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All Journal snimed Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Techno.Com: Jurnal Teknologi Informasi Jurnal Teknologi dan Manajemen Informatika Jurnal Informatika dan Teknik Elektro Terapan Sistemasi: Jurnal Sistem Informasi JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Sisfokom (Sistem Informasi dan Komputer) Technomedia Journal ScientiCO : Computer Science and Informatics Journal IT (INFORMATIC TECHNIQUE) JOURNAL Advance Sustainable Science, Engineering and Technology (ASSET) DedikasiMU: Journal of Community Service PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) The Indonesian Journal of Computer Science Tadulako Science and Technology Journal
Hajra Rasmita Ngemba
Information System Department, Faculty Of Engineering, Tadulako University, Jl. Soekarno Hatta No KM 9 Palu 94148, Central Sulawesi, Indonesia
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Social Sciences Other

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Utilization of EfficientNet-B0 to Identify Oncomelania Hupensis Lindoensis as a Schistosomiasis Host Lamadjido, Moh. Raihan Dirga Putra; Laila, Rahmah; Pusadan, Mohammad Yazdi; Yudhaswana, Yuri; Lapatta, Nouval Trezandy; Ngemba, Hajra Rasmita
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9058

Abstract

Schistosomiasis caused by the Schistosoma japonicum worm is a significant health problem in Indonesia, especially in endemic areas such as the Napu Plateau and Bada Plateau. The main problem in controlling this disease is the difficulty in rapid and accurate identification of Oncomelania hupensis lindoensis snails as intermediate hosts of the parasite. This research aims to develop an artificial intelligence-based system that can efficiently identify the snail species. The stages of this research include collecting snail image data from the Central Sulawesi Provincial Health Office, consisting of 2100 images covering seven snail species, then processed through preprocessing and augmentation stages. The model applied was EfficientNet-B0. The results showed that the EfficientNet-B0 model achieved 98.80% training accuracy and 98.33% validation accuracy. Confusion matrix testing showed good performance, with an accuracy of 98% and for the species Oncomelania hupensis lindoensis had a recall of 93%, precision of 100%, F1-score of 97%, and the resulting AUC value of 99.7%. This research successfully developed an efficient identification system, which is expected to help health surveillance personnel in accelerating the identification process of schistosomiasis intermediate hosts.
Comparison of Machine Learning Algorithms for Predicting Stunting Prevalence in Indonesia Pratama, Moh. Asry Eka; Hendra, Syaiful; Ngemba, Hajra Rasmita; Nur, Rosmala; Azhar, Ryfial; Laila, Rahmah
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 2 (2024): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i2.2097

Abstract

Stunting is a serious public health problem, especially among under-fives, which can cause serious short- and long-term impacts. Efforts to tackle stunting in Indonesia involve national strategies and development priorities. Therefore, this study aims to compare the performance of machine learning regression algorithms in predicting stunting prevalence in Indonesia. The data collected is secondary data. The data collection was done carefully, taking explicit details regarding the source, scope, extent, and analysis of the dataset, and using a careful sampling methodology. The model evaluation results show that the Random Forest Regression algorithm has the best performance, with a success rate of 90.537%. The application of this model to the new dataset shows that East Nusa Tenggara province has the highest percentage of stunting at 31.85%, while Bali has the lowest percentage at 12.07%. Visualization of the dashboard using Tableau provides a clear picture of the distribution of stunting in Indonesia. In conclusion, this research contributes to the development of science, especially in the field of machine learning and public health, and provides policy recommendations for tackling stunting in Indonesia.
Bahasa Inggris Abdillah Sani, Ilham; Lapatta, Nouval Trezandy; Ngemba, Hajra Rasmita; Fahlevi, Mohammad Fazrin
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.4307

Abstract

Implementing a digital-based hazardous work licensing management system at PT Citra Palu Minerals is intended to enhance the efficiency and transparency of the work permit process. The research methodology involves a qualitative approach, Agile methodology system development, and integration with WhatsApp for notifications. The research findings indicate that this system simplifies the submission, approval, and monitoring of work permits in a structured manner, thereby reducing the risk of work accidents. Black box testing demonstrates that the system's performance meets expectations, while the questionnaire results indicate a high level of user satisfaction with an average score of 4.3 out of 5. Implementing this system can serve as a model for enhancing occupational safety and health management in similar industries.
Co-Authors - Ifandi A Djufri, Isdar Abd. Rahman Sholeh Abdillah Sani, Ilham Abdul Mahatir Najar Aimar Anand Alamsyah - Alamsyah Alamsyah Alda Nur Pradinda Angraeni, Dwi Shinta Anita Ahmad Kasim Anugrah Aidin Yotolembah Anwar, Asriani Azizah Azizah Chandra, Ferri Rama Deny Wiria Nugraha Dharmakirti, Dharmakirti Djohari, Riyandi Dwitama Dwi Shinta Angreni Dwi Wijaya, Kadek Agus Dwiwijaya, Kadek Agus Elyana Aulia Chandra Fahlevi, Mohammad Fazrin Fathul Wahid Habibu, Rahmawati Hamid, Odai Amer Hardianti Hardianti, Hardianti I Gusti Ngurah Agung Kade Dwi Arsana Iman Setiawan Indrajaya, Muhammad Aristo Junaidi Junaidi Junus Widjaja Kade Dwi Arsana, I Gusti Ngurah Agung Laila, Rahma Lakatjinda, Adiatma Lamadjido, Moh. Raihan Dirga Putra Lapatta, Nouval Trezand Liza Amalia Mohamad Irfan, Mohamad Mohammad Fajri Mohammad Yazdi Pusadan Muh Alif Alghifari Muh. Aristo Indrajaya Muhammad Jindan Muhammad Nauval Daffa Ulhaq Muhammad Yazdi Nouval Trezandy Lapatta Nurpati Nurpati Nursalim Pratama, Moh. Asry Eka Putri Febrina, Annisa Rahmah Laila Raivandy, I Made Randhy Rina Marlina Risaldi Pata’Dungan, Adi Rizana Fauzi, Rizana Rizka Ardiansyah Rosmala Nur Ryfial Azhar, Ryfial Setijadi, Eko Smith, Jennifer Suryani Putri, Fadiah Syahrullah Syahrullah Syahrullah Syaiful Hendra Ulhaq, Muhammad Naufal Daffa Winarta, Ardhiansyah Wirdayanti Yudhaswana, Yuri Yuri Yudhaswana Joefrie Yusuf Anshori Zaharuddin