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All Journal Prosiding SNATIF Infotech Journal PROCEEDING IC-ITECHS 2014 JITEK (Jurnal Ilmiah Teknosains) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI INTECOMS: Journal of Information Technology and Computer Science IJISTECH (International Journal Of Information System & Technology) Indonesian Journal of Applied Informatics Jurnal Teras Kesehatan JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Teknologi Terpadu Journal of Information System, Applied, Management, Accounting and Research Jurnal ICT : Information Communication & Technology Jurnal Manajemen Informatika dan Sistem Informasi Generation Journal JTIK (Jurnal Teknik Informatika Kaputama) Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat Jurnal Bumigora Information Technology (BITe) Journal of Advanced in Information and Industrial Technology (JAIIT) Infotek : Jurnal Informatika dan Teknologi Madaniya Tematik : Jurnal Teknologi Informasi Komunikasi Infotech: Journal of Technology Information SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Teknik Informatika (JUTIF) IJISTECH Infotech: Jurnal Informatika & Teknologi Jurnal Pendidikan dan Teknologi Indonesia DEVICE International Journal Software Engineering and Computer Science (IJSECS) Jurnal Dinamika Informatika (JDI) Jurnal Teknik Informatika Jurnal Informatika Teknologi dan Sains (Jinteks) Duta.com : Jurnal Ilmiah Teknologi Informasi dan Komunikasi Asian Journal of Community Services (AJCS) Mafaza : Jurnal Pengabdian Masyarakat STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Proceeding of International Conference Health, Science And Technology (ICOHETECH) Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Innovative: Journal Of Social Science Research Journal of Software Engineering and Information System (SEIS) Journal Of Artificial Intelligence And Software Engineering SmartComp RESLAJ: Religion Education Social Laa Roiba Journal Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) International Journal of Community Service Implementation
Maulindar, Joni
Universitas Duta Bangsa Duta Surakarta
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Veterinary Other

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Journal : Proceeding of International Conference Health, Science And Technology (ICOHETECH)

 1 
Implementation of Artificial Intelligence and Big Data to Overcome Covid-19 Pandemic Christopher Jody Widiyono; Joni Maulindar
Proceeding of International Conference on Science, Health, And Technology 2021: Proceeding of the 2nd International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (407.443 KB) | DOI: 10.47701/icohetech.v1i1.1132

Abstract

At this time, where the COVID-19 pandemic caused by a new type of corona virus is occurring, the demands in the health sector are becoming heavy. Seeing the increasing number of confirmed cases of COVID-19 patients and the number of existing cases of death, a solution is needed to deal with this crisis. Artificial Intelligence and Big Data are technologies that can be adapted to deal with the COVID-19 pandemic. This review will discuss how technology based on Artificial Intelligence and Big Data can be implemented to help medical personnel carry out initial diagnoses and various kinds of research to assist drug and vaccine discovery.
EFFECT OF EDUCATION AND OCCUPATION ON THE QUANTITY OF RESEARCH Joni Maulindar; Zalizah Binti Awang Long; Jawahir Che Mustapha Yusuf
Proceeding of International Conference on Science, Health, And Technology Proceeding of the 3rd International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (404.33 KB) | DOI: 10.47701/icohetech.v3i1.2259

Abstract

The low interest in research in several places, especially in Indonesia, is influenced by several factors. This study identifies the validity of two factors that can affect the number of lecturers' research. The goal to be achieved in this research is to find out how much education and job title affect the number of lecturers' research. The research was conducted using descriptive quantitative methods with 41 samples of respondents. The results of this study indicate that based on the ANOVA test, the F value is 1.864 with a sig. 0.169. In the regression test, the t value was obtained at 1.153 with a sig value. 0.256 and 0.067. Through this value, it can be concluded that education and job position has a significant effect on the quantity of lecturer research.
PREDICTION AND PREVENTION OF DISEASE DIAGNOSIS DELAY USING DATA MINING METHODS IN HEALTHCARE QUALITY MANAGEMENT Maulindar, Joni; Guterres, Juvinal Ximenes; Rosita, Riska
Proceeding of the International Conference Health, Science And Technology (ICOHETECH) 2023: Proceeding of the 4th International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/icohetech.v4i1.3376

Abstract

This study analyzes the issue of disease diagnosis delay in healthcare quality management using data mining methods. The aim is to understand the relationship between several key variables and diagnosis delay for various diseases. The study focuses on the variables of Age, Symptom Duration, Physician Experience, and Diagnosis Delay. Advanced data mining methods are employed to predict and prevent disease diagnosis delays. The results of this study present the findings from the analysis of the collected dataset. The dataset consists of patient information, including attributes such as Patient ID, Age, Symptom Duration, Physician Experience, Diagnosis Delay, and Treatment Initiation. Each attribute plays a crucial role in understanding and predicting diagnosis delay. The approach using linear regression yields coefficients [0.03260123, 0.24605912, 0.01765057, 1.09631713], indicating the influence of each variable on Diagnosis Delay. The Mean Squared Error (MSE) value of 0.7926 signifies the model's ability to predict Diagnosis Delay accurately. The scatter plot illustrates the linear relationship between actual Diagnosis Delay and predicted Diagnosis Delay. The Pearson's Correlation Coefficient of 0.5222 indicates a moderate positive correlation between the two. However, the residual plot indicates a tendency for underestimation of Diagnosis Delay for higher values.
Challenges in The Academic Promotion Process: Perspectives From Faculty Members Maulindar, Joni; Awang Long, Zalizah; Che Mustapha, Jawahir; Purnomo, Singgih
Proceeding of the International Conference Health, Science And Technology (ICOHETECH) 2024: Proceeding of the 5th International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/icohetech.v5i1.4127

Abstract

The academic promotion process faces various challenges that cause delays for faculty members in reaching higher ranks. This research aims to identify and analyze the factors contributing to delays in the academic promotion process. The research method used is a quantitative approach, with data collection techniques involving the distribution of questionnaires to faculty members who are currently undergoing or about to undergo the academic promotion process. The research results indicate that the lack of transparency in rules, policy changes, evaluation complexity, communication limitations, and institutional support all have a significant and equal impact on the challenges of academic promotion, with each factor having a coefficient of 0.2000. The R-squared and Adjusted R-squared values of 1.000 indicate that this model can explain the entire variation in academic promotion challenges. The high statistical significance of all coefficients suggests that these results are almost certainly not due to chance. Data analysis also shows that there is little autocorrelation in the model's residuals, and the residual distribution is nearly normal. These findings highlight the importance of transparency, policy consistency, effective communication, and institutional support in the academic promotion process. Improvements in these areas are expected to reduce the challenges faced by faculty members during the promotion process
Intelligent Traffic Sign Detection Using Yolov9 Pradana, Afu Ichsan; Harsanto, Harsanto; Maulindar, Joni
Proceeding of the International Conference Health, Science And Technology (ICOHETECH) 2024: Proceeding of the 5th International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/icohetech.v5i1.4205

Abstract

This research examines the automatic detection and classification of traffic signs using artificial intelligence (AI) and computer vision technologies. As urban traffic increases, quickly and accurately recognizing traffic signs becomes a challenge, especially under adverse conditions such as bad weather and limited visibility. Conventional technologies that rely on human vision are prone to errors, so an automated solution is needed. This research uses the YOLOv9 algorithm for real-time traffic sign detection, utilizing the Generalized ELAN (GELAN) architecture that combines the advantages of CSPNet and ELAN for efficiency and accuracy. The dataset used consists of 1924 images processed through various stages, including data augmentation and normalization. The model was trained for 15 epochs with fairly high accuracy results in the prohibitory, danger, and mandatory sign categories. However, there were still some misclassifications, especially in the prohibitory category which was sometimes mistakenly detected as another category or background. Overall, the model performed well in detecting traffic signs in various environmental conditions, but still needs improvement to increase accuracy in certain cases.
Digital Training Recommendations for Enhancing Healthcare Human Resource Competence Using the Simple Additive Weighting Algorithm Maulindar, Joni; Dewi, Nandita Sekar Sukma; Mustapha, Jawahir Che
Proceeding of the International Conference Health, Science And Technology (ICOHETECH) 2025: Proceeding of the 6th International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/hr24wj75

Abstract

The main challenge in digital transformation within the healthcare sector lies in the limited mapping of human resources’ (HR) readiness toward information technology. This study aims to evaluate the digital readiness level of healthcare human resources using a data-driven approach. The Simple Additive Weighting (SAW) method was applied, utilizing six variables whose weights were determined through expert discussions. The results show that respondents’ digital readiness scores range from 0.800 to 0.858. The highest score was achieved by R011 (Doctor, Outpatient Department) with a score of 0.858, indicating excellent digital literacy and effective utilization of information systems. A score of 0.828 was obtained by R032 (Medical Records Administration, ICU), R035 (Midwife, Polyclinic), and R025 (Doctor, Outpatient Department), suggesting relatively even readiness levels. The lowest score, 0.800, belonged to R026 (Laboratory Analyst, Polyclinic), which still falls within the high-readiness category but indicates the need for improvement in health data analysis skills. The overall average score was 0.828, confirming that the majority of healthcare personnel demonstrate good digital readiness. These findings highlight the effectiveness of the SAW method in providing data-driven recommendations for digital capacity development. Moreover, the results can serve as a foundation for designing more targeted and efficient training programs. Overall, this study demonstrates that SAW-based mapping can effectively support strategic decision-making in the digital transformation of healthcare services.
PREDICTION AND PREVENTION OF DISEASE DIAGNOSIS DELAY USING DATA MINING METHODS IN HEALTHCARE QUALITY MANAGEMENT Maulindar, Joni; Guterres, Juvinal Ximenes; Rosita, Riska
Proceeding of the International Conference Health, Science And Technology (ICOHETECH) 2023: Proceeding of the 4th International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/icohetech.v4i1.3376

Abstract

This study analyzes the issue of disease diagnosis delay in healthcare quality management using data mining methods. The aim is to understand the relationship between several key variables and diagnosis delay for various diseases. The study focuses on the variables of Age, Symptom Duration, Physician Experience, and Diagnosis Delay. Advanced data mining methods are employed to predict and prevent disease diagnosis delays. The results of this study present the findings from the analysis of the collected dataset. The dataset consists of patient information, including attributes such as Patient ID, Age, Symptom Duration, Physician Experience, Diagnosis Delay, and Treatment Initiation. Each attribute plays a crucial role in understanding and predicting diagnosis delay. The approach using linear regression yields coefficients [0.03260123, 0.24605912, 0.01765057, 1.09631713], indicating the influence of each variable on Diagnosis Delay. The Mean Squared Error (MSE) value of 0.7926 signifies the model's ability to predict Diagnosis Delay accurately. The scatter plot illustrates the linear relationship between actual Diagnosis Delay and predicted Diagnosis Delay. The Pearson's Correlation Coefficient of 0.5222 indicates a moderate positive correlation between the two. However, the residual plot indicates a tendency for underestimation of Diagnosis Delay for higher values.
Challenges in The Academic Promotion Process: Perspectives From Faculty Members Maulindar, Joni; Awang Long, Zalizah; Che Mustapha, Jawahir; Purnomo, Singgih
Proceeding of the International Conference Health, Science And Technology (ICOHETECH) 2024: Proceeding of the 5th International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/icohetech.v5i1.4127

Abstract

The academic promotion process faces various challenges that cause delays for faculty members in reaching higher ranks. This research aims to identify and analyze the factors contributing to delays in the academic promotion process. The research method used is a quantitative approach, with data collection techniques involving the distribution of questionnaires to faculty members who are currently undergoing or about to undergo the academic promotion process. The research results indicate that the lack of transparency in rules, policy changes, evaluation complexity, communication limitations, and institutional support all have a significant and equal impact on the challenges of academic promotion, with each factor having a coefficient of 0.2000. The R-squared and Adjusted R-squared values of 1.000 indicate that this model can explain the entire variation in academic promotion challenges. The high statistical significance of all coefficients suggests that these results are almost certainly not due to chance. Data analysis also shows that there is little autocorrelation in the model's residuals, and the residual distribution is nearly normal. These findings highlight the importance of transparency, policy consistency, effective communication, and institutional support in the academic promotion process. Improvements in these areas are expected to reduce the challenges faced by faculty members during the promotion process
Intelligent Traffic Sign Detection Using Yolov9 Pradana, Afu Ichsan; Harsanto, Harsanto; Maulindar, Joni
Proceeding of the International Conference Health, Science And Technology (ICOHETECH) 2024: Proceeding of the 5th International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/icohetech.v5i1.4205

Abstract

This research examines the automatic detection and classification of traffic signs using artificial intelligence (AI) and computer vision technologies. As urban traffic increases, quickly and accurately recognizing traffic signs becomes a challenge, especially under adverse conditions such as bad weather and limited visibility. Conventional technologies that rely on human vision are prone to errors, so an automated solution is needed. This research uses the YOLOv9 algorithm for real-time traffic sign detection, utilizing the Generalized ELAN (GELAN) architecture that combines the advantages of CSPNet and ELAN for efficiency and accuracy. The dataset used consists of 1924 images processed through various stages, including data augmentation and normalization. The model was trained for 15 epochs with fairly high accuracy results in the prohibitory, danger, and mandatory sign categories. However, there were still some misclassifications, especially in the prohibitory category which was sometimes mistakenly detected as another category or background. Overall, the model performed well in detecting traffic signs in various environmental conditions, but still needs improvement to increase accuracy in certain cases.
Co-Authors Abdullah Abdullah Syaifudin Aditya Rachman Putra Afu Ichsan Pradana Agil Husnul Khotimah Agustina Srirahayu Ahmad Qashid H Ahmad Setiawan Aji, Sindhu Purnomo Akbar Galih Saputra Akbar Akbar, Akbar Galih Saputra Akbar, Reza Maulana Aldin Fathiray Ananda, Naufal Choirul Andreas Abi Permana Andrianto, Albertus Ari Anggita, Febri Arif Anisatul Farida Annas Setiawan Prabowo Anugrah Putra, Muhammad Ardani, Hasby Arif Ardhianto, Aan Ardiyanto, Kevin Ardiyanto, Marta Arif Eko Fitrianto Arif Wicaksono Septyanto Arnan Dwi Arsandy, Noelino Grevansha Atina, Vihi Avianto Adi Pratama Awang Long, Zalizah Azza Al Abbas, Abdullah A’an Jati Susilo Badrudin, Muhamad Bagas Mutaqqi Bagaskara, Ikrar Bagos Erwanto Bagus Prakoso, Ahmad Bahrul Aziz Rifai BAHTIAR, YUSUF Bayu Tri Pramono Berlian Agustina, Anggun Binuko, Raafika Studiviani Dwi brigitta harlim Che Mustapha, Jawahir Christopher Jody Widiyono Da Costa, Alexandre Dewi, Nandita Dewi, Nandita Sekar Sukma Dhimas Arya Rakadipa Didik Kurniawan Difan Agra Susilo Dika Adi Pratama Dimas Abimanyu Sutrisno Putro Dimas Cahyo utomo Dison Librado Dita Putra Pratama Divangga Revansa Arya Pradhana Dwi Hartanti Dwi Hartanti Dwi Hartanti Dwi Kurniawan Saputro Dwiirawan, Ridwan Dyah Aprimavista Cahyani Edy Kurniawan Eko Purwanto Em Sutrisna Enggar Wijaya Putra Erlinawati, Mira Ery Permana Yudha Ester Anugrayningtyas Fachruddin Edi Fandi Aziz Pratama Fathur Iqbal Hilmi Ulhadi FAULINDA ELY NASTITI Faulinda Ely Nastiti Firdaus, Azkha Brilliant Fitria Eko Nurjanah Fitroh Ahmad Abdul Aziz Frisca Tri Arumsari Guterres, Juvinal Ximenes Hafid Affan Wahid Hamna Zakiya , Nasywa Hani Rifdah Azizah Hanif Hilmi, Muhammad Hanif Nur Ahmad Hari Windiyastuti Harsanto, Harsanto Hartanti, Dwi Hartanto, Didik Mayur Hasanah, Herliyani Hidayat, Almaranda Aisyanissa Hiyarunnisa Kahes Waypi Ichsan Pradana, Afu Ichwani, Achmad Ila Aulia Rahmah immaculata yolia dewi Widayanti Indah, Ratna Puspita Indrastata, Ilham Buyung Indriyas Kukuh Wijayanti Intan Oktaviani Iqbal Hanan Junaidi Irawan, Egie Irawan, Ridwan Dwi Istiana Hanifah Istiqomah, Yasinta Jawahir Che Mustapha Yusuf Jawahir Che Mustapha Yusuf Jofan Fathurahman Juvinal Ximenes Guterres Karlina Kusuma Ningrum Kevin Yoga Ananta Kurniawan, Daniel Ade Leny Monica Lidia Earlene Rendhiva Lola Sekar Arum Lufti Puspitasari Margaretha Evi Yuliana Margaretha Evi Yuliana Mashkul Ryan Ibrahim Matin Muhith Meraldy Fiko Rastio Ajie Mink Poo Lexy Utomo Mink Poo Lexy Utomo Moh Muhtarom Moh. Muhtarom Muhammad Daivany Nur Auliya Saleh Muhammad Nur Ikhsanudin Mulyanto, Hari Munawaroh, Maysani Mustapha, Jawahir Che Nadia Hepyntha Nailurrizqi, Adistya Nanda Ramadhani, Fikko Nandita Sekar Sukma Dewi Nibras Faiq Muhammad Ningrum, Karlina Kusuma Nugroh, Heri Nur Auliya Saleh, Muhammad Daivany Nur Cahyo Nugroho Nurchim Nurchim Nurfadilah Nurfadilah Nurlaili, Dewi Nurohman Nurohman, Nurohman Nurrohman Oktaviyana Dwi Hendra Jati Pamekas, Bondan Wahyu Permatasari, Hanifah Praba M.A.R.K Pradana, Afu Ichsan Pradana, Gibran Arya Prajadi Cipto Utomo, Bangun Pramoedya Ananta Dzikri Pramono Pramono prastiwi, yuyun Prastya, Alvian Bagus Prastyo, Okik Dwi Pratiwi, Dinita Christy Pujiati Edy Santoso, Elysa Mei Puput Dwi Mandiri Puput Dwi Mandiri Purwanto, Eko Putra Pradana, Gibrand Raafika Studiviani Dwi Binuko Raditya Koesyan Dipo P Rais Suryo Wahono Rendi Enggar Bintang Pratama Restu Gilang Wijanarko Reza Mar Hendra Putra Rifa'i, Rifan Rifan Amirul Hafizh, Muhammad Rifqi Firdausi Arafad Riska Rosita Rizki Hendra Rizqy Mahendra Abdul Rahman Romy Rajawali Nusantara Saifullah Rudi Susanto Saifudin Umar Sandy Yustisio O Saputra, Muchammad Yoga Setiawan, Gilang Setya Pradhana, Wahyudi Shelvi Azizah Sindhu Purnomo Aji Singgih Purnomo SRI SUMARLINDA Studiviani Dwi Binuko, Raafika Suci Bunga Pritalina Sudarminingsih Sudarminingsih Sulistyo Adi Prasetyo Syahrul Agung Fathoni Tasya Mutiara Diva Tasya Mutiara Diva Tesalonika, Angel Titi Jayanti Tiur Bunga Gadissa Tory, Alfa Rado Andre Yusa Saka Vita Sofia Prihatini Wahyu Adi Pratama Wahyu Aji Saputro, Lintang Wahyu Kuncoro Wahyu Pamekas, Bondan Warihaji, Wijasena Warta, Danu Widayanti, immaculata yolia dewi Wihan Perkasa Nugraha Putra Wijayanti, Indriyas Kukuh Wijayanti, Sefi Ayuk Wiji Lestari WIJI LESTARI Wijiyanto Yafa Arsyida Aulia Rakhma Yasinta istiqomah Yeyen Santi Putri Yoma Patria Risky, Satya Yusuf Bahtiar Zaenuar Erfandi Zalizah Binti Awang Long