin, Fachruddin Faiza Rini Fery Purnama, Fery Fitriyani, Mia Frieyadie Frieyadie Govinda Saputra Gustinar, Gustinar Hafiz Nugraha Hafiz Nugraha Hafiz Nugraha Hario Tamtomo Helmina Helmina Helmina Helmina Helmina, Helmina Hendra Kurniawan Heri Santoso Hesti hesti, Hesti Yulianingsih Ibnu Sani Wijaya Ikke Yamalia Imam Saputra Indah Desmasari Indradewa, Rhian Irmanelly Irmanelly, Irmanelly Irvan Siahaan Jasmir Jasmir, Jasmir Jeperson Hutahaean Julian Chaniago Jumaryadi, Yuwan Kevin Kurniawansyah Khairul Imtihan Lubis, Ridha Maya Faza M. Reinaldi Mardiana, Ananda Sri Mesran, Mesran Muhamad Irsan Muhammad Alfareza Muhammad Alfareza Muhammad Choirul Umam Muhammad Fauzi Muhammad Fauzi Muhammad Ikhsan Muhammad Ikhsan Muhammad Syahrizal Nanjar, Agi Nazrul Azizi Noneng Marthiawati Novia, Tri Meli NOVITASARI Nurdiansyah Saputra Nurfazila, Aprilia Nursaka Putra Nurwijayanti Oka Ediansa Oktarino, Ade Pandapotan Siagian Pandapotan Siagian Pandapotan Siagian Partogi Simanjuntak Purba, Bister Purnama, Benni Putra Nugraha Syah R, Muhammad Rachmad Rahmi Handayani Rahmi Handayani Ramli, M Saidina Rendi Efdiansyah Rico Rico Rico Rico Rico Rico Rico Rico Rico, Rico Ridha Maya Faza Lubis Rifky Lana Rahardian Rifky Lana Rahardian Riski Ferita Wahyu Rizky Pratama Rohmat Indra Borman Safira Agustina Safira Agustina Sahrawi Sahrawi Sallaby, Achmad Fikri Salsabila Tadya Hasibuan Salsabila Tadya Hasibuan Santoso SANTOSO SANTOSO Saputri, Yolanda Sherina Intania Siagian, Jaya Sari Anggraini Siagian, Pandapotan Siswoyo Siswoyo Sitti Nur Alam Steven, Ferry Surjandy Surjandy Sussolaikah, Kelik Syam, Syahrull Hi Fi Tertia, Farhan Adhimukti Valian Yoga Pudya Ardhana Wahyu Hidayat Wella Sandria Wella Sandria Yaakub, Saleh Yuniar, Fira Yuvanda, Sesraria Zai, Iwanman Zulfikri Akbar Zulpandi, Zulpandi